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[N] New Trends to Power Faster Artificial Intelligence and Machine Learning Adoption?
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EsotericaCapitalThis week

[N] New Trends to Power Faster Artificial Intelligence and Machine Learning Adoption?

In 2012, Google X lab created a neural network that can identify cats. Since then, technology companies have been increasingly adopting AI/ML on a large scale to build better applications and services for consumers (ToC). On the other hand, AI/ML's adoption on the enterprises' side (ToB) has yet to see the same growth trajectory due to the costs and complexities in both hardware and software. However, Since 2020, we started noticing three emerging tech trends that can help accelerate enterprises' adoption of AI/ML. Breakthrough in semiconductors: In 2020, Nvidia debut the concept of "Data Processing Unit," a new class of programmable processors that combine high-performance CPU with SmartNiC (network interface controller). Data centers can deploy DPUs to optimize computing offload and frees up CPUs to focus on intended tasks, such as machine learning. DPUs help resolve a significant bottleneck for ML training, where models, sometimes with billions of parameters, are way too big for traditional CPUs and GPUs to handle. Other leading semiconductor players, such as Marvell and Xilinx, follow suit with their in-house or partner-designed DPUs. Industry analysts have forecasted that the market size for DPUs in data centers alone could reach $50 billion by 2025. ​ https://preview.redd.it/l436muluhnn61.png?width=1430&format=png&auto=webp&s=ba8d1298056ea31bddd25f1596ff64b7e107580a Breakthrough in software: we also saw significant progress of "Conversational AI," a new form of AI that can understand and speak languages with human-like accuracy, in 2020. Conversational AI allows two-way interactions and provides a much better user experience than traditional AI-powered Chatbot, mostly a one-way response system. The secret of conversational AI is its ability to handle lots of human conversation variance. Developers have designed innovative algorithms such as "Switch transformers" and "Sparse training" to enable models to handle vast amounts of data. The size of conversational AI training models is enormous and keeps expanding. For example, in February 2021, Google Brain announced a model with 1.6 trillion parameters, nine times the size of the famous Open AI GPT-3 (175 billion parameters) unveiled in July 2020. GPT-3 is 100+ times bigger than GPT-2 introduced in 2019. ​ https://preview.redd.it/oajpi2yvhnn61.png?width=1430&format=png&auto=webp&s=1482913a98e17ddc1d62cc79864598d4012ad6f7 Cloud giants are expanding machine-learning platforms for developers. Andy Jassy famously said that "AI is shifting from a niche experiment inside technical departments to becoming more mainstream in business processes." in the 2020 AWS reInvent. During the conference, AWS rolled out many AI products across the technology stack, including AI chips (AWS Trainium), database (Aurora Machine Learning), and vertical solutions (Amazon Healthlake), etc. However, the most significant development is the drastic expansion of "Amazon SageMaker," one of the largest cloud machine-learning platforms. SageMaker has been offering new features to make it easier for developers to automate machine learning workflow. Microsoft Azure and Google Cloud are also growing their ML developer platforms. ​ https://preview.redd.it/z9wf2o8xhnn61.png?width=1430&format=png&auto=webp&s=9f607acfe8f0dbf36fb9b472f3cb40b80f13879e Witnessing these breakthroughs in semiconductor and software, coupled with cloud giants' effort to democratize AI, we see a coming inflection point of accelerated AI adoption in both ToC and ToB markets. So how do we benefit from this megatrend? In semiconductors, we like companies with DPUs exposure. In AI development and processing, we favor multi-cloud AI platforms such as Databricks. In enterprise software, we believe there will be a strong wave of new AI-based enterprise applications that can be creative and efficient in solving real-world problems.

I spent 18 hours every week tracking marketing trends and latest news. Here are my predictions for 2024
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lazymentorsThis week

I spent 18 hours every week tracking marketing trends and latest news. Here are my predictions for 2024

1/ Securing Digital Footprint becomes #1 Priority For Chronically Online Users, Protecting their digital footprint will become one of the main things. We saw influencers getting cancelled over Old Content and Brands used Old Travis Kelce Tweets, we saw what could happen without digital footprint protection. Online Engagement Precautions will be taken again with Twitter & IG showing your usernames above ‘Algorithm Suggested Content’. What you like is more visible to other people in UI Design of these apps, another reason behind why Digital Footprint preservation will matter a lot in 2024. This will impact likes to viewership ratio on your organic and paid content. ​ 2/  TikTok wants Long Videos with Storytelling As I was writing this report, TikTok also released their What’s Next 2024 Report. It focuses heavily on how the audiences on the app demand better storytelling and from the examples in the report, you can judge what TikTok wants. They also rolled out a 30-minute video upload limit. Engaging Content over 1-Minute Mark to keep the audiences longer on the app. I highlighted in the first trend, every social media platform wants the same thing, more time spent. 3/ Use of Shop the Look While Streaming Netflix or Amazon Prime. This year’s one of the most successful TV series, The Bear caused Men to go mad for the T-Shirt worn by Jeremy Allen White in the show. Showing us how TV Shows influence or encourage us to dress in a particular way. It’s nothing new, TV Shows like Friends & Gossip Girl influenced all demographics when they came out. But now, Streamings Services such as Roku & Amazon enable consumers to shop the look while watching the TV Shows. Many Brands will jump on these opportunities in upcoming months. 4/ Brands in Comments & Memes are the new norm By Summer 2024, Most Online Users & Creators will no longer feel too excited or answered when they see your brand in the comments. Why? It’s becoming too common for Brands to show in comments under viral content about them. Or Brands being funny with Internet Culture Trends is known to most users. The Saturation of Every Brand being funny and being present leads to increased competition of levitating the content quality. ​ 5/ Marketers decrease their focus on Traffic & Views With AI recommendations taking over, The Structure of content distributing on social media is changing, the same goes for SEO. Conversational AIs are changing how web traffic is distributed to publishers. An Increased focus on managing the conversion rate and landing page relevancy will be the main focus. 6/ OOH is kind of making a comeback. First, US OOH Ads Industry grew 1.1% in Q3 2023. Second, Outfront Media reported slight revenue increase in Q3 as Billboard Ad Revenue grew in Q3. Many Brands in UK are also aligning more toward traditional media Channels. With Burger King in UK focusing on only OOH for Christmas this year and Fashion Brands like SSENSE launching Billboards as Branding Play. 7/ Rise of Curation Continues This Year, we witnessed success of Pinterest Shuffles App, Gen-Z loved it. Similar Success with formats like IG photo dump & TikTok ‘My Fav Finds’ Carousels being the center of Gen-Z Content. Just look at this recent trend and tell me Curation isn’t personal to Online Teens. Spotify won with their idea of curating Songs with Astrology-type signs. The Fashion Products with Curated Emojis and Stickers on them, that scrappy curated approach is predicted to grow in 2024, data from Pinterest. 8/ Use of AI to Trace Consumers in the wild This year we saw a huge trend of people using Image/ face recognition tools to find or dig dirt about famous people. The biggest example was Dillion Dannis exposing Multiple images of Logan Paul’s girlfriend using AI tools. (Which was Obviously bad) But next year, I believe with better rules, big brands like Adidas or Nike will be able to find worldwide micro-influencers & Online Consumers seen wearing adidas. And partnering with them on a large scale through automated outreach. 9/ More Cartoons than Influencer-Brand Products. All the Cartoon shows are seeing huge rise on IG and TikTok, Shaun the sheep is viral, Snoopy was big this year, Sesame Street’s TikTok is working. Aussie Show Bluey is making a huge spark in the US. More Brand collaborations are on the road. Why? Cartoons have built a very consistent identity and they have social channels. I know many see Cartoons as Kids Content but on social, looking at TikTok Account of Sesame Street & Snoopy. Last month, Powerpuff Girls launched a collaboration with Nike. ​ 10/ The Best Trend to get people off social media ​ Try to get people off the social media apps, build your own loops. You can’t rely on social and you clearly shouldn’t burn out trying to win on social and streaming with Paid Ads or without them. This matters a lot because data shares most of your customers buy from you once or twice a year. And then they interact with your content, how bad will you feel if the only thing they remember as your content is being on TikTok. Nothing about your brand. 11/ The Internet Aesthetic will Die for Cafes & Restaurants When I wrote my post about Instagram Marketing, I mentioned this issue of Every Account looking the same. In reality, It isn’t limited to IG Feeds, This Creator points out the same Problem, mentioning the aesthetic Standards from Internet are changing how new businesses approach their whole business. More Content from Cafes & Restaurants need to be around their people and neighbourhood. 12/ Echo Chambers & Sonic Influence All Podcasts are Echo Chambers because if people wanted a new perspective in form of value. We would have chosen debates, but we chose Podcasts to find new value while being in comfort. People are now looking for more value in comfort than ever, Podcasts will continue to rise. 13/ Clever AI Integration to Better Customer Journeys in B2B & B2C Marketing Agencies can provide clever solutions to B2B Companies, and help them overcome the tag of Boring Ads only. How? Ogilvy India created an AI Ad Campaign for Cadbury, allowing SMBs to have the Bollywood Actor endorse them. They used the AI voice generation allowing businesses to alter the voice and have Shah Rukh Khan endorse their shop. A similar approach was taken by IPG India, An AI Ad with Shah Rukh Khan allowing everyone to add their face in the Branded Content. ​ If I sounded like an Old head in this report or I missed on some elements like Programmatic Advertising and PPC. I will try to include better analysis and new content about future trends. You can find the post shared with examples & research, linked here.

What Are the Top Small Business Trends You Must Know for 2024 ?
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brycetychsenThis week

What Are the Top Small Business Trends You Must Know for 2024 ?

Are you excited about the new business horizons in 2024? Well, you should be! The small business landscape is evolving faster than anything right now, and here are the trends you absolutely need to know to keep your business game strong. Sustainable Swag In a world where eco-friendliness is the new black, businesses are carrying the badge of sustainability. From eco-packaging to carbon-neutral practices, customers are giving the side-eye to anything less green. So, if you want to be at the top, consider adopting some planet-friendly practices. Remote Work Revolution Office who? The 9-to-5 grind is getting a makeover, and the dress code is PJs. Remote work is no longer just a trend; it's a lifestyle. So, if your business can embrace the virtual office, you might just find your team doing the hustle and bustle with productivity. Tech-Tastic Ventures The future is now, and it's filled with tech wonders. Augmented reality (AR), artificial intelligence (AI), and all things tech are the new developments in this sector. Businesses incorporating these innovations are riding the digital wave straight to success. Personalization Party No one likes generic. Customers want products and services tailor-made just for them. So, businesses are using data to give customers an experience that feels as customized as a handmade suit. Say goodbye to one-size-fits-all! Community Crusaders In a world full of noise, community is the superhero we all need. Businesses are realizing the power of building a network around their brand. Whether it's through social media, events, or exclusive memberships, creating a community is like having an army of brand advocates. 2024 is the year to unleash your small business swagger. Embrace these trends, adapt with flair, and let your entrepreneurial spirit soar. Remember to sprinkle some personality into your business strategy—people love a brand with a sense of humor and a human touch!

Business Strategy Trends for 2024
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aidenleepingweiiThis week

Business Strategy Trends for 2024

As we gear up for 2024, it's time to gaze into the crystal ball and see what's reshaping the world of business strategy. From cutting-edge technology to how people are shopping, it's all happening. So let's check out the latest trends that are going to dominate the business world! Going Green and Doing Good Yep, you heard it right—being eco-friendly and socially responsible is all the rage. Businesses are jumping on the sustainability train, whether it's by using recycled materials or giving back to the community. It's not just good for the planet—it's good for business too! Tech Takeover From fancy AI to blockchain innovations, businesses are embracing all things digital. It's not just about staying up to date—it's about using technology to make things easier, faster, and way more amazing. Work from Anywhere Who says you have to be stuck in an office all day? Today, businesses are all about flexibility. Whether you're working from home, a coffee shop, or a hammock on the beach, it's all good. Remote work is here to stay, and people are loving the freedom it brings. Treat Yo' Customers Want to stand out in a sea of competition? It's all about making your customers feel special. Whether it's personalized recommendations or killer customer service, businesses are pulling out all the stops to keep folks coming back for more. Roll with the Punches In today's fast-paced world, you've got to be quick on your feet. That's why businesses are ditching rigid plans and embracing agile strategies. It's all about being able to adapt to whatever curveballs the world throws your way. Click, Buy, and Repeat Online shopping is getting bigger. Businesses are getting creative with their online offerings, whether it's through slick new websites, social media shenanigans, or funky new delivery options. The future of shopping is digital! Conclusion: The lowdown on what's shaking up the world of business strategy in 2024. Whether it's going green, embracing tech, or keeping customers happy, there's plenty of excitement on the horizon.

What Are the Top Small Business Trends You Must Know for 2024 ?
reddit
LLM Vibe Score0
Human Vibe Score1
brycetychsenThis week

What Are the Top Small Business Trends You Must Know for 2024 ?

Are you excited about the new business horizons in 2024? Well, you should be! The small business landscape is evolving faster than anything right now, and here are the trends you absolutely need to know to keep your business game strong. Sustainable Swag In a world where eco-friendliness is the new black, businesses are carrying the badge of sustainability. From eco-packaging to carbon-neutral practices, customers are giving the side-eye to anything less green. So, if you want to be at the top, consider adopting some planet-friendly practices. Remote Work Revolution Office who? The 9-to-5 grind is getting a makeover, and the dress code is PJs. Remote work is no longer just a trend; it's a lifestyle. So, if your business can embrace the virtual office, you might just find your team doing the hustle and bustle with productivity. Tech-Tastic Ventures The future is now, and it's filled with tech wonders. Augmented reality (AR), artificial intelligence (AI), and all things tech are the new developments in this sector. Businesses incorporating these innovations are riding the digital wave straight to success. Personalization Party No one likes generic. Customers want products and services tailor-made just for them. So, businesses are using data to give customers an experience that feels as customized as a handmade suit. Say goodbye to one-size-fits-all! Community Crusaders In a world full of noise, community is the superhero we all need. Businesses are realizing the power of building a network around their brand. Whether it's through social media, events, or exclusive memberships, creating a community is like having an army of brand advocates. 2024 is the year to unleash your small business swagger. Embrace these trends, adapt with flair, and let your entrepreneurial spirit soar. Remember to sprinkle some personality into your business strategy—people love a brand with a sense of humor and a human touch!

How to get that big idea for your next business? Use trends!
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IRemember123This week

How to get that big idea for your next business? Use trends!

Hello entrepreneurs and aspiring business owners, I am Mikael and I want to share a post about how to spot business ideas. If you're wondering who the owl is, it's Agent O, my sidekick (please bear with him... or me, if you can). Let's get on to it. So, there are basically two ways of getting ideas for your new business: Find a service, product or experience that's already working. Identify and ride a trend. 🦉 : Third, have a rich relative pass you their business and sip margaritas by the sea while scrolling Reddit for the rest of your life! 🕵️ : Refrain yourself, I just got started ffs, I don't want to get banned! So, what are trends? Trends are patterns of adoption of a product, service or experience by people who want to satisfy a common need. Cool, huh? How trends start Trends emerge and evolve as temporary or permanent solutions to human needs. All products, services and experiences are the expression of human needs manifested through a perceived lack, which we humans interpret as problems. Let me make this more clear. Humans have needs: from basic (food, shelter, safety) to advanced (community, knowledge) to evolved (self actualization, spirituality) and everything in between. Don’t see this as a hierarchy, as it’s usually depicted with Maslow’s pyramid. See it as cycles with different degrees of impact on humans that vary in time and intensity. 🦉 : WHAT!?? 🕵️ : Hear me out… How Trends Affect Society Human needs are physical, emotional, intellectual and spiritual. Every day we feel the impact of those needs with different degrees of required fulfillment. You can’t go on without air for more than a few minutes. You can’t live without food and water for more than a few days. So, when it comes to the needs of the body, these have a shorter timeframe in which they need to be addressed. 🦉 : Ahh, I see what you did there… \\🕵️ \\: Thanks! But you can also live with an unfulfilled need for love or friends for a long time. You can live with a decaying health as well. And you also can live your entire life without finding out if there is a God or not. Humans perceive needs as something they lack within, which in turn is expressed as a problem on the outside. I lack food or water, this will create a problem for my survival. So I need to find food and water in my environment. This lack creates a behavior seeking a product, service or experience to fulfill that need. Makes sense? 🦉 : I just went out and got me a “Mice à la Forest” dinner! 🕵️: Bon appétit! See, Agent O fulfilled a bodily need. That’s what animals do, as they’re driven by instinct and are governed by natural laws (survive, reproduce, sleep, repeat). Humans are driven by more complex needs, as our intellect and emotions allow us to override those basic primary instincts. Why Trends Are Important What an entrepreneur does is to shift the perspective: instead of seeing a lack, he/she sees an opportunity by asking the question: how can I fulfill this need? Or, even better put: how can I help people by solving their problem? That’s the first step to solving a problem: asking a question. That is why the best products are actually problems solved by entrepreneurs who work to solve their own need for a product, service or experience. They then provide it to other people for a cost. Easy, right? That’s what entrepreneurship is: solving a problem. The bigger the problem, the bigger the impact. The bigger the impact, the higher the revenue. It’s easier to understand trends now, isn’t it? You can see that trends are nothing more than the initial adoption of a product, service or experience by a group of people who are looking for a solution to their common need. 🦉 : Did you get that from a book? 🕵️ : You snore when you sleep… ¯\\(\ツ)/\¯ 🦉 : $@#&\*! Hooman! Needs are the foundation on which the modern world is built. Once you understand needs, you fundamentally change your perception of problems into opportunities. This mental shift is the entrepreneurial mindset: where others see problems, you see solutions. Where Do Trends Start So, to recap: human needs are translated into problems. Founders understand the root of the problem (the need) and create products, services, experiences as solutions to those needs. They offer the solution to the public through startups and companies, which belong to a specific niche in a particular industry. 🦉 : Aaah, so that’s why it’s called venture capital? 🕵️ : Yeah, because you’re venturing into a new endeavor to let people know about your solution to their (and ideally your) problem. 🦉 : So if you use ads to market your venture, it’s an adventure? 🕵️ : I see what you did there… If the need behind the adoption is strong and real enough, that trend will translate into a niche within an industry. If the adoption isn’t driven by strong fundamental needs, it will turn into a fad and disappear from the perception of the public, no matter how much marketing money is thrown at it. This happens because the solution (product/service/experience) to the need didn’t create the physical, intellectual or emotional response required to create a recurring behavior around it. Remember this: Problem (why) -> Behavior (how) -> Solution (what) Understand this: there are multiple types of trends. There are product or service trends. There are industry driven trends. There are tendency driven trends, like the emergence of a new paradigm that improves a lot of industries (yes, I’m looking at you, AI). Where Do Trends Come From So now you can see that trends are patterns of adoption related to a specific human need that is addressed through one or multiple products or services. This is a bottom up direction coming from evolution. Multiple trends in different industries also emerge from a theme, which is a bigger vision of a human effort to address a high level problem. This is a top down direction, coming from implementation (by governments, different organizations or other interested parties with the power to influence changes at mass level). Conclusion Now you have a better understanding of trends by looking at them through the lens of human needs. Also, you might also understand time better because you realize that human needs have different degrees of impact in time and intensity. So you now see that trends don’t only relate to individuals, but also to groups of people, from the smallest community to countries and even global needs. That is the reason you’ll sometimes hear some say that time is a flat circle: because clothes change, but humans are quite the same. Needs don’t change a lot in time, just the way we address and solve them. Here’s an interesting game for you: take a look at some behaviors in your life. Which of them are driven by a bodily need, which by an intellectual or emotional one? Which ones are completely automated and you had no idea you were doing? How are these behaviors controlling parts of your life that you were unaware of until now? If you made it this far, thank you for taking the time to read this. I hope you enjoyed it, found it useful and entertaining. Ofc, I value your opinion and welcome it in the comments. Thank you!

How to get that big idea for your next business? Use trends!
reddit
LLM Vibe Score0
Human Vibe Score1
IRemember123This week

How to get that big idea for your next business? Use trends!

Hello entrepreneurs and aspiring business owners, I am Mikael and I want to share a post about how to spot business ideas. If you're wondering who the owl is, it's Agent O, my sidekick (please bear with him... or me, if you can). Let's get on to it. So, there are basically two ways of getting ideas for your new business: Find a service, product or experience that's already working. Identify and ride a trend. 🦉 : Third, have a rich relative pass you their business and sip margaritas by the sea while scrolling Reddit for the rest of your life! 🕵️ : Refrain yourself, I just got started ffs, I don't want to get banned! So, what are trends? Trends are patterns of adoption of a product, service or experience by people who want to satisfy a common need. Cool, huh? How trends start Trends emerge and evolve as temporary or permanent solutions to human needs. All products, services and experiences are the expression of human needs manifested through a perceived lack, which we humans interpret as problems. Let me make this more clear. Humans have needs: from basic (food, shelter, safety) to advanced (community, knowledge) to evolved (self actualization, spirituality) and everything in between. Don’t see this as a hierarchy, as it’s usually depicted with Maslow’s pyramid. See it as cycles with different degrees of impact on humans that vary in time and intensity. 🦉 : WHAT!?? 🕵️ : Hear me out… How Trends Affect Society Human needs are physical, emotional, intellectual and spiritual. Every day we feel the impact of those needs with different degrees of required fulfillment. You can’t go on without air for more than a few minutes. You can’t live without food and water for more than a few days. So, when it comes to the needs of the body, these have a shorter timeframe in which they need to be addressed. 🦉 : Ahh, I see what you did there… \\🕵️ \\: Thanks! But you can also live with an unfulfilled need for love or friends for a long time. You can live with a decaying health as well. And you also can live your entire life without finding out if there is a God or not. Humans perceive needs as something they lack within, which in turn is expressed as a problem on the outside. I lack food or water, this will create a problem for my survival. So I need to find food and water in my environment. This lack creates a behavior seeking a product, service or experience to fulfill that need. Makes sense? 🦉 : I just went out and got me a “Mice à la Forest” dinner! 🕵️: Bon appétit! See, Agent O fulfilled a bodily need. That’s what animals do, as they’re driven by instinct and are governed by natural laws (survive, reproduce, sleep, repeat). Humans are driven by more complex needs, as our intellect and emotions allow us to override those basic primary instincts. Why Trends Are Important What an entrepreneur does is to shift the perspective: instead of seeing a lack, he/she sees an opportunity by asking the question: how can I fulfill this need? Or, even better put: how can I help people by solving their problem? That’s the first step to solving a problem: asking a question. That is why the best products are actually problems solved by entrepreneurs who work to solve their own need for a product, service or experience. They then provide it to other people for a cost. Easy, right? That’s what entrepreneurship is: solving a problem. The bigger the problem, the bigger the impact. The bigger the impact, the higher the revenue. It’s easier to understand trends now, isn’t it? You can see that trends are nothing more than the initial adoption of a product, service or experience by a group of people who are looking for a solution to their common need. 🦉 : Did you get that from a book? 🕵️ : You snore when you sleep… ¯\\(\ツ)/\¯ 🦉 : $@#&\*! Hooman! Needs are the foundation on which the modern world is built. Once you understand needs, you fundamentally change your perception of problems into opportunities. This mental shift is the entrepreneurial mindset: where others see problems, you see solutions. Where Do Trends Start So, to recap: human needs are translated into problems. Founders understand the root of the problem (the need) and create products, services, experiences as solutions to those needs. They offer the solution to the public through startups and companies, which belong to a specific niche in a particular industry. 🦉 : Aaah, so that’s why it’s called venture capital? 🕵️ : Yeah, because you’re venturing into a new endeavor to let people know about your solution to their (and ideally your) problem. 🦉 : So if you use ads to market your venture, it’s an adventure? 🕵️ : I see what you did there… If the need behind the adoption is strong and real enough, that trend will translate into a niche within an industry. If the adoption isn’t driven by strong fundamental needs, it will turn into a fad and disappear from the perception of the public, no matter how much marketing money is thrown at it. This happens because the solution (product/service/experience) to the need didn’t create the physical, intellectual or emotional response required to create a recurring behavior around it. Remember this: Problem (why) -> Behavior (how) -> Solution (what) Understand this: there are multiple types of trends. There are product or service trends. There are industry driven trends. There are tendency driven trends, like the emergence of a new paradigm that improves a lot of industries (yes, I’m looking at you, AI). Where Do Trends Come From So now you can see that trends are patterns of adoption related to a specific human need that is addressed through one or multiple products or services. This is a bottom up direction coming from evolution. Multiple trends in different industries also emerge from a theme, which is a bigger vision of a human effort to address a high level problem. This is a top down direction, coming from implementation (by governments, different organizations or other interested parties with the power to influence changes at mass level). Conclusion Now you have a better understanding of trends by looking at them through the lens of human needs. Also, you might also understand time better because you realize that human needs have different degrees of impact in time and intensity. So you now see that trends don’t only relate to individuals, but also to groups of people, from the smallest community to countries and even global needs. That is the reason you’ll sometimes hear some say that time is a flat circle: because clothes change, but humans are quite the same. Needs don’t change a lot in time, just the way we address and solve them. Here’s an interesting game for you: take a look at some behaviors in your life. Which of them are driven by a bodily need, which by an intellectual or emotional one? Which ones are completely automated and you had no idea you were doing? How are these behaviors controlling parts of your life that you were unaware of until now? If you made it this far, thank you for taking the time to read this. I hope you enjoyed it, found it useful and entertaining. Ofc, I value your opinion and welcome it in the comments. Thank you!

Marketing Automation Trends To Look For in 2018
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Human Vibe Score1
SoffrontHQThis week

Marketing Automation Trends To Look For in 2018

As the new year is upon us, marketing automation software and AI continue to soar in the CRM Industry. In 2018, keep an eye out for these constraints as they will revolutionize marketing, keeping customer engagement in the forefront. Customer experience: In 2018, customer experience will be instrumental in driving the marketing automation software market. The recent shift in the trends of markets has forced the companies to develop new ways of engaging the customer and giving them an enriched experience. As new strategies are deployed to the target customer, shorter content, full streaming videos or infographics will be preferred. Content marketing automation: After the content is finished, it is only left to communicate it to the right channels, at the right time. But in order to have an edge in the regularity of publications and efficiency, companies are opting for automation tools to communicate and promote content through various channels. The results are obvious, not only you gain efficiency but this method helps in reaching and retaining those group of individuals whose appointments happen on a daily basis thus putting your company in the expert bracket. Chatbots: Chatbots are perfect examples of online CRM applications impacting the business in 2018. These intelligent programs have the ability to comprehend, analyze and then formulate an adequate reply to customer queries in real time. Ever since Facebook messenger opened its API, the ease and simplicity of installing these on CMS have inspired a lot of companies to implement it. In the future, their challenge will be to innovate customer engagement providing a better user experience rather than mere customer service using customer data. Further expectations will shape up in the form of artificial empathy where they will be able to connect to the customer emotionally and listen to their wanting. This will automate customer expectations and enable humans to focus on their “real” customer holding strong added value. The future looks bright with thought-leaders pioneering digital transformation and paving the way to tremendous opportunities. If they can manage to anticipate the consequence of the current mutations, companies will evolve and marketing resources will experience growth like never before.

With Vibe Coding Say Goodbye to Boring Coding!
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LLM Vibe Score0.321
Human Vibe Score0.44
GeeksforGeeksMar 27, 2025

With Vibe Coding Say Goodbye to Boring Coding!

Coding doesn’t have to be boring anymore! With the rise of AI-powered tools and innovative development approaches, the way we write code is changing drastically. Are you ready to embrace this new era of vibe coding? 🚀 💡 Want to level up your coding and problem-solving skills? Join the Three 90 Challenge by GeeksforGeeks—ending on 31st March! ✅ Complete 90% of your course in 90 days ✅ Get 90% of your fee refunded! Yes, you read that right! 🌟 Over ₹5 CRORE in refunds already processed—yours could be next! 👉 Start the challenge now: https://gfgcdn.com/tu/U4a/ 📌 Stay Connected for More Coding Challenges & Learning Resources: 📱 Download the GeeksforGeeks App: https://play.google.com/store/apps/details?id=free.programming.programming 💬 Twitter: https://twitter.com/geeksforgeeks 🧑‍💼 LinkedIn: https://www.linkedin.com/company/geeksforgeeks 📷 Instagram: https://www.instagram.com/geeksforgeeks/ 💌 Telegram: https://t.me/geeksforgeeks_official 📌 Pinterest: https://in.pinterest.com/geeksforgeeks/ 🎮 Discord: https://discord.gg/geeksforgeeks 🔍 Tags: AI Coding, AI-Powered Development, Vibe Coding, Future of Programming, Software Development Trends, Coding with AI, AI-Assisted Programming, Tech Innovations, Machine Learning in Coding, AI Coding Assistants, Software Engineering Revolution, AI for Developers, ChatGPT Coding, AI Coding Tools, gfg, gfg courses, gfg classes, it jobs, it job market, ai trends, ai news, ai vs software developers 🔥 Hashtags: #AICoding #FutureOfProgramming #VibeCoding #SoftwareDevelopment #TechTrends #CodingWithAI #AIRevolution #AIInTech #MachineLearning #CodingFuture #GeeksforGeeks #CodeSmarter #AIforDevelopers

160 of Y Combinators 229 Startup Cohort are AI Startups with and 75% of the Cohort has 0 revenue
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160 of Y Combinators 229 Startup Cohort are AI Startups with and 75% of the Cohort has 0 revenue

Y Combinator (YC), one of the most prestigious startup accelerators in the world, has just unveiled its latest batch of innovative startups, providing key insights into what the future might hold. Y Combinators Summer 2023 Batch In a recent post by Garry Tan, YC's president, Tan offers a nostalgic look back at his first YC Demo Day in 2008, where he, as a budding entrepreneur, pitched his startup. Now, fifteen years later, he's at the helm, proudly launching the 37th Demo Day, this time for the Summer 2023 batch. Tan proudly declares this batch as one of YC's most impressive yet, emphasizing the deep technical talent of the participants. From a staggering pool of over 24,000 applications, only 229 startups were chosen, making this one of the most competitive batches to date. This batch marks a number of firsts and solidifies several rising trends within the startups landscape. 75% of these companies began their YC journey with zero revenue, and 81% hadn't raised any funding before joining the accelerator. YC's decision to focus on early-stage startups this round signals their commitment to nurturing raw, untapped potential. A Return to Face-to-Face Interaction After three years, YC has brought back the in-person Demo Day format, allowing startups, investors, and mentors to connect directly. While the virtual format has its merits, there's an unmistakable magic in the YC Demo Day room, filled with anticipation, hope, and innovation. AI Takes Center Stage Artificial Intelligence is the standout sector in the Summer 2023 batch. With recent advancements making waves across various industries, there's arguably no better time to launch an AI-focused startup, and no better platform than YC to foster its growth. This signals a clear trend in the startup investing and venture capital space: AI is just getting started. Of the entire Summer 2023 batch, 160 out of the entire 229 Summer 2023 batch that are utilizing or implementing artificial intelligence in some capacity. This means over 2 out of every 3 startups accepted is focused on artificial intelligence in some capacity. Some of the startups include: Quill AI: Automating the job of a financial analyst Fiber AI: Automating prospecting and outbound marketing Reworkd AI: Open Source Zapier of AI Agents Watto AI: AI-powered McKinsey-quality reports in seconds Agentive: AI-powered auditing platform Humanlike: Replace your call center with voice bots that sound human Greenlite: AI compliance team for fintech and banking atla: AI assistants to help in-house lawyers answer legal questions Studdy: An AI Match tutor Glade: League of Legends with AI-generated maps and gameplay and literally over 100 others. As you can see, there's a startup covering nearly every sector of AI in the new batch. YC By The Numbers YC continues to grow as a community. The accelerator now boasts over 10,000 founders spanning more than 4,500 startups. The success stories are impressive: over 350 startups valued at over $150 million and 90 valued at more than $1 billion. The unicorn creation rate of 5% is truly unparalleled in the industry. To cater to the ever-growing community, YC has added more full-time Group Partners than ever. This includes industry veterans such as Tom Blomfield, co-founder of billion-dollar startups GoCardless and Monzo, and YC alumni like Wayne Crosby (Zenter) and Emmett Shear (Twitch). YC Core Values YC's commitment to diversity is evident in the demographics of the S23 batch. They've also spotlighted the industries these startups operate in, with 70% in B2B SaaS/Enterprise, followed by fintech, healthcare, consumer, and proptech/industrials. Garry Tan emphasizes three core tenets for YC investors: to act ethically, to make decisions swiftly, and to commit long-term. He underlines the importance of the YC community, urging investors to provide valuable introductions and guidance to founders. The Road Ahead With YC's track record and the promise shown by the Summer 2023 batch, the future of the startup ecosystem looks promising. As always, YC remains at the forefront, championing innovation and shaping the next generation of global startups. Original Post: https://www.democratizing.finance/post/take-a-peek-into-the-future-with-y-combinators-finalized-summer-2023-batch

How a founder built a B2B AI startup to serve with 65+ global brands (including Fortune500 companies) (I will not promote)
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How a founder built a B2B AI startup to serve with 65+ global brands (including Fortune500 companies) (I will not promote)

AI Palette is an AI-driven platform that helps food and beverage companies predict emerging product trends. I had the opportunity recently to sit down with the founder to get his advice on building an AI-first startup, which he'll be going through in this post. (I will not promote) About AI Palette: Co-founders: >!2 (Somsubhra GanChoudhuri, Himanshu Upreti)!!100+!!$12.7M USD!!AI-powered predictive analytics for the CPG (Consumer Packaged Goods) industry!!Signed first paying customer in the first year!!65+ global brands, including Cargill, Diageo, Ajinomoto, Symrise, Mondelez, and L’Oréal, use AI Palette!!Every new product launched has secured a paying client within months!!Expanded into Beauty & Personal Care (BPC), onboarding one of India’s largest BPC companies within weeks!!Launched multiple new product lines in the last two years, creating a unified suite for brand innovation!Identify the pain points in your industry for ideas* When I was working in the flavour and fragrance industry, I noticed a major issue CPG companies faced: launching a product took at least one to two years. For instance, if a company decided today to launch a new juice, it wouldn’t hit the market until 2027. This long timeline made it difficult to stay relevant and on top of trends. Another big problem I noticed was that companies relied heavily on market research to determine what products to launch. While this might work for current consumer preferences, it was highly inefficient since the product wouldn’t actually reach the market for several years. By the time the product launched, the consumer trends had already shifted, making that research outdated. That’s where AI can play a crucial role. Instead of looking at what consumers like today, we realised that companies should use AI to predict what they will want next. This allows businesses to create products that are ahead of the curve. Right now, the failure rate for new product launches is alarmingly high, with 8 out of 10 products failing. By leveraging AI, companies can avoid wasting resources on products that won’t succeed, leading to better, more successful launches. Start by talking to as many industry experts as possible to identify the real problems When we first had the idea for AI Palette, it was just a hunch, a gut feeling—we had no idea whether people would actually pay for it. To validate the idea, we reached out to as many people as we could within the industry. Since our focus area was all about consumer insights, we spoke to professionals in the CPG sector, particularly those in the insights departments of CPG companies. Through these early conversations, we began to see a common pattern emerge and identified the exact problem we wanted to solve. Don’t tell people what you’re building—listen to their frustrations and challenges first. Going into these early customer conversations, our goal was to listen and understand their challenges without telling them what we were trying to build. This is crucial as it ensures that you can gather as much data about the problem to truly understand it and that you aren't biasing their answers by showing your solution. This process helped us in two key ways: First, it validated that there was a real problem in the industry through the number of people who spoke about experiencing the same problem. Second, it allowed us to understand the exact scale and depth of the problem—e.g., how much money companies were spending on consumer research, what kind of tools they were currently using, etc. Narrow down your focus to a small, actionable area to solve initially. Once we were certain that there was a clear problem worth solving, we didn’t try to tackle everything at once. As a small team of two people, we started by focusing on a specific area of the problem—something big enough to matter but small enough for us to handle. Then, we approached customers with a potential solution and asked them for feedback. We learnt that our solution seemed promising, but we wanted to validate it further. If customers are willing to pay you for the solution, it’s a strong validation signal for market demand. One of our early customer interviewees even asked us to deliver the solution, which we did manually at first. We used machine learning models to analyse the data and presented the results in a slide deck. They paid us for the work, which was a critical moment. It meant we had something with real potential, and we had customers willing to pay us before we had even built the full product. This was the key validation that we needed. By the time we were ready to build the product, we had already gathered crucial insights from our early customers. We understood the specific information they wanted and how they wanted the results to be presented. This input was invaluable in shaping the development of our final product. Building & Product Development Start with a simple concept/design to validate with customers before building When we realised the problem and solution, we began by designing the product, but not by jumping straight into coding. Instead, we created wireframes and user interfaces using tools like InVision and Figma. This allowed us to visually represent the product without the need for backend or frontend development at first. The goal was to showcase how the product would look and feel, helping potential customers understand its value before we even started building. We showed these designs to potential customers and asked for feedback. Would they want to buy this product? Would they pay for it? We didn’t dive into actual development until we found a customer willing to pay a significant amount for the solution. This approach helped us ensure we were on the right track and didn’t waste time or resources building something customers didn’t actually want. Deliver your solution using a manual consulting approach before developing an automated product Initially, we solved problems for customers in a more "consulting" manner, delivering insights manually. Recall how I mentioned that when one of our early customer interviewees asked us to deliver the solution, we initially did it manually by using machine learning models to analyse the data and presenting the results to them in a slide deck. This works for the initial stages of validating your solution, as you don't want to invest too much time into building a full-blown MVP before understanding the exact features and functionalities that your users want. However, after confirming that customers were willing to pay for what we provided, we moved forward with actual product development. This shift from a manual service to product development was key to scaling in a sustainable manner, as our building was guided by real-world feedback and insights rather than intuition. Let ongoing customer feedback drive iteration and the product roadmap Once we built the first version of the product, it was basic, solving only one problem. But as we worked closely with customers, they requested additional features and functionalities to make it more useful. As a result, we continued to evolve the product to handle more complex use cases, gradually developing new modules based on customer feedback. Product development is a continuous process. Our early customers pushed us to expand features and modules, from solving just 20% of their problems to tackling 50–60% of their needs. These demands shaped our product roadmap and guided the development of new features, ultimately resulting in a more complete solution. Revenue and user numbers are key metrics for assessing product-market fit. However, critical mass varies across industries Product-market fit (PMF) can often be gauged by looking at the size of your revenue and the number of customers you're serving. Once you've reached a certain critical mass of customers, you can usually tell that you're starting to hit product-market fit. However, this critical mass varies by industry and the type of customers you're targeting. For example, if you're building an app for a broad consumer market, you may need thousands of users. But for enterprise software, product-market fit may be reached with just a few dozen key customers. Compare customer engagement and retention with other available solutions on the market for product-market fit Revenue and the number of customers alone isn't always enough to determine if you're reaching product-market fit. The type of customer and the use case for your product also matter. The level of engagement with your product—how much time users are spending on the platform—is also an important metric to track. The more time they spend, the more likely it is that your product is meeting a crucial need. Another way to evaluate product-market fit is by assessing retention, i.e whether users are returning to your platform and relying on it consistently, as compared to other solutions available. That's another key indication that your solution is gaining traction in the market. Business Model & Monetisation Prioritise scalability Initially, we started with a consulting-type model where we tailor-made specific solutions for each customer use-case we encountered and delivered the CPG insights manually, but we soon realized that this wasn't scalable. The problem with consulting is that you need to do the same work repeatedly for every new project, which requires a large team to handle the workload. That is not how you sustain a high-growth startup. To solve this, we focused on building a product that would address the most common problems faced by our customers. Once built, this product could be sold to thousands of customers without significant overheads, making the business scalable. With this in mind, we decided on a SaaS (Software as a Service) business model. The benefit of SaaS is that once you create the software, you can sell it to many customers without adding extra overhead. This results in a business with higher margins, where the same product can serve many customers simultaneously, making it much more efficient than the consulting model. Adopt a predictable, simplistic business model for efficiency. Look to industry practices for guidance When it came to monetisation, we considered the needs of our CPG customers, who I knew from experience were already accustomed to paying annual subscriptions for sales databases and other software services. We decided to adopt the same model and charge our customers an annual upfront fee. This model worked well for our target market, aligning with industry standards and ensuring stable, recurring revenue. Moreover, our target CPG customers were already used to this business model and didn't have to choose from a huge variety of payment options, making closing sales a straightforward and efficient process. Marketing & Sales Educate the market to position yourself as a thought leader When we started, AI was not widely understood, especially in the CPG industry. We had to create awareness around both AI and its potential value. Our strategy focused on educating potential users and customers about AI, its relevance, and why they should invest in it. This education was crucial to the success of our marketing efforts. To establish credibility, we adopted a thought leadership approach. We wrote blogs on the importance of AI and how it could solve problems for CPG companies. We also participated in events and conferences to demonstrate our expertise in applying AI to the industry. This helped us build our brand and reputation as leaders in the AI space for CPG, and word-of-mouth spread as customers recognized us as the go-to company for AI solutions. It’s tempting for startups to offer products for free in the hopes of gaining early traction with customers, but this approach doesn't work in the long run. Free offerings don’t establish the value of your product, and customers may not take them seriously. You should always charge for pilots, even if the fee is minimal, to ensure that the customer is serious about potentially working with you, and that they are committed and engaged with the product. Pilots/POCs/Demos should aim to give a "flavour" of what you can deliver A paid pilot/POC trial also gives you the opportunity to provide a “flavour” of what your product can deliver, helping to build confidence and trust with the client. It allows customers to experience a detailed preview of what your product can do, which builds anticipation and desire for the full functionality. During this phase, ensure your product is built to give them a taste of the value you can provide, which sets the stage for a broader, more impactful adoption down the line. Fundraising & Financial Management Leverage PR to generate inbound interest from VCs When it comes to fundraising, our approach was fairly traditional—we reached out to VCs and used connections from existing investors to make introductions. However, looking back, one thing that really helped us build momentum during our fundraising process was getting featured in Tech in Asia. This wasn’t planned; it just so happened that Tech in Asia was doing a series on AI startups in Southeast Asia and they reached out to us for an article. During the interview, they asked if we were fundraising, and we mentioned that we were. As a result, several VCs we hadn’t yet contacted reached out to us. This inbound interest was incredibly valuable, and we found it far more effective than our outbound efforts. So, if you can, try to generate some PR attention—it can help create inbound interest from VCs, and that interest is typically much stronger and more promising than any outbound strategies because they've gone out of their way to reach out to you. Be well-prepared and deliberate about fundraising. Keep trying and don't lose heart When pitching to VCs, it’s crucial to be thoroughly prepared, as you typically only get one shot at making an impression. If you mess up, it’s unlikely they’ll give you a second chance. You need to have key metrics at your fingertips, especially if you're running a SaaS company. Be ready to answer questions like: What’s your retention rate? What are your projections for the year? How much will you close? What’s your average contract value? These numbers should be at the top of your mind. Additionally, fundraising should be treated as a structured process, not something you do on the side while juggling other tasks. When you start, create a clear plan: identify 20 VCs to reach out to each week. By planning ahead, you’ll maintain momentum and speed up the process. Fundraising can be exhausting and disheartening, especially when you face multiple rejections. Remember, you just need one investor to say yes to make it all worthwhile. When using funds, prioritise profitability and grow only when necessary. Don't rely on funding to survive. In the past, the common advice for startups was to raise money, burn through it quickly, and use it to boost revenue numbers, even if that meant operating at a loss. The idea was that profitability wasn’t the main focus, and the goal was to show rapid growth for the next funding round. However, times have changed, especially with the shift from “funding summer” to “funding winter.” My advice now is to aim for profitability as soon as possible and grow only when it's truly needed. For example, it’s tempting to hire a large team when you have substantial funds in the bank, but ask yourself: Do you really need 10 new hires, or could you get by with just four? Growing too quickly can lead to unnecessary expenses, so focus on reaching profitability as soon as possible, rather than just inflating your team or burn rate. The key takeaway is to spend your funds wisely and only when absolutely necessary to reach profitability. You want to avoid becoming dependent on future VC investments to keep your company afloat. Instead, prioritize reaching break-even as quickly as you can, so you're not reliant on external funding to survive in the long run. Team-Building & Leadership Look for complementary skill sets in co-founders When choosing a co-founder, it’s important to find someone with a complementary skill set, not just someone you’re close to. For example, I come from a business and commercial background, so I needed someone with technical expertise. That’s when I found my co-founder, Himanshu, who had experience in machine learning and AI. He was a great match because his technical knowledge complemented my business skills, and together we formed a strong team. It might seem natural to choose your best friend as your co-founder, but this can often lead to conflict. Chances are, you and your best friend share similar interests, skills, and backgrounds, which doesn’t bring diversity to the table. If both of you come from the same industry or have the same strengths, you may end up butting heads on how things should be done. Having diverse skill sets helps avoid this and fosters a more collaborative working relationship. Himanshu (left) and Somsubhra (right) co-founded AI Palette in 2018 Define roles clearly to prevent co-founder conflict To avoid conflict, it’s essential that your roles as co-founders are clearly defined from the beginning. If your co-founder and you have distinct responsibilities, there is no room for overlap or disagreement. This ensures that both of you can work without stepping on each other's toes, and there’s mutual respect for each other’s expertise. This is another reason as to why it helps to have a co-founder with a complementary skillset to yours. Not only is having similar industry backgrounds and skillsets not particularly useful when building out your startup, it's also more likely to lead to conflicts since you both have similar subject expertise. On the other hand, if your co-founder is an expert in something that you're not, you're less likely to argue with them about their decisions regarding that aspect of the business and vice versa when it comes to your decisions. Look for employees who are driven by your mission, not salary For early-stage startups, the first hires are crucial. These employees need to be highly motivated and excited about the mission. Since the salary will likely be low and the work demanding, they must be driven by something beyond just the paycheck. The right employees are the swash-buckling pirates and romantics, i.e those who are genuinely passionate about the startup’s vision and want to be part of something impactful beyond material gains. When employees are motivated by the mission, they are more likely to stick around and help take the startup to greater heights. A litmus test for hiring: Would you be excited to work with them on a Sunday? One of the most important rounds in the hiring process is the culture fit round. This is where you assess whether a candidate shares the same values as you and your team. A key question to ask yourself is: "Would I be excited to work with this person on a Sunday?" If there’s any doubt about your answer, it’s likely not a good fit. The idea is that you want employees who align with the company's culture and values and who you would enjoy collaborating with even outside of regular work hours. How we structure the team at AI Palette We have three broad functions in our organization. The first two are the big ones: Technical Team – This is the core of our product and technology. This team is responsible for product development and incorporating customer feedback into improving the technology Commercial Team – This includes sales, marketing, customer service, account managers, and so on, handling everything related to business growth and customer relations. General and Administrative Team – This smaller team supports functions like finance, HR, and administration. As with almost all businesses, we have teams that address the two core tasks of building (technical team) and selling (commercial team), but given the size we're at now, having the administrative team helps smoothen operations. Set broad goals but let your teams decide on execution What I've done is recruit highly skilled people who don't need me to micromanage them on a day-to-day basis. They're experts in their roles, and as Steve Jobs said, when you hire the right person, you don't have to tell them what to do—they understand the purpose and tell you what to do. So, my job as the CEO is to set the broader goals for them, review the plans they have to achieve those goals, and periodically check in on progress. For example, if our broad goal is to meet a certain revenue target, I break it down across teams: For the sales team, I’ll look at how they plan to hit that target—how many customers they need to sell to, how many salespeople they need, and what tactics and strategies they plan to use. For the technical team, I’ll evaluate our product offerings—whether they think we need to build new products to attract more customers, and whether they think it's scalable for the number of customers we plan to serve. This way, the entire organization's tasks are cascaded in alignment with our overarching goals, with me setting the direction and leaving the details of execution to the skilled team members that I hire.

From “Green” to “Smart” – Tom Gorski’s Word of Advice
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From “Green” to “Smart” – Tom Gorski’s Word of Advice

Sharing this interview with entrepreneur Tom Gorski. I think it contains a few nice tips for beginner entrepreneurs. What is the problem with the term “Green?” what are the top 3 mistakes entrepreneurs make that can prevent them from enjoying the sweet taste of success? And what should young entrepreneurs always keep in mind? Continuing our expert interview series, we asked entrepreneur Tom Gorski to share some of his secrets to success with us. Gorski is the CEO and Co-Founder at SaaSGenius.com, and an Inbound Marketer & Growth Hacker at InboundWay.com. His career spans over 12 years of developing and implementing online marketing, SEO and conversion optimization campaigns. He defines his biggest accomplishment to date as “achieving 4500% growth for one of my clients over a three­year period.” logo-saasgenius Q: It’s no secret that the SaaS market is saturated, as new companies are having very hard time acquiring, retaining and monetizing users. In your view – what are the top 3 mistakes SaaS companies make? What are some key differentiators you recognize in a successful product? A: Mistake No. 1: Product-market fit is not good enough There are a number of reasons for this, including the fact that inertia, incumbency and bureaucracy are all working against you. For emerging companies, this means finding a way to be exponentially better with fewer resources. As a result, focus is key. Mistake No. 2: Not Specializing Your Sales Roles When you specialize your sales people, you allow them to focus, which creates greater output form your sales team. Mistake No. 3: You Need a Niche To be able to market and sell well, you need to have a niche. The world is noisy and messy, and you’ll struggle if you don’t have a sharp, direct message. When you try to speak to everyone, no one can hear you. Q: Which innovative trends do you recognize in the high tech world nowadays? A: “Green” was a mega trend of the last decade and while it will continue to be very important, there will be a shift towards “smart” solutions, which are intelligent, connected and have the ability to sense, report, and take the right action. Smart solutions will be everywhere around us from smart clothing, phones, to smart homes and smart cities. Q: What is the most significant advice you can give young entrepreneurs? A: Being very successful means learning from those who have already achieved success. Having a mentor is an amazing blessing to an entrepreneur, but not everyone can find one in person. My advice is to work smarter, not harder. This is the most non-intuitive observation I will probably make. If you want to compete in the arena, hard work isn’t enough. And judging yourself on how hard you work, rather than how smart you work can be fatal. Q: We are flooded with buzzwords lately – VR / AI / Bots… where do you think the software world is heading? A: AI and bots are a very hot topic in 2016 and it’s sometimes hard to distinguish the real potential behind the hype. My point of view is that, like with many things, there’s no revolution but evolution. It’s unrealistic to think that AI can become mainstream in SaaS products without proper AI infrastructure. SaaS delivery will significantly outpace traditional software product delivery, growing nearly five times faster than the traditional software market and will become a significant growth driver for all functional software markets. By 2019, the SaaS software model will account for $1 of every $4 spent on software. Q: Let us in on some of your secrets… where do you look for innovation? For inspiration and revolutionary ideas? A: Ideas for new startups often begin with a real problem that needs to be solved. And they don’t come while you’re sitting around sipping coffee and contemplating life. They tend to reveal themselves while you’re at work on something else. Start with brainstorming with problems that you are personally invested in. Building a business is hard and takes the kind of relentless dedication that comes from personal passion. Perhaps the greatest factor that determines whether or not an entrepreneur will be successful isn’t the business idea itself, but rather the entrepreneur’s willingness to try to turn the idea into reality. Great ideas are abundant, but it’s what we decide to do with them that counts. Original post: http://saasaddict.walkme.com/from-green-to-smart-tom-gorskis-words-of-advice/

How a founder built a B2B AI startup to serve with 65+ global brands (including Fortune500 companies) (I will not promote)
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Royal_Rest8409This week

How a founder built a B2B AI startup to serve with 65+ global brands (including Fortune500 companies) (I will not promote)

AI Palette is an AI-driven platform that helps food and beverage companies predict emerging product trends. I had the opportunity recently to sit down with the founder to get his advice on building an AI-first startup, which he'll be going through in this post. (I will not promote) About AI Palette: Co-founders: >!2 (Somsubhra GanChoudhuri, Himanshu Upreti)!!100+!!$12.7M USD!!AI-powered predictive analytics for the CPG (Consumer Packaged Goods) industry!!Signed first paying customer in the first year!!65+ global brands, including Cargill, Diageo, Ajinomoto, Symrise, Mondelez, and L’Oréal, use AI Palette!!Every new product launched has secured a paying client within months!!Expanded into Beauty & Personal Care (BPC), onboarding one of India’s largest BPC companies within weeks!!Launched multiple new product lines in the last two years, creating a unified suite for brand innovation!Identify the pain points in your industry for ideas* When I was working in the flavour and fragrance industry, I noticed a major issue CPG companies faced: launching a product took at least one to two years. For instance, if a company decided today to launch a new juice, it wouldn’t hit the market until 2027. This long timeline made it difficult to stay relevant and on top of trends. Another big problem I noticed was that companies relied heavily on market research to determine what products to launch. While this might work for current consumer preferences, it was highly inefficient since the product wouldn’t actually reach the market for several years. By the time the product launched, the consumer trends had already shifted, making that research outdated. That’s where AI can play a crucial role. Instead of looking at what consumers like today, we realised that companies should use AI to predict what they will want next. This allows businesses to create products that are ahead of the curve. Right now, the failure rate for new product launches is alarmingly high, with 8 out of 10 products failing. By leveraging AI, companies can avoid wasting resources on products that won’t succeed, leading to better, more successful launches. Start by talking to as many industry experts as possible to identify the real problems When we first had the idea for AI Palette, it was just a hunch, a gut feeling—we had no idea whether people would actually pay for it. To validate the idea, we reached out to as many people as we could within the industry. Since our focus area was all about consumer insights, we spoke to professionals in the CPG sector, particularly those in the insights departments of CPG companies. Through these early conversations, we began to see a common pattern emerge and identified the exact problem we wanted to solve. Don’t tell people what you’re building—listen to their frustrations and challenges first. Going into these early customer conversations, our goal was to listen and understand their challenges without telling them what we were trying to build. This is crucial as it ensures that you can gather as much data about the problem to truly understand it and that you aren't biasing their answers by showing your solution. This process helped us in two key ways: First, it validated that there was a real problem in the industry through the number of people who spoke about experiencing the same problem. Second, it allowed us to understand the exact scale and depth of the problem—e.g., how much money companies were spending on consumer research, what kind of tools they were currently using, etc. Narrow down your focus to a small, actionable area to solve initially. Once we were certain that there was a clear problem worth solving, we didn’t try to tackle everything at once. As a small team of two people, we started by focusing on a specific area of the problem—something big enough to matter but small enough for us to handle. Then, we approached customers with a potential solution and asked them for feedback. We learnt that our solution seemed promising, but we wanted to validate it further. If customers are willing to pay you for the solution, it’s a strong validation signal for market demand. One of our early customer interviewees even asked us to deliver the solution, which we did manually at first. We used machine learning models to analyse the data and presented the results in a slide deck. They paid us for the work, which was a critical moment. It meant we had something with real potential, and we had customers willing to pay us before we had even built the full product. This was the key validation that we needed. By the time we were ready to build the product, we had already gathered crucial insights from our early customers. We understood the specific information they wanted and how they wanted the results to be presented. This input was invaluable in shaping the development of our final product. Building & Product Development Start with a simple concept/design to validate with customers before building When we realised the problem and solution, we began by designing the product, but not by jumping straight into coding. Instead, we created wireframes and user interfaces using tools like InVision and Figma. This allowed us to visually represent the product without the need for backend or frontend development at first. The goal was to showcase how the product would look and feel, helping potential customers understand its value before we even started building. We showed these designs to potential customers and asked for feedback. Would they want to buy this product? Would they pay for it? We didn’t dive into actual development until we found a customer willing to pay a significant amount for the solution. This approach helped us ensure we were on the right track and didn’t waste time or resources building something customers didn’t actually want. Deliver your solution using a manual consulting approach before developing an automated product Initially, we solved problems for customers in a more "consulting" manner, delivering insights manually. Recall how I mentioned that when one of our early customer interviewees asked us to deliver the solution, we initially did it manually by using machine learning models to analyse the data and presenting the results to them in a slide deck. This works for the initial stages of validating your solution, as you don't want to invest too much time into building a full-blown MVP before understanding the exact features and functionalities that your users want. However, after confirming that customers were willing to pay for what we provided, we moved forward with actual product development. This shift from a manual service to product development was key to scaling in a sustainable manner, as our building was guided by real-world feedback and insights rather than intuition. Let ongoing customer feedback drive iteration and the product roadmap Once we built the first version of the product, it was basic, solving only one problem. But as we worked closely with customers, they requested additional features and functionalities to make it more useful. As a result, we continued to evolve the product to handle more complex use cases, gradually developing new modules based on customer feedback. Product development is a continuous process. Our early customers pushed us to expand features and modules, from solving just 20% of their problems to tackling 50–60% of their needs. These demands shaped our product roadmap and guided the development of new features, ultimately resulting in a more complete solution. Revenue and user numbers are key metrics for assessing product-market fit. However, critical mass varies across industries Product-market fit (PMF) can often be gauged by looking at the size of your revenue and the number of customers you're serving. Once you've reached a certain critical mass of customers, you can usually tell that you're starting to hit product-market fit. However, this critical mass varies by industry and the type of customers you're targeting. For example, if you're building an app for a broad consumer market, you may need thousands of users. But for enterprise software, product-market fit may be reached with just a few dozen key customers. Compare customer engagement and retention with other available solutions on the market for product-market fit Revenue and the number of customers alone isn't always enough to determine if you're reaching product-market fit. The type of customer and the use case for your product also matter. The level of engagement with your product—how much time users are spending on the platform—is also an important metric to track. The more time they spend, the more likely it is that your product is meeting a crucial need. Another way to evaluate product-market fit is by assessing retention, i.e whether users are returning to your platform and relying on it consistently, as compared to other solutions available. That's another key indication that your solution is gaining traction in the market. Business Model & Monetisation Prioritise scalability Initially, we started with a consulting-type model where we tailor-made specific solutions for each customer use-case we encountered and delivered the CPG insights manually, but we soon realized that this wasn't scalable. The problem with consulting is that you need to do the same work repeatedly for every new project, which requires a large team to handle the workload. That is not how you sustain a high-growth startup. To solve this, we focused on building a product that would address the most common problems faced by our customers. Once built, this product could be sold to thousands of customers without significant overheads, making the business scalable. With this in mind, we decided on a SaaS (Software as a Service) business model. The benefit of SaaS is that once you create the software, you can sell it to many customers without adding extra overhead. This results in a business with higher margins, where the same product can serve many customers simultaneously, making it much more efficient than the consulting model. Adopt a predictable, simplistic business model for efficiency. Look to industry practices for guidance When it came to monetisation, we considered the needs of our CPG customers, who I knew from experience were already accustomed to paying annual subscriptions for sales databases and other software services. We decided to adopt the same model and charge our customers an annual upfront fee. This model worked well for our target market, aligning with industry standards and ensuring stable, recurring revenue. Moreover, our target CPG customers were already used to this business model and didn't have to choose from a huge variety of payment options, making closing sales a straightforward and efficient process. Marketing & Sales Educate the market to position yourself as a thought leader When we started, AI was not widely understood, especially in the CPG industry. We had to create awareness around both AI and its potential value. Our strategy focused on educating potential users and customers about AI, its relevance, and why they should invest in it. This education was crucial to the success of our marketing efforts. To establish credibility, we adopted a thought leadership approach. We wrote blogs on the importance of AI and how it could solve problems for CPG companies. We also participated in events and conferences to demonstrate our expertise in applying AI to the industry. This helped us build our brand and reputation as leaders in the AI space for CPG, and word-of-mouth spread as customers recognized us as the go-to company for AI solutions. It’s tempting for startups to offer products for free in the hopes of gaining early traction with customers, but this approach doesn't work in the long run. Free offerings don’t establish the value of your product, and customers may not take them seriously. You should always charge for pilots, even if the fee is minimal, to ensure that the customer is serious about potentially working with you, and that they are committed and engaged with the product. Pilots/POCs/Demos should aim to give a "flavour" of what you can deliver A paid pilot/POC trial also gives you the opportunity to provide a “flavour” of what your product can deliver, helping to build confidence and trust with the client. It allows customers to experience a detailed preview of what your product can do, which builds anticipation and desire for the full functionality. During this phase, ensure your product is built to give them a taste of the value you can provide, which sets the stage for a broader, more impactful adoption down the line. Fundraising & Financial Management Leverage PR to generate inbound interest from VCs When it comes to fundraising, our approach was fairly traditional—we reached out to VCs and used connections from existing investors to make introductions. However, looking back, one thing that really helped us build momentum during our fundraising process was getting featured in Tech in Asia. This wasn’t planned; it just so happened that Tech in Asia was doing a series on AI startups in Southeast Asia and they reached out to us for an article. During the interview, they asked if we were fundraising, and we mentioned that we were. As a result, several VCs we hadn’t yet contacted reached out to us. This inbound interest was incredibly valuable, and we found it far more effective than our outbound efforts. So, if you can, try to generate some PR attention—it can help create inbound interest from VCs, and that interest is typically much stronger and more promising than any outbound strategies because they've gone out of their way to reach out to you. Be well-prepared and deliberate about fundraising. Keep trying and don't lose heart When pitching to VCs, it’s crucial to be thoroughly prepared, as you typically only get one shot at making an impression. If you mess up, it’s unlikely they’ll give you a second chance. You need to have key metrics at your fingertips, especially if you're running a SaaS company. Be ready to answer questions like: What’s your retention rate? What are your projections for the year? How much will you close? What’s your average contract value? These numbers should be at the top of your mind. Additionally, fundraising should be treated as a structured process, not something you do on the side while juggling other tasks. When you start, create a clear plan: identify 20 VCs to reach out to each week. By planning ahead, you’ll maintain momentum and speed up the process. Fundraising can be exhausting and disheartening, especially when you face multiple rejections. Remember, you just need one investor to say yes to make it all worthwhile. When using funds, prioritise profitability and grow only when necessary. Don't rely on funding to survive. In the past, the common advice for startups was to raise money, burn through it quickly, and use it to boost revenue numbers, even if that meant operating at a loss. The idea was that profitability wasn’t the main focus, and the goal was to show rapid growth for the next funding round. However, times have changed, especially with the shift from “funding summer” to “funding winter.” My advice now is to aim for profitability as soon as possible and grow only when it's truly needed. For example, it’s tempting to hire a large team when you have substantial funds in the bank, but ask yourself: Do you really need 10 new hires, or could you get by with just four? Growing too quickly can lead to unnecessary expenses, so focus on reaching profitability as soon as possible, rather than just inflating your team or burn rate. The key takeaway is to spend your funds wisely and only when absolutely necessary to reach profitability. You want to avoid becoming dependent on future VC investments to keep your company afloat. Instead, prioritize reaching break-even as quickly as you can, so you're not reliant on external funding to survive in the long run. Team-Building & Leadership Look for complementary skill sets in co-founders When choosing a co-founder, it’s important to find someone with a complementary skill set, not just someone you’re close to. For example, I come from a business and commercial background, so I needed someone with technical expertise. That’s when I found my co-founder, Himanshu, who had experience in machine learning and AI. He was a great match because his technical knowledge complemented my business skills, and together we formed a strong team. It might seem natural to choose your best friend as your co-founder, but this can often lead to conflict. Chances are, you and your best friend share similar interests, skills, and backgrounds, which doesn’t bring diversity to the table. If both of you come from the same industry or have the same strengths, you may end up butting heads on how things should be done. Having diverse skill sets helps avoid this and fosters a more collaborative working relationship. Himanshu (left) and Somsubhra (right) co-founded AI Palette in 2018 Define roles clearly to prevent co-founder conflict To avoid conflict, it’s essential that your roles as co-founders are clearly defined from the beginning. If your co-founder and you have distinct responsibilities, there is no room for overlap or disagreement. This ensures that both of you can work without stepping on each other's toes, and there’s mutual respect for each other’s expertise. This is another reason as to why it helps to have a co-founder with a complementary skillset to yours. Not only is having similar industry backgrounds and skillsets not particularly useful when building out your startup, it's also more likely to lead to conflicts since you both have similar subject expertise. On the other hand, if your co-founder is an expert in something that you're not, you're less likely to argue with them about their decisions regarding that aspect of the business and vice versa when it comes to your decisions. Look for employees who are driven by your mission, not salary For early-stage startups, the first hires are crucial. These employees need to be highly motivated and excited about the mission. Since the salary will likely be low and the work demanding, they must be driven by something beyond just the paycheck. The right employees are the swash-buckling pirates and romantics, i.e those who are genuinely passionate about the startup’s vision and want to be part of something impactful beyond material gains. When employees are motivated by the mission, they are more likely to stick around and help take the startup to greater heights. A litmus test for hiring: Would you be excited to work with them on a Sunday? One of the most important rounds in the hiring process is the culture fit round. This is where you assess whether a candidate shares the same values as you and your team. A key question to ask yourself is: "Would I be excited to work with this person on a Sunday?" If there’s any doubt about your answer, it’s likely not a good fit. The idea is that you want employees who align with the company's culture and values and who you would enjoy collaborating with even outside of regular work hours. How we structure the team at AI Palette We have three broad functions in our organization. The first two are the big ones: Technical Team – This is the core of our product and technology. This team is responsible for product development and incorporating customer feedback into improving the technology Commercial Team – This includes sales, marketing, customer service, account managers, and so on, handling everything related to business growth and customer relations. General and Administrative Team – This smaller team supports functions like finance, HR, and administration. As with almost all businesses, we have teams that address the two core tasks of building (technical team) and selling (commercial team), but given the size we're at now, having the administrative team helps smoothen operations. Set broad goals but let your teams decide on execution What I've done is recruit highly skilled people who don't need me to micromanage them on a day-to-day basis. They're experts in their roles, and as Steve Jobs said, when you hire the right person, you don't have to tell them what to do—they understand the purpose and tell you what to do. So, my job as the CEO is to set the broader goals for them, review the plans they have to achieve those goals, and periodically check in on progress. For example, if our broad goal is to meet a certain revenue target, I break it down across teams: For the sales team, I’ll look at how they plan to hit that target—how many customers they need to sell to, how many salespeople they need, and what tactics and strategies they plan to use. For the technical team, I’ll evaluate our product offerings—whether they think we need to build new products to attract more customers, and whether they think it's scalable for the number of customers we plan to serve. This way, the entire organization's tasks are cascaded in alignment with our overarching goals, with me setting the direction and leaving the details of execution to the skilled team members that I hire.

Looking for a Marketing Partner for an Innovative AI Mobile App [i will not promote]
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Altruistic-Flan-8222This week

Looking for a Marketing Partner for an Innovative AI Mobile App [i will not promote]

Hello everyone! I'm a software engineer and AI developer working on something great in the mobile AI space. If you have been following the trends on TikTok and similar platforms, you have probably noticed the explosion of AI apps (like Rizz AI and similar) that follow the simple "scan → solve" concept. These apps have been massively successful because they solve specific problems with minimal user friction. Here's what makes my project different: I have identified an unique market where there is currently zero competition for this app idea that I'm creating and the potential user base is massive - we are talking about 200M+ potential users in the US alone (60% of the US population could use this app). Even capturing just 0.05% of this market could generate significant revenue, considering similar apps typically charge $4-6 per user. What I'm looking for: A marketing partner (preferably US-based or someone familiar with the US market/audience) who can help grow this app. Initially, it requires about 30–60 minutes per day for content creation and posting. No experience is required. If you don't have marketing experience, don't worry. In today's marketing, passion is often more important than skills (and a bit of luck, haha). What I'm offering: For now, it's a revenue share partnership. I have invested my savings into the development of the app and the necessary equipment and I'm offering a revenue share until we generate enough profit for paid positions. Once we gain traction, the goal is to transition this into a part-time or full-time role. If you have zero creativity skills, I can provide you with my automated content generation tool to assist with marketing. It is basically a script that generates the type of content that gets the most views on other AI apps promoted on social media platforms. This is also a long-term partnership, if we achieve some results but not good enough with one app, we can try a new niche or just continue on this one. About the project: The app is almost complete and will likely launch in mid-February. It is a self-funded venture, meaning all profits will be reinvested into growth, including ads, revenue sharing and potentially useful tools to improve marketing. Also, the app is unique, I made a deep research and there is no similar app in this niche and it is very easy to promote. Overall, it follows a simple and effective business model with a clear monetization strategy. If you're interested in being part of something with genuine growth potential and want to learn more, DM me. We can discuss details on Reddit, Discord, LinkedIn, anything you like. The app launches in mid-February so I'm looking to bring someone on board soon to help out. Note: I will share specific details about the niche and app functionality in private messages to protect the idea before launch.

The Advantages of a Custom CRM Solution
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NeerajKumarChaurasiaThis week

The Advantages of a Custom CRM Solution

The growth in the global CRM market continues to accelerate. According to techspective, the global CRM market is now worth \~ $40B USD and is expected to surpass $80B USD by 2025. Despite this phenomenal growth, the CRM market is still dominated by off-the-shelf solutions that are “cookie-cutter” in design and that provide little to no options for customization. These non-customized CRM solutions can significantly inhibit an enterprise’s ability to maximize the advantages of CRM adoption and to realize a robust ROI. As a result, companies are increasingly opting for digital CRM solutions that are customized to meet the unique needs of the enterprise. What is driving the increased demand for custom CRM solutions? What are some of the inherent advantages of a custom CRM solution when compared to a typical off-the-shelf product? Off-the-Shelf CRM Solutions – the Limitations Static CRM solutions are inflexible and self-limiting. Enterprises saddled with these cookie-cutter solutions increasingly report a consistent listing of issues that limit business growth.  These include…. A lack of real-time visibility into shifting customer trends and demands Delayed reaction to coordination of internal resources to meet changing business conditions Lost business opportunities due to lack of flexible, and real-time, opportunity lifecycle management Reporting and dashboarding capabilities that are slow, static, and disconnected Poor quote-to-cash performance that degrades financial performance A CRM investment that delivers poor ROI and that cannot grow with the enterprise All of the above can combine to limit the enterprise’s ability to fully capitalize on its hard-won business opportunities and, over time, limit its ability to create new opportunities. A Customized CRM – What is it? What is a “customized CRM”? Simply put, it is a holistic CRM solution that has been specifically tailored for the individual enterprise. The provider of a truly customized CRM solution will deliver a solution that has been designed to meet the specific—and unique—demands and objectives of the enterprise. A tailored CRM solution will address the enterprise’s sales and operational requirements as well as its customer experience objectives. Unlike standard off-the-shelf CRM providers, a provider of enterprise-grade custom CRM solutions will employ a comprehensive project discovery and requirements gathering process. This is an integral process that provides the foundation for the development of a custom solution that will provide the enterprise with long term flexibility and scalability. A customized digital CRM solution can provide distinct competitive advantages; including: Dynamic, Flexible, Powerful, Real-Time Management and Engagement A customized, technology\-fueled, CRM solution provides the enterprise with the means with which to dynamically engage with customers in ways that build customer loyalty, generate market growth, and drive strong financial performance. Distinct advantages include: Real-time sales opportunity tracking. Helps eliminate lost opportunities due to slow or inadequate reaction. Customized, AI and IoT-fueled, data analytics. A customized CRM solution can be designed to deliver real-time insights. Allows the enterprise to anticipate, and then satisfy, the needs of the customer. Customizable Dashboards and Reports. Widget-based, customized, dashboards and reports that provide real-time data and actionable insights. Sales process automation. Intelligent Workflow-based automation and control of critical sales processes. Increases overall operational efficiency. Outstanding ROI. A custom CRM solution typically delivers superior ROI when compared to off-the-shelf CRM products. Enterprises today spend considerable time, money, and effort in the development of customer relationships. For many enterprises, the continued use of CRM solutions that are rigid and outdated can prove to be impediments to business growth. When considering investment in a new CRM solution any enterprise will be well served by full consideration of a CRM solution that can be fully customized to meet its long-range requirements.

The Evolution of Financial Technology: How CAs Are Embracing the Digital Age
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ExpenectThis week

The Evolution of Financial Technology: How CAs Are Embracing the Digital Age

The Evolution of Financial Technology: How CAs Are Embracing the Digital Age Introduction In an era characterized by rapid technological advancements, the field of finance is undergoing a transformative journey. The emergence of financial technology, or fintech, is reshaping the way businesses manage their finances, and Chartered Accountants (CAs) are at the forefront of this evolution. In this blog post, we'll explore how CAs are embracing fintech and leveraging its potential to enhance financial management, analysis, and advisory services. Fintech's Impact on Financial Services Fintech encompasses a wide range of technologies that leverage data analytics, artificial intelligence, blockchain, and automation to improve financial services. For CAs, this means new tools to streamline processes, enhance decision-making, and offer innovative solutions to clients. Automation of Routine Tasks CAs are increasingly using automation tools to handle repetitive tasks such as data entry, reconciliations, and transaction processing. This not only reduces the risk of human error but also frees up CAs to focus on higher-value tasks like strategic planning and analysis. Advanced Data Analytics Data analytics tools enable CAs to extract meaningful insights from large volumes of financial data. These insights can help businesses identify trends, anticipate risks, and make informed decisions to drive growth. Real-Time Financial Reporting Fintech enables CAs to provide clients with real-time financial reporting, giving businesses immediate access to critical information. This enhances transparency and empowers business owners to respond quickly to changing market conditions. Enhancing Audit Efficiency Fintech tools are revolutionizing the audit process. CAs can use AI-powered algorithms to analyze vast amounts of data, detect anomalies, and identify potential instances of fraud more efficiently. Personalized Financial Planning CAs can leverage fintech to offer personalized financial planning services. With access to detailed financial data, CAs can create tailored strategies that align with a client's unique goals and circumstances. Strengthening Cybersecurity As businesses become more reliant on digital tools, cybersecurity becomes paramount. CAs are playing a critical role in advising clients on cybersecurity measures to protect sensitive financial information. Virtual CFO Services Fintech enables CAs to offer virtual CFO services to startups and small businesses. Through digital platforms, CAs can provide expert financial advice and guidance remotely, making their expertise accessible to a wider range of clients. Embracing Blockchain Technology Blockchain's potential for secure and transparent record-keeping is of interest to CAs. They can explore applications in supply chain finance, smart contracts, and even audit trail verification. Continuous Learning in Fintech CAs recognize the importance of staying updated with fintech trends. Many are investing in continuous learning to master the use of new tools and technologies that can optimize their services. Conclusion The integration of fintech into the realm of finance is reshaping the landscape in profound ways. CAs are embracing these technologies to elevate their roles from traditional number-crunchers to strategic advisors, equipped with tools that enhance efficiency, accuracy, and insight. As fintech continues to evolve, CAs will remain pivotal in guiding businesses through the ever-changing financial landscape, leveraging technology to drive growth, innovation, and success. Find the top verified CA in your City Feel free to let me know if you'd like more blogs on different topics or if you have specific requirements for the content.

The Future of AI in eCommerce Marketing: What to Expect 🚀
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McFlyAdsThis week

The Future of AI in eCommerce Marketing: What to Expect 🚀

Hey Reddit community! As we dive deeper into 2025, the integration of AI in eCommerce marketing is becoming more sophisticated and impactful. Here’s a look at where AI is headed and how it's revolutionizing the industry: Personalized Shopping Experiences: AI is enhancing personalization by analyzing consumer behavior and preferences, allowing retailers to offer tailored recommendations and promotions. This not only boosts customer satisfaction but also increases conversion rates. Chatbots and Virtual Assistants: AI-powered chatbots are becoming more intuitive and capable of handling complex queries, providing 24/7 customer support, and improving overall user experience. They’re a game-changer for eCommerce businesses looking to enhance customer engagement. Predictive Analytics: With AI, businesses can leverage predictive analytics to forecast trends, optimize inventory, and refine marketing strategies. This helps in making data-driven decisions that align with consumer demands and market dynamics. Automated Content Creation: AI tools are being used to generate product descriptions, social media posts, and even ad copy. This automation saves time and ensures consistency across marketing channels. Visual and Voice Search: AI is powering visual and voice search capabilities, making it easier for consumers to find products using images or voice commands. This technology is set to transform how users interact with eCommerce platforms. Fraud Detection: AI algorithms are improving fraud detection by analyzing transaction patterns and identifying anomalies. This is crucial for maintaining trust and security in online shopping. As AI continues to evolve, it will undoubtedly reshape the eCommerce landscape, offering new opportunities for innovation and growth. What are your thoughts on the future of AI in eCommerce marketing? Let's discuss!

Ai C-Level team
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thestoicdesignerThis week

Ai C-Level team

I've been exploring ways to run a company where I'm essentially the only internal team member, relying entirely on a suite of specialized AIs for executive roles, supported occasionally by external consultants for niche expertise. My goal is to stay lean, agile, and highly creative, especially in a fashion/tech brand context. Essentially, I'm building an AI-driven C-Level team, or what I like to call a "C-Level AI Wallet." Here's what I'm thinking for the key executive roles I'd need to cover with AI: CEO AI – Responsible for overall strategy, decision-making, trend analysis, and guiding the company's vision. I'd probably lean on something advanced like Gemini, GPT-4, or similar models, fine-tuned with market-specific data. COO AI (Operations): I'd need tools that streamline and automate logistics, supply chain management, and day-to-day operations (think something along the lines of Zapier AI integrations or Make). CMO AI (Marketing & Content): For branding, content creation, digital marketing, and consumer insights, I'd use Jasper or Copy.ai, combined with predictive analytics tools like Google Vertex AI to understand trends better. Additionally, for generating engaging visual and multimedia content, tools like Midjourney, DALL·E, Adobe Firefly, and Runway ML would be perfect. CFO AI (Financial Management): For financial management, cash flow control, and investment decisions, I'd probably leverage AI tools like Bloomberg GPT, combined with AI-powered forecasting platforms. CHRO AI (Human Resources & Culture): Although the internal team is minimal (just myself!), I'd still rely on AI for tasks like project management, freelancer hiring, and performance tracking—tools like HireVue AI, Motion, or even Notion's AI could be beneficial here. CSO AI (Sustainability & Compliance): Since sustainability and ethical sourcing are critical, I'd integrate ESG-focused AI tools to ensure transparency and responsible sourcing. My idea is that, with the right AI tools seamlessly integrated, I can manage the strategic vision and creative direction personally, leveraging external consultants only when necessary. This setup would ideally allow me to operate as a one-person internal team supported by a robust "wallet" of AI executives. Has anyone tried a similar approach? What AI tools would you recommend for a truly lean, innovative brand structure? I'm very curious about your experiences or suggestions—let me know your thoughts!

The Evolution of Financial Technology: How CAs Are Embracing the Digital Age
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ExpenectThis week

The Evolution of Financial Technology: How CAs Are Embracing the Digital Age

The Evolution of Financial Technology: How CAs Are Embracing the Digital Age Introduction In an era characterized by rapid technological advancements, the field of finance is undergoing a transformative journey. The emergence of financial technology, or fintech, is reshaping the way businesses manage their finances, and Chartered Accountants (CAs) are at the forefront of this evolution. In this blog post, we'll explore how CAs are embracing fintech and leveraging its potential to enhance financial management, analysis, and advisory services. Fintech's Impact on Financial Services Fintech encompasses a wide range of technologies that leverage data analytics, artificial intelligence, blockchain, and automation to improve financial services. For CAs, this means new tools to streamline processes, enhance decision-making, and offer innovative solutions to clients. Automation of Routine Tasks CAs are increasingly using automation tools to handle repetitive tasks such as data entry, reconciliations, and transaction processing. This not only reduces the risk of human error but also frees up CAs to focus on higher-value tasks like strategic planning and analysis. Advanced Data Analytics Data analytics tools enable CAs to extract meaningful insights from large volumes of financial data. These insights can help businesses identify trends, anticipate risks, and make informed decisions to drive growth. Real-Time Financial Reporting Fintech enables CAs to provide clients with real-time financial reporting, giving businesses immediate access to critical information. This enhances transparency and empowers business owners to respond quickly to changing market conditions. Enhancing Audit Efficiency Fintech tools are revolutionizing the audit process. CAs can use AI-powered algorithms to analyze vast amounts of data, detect anomalies, and identify potential instances of fraud more efficiently. Personalized Financial Planning CAs can leverage fintech to offer personalized financial planning services. With access to detailed financial data, CAs can create tailored strategies that align with a client's unique goals and circumstances. Strengthening Cybersecurity As businesses become more reliant on digital tools, cybersecurity becomes paramount. CAs are playing a critical role in advising clients on cybersecurity measures to protect sensitive financial information. Virtual CFO Services Fintech enables CAs to offer virtual CFO services to startups and small businesses. Through digital platforms, CAs can provide expert financial advice and guidance remotely, making their expertise accessible to a wider range of clients. Embracing Blockchain Technology Blockchain's potential for secure and transparent record-keeping is of interest to CAs. They can explore applications in supply chain finance, smart contracts, and even audit trail verification. Continuous Learning in Fintech CAs recognize the importance of staying updated with fintech trends. Many are investing in continuous learning to master the use of new tools and technologies that can optimize their services. Conclusion The integration of fintech into the realm of finance is reshaping the landscape in profound ways. CAs are embracing these technologies to elevate their roles from traditional number-crunchers to strategic advisors, equipped with tools that enhance efficiency, accuracy, and insight. As fintech continues to evolve, CAs will remain pivotal in guiding businesses through the ever-changing financial landscape, leveraging technology to drive growth, innovation, and success. Find the top verified CA in your City Feel free to let me know if you'd like more blogs on different topics or if you have specific requirements for the content.

The Advantages of a Custom CRM Solution
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NeerajKumarChaurasiaThis week

The Advantages of a Custom CRM Solution

The growth in the global CRM market continues to accelerate. According to techspective, the global CRM market is now worth \~ $40B USD and is expected to surpass $80B USD by 2025. Despite this phenomenal growth, the CRM market is still dominated by off-the-shelf solutions that are “cookie-cutter” in design and that provide little to no options for customization. These non-customized CRM solutions can significantly inhibit an enterprise’s ability to maximize the advantages of CRM adoption and to realize a robust ROI. As a result, companies are increasingly opting for digital CRM solutions that are customized to meet the unique needs of the enterprise. What is driving the increased demand for custom CRM solutions? What are some of the inherent advantages of a custom CRM solution when compared to a typical off-the-shelf product? Off-the-Shelf CRM Solutions – the Limitations Static CRM solutions are inflexible and self-limiting. Enterprises saddled with these cookie-cutter solutions increasingly report a consistent listing of issues that limit business growth.  These include…. A lack of real-time visibility into shifting customer trends and demands Delayed reaction to coordination of internal resources to meet changing business conditions Lost business opportunities due to lack of flexible, and real-time, opportunity lifecycle management Reporting and dashboarding capabilities that are slow, static, and disconnected Poor quote-to-cash performance that degrades financial performance A CRM investment that delivers poor ROI and that cannot grow with the enterprise All of the above can combine to limit the enterprise’s ability to fully capitalize on its hard-won business opportunities and, over time, limit its ability to create new opportunities. A Customized CRM – What is it? What is a “customized CRM”? Simply put, it is a holistic CRM solution that has been specifically tailored for the individual enterprise. The provider of a truly customized CRM solution will deliver a solution that has been designed to meet the specific—and unique—demands and objectives of the enterprise. A tailored CRM solution will address the enterprise’s sales and operational requirements as well as its customer experience objectives. Unlike standard off-the-shelf CRM providers, a provider of enterprise-grade custom CRM solutions will employ a comprehensive project discovery and requirements gathering process. This is an integral process that provides the foundation for the development of a custom solution that will provide the enterprise with long term flexibility and scalability. A customized digital CRM solution can provide distinct competitive advantages; including: Dynamic, Flexible, Powerful, Real-Time Management and Engagement A customized, technology\-fueled, CRM solution provides the enterprise with the means with which to dynamically engage with customers in ways that build customer loyalty, generate market growth, and drive strong financial performance. Distinct advantages include: Real-time sales opportunity tracking. Helps eliminate lost opportunities due to slow or inadequate reaction. Customized, AI and IoT-fueled, data analytics. A customized CRM solution can be designed to deliver real-time insights. Allows the enterprise to anticipate, and then satisfy, the needs of the customer. Customizable Dashboards and Reports. Widget-based, customized, dashboards and reports that provide real-time data and actionable insights. Sales process automation. Intelligent Workflow-based automation and control of critical sales processes. Increases overall operational efficiency. Outstanding ROI. A custom CRM solution typically delivers superior ROI when compared to off-the-shelf CRM products. Enterprises today spend considerable time, money, and effort in the development of customer relationships. For many enterprises, the continued use of CRM solutions that are rigid and outdated can prove to be impediments to business growth. When considering investment in a new CRM solution any enterprise will be well served by full consideration of a CRM solution that can be fully customized to meet its long-range requirements.

Ai C-Level team
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thestoicdesignerThis week

Ai C-Level team

I've been exploring ways to run a company where I'm essentially the only internal team member, relying entirely on a suite of specialized AIs for executive roles, supported occasionally by external consultants for niche expertise. My goal is to stay lean, agile, and highly creative, especially in a fashion/tech brand context. Essentially, I'm building an AI-driven C-Level team, or what I like to call a "C-Level AI Wallet." Here's what I'm thinking for the key executive roles I'd need to cover with AI: CEO AI – Responsible for overall strategy, decision-making, trend analysis, and guiding the company's vision. I'd probably lean on something advanced like Gemini, GPT-4, or similar models, fine-tuned with market-specific data. COO AI (Operations): I'd need tools that streamline and automate logistics, supply chain management, and day-to-day operations (think something along the lines of Zapier AI integrations or Make). CMO AI (Marketing & Content): For branding, content creation, digital marketing, and consumer insights, I'd use Jasper or Copy.ai, combined with predictive analytics tools like Google Vertex AI to understand trends better. Additionally, for generating engaging visual and multimedia content, tools like Midjourney, DALL·E, Adobe Firefly, and Runway ML would be perfect. CFO AI (Financial Management): For financial management, cash flow control, and investment decisions, I'd probably leverage AI tools like Bloomberg GPT, combined with AI-powered forecasting platforms. CHRO AI (Human Resources & Culture): Although the internal team is minimal (just myself!), I'd still rely on AI for tasks like project management, freelancer hiring, and performance tracking—tools like HireVue AI, Motion, or even Notion's AI could be beneficial here. CSO AI (Sustainability & Compliance): Since sustainability and ethical sourcing are critical, I'd integrate ESG-focused AI tools to ensure transparency and responsible sourcing. My idea is that, with the right AI tools seamlessly integrated, I can manage the strategic vision and creative direction personally, leveraging external consultants only when necessary. This setup would ideally allow me to operate as a one-person internal team supported by a robust "wallet" of AI executives. Has anyone tried a similar approach? What AI tools would you recommend for a truly lean, innovative brand structure? I'm very curious about your experiences or suggestions—let me know your thoughts!

How I Started Learning Machine Learning
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TechPrimoThis week

How I Started Learning Machine Learning

Hello, everyone. As promised, I'll write a longer post about how I entered the world of ML, hoping it will help someone shape their path. I'll include links to all the useful materials I used alongside the story, which you can use for learning. I like to call myself an AI Research Scientist who enjoys exploring new AI trends, delving deeper into understanding their background, and applying them to real products. This way, I try to connect science and entrepreneurship because I believe everything that starts as scientific research ends up "on the shelves" as a product that solves a specific user problem. I began my journey in ML in 2016 when it wasn't such a popular field. Everyone had heard of it, but few were applying it. I have several years of development experience and want to try my hand at ML. The first problem I encountered was where to start - whether to learn mathematics, statistics, or something else. That's when I came across a name and a course that completely changed my career. Let's start You guessed it. It was Professor Andrew Ng and his globally popular Machine Learning course available on Coursera (I still have the certificate, hehe). This was also my first official online course ever. Since that course no longer exists as it's been replaced by a new one, I recommend you check out: Machine Learning (Stanford CS229) Machine Learning Specialization These two courses start from the basics of ML and all the necessary calculus you need to know. Many always ask questions like whether to learn linear algebra, statistics, or probability, but you don't need to know everything in depth. This knowledge helps if you're a scientist developing a new architecture, but as an engineer, not really. You need to know some basics to understand, such as how the backpropagation algorithm works. I know that Machine Learning (Stanford CS229) is a very long and arduous course, but it's the right start if you want to be really good at ML. In my time, I filled two thick notebooks by hand while taking the course mentioned above. TensorFlow and Keras After the course, I didn't know how to apply my knowledge because I hadn't learned specifically how to code things. Then, I was looking for ways to learn how to code it. That's when I came across a popular framework called Keras, now part of TensorFlow. I started with a new course and acquiring practical knowledge: Deep Learning Specialization Deep Learning by Ian Goodfellow Machine Learning Yearning by Andrew Ng These resources above were my next step. I must admit that I learned the most from that course and from the book Deep Learning by Ian Goodfellow because I like reading books (although this one is quite difficult to read). Learn by coding To avoid just learning, I went through various GitHub repositories that I manually retyped and learned that way. It may be an old-fashioned technique, but it helped me a lot. Now, most of those repositories don't exist, so I'll share some that I found to be good: Really good Jupyter notebooks that can teach you the basics of TensorFlow Another good repo for learning TF and Keras Master the challenge After mastering the basics in terms of programming in TF/Keras, I wanted to try solving some real problems. There's no better place for that challenge than Kaggle and the popular Titanic dataset. Here, you can really find a bunch of materials and simple examples of ML applications. Here are some of my favorites: Titanic - Machine Learning from Disaster Home Credit Default Risk House Prices - Advanced Regression Techniques Two Sigma: Using News to Predict Stock Movements I then decided to further develop my career in the direction of applying ML to the stock market, first using predictions on time series and then using natural language processing. I've remained in this field until today and will defend my doctoral dissertation soon. How to deploy models To continue, before I move on to the topic of specialization, we need to address the topic of deployment. Now that we've learned how to make some basic models in Keras and how to use them, there are many ways and services, but I'll only mention what I use today. For all my ML models, whether simple regression models or complex GPT models, I use FastAPI. It's a straightforward framework, and you can quickly create API endpoints. I'll share a few older and useful tutorials for beginners: AI as an API tutorial series A step-by-step guide Productizing an ML Model with FastAPI and Cloud Run Personally, I've deployed on various cloud providers, of which I would highlight GCP and AWS because they have everything needed for model deployment, and if you know how to use them, they can be quite cheap. Chose your specialization The next step in developing my career, besides choosing finance as the primary area, was my specialization in the field of NLP. This happened in early 2020 when I started working with models based on the Transformer architecture. The first model I worked with was BERT, and the first tasks were related to classifications. My recommendations are to master the Transformer architecture well because 99% of today's LLM models are based on it. Here are some resources: The legendary paper "Attention Is All You Need" Hugging Face Course on Transformers Illustrated Guide to Transformers - Step by Step Explanation Good repository How large language models work, a visual intro to transformers After spending years using encoder-based Transformer models, I started learning GPT models. Good open-source models like Llama 2 then appear. Then, I started fine-tuning these models using the excellent Unsloth library: How to Finetune Llama-3 and Export to Ollama Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth After that, I focused on studying various RAG techniques and developing Agent AI systems. This is now called AI engineering, and, as far as I can see, it has become quite popular. So I'll write more about that in another post, but here I'll leave what I consider to be the three most famous representatives, i.e., their tutorials: LangChain tutorial LangGraph tutorial CrewAI examples Here I am today Thanks to the knowledge I've generated over all these years in the field of ML, I've developed and worked on numerous projects. The most significant publicly available project is developing an agent AI system for well-being support, which I turned into a mobile application. Also, my entire doctoral dissertation is related to applying ML to the stock market in combination with the development of GPT models and reinforcement learning (more on that in a separate post). After long 6 years, I've completed my dissertation, and now I'm just waiting for its defense. I'll share everything I'm working on for the dissertation publicly on the project, and in tutorials I'm preparing to write. If you're interested in these topics, I announce that I'll soon start with activities of publishing content on Medium and a blog, but I'll share all of that here on Reddit as well. Now that I've gathered years of experience and knowledge in this field, I'd like to share it with others and help as much as possible. If you have any questions, feel free to ask them, and I'll try to answer all of them. Thank you for reading.

How I Started Learning Machine Learning
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TechPrimoThis week

How I Started Learning Machine Learning

Hello, everyone. As promised, I'll write a longer post about how I entered the world of ML, hoping it will help someone shape their path. I'll include links to all the useful materials I used alongside the story, which you can use for learning. I like to call myself an AI Research Scientist who enjoys exploring new AI trends, delving deeper into understanding their background, and applying them to real products. This way, I try to connect science and entrepreneurship because I believe everything that starts as scientific research ends up "on the shelves" as a product that solves a specific user problem. I began my journey in ML in 2016 when it wasn't such a popular field. Everyone had heard of it, but few were applying it. I have several years of development experience and want to try my hand at ML. The first problem I encountered was where to start - whether to learn mathematics, statistics, or something else. That's when I came across a name and a course that completely changed my career. Let's start You guessed it. It was Professor Andrew Ng and his globally popular Machine Learning course available on Coursera (I still have the certificate, hehe). This was also my first official online course ever. Since that course no longer exists as it's been replaced by a new one, I recommend you check out: Machine Learning (Stanford CS229) Machine Learning Specialization These two courses start from the basics of ML and all the necessary calculus you need to know. Many always ask questions like whether to learn linear algebra, statistics, or probability, but you don't need to know everything in depth. This knowledge helps if you're a scientist developing a new architecture, but as an engineer, not really. You need to know some basics to understand, such as how the backpropagation algorithm works. I know that Machine Learning (Stanford CS229) is a very long and arduous course, but it's the right start if you want to be really good at ML. In my time, I filled two thick notebooks by hand while taking the course mentioned above. TensorFlow and Keras After the course, I didn't know how to apply my knowledge because I hadn't learned specifically how to code things. Then, I was looking for ways to learn how to code it. That's when I came across a popular framework called Keras, now part of TensorFlow. I started with a new course and acquiring practical knowledge: Deep Learning Specialization Deep Learning by Ian Goodfellow Machine Learning Yearning by Andrew Ng These resources above were my next step. I must admit that I learned the most from that course and from the book Deep Learning by Ian Goodfellow because I like reading books (although this one is quite difficult to read). Learn by coding To avoid just learning, I went through various GitHub repositories that I manually retyped and learned that way. It may be an old-fashioned technique, but it helped me a lot. Now, most of those repositories don't exist, so I'll share some that I found to be good: Really good Jupyter notebooks that can teach you the basics of TensorFlow Another good repo for learning TF and Keras Master the challenge After mastering the basics in terms of programming in TF/Keras, I wanted to try solving some real problems. There's no better place for that challenge than Kaggle and the popular Titanic dataset. Here, you can really find a bunch of materials and simple examples of ML applications. Here are some of my favorites: Titanic - Machine Learning from Disaster Home Credit Default Risk House Prices - Advanced Regression Techniques Two Sigma: Using News to Predict Stock Movements I then decided to further develop my career in the direction of applying ML to the stock market, first using predictions on time series and then using natural language processing. I've remained in this field until today and will defend my doctoral dissertation soon. How to deploy models To continue, before I move on to the topic of specialization, we need to address the topic of deployment. Now that we've learned how to make some basic models in Keras and how to use them, there are many ways and services, but I'll only mention what I use today. For all my ML models, whether simple regression models or complex GPT models, I use FastAPI. It's a straightforward framework, and you can quickly create API endpoints. I'll share a few older and useful tutorials for beginners: AI as an API tutorial series A step-by-step guide Productizing an ML Model with FastAPI and Cloud Run Personally, I've deployed on various cloud providers, of which I would highlight GCP and AWS because they have everything needed for model deployment, and if you know how to use them, they can be quite cheap. Chose your specialization The next step in developing my career, besides choosing finance as the primary area, was my specialization in the field of NLP. This happened in early 2020 when I started working with models based on the Transformer architecture. The first model I worked with was BERT, and the first tasks were related to classifications. My recommendations are to master the Transformer architecture well because 99% of today's LLM models are based on it. Here are some resources: The legendary paper "Attention Is All You Need" Hugging Face Course on Transformers Illustrated Guide to Transformers - Step by Step Explanation Good repository How large language models work, a visual intro to transformers After spending years using encoder-based Transformer models, I started learning GPT models. Good open-source models like Llama 2 then appear. Then, I started fine-tuning these models using the excellent Unsloth library: How to Finetune Llama-3 and Export to Ollama Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth After that, I focused on studying various RAG techniques and developing Agent AI systems. This is now called AI engineering, and, as far as I can see, it has become quite popular. So I'll write more about that in another post, but here I'll leave what I consider to be the three most famous representatives, i.e., their tutorials: LangChain tutorial LangGraph tutorial CrewAI examples Here I am today Thanks to the knowledge I've generated over all these years in the field of ML, I've developed and worked on numerous projects. The most significant publicly available project is developing an agent AI system for well-being support, which I turned into a mobile application. Also, my entire doctoral dissertation is related to applying ML to the stock market in combination with the development of GPT models and reinforcement learning (more on that in a separate post). After long 6 years, I've completed my dissertation, and now I'm just waiting for its defense. I'll share everything I'm working on for the dissertation publicly on the project, and in tutorials I'm preparing to write. If you're interested in these topics, I announce that I'll soon start with activities of publishing content on Medium and a blog, but I'll share all of that here on Reddit as well. Now that I've gathered years of experience and knowledge in this field, I'd like to share it with others and help as much as possible. If you have any questions, feel free to ask them, and I'll try to answer all of them. Thank you for reading.

6 principles to data architecture that facilitate innovation
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Competitive_Speech36This week

6 principles to data architecture that facilitate innovation

My team and I have been re-building our company's data architecture. In the process of doing so, I got together six key principles to transforming data architectures and thought I would share them, as a strong data architecture is crucial for businesses looking to stay competitive in the digital landscape, as it improves decision-making, time to market, and data security. When executed with efficiency, a resilient data architecture unleashes unparalleled degrees of agility. Principle 1: Agility and flexibility To quickly adjust to market fluctuations, businesses must create adaptable data infrastructures that can effortlessly manage an ever-growing influx of data. To accomplish this objective, we recommend to our clients to implement Enterprise Service Bus, Enterprise Data Warehouse, and Master Data Management integrated together. ​ I believe the best option is this: \- By centralizing communication, ESB reduces the time and effort required to integrate new systems; \- EDW consolidates data from different sources, resulting in a 50% reduction in software implementation time; \- Finally, MDM ensures consistency and accuracy across the organization, leading to better decision-making and streamlined operations. Implementing these solutions can lead to reduced software implementation time, better ROI, and more manageable data architecture. By fostering a culture of collaboration and adopting modern technologies and practices, businesses can prioritize agility and flexibility in their data architecture to increase the pace of innovation. Principle 2: Modularity and reusability Data architecture that fosters modularity and reusability is essential for accelerating innovation within an organization. By breaking data architecture components into smaller, more manageable pieces, businesses can enable different teams to leverage existing architecture components, reducing redundancy and improving overall efficiency. MDM can promote modularity and reusability by creating a central repository for critical business data. This prevents duplication and errors, improving efficiency and decision-making. MDM enables a single source of truth for data, accessible across multiple systems, which promotes integration and scalability. MDM also provides standardized data models, rules, and governance policies that reduce development time, increase quality, and ensure proper management throughout the data’s lifecycle. Another way to achieve modularity in data architecture is through the use of microservices and scripts for Extract, Transform, and Load (ETL) processes. Adopting a structured methodology and framework can ensure these components are well-organized, making it easier for teams to collaborate and maintain the system. Microservices can also contribute to modularity and reusability in data architecture. These small, independent components can be developed, deployed, and scaled independently of one another. By utilizing microservices, organizations can update or replace individual components without affecting the entire system, improving flexibility and adaptability. Principle 3: Data quality and consistency The efficiency of operations depends on data’s quality, so a meticulously crafted data architecture plays a pivotal role in preserving it, empowering enterprises to make well-informed decisions based on credible information. Here are some key factors to consider that will help your company ensure quality: \- Implementing Master Data Management (MDM) – this way, by consolidating, cleansing, and standardizing data from multiple sources, your IT department will be able to create a single, unified view of the most important data entities (customers, products, and suppliers); \- Assigning data stewardship responsibilities to a small team or an individual specialist; \- Considering implementing data validation, data lineage, and data quality metrics; \- By implementing MDM and adopting a minimal data stewardship approach, organizations can maintain high-quality data that drives innovation and growth. Principle 4: Data governance Data governance is a strategic framework that goes beyond ensuring data quality and consistency. It includes ensuring data security, privacy, accessibility, regulatory compliance, and lifecycle management. Here are some key aspects of data governance: \- Implementing robust measures and controls to protect sensitive data from unauthorized access, breaches, and theft. This is only possible through including encryption, access controls, and intrusion detection systems into your company’s IT architecture; \- Adhering to data privacy regulations and guidelines, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA); \- Defining stringent conditions for who has access to specific data assets to maintain control over data and ensure its accessibility only for legitimate purposes. Managing the entire lifecycle of data, from creation and storage to archiving and disposal, including defining policies for data retention, archiving, and deletion in compliance with legal and regulatory requirements. To facilitate effective data governance, organizations can leverage various tools and technologies, such as: \- Data cataloging tools: Solutions like Collibra, Alation, or Informatica Enterprise Data Catalog help organizations discover, understand, and manage their data assets. \- Data lineage tools: Tools like Talend, IBM InfoSphere, or Apache Atlas help track data’s origin, transformation, and usage, providing insights into data quality issues and potential areas for improvement. \- Data quality tools: Solutions like Informatica Data Quality, Trifacta, or SAS Data Quality help organizations maintain high-quality data by identifying and correcting errors, inconsistencies, and inaccuracies. \- Data security and privacy tools: Tools like Varonis, BigID, or Spirion help protect sensitive data and ensure compliance with data privacy regulations. Principle 5: Cloud-first approach A cloud-first approach prioritizes cloud-based solutions over on-premises ones when it comes to data management. Cloud-based data management pros: \- Virtually limitless scalability, so that organizations can grow and adapt to changing data requirements without significant infrastructure investments; \- The pay-as-you-go model of cloud services reduces maintenance costs usually associated with the on-premise choice; \- Greater flexibility for deploying and integrating new technologies and services; \- Cloud can be accessed from anywhere, at any time, turning team collaboration and remote work into a breeze; \- Built-in backup and disaster recovery capabilities, ensuring data safety and minimizing downtime in case of emergencies. Cloud-based data management cons: \- Cloud-first approach raises many data security, privacy, and compliance concerns; \- Transferring large data volumes to and from cloud is often time-consuming and results in increased latency for certain apps; \- Relying on a single cloud provider makes it difficult to switch them or move back to the on-premises option without significant funds and effort. Challenges that organizations that choose a cloud-first approach face: \- Integrating cloud-based systems with on-premises ones can be complex and time-consuming; \- Ensuring data governance and compliance in a multi-cloud or hybrid environment is also another problem reported by my clients. How EDW, ESB, and MDM promote cloud-first approach: A cloud-based EDW centralizes data from multiple sources, enabling a unified view of the organization’s data and simplifying data integration across cloud and on-premises systems. An ESB facilitates communication between disparate cloud and on-premises systems, streamlining data integration and promoting a modular architecture. Cloud-based MDM solutions are used for maintaining data quality and consistency across multiple data sources and environments. Principle 6: Automation and artificial intelligence Incorporating automation tools and AI technologies into data architecture can optimize processes and decision-making. Key Applications: \- Data ingestion and integration: Automation simplifies data schema updates and identifies data quality issues, while AI-assisted development helps create tailored connectors, scripts, and microservices. \- Data quality management: Machine learning algorithms improve data quality and consistency by automatically detecting and correcting inconsistencies and duplicates. \- Predictive analytics: AI and machine learning models analyze historical data to predict trends, identify opportunities, and uncover hidden patterns for better-informed decisions. How No-Code Tools and AI-Assisted Development Work: Business users define data requirements and workflows using no-code tools, enabling AI models to understand their needs. AI models process the information, generating recommendations for connector creation, ETL scripts, and microservices. Developers use AI-generated suggestions to accelerate development and tailor solutions to business needs. By combining automation, AI technologies, and no-code tools, organizations can streamline data architecture processes and bridge the gap between business users and developers, ultimately accelerating innovation. I share more tips on building an agile data architectures in my blog.

Backend dev wants to learn ML
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chipmuxThis week

Backend dev wants to learn ML

Hello ML Experts, I am staff engineer, working in a product based organization, handling the backend services. I see myself becoming Solution Architect and then Enterprise Architect one day. With the AI and ML trending now a days, So i feel ML should be an additional skill that i should acquire which can help me leading and architecting providing solutions to the problems more efficiently, I think however it might not replace the traditional SWEs working on backend APIs completely, but ML will be just an additional diamention similar to the knowledge of Cloud services and DevOps. So i would like to acquire ML knowledge, I dont have any plans to be an expert at it right now, nor i want to become a full time data scientist or ML engineer as of today. But who knows i might diverge, but thats not the plan currently. I did some quick promting with ChatGPT and was able to comeup with below learning path for me. So i would appreciate if some of you ML experts can take a look at below learning path and provide your suggestions 📌 PHASE 1: Core AI/ML & Python for AI (3-4 Months) Goal: Build a solid foundation in AI/ML with Python, focusing on practical applications. 1️⃣ Python for AI/ML (2-3 Weeks) Course: [Python for Data Science and Machine Learning Bootcamp]() (Udemy) Topics: Python, Pandas, NumPy, Matplotlib, Scikit-learn basics 2️⃣ Machine Learning Fundamentals (4-6 Weeks) Course: Machine Learning Specialization by Andrew Ng (C0ursera) Topics: Linear & logistic regression, decision trees, SVMs, overfitting, feature engineering Project: Build an ML model using Scikit-learn (e.g., predicting house prices) 3️⃣ Deep Learning & AI Basics (4-6 Weeks) Course: Deep Learning Specialization by Andrew Ng (C0ursera) Topics: Neural networks, CNNs, RNNs, transformers, generative AI (GPT, Stable Diffusion) Project: Train an image classifier using TensorFlow/Keras 📌 PHASE 2: AI/ML for Enterprise & Cloud Applications (3-4 Months) Goal: Learn how AI is integrated into cloud applications & enterprise solutions. 4️⃣ AI/ML Deployment & MLOps (4 Weeks) Course: MLOps Specialization by Andrew Ng (C0ursera) Topics: Model deployment, monitoring, CI/CD for ML, MLflow, TensorFlow Serving Project: Deploy an ML model as an API using FastAPI & Docker 5️⃣ AI/ML in Cloud (Azure, AWS, OpenAI APIs) (4-6 Weeks) Azure AI Services: Course: Microsoft AI Fundamentals (C0ursera) Topics: Azure ML, Azure OpenAI API, Cognitive Services AWS AI Services: Course: [AWS Certified Machine Learning – Specialty]() (Udemy) Topics: AWS Sagemaker, AI workflows, AutoML 📌 PHASE 3: AI Applications in Software Development & Future Trends (Ongoing Learning) Goal: Explore AI-powered tools & future-ready AI applications. 6️⃣ Generative AI & LLMs (ChatGPT, GPT-4, LangChain, RAG, Vector DBs) (4 Weeks) Course: [ChatGPT Prompt Engineering for Developers]() (DeepLearning.AI) Topics: LangChain, fine-tuning, RAG (Retrieval-Augmented Generation) Project: Build an LLM-based chatbot with Pinecone + OpenAI API 7️⃣ AI-Powered Search & Recommendations (Semantic Search, Personalization) (4 Weeks) Course: [Building Recommendation Systems with Python]() (Udemy) Topics: Collaborative filtering, knowledge graphs, AI search 8️⃣ AI-Driven Software Development (Copilot, AI Code Generation, Security) (Ongoing) Course: AI-Powered Software Engineering (C0ursera) Topics: AI code completion, AI-powered security scanning 🚀 Final Step: Hands-on Projects & Portfolio Once comfortable, work on real-world AI projects: AI-powered document processing (OCR + LLM) AI-enhanced search (Vector Databases) Automated ML pipelines with MLOps Enterprise AI Chatbot using LLMs ⏳ Suggested Timeline 📅 6-9 Months Total (10-12 hours/week) 1️⃣ Core ML & Python (3-4 months) 2️⃣ Enterprise AI/ML & Cloud (3-4 months) 3️⃣ AI Future Trends & Applications (Ongoing) Would you like a customized plan with weekly breakdowns? 🚀

ChatPDF and PDF.ai are making millions using open source tech... here's the code
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Level-Thought6152This week

ChatPDF and PDF.ai are making millions using open source tech... here's the code

Why "copy" an existing product? The best SaaS products weren’t the first of their kind - think Slack, Shopify, Zoom, Dropbox, or HubSpot. They didn’t invent team communication, e-commerce, video conferencing, cloud storage, or marketing tools; they just made them better. What is a "Chat with PDF" SaaS? These are AI-powered PDF assistants that let you upload a PDF and ask questions about its content. You can summarize articles, extract key details from a contract, analyze a research paper, and more. To see this in action or dive deeper into the tech behind it, check out this YouTube video. Let's look at the market Made possible by advances in AI like ChatGPT and Retrieval-Augmented Generation (RAG), PDF chat tools started gaining traction in early 2023 and have seen consistent growth in market interest, which is currently at an all-time high (source:google trends) Keywords like "chat PDF" and "PDF AI" get between 1 to 10 million searches every month (source:keyword planner), with a broad target audience that includes researchers, students, and professionals across various industries. Leaders like PDF.ai and ChatPDF have already gained millions of users within a year of launch, driven by the growing market demand, with paid users subscribing at around $20/month. Alright, so how do we build this with open source? The core tech for most PDF AI tools are based on the same architecture. You generate text embeddings (AI-friendly text representations; usually via OpenAI APIs) for the uploaded PDF’s chapters/topics and store them in a vector database (like Pinecone). Now, every time the user asks a question, a similarity search is performed to find the most similar PDF topics from the vector database. The selected topic contents are then sent to an LLM (like ChatGPT) along with the question, which generates a contextual answer! Here are some of the best open source implementations for this process: GPT4 & LangChain Chatbot for large PDF docs by Mayo Oshin MultiPDF Chat App by Alejandro AO PDFToChat by Hassan El Mghari Worried about building signups, user management, payments, etc.? Here are my go-to open-source SaaS boilerplates that include everything you need out of the box: SaaS Boilerplate by Remi Wg Open SaaS by wasp-lang A few ideas to stand out from the noise: Here are a few strategies that could help you differentiate and achieve product market fit (based on the pivot principles from The Lean Startup by Eric Ries): Narrow down your target audience for a personalized UX: For instance, an exam prep assistant for students with study notes and quiz generator; or a document due diligence and analysis tool for lawyers. Add unique features to increase switching cost: You could autogenerate APIs for the uploaded PDFs to enable remote integrations (eg. support chatbot knowledge base); or build in workflow automation features for bulk analyses of PDFs. Offer platform level advantages: You could ship a native mobile/desktop apps for a more integrated UX; or (non-trivial) offer private/offline support by replacing the APIs with local open source deployments (eg. llama for LLM, an embedding model from the MTEB list, and FAISS for vector search). TMI? I’m an ex-AI engineer and product lead, so don’t hesitate to reach out with any questions! P.S. I've started a free weekly newsletter to share open-source/turnkey resources behind popular products (like this one). If you’re a founder looking to launch your next product without reinventing the wheel, please subscribe :)

Introducing Stratify: Your Ultimate AI Strategy Builder for Business Success
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vsengarThis week

Introducing Stratify: Your Ultimate AI Strategy Builder for Business Success

Hello, I’m thrilled to announce the launch of my new startup, Stratify! 🔍 What is Stratify? Stratify is an AI Strategy Builder designed to help businesses of all sizes develop, implement, and optimize their strategic plans using cutting-edge artificial intelligence. Whether you're a startup looking to scale or an established company aiming to innovate, Stratify provides the tools and insights you need to stay ahead in today's competitive landscape. 🌟 Key Features: Automated Strategy Development: Leverage AI to analyze market trends, competitor data, and internal metrics to create comprehensive strategic plans tailored to your business goals. Real-Time Analytics & Insights: Monitor your strategy's performance with real-time data dashboards, enabling you to make informed decisions quickly. Scenario Planning: Use AI-driven simulations to forecast different business scenarios and understand potential outcomes, helping you prepare for uncertainties. Collaborative Tools: Facilitate team collaboration with integrated communication features, ensuring everyone is aligned and contributing to the strategy development process. Customizable Templates: Access a library of industry-specific strategy templates that can be customized to fit your unique business needs. 💡 Why Stratify? In today's fast-paced business environment, creating and adapting effective strategies can be challenging. Many companies struggle with data overload, lack of actionable insights, and inefficient planning processes. Stratify addresses these pain points by harnessing the power of AI to streamline strategy building, making it more efficient, data-driven, and adaptable. 🚀 Our Journey So Far: Founded: August 2024 Milestones Achieved: Developed and tested our MVP with a select group of beta users What's Next: Launching our public beta in Q4 2024 Expanding our feature set based on user feedback Growing our team with experts in AI, business strategy, and customer success 🤝 How You Can Help: We’re eager to connect with early adopters, business strategists, and industry experts who can benefit from or contribute to Stratify. Here’s how you can get involved: Join Our Beta Program: Be among the first to experience Stratify and provide valuable feedback. Share Your Insights: Help us refine our features by sharing your business strategy challenges and needs. Spread the Word: If you know someone who could benefit from an AI-driven strategy builder, please share our mission and be an affiliate to earn rewards! 🌐 Learn More: Visit our website at AI-Powered Brand Strategy & Content Creation | Stratify (brandprovoke.com) and follow us for the latest updates and insights. 🙏 Thank You! A heartfelt thank you to the Reddit community for your support and encouragement. We’re excited to embark on this journey and look forward to your feedback and suggestions! Looking forward to your thoughts and questions!

ChatPDF and PDF.ai are making millions using open source tech... here's the code
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Level-Thought6152This week

ChatPDF and PDF.ai are making millions using open source tech... here's the code

Why "copy" an existing product? The best SaaS products weren’t the first of their kind - think Slack, Shopify, Zoom, Dropbox, or HubSpot. They didn’t invent team communication, e-commerce, video conferencing, cloud storage, or marketing tools; they just made them better. What is a "Chat with PDF" SaaS? These are AI-powered PDF assistants that let you upload a PDF and ask questions about its content. You can summarize articles, extract key details from a contract, analyze a research paper, and more. To see this in action or dive deeper into the tech behind it, check out this YouTube video. Let's look at the market Made possible by advances in AI like ChatGPT and Retrieval-Augmented Generation (RAG), PDF chat tools started gaining traction in early 2023 and have seen consistent growth in market interest, which is currently at an all-time high (source:google trends) Keywords like "chat PDF" and "PDF AI" get between 1 to 10 million searches every month (source:keyword planner), with a broad target audience that includes researchers, students, and professionals across various industries. Leaders like PDF.ai and ChatPDF have already gained millions of users within a year of launch, driven by the growing market demand, with paid users subscribing at around $20/month. Alright, so how do we build this with open source? The core tech for most PDF AI tools are based on the same architecture. You generate text embeddings (AI-friendly text representations; usually via OpenAI APIs) for the uploaded PDF’s chapters/topics and store them in a vector database (like Pinecone). Now, every time the user asks a question, a similarity search is performed to find the most similar PDF topics from the vector database. The selected topic contents are then sent to an LLM (like ChatGPT) along with the question, which generates a contextual answer! Here are some of the best open source implementations for this process: GPT4 & LangChain Chatbot for large PDF docs by Mayo Oshin MultiPDF Chat App by Alejandro AO PDFToChat by Hassan El Mghari Worried about building signups, user management, payments, etc.? Here are my go-to open-source SaaS boilerplates that include everything you need out of the box: SaaS Boilerplate by Remi Wg Open SaaS by wasp-lang A few ideas to stand out from the noise: Here are a few strategies that could help you differentiate and achieve product market fit (based on the pivot principles from The Lean Startup by Eric Ries): Narrow down your target audience for a personalized UX: For instance, an exam prep assistant for students with study notes and quiz generator; or a document due diligence and analysis tool for lawyers. Add unique features to increase switching cost: You could autogenerate APIs for the uploaded PDFs to enable remote integrations (eg. support chatbot knowledge base); or build in workflow automation features for bulk analyses of PDFs. Offer platform level advantages: You could ship a native mobile/desktop apps for a more integrated UX; or (non-trivial) offer private/offline support by replacing the APIs with local open source deployments (eg. llama for LLM, an embedding model from the MTEB list, and FAISS for vector search). TMI? I’m an ex-AI engineer and product lead, so don’t hesitate to reach out with any questions! P.S. I've started a free weekly newsletter to share open-source/turnkey resources behind popular products (like this one). If you’re a founder looking to launch your next product without reinventing the wheel, please subscribe :)

I just built footyGPT.com , an AI assistant for football stats, data and data-backed predictions
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footyamigoThis week

I just built footyGPT.com , an AI assistant for football stats, data and data-backed predictions

Hey Amigos! I am based in London and I build lots of fun tools and websites in the sports data space to help sports bettors make better data-driven decisions. One of my recently launched ones is footyGPT.com I have tried to launch this earlier in 2023 but my team were not able to achieve the end goal (I am a non-technical founder) Funny story, I bought the domain footygpt(dot)com back in 2023 and let it expire because i felt like since we weren't able to acheieve the end goal, there was no need to keep the domain. But 2 months ago I started working on it one more time with a different team, and since AI has become "more friendly" than it was early in 2023, it was a much smoother ride and i was so scared someone else had already bought the domain, only to find out it was still available to buy! I was so scared I lost such a clean domain. Below is an example of how footyGPT works: Ask FootyGPT for Predictions for a Given Fixture how footyGPT works \(case study\) Ask FootyGPT to Scrape and Summarize Data from a Stats Website using footyGPT to scrape football stats websites Ask FootyGPT for Sentiments and Online Opinion About a Given Match or Events. ask footygpt for up to date sentiments and opinions about football matches Ask FootyGPT to Find Teams with Certain Streaks, Trends, or Stats https://reddit.com/link/1frdk04/video/9oyaj5ldqjrd1/player If you are into football (soccer) please give it a spin and let me know your thoughts! Cheers Amigos

How I Automated Amazon Affiliate Marketing: A Developer's Journey
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siom_cThis week

How I Automated Amazon Affiliate Marketing: A Developer's Journey

From Manual Labor to 1000x Efficiency As a developer who ventured into affiliate marketing, I discovered a significant gap between technical possibilities and current practices. This revelation led me to create AutoPin, a tool that's now helping hundreds of affiliate marketers reclaim their time. The Problem: A Time-Consuming Reality Every affiliate marketer knows this scenario: you spend hours copying and pasting links, checking prices, and updating product information. I found myself dedicating 4-6 hours daily to these repetitive tasks. As a programmer, this felt fundamentally wrong. The typical affiliate marketing workflow looked like this: Find promising products Generate affiliate links one by one Monitor price changes manually Check product availability regularly Update content when things change Repeat this process daily This manual process had several critical issues: Time Waste: 20-30 hours weekly on repetitive tasks Missed Opportunities: Unable to scale beyond 100 products Human Error: Inevitable mistakes in manual updates Delayed Updates: Lost commissions due to outdated information The Solution: Building AutoPin After three months of development and six months of testing, I created a system that could: Generate hundreds of affiliate links in minutes Monitor price changes automatically Update product availability in real-time Export data in multiple formats Scale infinitely without additional effort Real Results, Real Impact The impact was immediate and significant: 📊 Efficiency Metrics: Link generation: From 2 minutes per link to 0.1 seconds Monitoring capacity: From 50 to 5000+ products Update frequency: From daily to real-time Error rate: Reduced by 99.9% 💡 User Success Stories: "Increased my product portfolio by 10x without adding work hours" "Revenue grew 300% in the first month" "Finally able to focus on content creation instead of link management" Technical Insights The system architecture focuses on three core components: Data Extraction Engine Efficient web scraping Rate limiting and proxy management Data validation and cleaning Real-time Monitoring System Websocket connections for instant updates Queue management for large-scale monitoring Smart scheduling based on price volatility Export Framework Multiple format support (CSV, HTML, Markdown) Custom templating engine Batch processing capabilities The Future of Affiliate Marketing Automation We're currently developing AI capabilities to: Generate product descriptions automatically Optimize link placement for conversion Predict price trends and best promotion times Create content variations for different platforms Key Learnings Automation is Essential The future of affiliate marketing lies in automation. Manual processes simply can't compete with automated systems in terms of efficiency and accuracy. Focus on Value Creation When marketers spend less time on repetitive tasks, they can focus on strategy and content quality. Scale Matters With automation, the difference between managing 10 products and 1000 products becomes minimal. Getting Started If you're an affiliate marketer spending hours on manual tasks, it's time to automate. Here's what you can do: Analyze your current workflow Identify repetitive tasks Start with basic automation Scale gradually Monitor and optimize Conclusion The transformation from manual to automated affiliate marketing isn't just about saving time—it's about unlocking potential. When you remove the tedious aspects of affiliate marketing, you create space for creativity, strategy, and growth. Want to experience the difference? Visit AutoPin at autopin.pro and join the automation revolution. Remember: The best time to automate was yesterday. The second best time is now. About the Author: A developer turned affiliate marketer who believes in the power of automation to transform digital marketing. #AffiliateMarketing #Automation #Programming #DigitalMarketing #SaaS #ProductivityTools

My humble analysis on how @levelsio grew PhotoAI to 155K/m
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PodcastSummaryGuyThis week

My humble analysis on how @levelsio grew PhotoAI to 155K/m

In 2023,@levelsiol aunched PhotoAi and nailed perfect timing with the AI-generated photo trend He saw the rise of AI Image Generators as early as Jan 2023—when search volume was just 246K. Fast forward to now, that’s exploded to 1M+ searches/month! @levelsio didn’t guess—he tracked this exponential growth, built fast, and launched a product that people needed before they even realized it themselves. That’s how you go from idea to$155K/month in revenue. But why does this work? Demand Explosion: The need for AI tools like PhotoAI surged as more creators, marketers, and businesses searched for automated ways to generate images (graph 1 shows this insane growth curve). Timing is everything: When trends move this fast, you have to act quicker. He launched at the right time, capturing the early adopters and setting the foundation for sustainable revenue (graph 2 showcases the clear spike). Build based on data, not assumptions: The growth of AI Character Generators also hit major traction (graph 3). Both trends signaled an opportunity, and he jumped in before the market was saturated. Lesson: Don’t guess what people want—watch the trends and build fast. Trends show you what’s working, before the mainstream even knows it. This is how you launch products that solve real problems. Check out the graphs for context: AI Image Generator Growth \(graph 1\) Search volume spiking \(graph 2\) AI Character Generators \(graph 3\) Use data, not just intuition. Track the trends and execute. Simple but powerful. I hope you liked this thread.

Introducing Stratify: Your Ultimate AI Strategy Builder for Business Success
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vsengarThis week

Introducing Stratify: Your Ultimate AI Strategy Builder for Business Success

Hello, I’m thrilled to announce the launch of my new startup, Stratify! 🔍 What is Stratify? Stratify is an AI Strategy Builder designed to help businesses of all sizes develop, implement, and optimize their strategic plans using cutting-edge artificial intelligence. Whether you're a startup looking to scale or an established company aiming to innovate, Stratify provides the tools and insights you need to stay ahead in today's competitive landscape. 🌟 Key Features: Automated Strategy Development: Leverage AI to analyze market trends, competitor data, and internal metrics to create comprehensive strategic plans tailored to your business goals. Real-Time Analytics & Insights: Monitor your strategy's performance with real-time data dashboards, enabling you to make informed decisions quickly. Scenario Planning: Use AI-driven simulations to forecast different business scenarios and understand potential outcomes, helping you prepare for uncertainties. Collaborative Tools: Facilitate team collaboration with integrated communication features, ensuring everyone is aligned and contributing to the strategy development process. Customizable Templates: Access a library of industry-specific strategy templates that can be customized to fit your unique business needs. 💡 Why Stratify? In today's fast-paced business environment, creating and adapting effective strategies can be challenging. Many companies struggle with data overload, lack of actionable insights, and inefficient planning processes. Stratify addresses these pain points by harnessing the power of AI to streamline strategy building, making it more efficient, data-driven, and adaptable. 🚀 Our Journey So Far: Founded: August 2024 Milestones Achieved: Developed and tested our MVP with a select group of beta users What's Next: Launching our public beta in Q4 2024 Expanding our feature set based on user feedback Growing our team with experts in AI, business strategy, and customer success 🤝 How You Can Help: We’re eager to connect with early adopters, business strategists, and industry experts who can benefit from or contribute to Stratify. Here’s how you can get involved: Join Our Beta Program: Be among the first to experience Stratify and provide valuable feedback. Share Your Insights: Help us refine our features by sharing your business strategy challenges and needs. Spread the Word: If you know someone who could benefit from an AI-driven strategy builder, please share our mission and be an affiliate to earn rewards! 🌐 Learn More: Visit our website at AI-Powered Brand Strategy & Content Creation | Stratify (brandprovoke.com) and follow us for the latest updates and insights. 🙏 Thank You! A heartfelt thank you to the Reddit community for your support and encouragement. We’re excited to embark on this journey and look forward to your feedback and suggestions! Looking forward to your thoughts and questions!

What I learn from my $200 MRR App I built 4 months ago
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ricky0603This week

What I learn from my $200 MRR App I built 4 months ago

4 month ago, I am just a 10-years experienced product manager without any software development experience. I have an $3K/month job, but I am so tired, I don’t like my life, don’t like my boss, don’t like my daily work, that make me feeling I already died however I am still living. I yearn for freedom and want to live each day the way I want to. So I quit my job, and become a Indie developer to build my own business, my own app, even my own life. I am so grateful for this time and experience, now my app reach $200 MRR, still very little compared to my previous salary, but I never regret. I have learned lots of things from this time and experience, more than I had in last 10 years. Here is the time-line of my App: ​ Sep 2023: Launch first version to iOS App store Oct 2023: Release in-app-purchase features and have first subscriber, the revenue in October is $154 Nov 2023: Change from subscription to pay per use, and I did lots of marketing jobs in November, however, the revenue reduced to only $40. Dec 2023: Change back to subscription, and stop some invalid marketing jobs, only keep the ones that actually work. I almost did nothing in December, and the revenue come to $243. During this process, I have learned lots of things, there are some of them that I think could help you as well. Web or App My App is an iOS app that only can running on Apple’s device such like iPhone/iPad or Mac with Apple silicon. Many people ask me why my product is an iOS app not a website, because they don’t have any Apple device. It's true that promoting an app is much harder than promoting a website. However I am now very glad I made an App and not a website! If I make a website, I don't think it's possible to make $100 in the first month. My App is about keyword research, to help people find some ideas from search keyword, because every keyword people searched in Google are representing a real need of them, also can be used in SEO field. However there are a lot of website tools about keyword research, some of them are famous like Ahrefs, SEMrush… I have no intention of competing with them. Actually I don’t have any chance. While in app store, there are little apps about keyword research, each of them have terrible data and user experience, that means if my app has better data and experience that could be my chance. In fact, the App store brings me 20 organic installs a day that Google would never have been able to bring me if I had a website, at least for the first few months. Furthermore, Apple nearly did everything for developer, I don’t need to care about user login, payment and so on, Apple did everything, I just need to call their API, that save lots of time, if I build a website, I need to implement login and payment by myself, that would add some extra work. Not to mention I'd need to buy servers and domains, that would cost me a lot of money. Although Apple will take 30% of the revenue, I can live with that in the early stages because the most important thing for me is to get the product to market as soon as possible. Actually thought Apple’s SMB program, the take rate is 15% now. So Web or App is not important in the early stage, time is important, if people need my product, it's easy to make a website one. More Users or More Valuable Users In November, I notice some users would like use my app, and they were meet paywall, but they never subscribe. I provided 7 day free trail, but it seem that they don’t like it. So I decide to change subscription to pay per use. Because as a user, I don’t like subscription as well, pay per use seem like more friendly. So I change from subscription to pay per use. People can afford $9.99 to subscribe monthly for unlimited use or pay $1.99 for each data they want(First purchase is $0.99 then $1.99). I was expecting more user to pay, but it was the complete opposite! Some users who would have paid a higher subscription fee are switching to a lower priced single payment. Users are encountering paywalls more often, and each time they need to make a decision about whether or not to pay, which increases the probability that they will abandon payment. This resulted in a 75% decrease in revenue in November. In fact, the mostly of my revenue comes from a handful of long-cycle subscribers, such as annual subscription. Few bring in most of the revenue, that is the most important thing I learned. You don't need a lot of customers, you just need more valuable ones. That's why it's only right to design a mechanism to filter out high-value customers and focus on them, all the things you want do is just let more people into the filter, and from that point of view, subscription with free trial period is the best way, even if most people don't like it. The rule of 20/80 will always be there. The most important thing is always focus on the 20 percent things and people. Effort does not always guarantee rewards. Unless one engages in deep thinking, or most efforts are invalid. I have been working very hard to promote my product for a period of time. It’s about in November. I did a lot of job, such as write script to send message to my potential clients on Fiverr, post and write comments on others post on Reddit, find related questions and answer them on Quora, post and comments on Twitte, etc. During that period, I was exhausted every day, but the outcome did not meet my expectations. There is only little growth on App installation, even less revenue than before. That make me frustrated. I finally realized that If I need to put in a tremendous amount of effort just to make a little progress, there is must something wrong. So I stop 80% of promote work I have ever did, only keep app store search ad, which will bring a installation with less than $0.5 cost. Then I dive into long time and deeply thinking, I spent more time on reading books, investigate other product with great MRR, watch interviews with people who are already living the kind of life I aspire to live, for example, u/levelsio. These things have given me great inspiration, and my life has become easier. It seems that the life I anticipated when I resigned is getting closer. I also have a clearer understanding of my app. Meanwhile, MRR has been growing. This experience let me learn that effort does not always guarantee results. Many times, our efforts are just wishful thinking, they are invalid, do the right thing after deeply thinking is more important. What Next? My goal is reach $3K MRR, as same as my job payment, I will never stop to building things, and I will keep my currently lifestyle. I still don't know how to get more people to use my app, but levelsio's interviews give me some inspiration that I can verified something by manually instead of build a software. I plan to launch a trend analysis product based on the keyword data provided by my current app. I have always wanted to combine AI to build such a product, but I didn't know how to do it. Now I intend to manually complete it first and start software development once there are paying users. If you are interested to my App, you could try it. Gotrends

I built an AI Stock Analysis Tool
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HenryObjThis week

I built an AI Stock Analysis Tool

Hi Reddit, TL;DR: I am sharing the tool I built to assist me with my investments I have been investing for over a decade, and I have always struggled with: Putting the time to do actual research Trusting analyst’s recommendations To \ invest \ we want to make sure that the price is right and the company will keep improving. How do we know that “the price is right?” How can we predict that the company will perform better in the future? To answer the above, we have to look at the company’s financials and their trends. We have to compare the company with its peers/competitors. We should understand its business model, the sector and geography the company evolves in and the perspective of the economy in general. We can also look at additional signals like insiders selling or buying. Just for one investment, this is already a lot of work. And a work that we need to repeat every time there is a significant change - for example, a significant price change from our last analysis or new quarterly results, etc. To automate all the above, I have built a stock analysis tool and have been using it the past years for my own investments. I have been adding LLMs agents (GPT 4o & Claude 3.5) to perform the qualitative analysis. Recently, I decided to share it and keep on building it in public. In this initial version, you can get the summary of the stock analysis my model generates. For now, it covers most of the S&P and Nasdaq stocks. Here is the link 👉 https://undervalued.ai If you are into investing yourself, please feel free to reach out. I would love to get your feedback and know more about your methodology.

[Discussion] When ML and Data Science are the death of a good company: A cautionary tale.
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AlexSnakeKingThis week

[Discussion] When ML and Data Science are the death of a good company: A cautionary tale.

TD;LR: At Company A, Team X does advanced analytics using on-prem ERP tools and older programming languages. Their tools work very well and are designed based on very deep business and domain expertise. Team Y is a new and ambitious Data Science team that thinks they can replace Team X's tools with a bunch of R scripts and a custom built ML platform. Their models are simplistic, but more "fashionable" compared to the econometric models used by Team X, and team Y benefits from the ML/DS moniker so leadership is allowing Team Y to start a large scale overhaul of the analytics platform in question. Team Y doesn't have the experience for such a larger scale transformation, and is refusing to collaborate with team X. This project is very likely going to fail, and cause serious harm to the company as a whole financially and from a people perspective. I argue that this is not just because of bad leadership, but also because of various trends and mindsets in the DS community at large. Update (Jump to below the line for the original story): Several people in the comments are pointing out that this just a management failure, not something due to ML/DS, and that you can replace DS with any buzz tech and the story will still be relevant. My response: Of course, any failure at an organization level is ultimately a management failure one way or the other. Moreover, it is also the case that ML/DS when done correctly, will always improve a company's bottom line. There is no scenario where the proper ML solution, delivered at a reasonable cost and in a timely fashion, will somehow hurt the company's bottom line. My point is that in this case management is failing because of certain trends and practices that are specific to the ML/DS community, namely: The idea that DS teams should operate independently of tech and business orgs -- too much autonomy for DS teams The disregard for domain knowledge that seems prevalent nowadays thanks to the ML hype, that DS can be generalists and someone with good enough ML chops can solve any business problem. That wasn't the case when I first left academia for the industry in 2009 (back then nobody would even bother with a phone screen if you didn't have the right domain knowledge). Over reliance on resources who check all the ML hype related boxes (knows Python, R, Tensorflow, Shiny, etc..., has the right Coursera certifications, has blogged on the topic, etc...), but are lacking in depth of experience. DS interviews nowadays all seem to be: Can you tell me what a p-value is? What is elastic net regression? Show me how to fit a model in sklearn? How do you impute NAs in an R dataframe? Any smart person can look those up on Stackoverflow or Cross-Validated,.....Instead teams should be asking stuff like: why does portfolio optimization use QP not LP? How does a forecast influence a customer service level? When should a recommendation engine be content based and when should it use collaborative filtering? etc... (This is a true story, happening to the company I currently work for. Names, domains, algorithms, and roles have been shuffled around to protect my anonymity)  Company A has been around for several decades. It is not the biggest name in its domain, but it is a well respected one. Risk analysis and portfolio optimization have been a core of Company A's business since the 90s. They have a large team of 30 or so analysts who perform those tasks on a daily basis. These analysts use ERP solutions implemented for them by one the big ERP companies (SAP, Teradata, Oracle, JD Edwards,...) or one of the major tech consulting companies (Deloitte, Accenture, PWC, Capgemini, etc...) in collaboration with their own in house engineering team. The tools used are embarrassingly old school: Classic RDBMS running on on-prem servers or maybe even on mainframes, code written in COBOL, Fortran, weird proprietary stuff like ABAP or SPSS.....you get the picture. But the models and analytic functions were pretty sophisticated, and surprisingly cutting edge compared to the published academic literature. Most of all, they fit well with the company's enterprise ecosystem, and were honed based on years of deep domain knowledge.  They have a tech team of several engineers (poached from the aforementioned software and consulting companies) and product managers (who came from the experienced pools of analysts and managers who use the software, or poached from business rivals) maintaining and running this software. Their technology might be old school, but collectively, they know the domain and the company's overall architecture very, very well. They've guided the company through several large scale upgrades and migrations and they have a track record of delivering on time, without too much overhead. The few times they've stumbled, they knew how to pick themselves up very quickly. In fact within their industry niche, they have a reputation for their expertise, and have very good relations with the various vendors they've had to deal with. They were the launching pad of several successful ERP consulting careers.  Interestingly, despite dealing on a daily basis with statistical modeling and optimization algorithms, none of the analysts, engineers, or product managers involved describe themselves as data scientists or machine learning experts. It is mostly a cultural thing: Their expertise predates the Data Science/ML hype that started circa 2010, and they got most of their chops using proprietary enterprise tools instead of the open source tools popular nowadays. A few of them have formal statistical training, but most of them came from engineering or domain backgrounds and learned stats on the fly while doing their job. Call this team "Team X".  Sometime around the mid 2010s, Company A started having some serious anxiety issues: Although still doing very well for a company its size, overall economic and demographic trends were shrinking its customer base, and a couple of so called disruptors came up with a new app and business model that started seriously eating into their revenue. A suitable reaction to appease shareholders and Wall Street was necessary. The company already had a decent website and a pretty snazzy app, what more could be done? Leadership decided that it was high time that AI and ML become a core part of the company's business. An ambitious Manager, with no science or engineering background, but who had very briefly toyed with a recommender system a couple of years back, was chosen to build a data science team, call it team "Y" (he had a bachelor's in history from the local state college and worked for several years in the company's marketing org). Team "Y" consists mostly of internal hires who decided they wanted to be data scientists and completed a Coursera certification or a Galvanize boot camp, before being brought on to the team, along with a few of fresh Ph.D or M.Sc holders who didn't like academia and wanted to try their hand at an industry role. All of them were very bright people, they could write great Medium blog posts and give inspiring TED talks, but collectively they had very little real world industry experience. As is the fashion nowadays, this group was made part of a data science org that reported directly to the CEO and Board, bypassing the CIO and any tech or business VPs, since Company A wanted to claim the monikers "data driven" and "AI powered" in their upcoming shareholder meetings. In 3 or 4 years of existence, team Y produced a few Python and R scripts. Their architectural experience  consisted almost entirely in connecting Flask to S3 buckets or Redshift tables, with a couple of the more resourceful ones learning how to plug their models into Tableau or how to spin up a Kuberneties pod.  But they needn't worry: The aforementioned manager, who was now a director (and was also doing an online Masters to make up for his qualifications gap and bolster his chances of becoming VP soon - at least he now understands what L1 regularization is), was a master at playing corporate politics and self-promotion. No matter how few actionable insights team Y produced or how little code they deployed to production, he always had their back and made sure they had ample funding. In fact he now had grandiose plans for setting up an all-purpose machine learning platform that can be used to solve all of the company's data problems.  A couple of sharp minded members of team Y, upon googling their industry name along with the word "data science", realized that risk analysis was a prime candidate for being solved with Bayesian models, and there was already a nifty R package for doing just that, whose tutorial they went through on R-Bloggers.com. One of them had even submitted a Bayesian classifier Kernel for a competition on Kaggle (he was 203rd on the leaderboard), and was eager to put his new-found expertise to use on a real world problem. They pitched the idea to their director, who saw a perfect use case for his upcoming ML platform. They started work on it immediately, without bothering to check whether anybody at Company A was already doing risk analysis. Since their org was independent, they didn't really need to check with anybody else before they got funding for their initiative. Although it was basically a Naive Bayes classifier, the term ML was added to the project tile, to impress the board.  As they progressed with their work however, tensions started to build. They had asked the data warehousing and CA analytics teams to build pipelines for them, and word eventually got out to team X about their project. Team X was initially thrilled: They offered to collaborate whole heartedly, and would have loved to add an ML based feather to their already impressive cap. The product owners and analysts were totally onboard as well: They saw a chance to get in on the whole Data Science hype that they kept hearing about. But through some weird mix of arrogance and insecurity, team Y refused to collaborate with them or share any of their long term goals with them, even as they went to other parts of the company giving brown bag presentations and tutorials on the new model they created.  Team X got resentful: from what they saw of team Y's model, their approach was hopelessly naive and had little chances of scaling or being sustainable in production, and they knew exactly how to help with that. Deploying the model to production would have taken them a few days, given how comfortable they were with DevOps and continuous delivery (team Y had taken several months to figure out how to deploy a simple R script to production). And despite how old school their own tech was, team X were crafty enough to be able to plug it in to their existing architecture. Moreover, the output of the model was such that it didn't take into account how the business will consume it or how it was going to be fed to downstream systems, and the product owners could have gone a long way in making the model more amenable to adoption by the business stakeholders. But team Y wouldn't listen, and their leads brushed off any attempts at communication, let alone collaboration. The vibe that team Y was giving off was "We are the cutting edge ML team, you guys are the legacy server grunts. We don't need your opinion.", and they seemed to have a complete disregard for domain knowledge, or worse, they thought that all that domain knowledge consisted of was being able to grasp the definitions of a few business metrics.  Team X got frustrated and tried to express their concerns to leadership. But despite owning a vital link in Company A's business process, they were only \~50 people in a large 1000 strong technology and operations org, and they were several layers removed from the C-suite, so it was impossible for them to get their voices heard.  Meanwhile, the unstoppable director was doing what he did best: Playing corporate politics. Despite how little his team had actually delivered, he had convinced the board that all analysis and optimization tasks should now be migrated to his yet to be delivered ML platform. Since most leaders now knew that there was overlap between team Y and team X's objectives, his pitch was no longer that team Y was going to create a new insight, but that they were going to replace (or modernize) the legacy statistics based on-prem tools with more accurate cloud based ML tools. Never mind that there was no support in the academic literature for the idea that Naive Bayes works better than the Econometric approaches used by team X, let alone the additional wacky idea that Bayesian Optimization would definitely outperform the QP solvers that were running in production.  Unbeknownst to team X, the original Bayesian risk analysis project has now grown into a multimillion dollar major overhaul initiative, which included the eventual replacement of all of the tools and functions supported by team X along with the necessary migration to the cloud. The CIO and a couple of business VPs are on now board, and tech leadership is treating it as a done deal. An outside vendor, a startup who nobody had heard of, was contracted to help build the platform, since team Y has no engineering skills. The choice was deliberate, as calling on any of the established consulting or software companies would have eventually led leadership to the conclusion that team X was better suited for a transformation on this scale than team Y.  Team Y has no experience with any major ERP deployments, and no domain knowledge, yet they are being tasked with fundamentally changing the business process that is at the core of Company A's business. Their models actually perform worse than those deployed by team X, and their architecture is hopelessly simplistic, compared to what is necessary for running such a solution in production.  Ironically, using Bayesian thinking and based on all the evidence, the likelihood that team Y succeeds is close to 0%. At best, the project is going to end up being a write off of 50 million dollars or more. Once the !@#$!@hits the fan, a couple of executive heads are going to role, and dozens of people will get laid off. At worst, given how vital risk analysis and portfolio optimization is to Company A's revenue stream, the failure will eventually sink the whole company. It probably won't go bankrupt, but it will lose a significant portion of its business and work force. Failed ERP implementations can and do sink large companies: Just see what happened to National Grid US, SuperValu or Target Canada.  One might argue that this is more about corporate disfunction and bad leadership than about data science and AI. But I disagree. I think the core driver of this debacle is indeed the blind faith in Data Scientists, ML models and the promise of AI, and the overall culture of hype and self promotion that is very common among the ML crowd.  We haven't seen the end of this story: I sincerely hope that this ends well for the sake of my colleagues and all involved. Company A is a good company, and both its customers and its employees deserver better. But the chances of that happening are negligible given all the information available, and this failure will hit my company hard.

[P] The Big Sleep: Text-to-image generation using BigGAN and OpenAI's CLIP via a Google Colab notebook from Twitter user Adverb
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[P] The Big Sleep: Text-to-image generation using BigGAN and OpenAI's CLIP via a Google Colab notebook from Twitter user Adverb

From https://twitter.com/advadnoun/status/1351038053033406468: The Big Sleep Here's the notebook for generating images by using CLIP to guide BigGAN. It's very much unstable and a prototype, but it's also a fair place to start. I'll likely update it as time goes on. colab.research.google.com/drive/1NCceX2mbiKOSlAd\o7IU7nA9UskKN5WR?usp=sharing I am not the developer of The Big Sleep. This is the developer's Twitter account; this is the developer's Reddit account. Steps to follow to generate the first image in a given Google Colab session: Optionally, if this is your first time using Google Colab, view this Colab introduction and/or this Colab FAQ. Click this link. Sign into your Google account if you're not already signed in. Click the "S" button in the upper right to do this. Note: Being signed into a Google account has privacy ramifications, such as your Google search history being recorded in your Google account. In the Table of Contents, click "Parameters". Find the line that reads "tx = clip.tokenize('''a cityscape in the style of Van Gogh''')" and change the text inside of the single quote marks to your desired text; example: "tx = clip.tokenize('''a photo of New York City''')". The developer recommends that you keep the three single quote marks on both ends of your desired text so that mult-line text can be used An alternative is to remove two of the single quotes on each end of your desired text; example: "tx = clip.tokenize('a photo of New York City')". In the Table of Contents, click "Restart the kernel...". Position the pointer over the first cell in the notebook, which starts with text "import subprocess". Click the play button (the triangle) to run the cell. Wait until the cell completes execution. Click menu item "Runtime->Restart and run all". In the Table of Contents, click "Diagnostics". The output appears near the end of the Train cell that immediately precedes the Diagnostics cell, so scroll up a bit. Every few minutes (or perhaps 10 minutes if Google assigned you relatively slow hardware for this session), a new image will appear in the Train cell that is a refinement of the previous image. This process can go on for as long as you want until Google ends your Google Colab session, which is a total of up to 12 hours for the free version of Google Colab. Steps to follow if you want to start a different run using the same Google Colab session: Click menu item "Runtime->Interrupt execution". Save any images that you want to keep by right-clicking on them and using the appropriate context menu command. Optionally, change the desired text. Different runs using the same desired text almost always results in different outputs. Click menu item "Runtime->Restart and run all". Steps to follow when you're done with your Google Colab session: Click menu item "Runtime->Manage sessions". Click "Terminate" to end the session. Optionally, log out of your Google account due to the privacy ramifications of being logged into a Google account. The first output image in the Train cell (using the notebook's default of seeing every 100th image generated) usually is a very poor match to the desired text, but the second output image often is a decent match to the desired text. To change the default of seeing every 100th image generated, change the number 100 in line "if itt % 100 == 0:" in the Train cell to the desired number. For free-tier Google Colab users, I recommend changing 100 to a small integer such as 5. Tips for the text descriptions that you supply: In Section 3.1.4 of OpenAI's CLIP paper (pdf), the authors recommend using a text description of the form "A photo of a {label}." or "A photo of a {label}, a type of {type}." for images that are photographs. A Reddit user gives these tips. The Big Sleep should generate these 1,000 types of things better on average than other types of things. Here is an article containing a high-level description of how The Big Sleep works. The Big Sleep uses a modified version of BigGAN as its image generator component. The Big Sleep uses the ViT-B/32 CLIP model to rate how well a given image matches your desired text. The best CLIP model according to the CLIP paper authors is the (as of this writing) unreleased ViT-L/14-336px model; see Table 10 on page 40 of the CLIP paper (pdf) for a comparison. There are many other sites/programs/projects that use CLIP to steer image/video creation to match a text description. Some relevant subreddits: r/bigsleep (subreddit for images/videos generated from text-to-image machine learning algorithms). r/deepdream (subreddit for images/videos generated from machine learning algorithms). r/mediasynthesis (subreddit for media generation/manipulation techniques that use artificial intelligence; this subreddit shouldn't be used to post images/videos unless new techniques are demonstrated, or the images/videos are of high quality relative to other posts). Example using text 'a black cat sleeping on top of a red clock': https://preview.redd.it/7xq58v7022c61.png?width=512&format=png&auto=webp&s=a229ae9add555cd1caba31c42b60d907ffe67773 Example using text 'the word ''hot'' covered in ice': https://preview.redd.it/6kxdp8u3k2c61.png?width=512&format=png&auto=webp&s=5bd078b0111575f5d88a1dc53b0aeb933f3b0da6 Example using text 'a monkey holding a green lightsaber': https://preview.redd.it/rdsybsoaz2c61.png?width=512&format=png&auto=webp&s=2769d4c6c883c1c35ae0b1c629bebe9bc1d41393 Example using text 'The White House in Washington D.C. at night with green and red spotlights shining on it': https://preview.redd.it/w4mg90xsf5c61.png?width=512&format=png&auto=webp&s=5f18318de2f77bcd8a86e71e87048fadd30383d1 Example using text '''A photo of the Golden Gate Bridge at night, illuminated by spotlights in a tribute to Prince''': https://preview.redd.it/cn4ecuafhic61.png?width=512&format=png&auto=webp&s=397c838fdc49f13c5f17110b92c78b95bf0dcac0 Example using text '''a Rembrandt-style painting titled "Robert Plant decides whether to take the stairway to heaven or the ladder to heaven"''': https://preview.redd.it/h7rb3y6j5jc61.png?width=512&format=png&auto=webp&s=537bfe8210af185647b00e7585c948aa2c4e0ffb Example using text '''A photo of the Empire State Building being shot at with the laser cannons of a TIE fighter.''': https://preview.redd.it/cwi7i639c5d61.png?width=512&format=png&auto=webp&s=0510c8b93adb40eee4d3f41607f1c215d41e55ff Example using text '''A cartoon of a new mascot for the Reddit subreddit DeepDream that has a mouse-like face and wears a cape''': https://preview.redd.it/wtxbduevcbd61.png?width=512&format=png&auto=webp&s=c5d266258922bc62f25c80a08cd9cabc07d9cb1c Example using text '''Bugs Bunny meets the Eye of Sauron, drawn in the Looney Tunes cartoon style''': https://preview.redd.it/gmljaeekuid61.png?width=512&format=png&auto=webp&s=9ea578de165e12afc3a62bf6886bc1ae9dc19bec Example using text '''Photo of a blue and red neon-colored frog at night.''': https://preview.redd.it/nzlypte6wzd61.png?width=512&format=png&auto=webp&s=7e10b06f22cfc57c64b6d05738c7486b895083df Example using text '''Hell begins to freeze over''': https://preview.redd.it/vn99we9ngmf61.png?width=512&format=png&auto=webp&s=2408efd607f0ab40a08db6ee67448791aa813993 Example using text '''A scene with vibrant colors''': https://preview.redd.it/4z133mvrgmf61.png?width=512&format=png&auto=webp&s=b78e7a8e3f736769655056093a9904ff09a355a1 Example using text '''The Great Pyramids were turned into prisms by a wizard''': https://preview.redd.it/zxt6op7vgmf61.png?width=512&format=png&auto=webp&s=53e578cfde14b28afe27957e95e610b89afadd44

[D] Why is the AI Hype Absolutely Bonkers
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good_riceThis week

[D] Why is the AI Hype Absolutely Bonkers

Edit 2: Both the repo and the post were deleted. Redacting identifying information as the author has appeared to make rectifications, and it’d be pretty damaging if this is what came up when googling their name / GitHub (hopefully they’ve learned a career lesson and can move on). TL;DR: A PhD candidate claimed to have achieved 97% accuracy for coronavirus from chest x-rays. Their post gathered thousands of reactions, and the candidate was quick to recruit branding, marketing, frontend, and backend developers for the project. Heaps of praise all around. He listed himself as a Director of XXXX (redacted), the new name for his project. The accuracy was based on a training dataset of ~30 images of lesion / healthy lungs, sharing of data between test / train / validation, and code to train ResNet50 from a PyTorch tutorial. Nonetheless, thousands of reactions and praise from the “AI | Data Science | Entrepreneur” community. Original Post: I saw this post circulating on LinkedIn: https://www.linkedin.com/posts/activity-6645711949554425856-9Dhm Here, a PhD candidate claims to achieve great performance with “ARTIFICIAL INTELLIGENCE” to predict coronavirus, asks for more help, and garners tens of thousands of views. The repo housing this ARTIFICIAL INTELLIGENCE solution already has a backend, front end, branding, a README translated in 6 languages, and a call to spread the word for this wonderful technology. Surely, I thought, this researcher has some great and novel tech for all of this hype? I mean dear god, we have branding, and the author has listed himself as the founder of an organization based on this project. Anything with this much attention, with dozens of “AI | Data Scientist | Entrepreneur” members of LinkedIn praising it, must have some great merit, right? Lo and behold, we have ResNet50, from torchvision.models import resnet50, with its linear layer replaced. We have a training dataset of 30 images. This should’ve taken at MAX 3 hours to put together - 1 hour for following a tutorial, and 2 for obfuscating the training with unnecessary code. I genuinely don’t know what to think other than this is bonkers. I hope I’m wrong, and there’s some secret model this author is hiding? If so, I’ll delete this post, but I looked through the repo and (REPO link redacted) that’s all I could find. I’m at a loss for thoughts. Can someone explain why this stuff trends on LinkedIn, gets thousands of views and reactions, and gets loads of praise from “expert data scientists”? It’s almost offensive to people who are like ... actually working to treat coronavirus and develop real solutions. It also seriously turns me off from pursuing an MS in CV as opposed to CS. Edit: It turns out there were duplicate images between test / val / training, as if ResNet50 on 30 images wasn’t enough already. He’s also posted an update signed as “Director of XXXX (redacted)”. This seems like a straight up sleazy way to capitalize on the pandemic by advertising himself to be the head of a made up organization, pulling resources away from real biomedical researchers.

[Discussion] When ML and Data Science are the death of a good company: A cautionary tale.
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[Discussion] When ML and Data Science are the death of a good company: A cautionary tale.

TD;LR: At Company A, Team X does advanced analytics using on-prem ERP tools and older programming languages. Their tools work very well and are designed based on very deep business and domain expertise. Team Y is a new and ambitious Data Science team that thinks they can replace Team X's tools with a bunch of R scripts and a custom built ML platform. Their models are simplistic, but more "fashionable" compared to the econometric models used by Team X, and team Y benefits from the ML/DS moniker so leadership is allowing Team Y to start a large scale overhaul of the analytics platform in question. Team Y doesn't have the experience for such a larger scale transformation, and is refusing to collaborate with team X. This project is very likely going to fail, and cause serious harm to the company as a whole financially and from a people perspective. I argue that this is not just because of bad leadership, but also because of various trends and mindsets in the DS community at large. Update (Jump to below the line for the original story): Several people in the comments are pointing out that this just a management failure, not something due to ML/DS, and that you can replace DS with any buzz tech and the story will still be relevant. My response: Of course, any failure at an organization level is ultimately a management failure one way or the other. Moreover, it is also the case that ML/DS when done correctly, will always improve a company's bottom line. There is no scenario where the proper ML solution, delivered at a reasonable cost and in a timely fashion, will somehow hurt the company's bottom line. My point is that in this case management is failing because of certain trends and practices that are specific to the ML/DS community, namely: The idea that DS teams should operate independently of tech and business orgs -- too much autonomy for DS teams The disregard for domain knowledge that seems prevalent nowadays thanks to the ML hype, that DS can be generalists and someone with good enough ML chops can solve any business problem. That wasn't the case when I first left academia for the industry in 2009 (back then nobody would even bother with a phone screen if you didn't have the right domain knowledge). Over reliance on resources who check all the ML hype related boxes (knows Python, R, Tensorflow, Shiny, etc..., has the right Coursera certifications, has blogged on the topic, etc...), but are lacking in depth of experience. DS interviews nowadays all seem to be: Can you tell me what a p-value is? What is elastic net regression? Show me how to fit a model in sklearn? How do you impute NAs in an R dataframe? Any smart person can look those up on Stackoverflow or Cross-Validated,.....Instead teams should be asking stuff like: why does portfolio optimization use QP not LP? How does a forecast influence a customer service level? When should a recommendation engine be content based and when should it use collaborative filtering? etc... (This is a true story, happening to the company I currently work for. Names, domains, algorithms, and roles have been shuffled around to protect my anonymity)  Company A has been around for several decades. It is not the biggest name in its domain, but it is a well respected one. Risk analysis and portfolio optimization have been a core of Company A's business since the 90s. They have a large team of 30 or so analysts who perform those tasks on a daily basis. These analysts use ERP solutions implemented for them by one the big ERP companies (SAP, Teradata, Oracle, JD Edwards,...) or one of the major tech consulting companies (Deloitte, Accenture, PWC, Capgemini, etc...) in collaboration with their own in house engineering team. The tools used are embarrassingly old school: Classic RDBMS running on on-prem servers or maybe even on mainframes, code written in COBOL, Fortran, weird proprietary stuff like ABAP or SPSS.....you get the picture. But the models and analytic functions were pretty sophisticated, and surprisingly cutting edge compared to the published academic literature. Most of all, they fit well with the company's enterprise ecosystem, and were honed based on years of deep domain knowledge.  They have a tech team of several engineers (poached from the aforementioned software and consulting companies) and product managers (who came from the experienced pools of analysts and managers who use the software, or poached from business rivals) maintaining and running this software. Their technology might be old school, but collectively, they know the domain and the company's overall architecture very, very well. They've guided the company through several large scale upgrades and migrations and they have a track record of delivering on time, without too much overhead. The few times they've stumbled, they knew how to pick themselves up very quickly. In fact within their industry niche, they have a reputation for their expertise, and have very good relations with the various vendors they've had to deal with. They were the launching pad of several successful ERP consulting careers.  Interestingly, despite dealing on a daily basis with statistical modeling and optimization algorithms, none of the analysts, engineers, or product managers involved describe themselves as data scientists or machine learning experts. It is mostly a cultural thing: Their expertise predates the Data Science/ML hype that started circa 2010, and they got most of their chops using proprietary enterprise tools instead of the open source tools popular nowadays. A few of them have formal statistical training, but most of them came from engineering or domain backgrounds and learned stats on the fly while doing their job. Call this team "Team X".  Sometime around the mid 2010s, Company A started having some serious anxiety issues: Although still doing very well for a company its size, overall economic and demographic trends were shrinking its customer base, and a couple of so called disruptors came up with a new app and business model that started seriously eating into their revenue. A suitable reaction to appease shareholders and Wall Street was necessary. The company already had a decent website and a pretty snazzy app, what more could be done? Leadership decided that it was high time that AI and ML become a core part of the company's business. An ambitious Manager, with no science or engineering background, but who had very briefly toyed with a recommender system a couple of years back, was chosen to build a data science team, call it team "Y" (he had a bachelor's in history from the local state college and worked for several years in the company's marketing org). Team "Y" consists mostly of internal hires who decided they wanted to be data scientists and completed a Coursera certification or a Galvanize boot camp, before being brought on to the team, along with a few of fresh Ph.D or M.Sc holders who didn't like academia and wanted to try their hand at an industry role. All of them were very bright people, they could write great Medium blog posts and give inspiring TED talks, but collectively they had very little real world industry experience. As is the fashion nowadays, this group was made part of a data science org that reported directly to the CEO and Board, bypassing the CIO and any tech or business VPs, since Company A wanted to claim the monikers "data driven" and "AI powered" in their upcoming shareholder meetings. In 3 or 4 years of existence, team Y produced a few Python and R scripts. Their architectural experience  consisted almost entirely in connecting Flask to S3 buckets or Redshift tables, with a couple of the more resourceful ones learning how to plug their models into Tableau or how to spin up a Kuberneties pod.  But they needn't worry: The aforementioned manager, who was now a director (and was also doing an online Masters to make up for his qualifications gap and bolster his chances of becoming VP soon - at least he now understands what L1 regularization is), was a master at playing corporate politics and self-promotion. No matter how few actionable insights team Y produced or how little code they deployed to production, he always had their back and made sure they had ample funding. In fact he now had grandiose plans for setting up an all-purpose machine learning platform that can be used to solve all of the company's data problems.  A couple of sharp minded members of team Y, upon googling their industry name along with the word "data science", realized that risk analysis was a prime candidate for being solved with Bayesian models, and there was already a nifty R package for doing just that, whose tutorial they went through on R-Bloggers.com. One of them had even submitted a Bayesian classifier Kernel for a competition on Kaggle (he was 203rd on the leaderboard), and was eager to put his new-found expertise to use on a real world problem. They pitched the idea to their director, who saw a perfect use case for his upcoming ML platform. They started work on it immediately, without bothering to check whether anybody at Company A was already doing risk analysis. Since their org was independent, they didn't really need to check with anybody else before they got funding for their initiative. Although it was basically a Naive Bayes classifier, the term ML was added to the project tile, to impress the board.  As they progressed with their work however, tensions started to build. They had asked the data warehousing and CA analytics teams to build pipelines for them, and word eventually got out to team X about their project. Team X was initially thrilled: They offered to collaborate whole heartedly, and would have loved to add an ML based feather to their already impressive cap. The product owners and analysts were totally onboard as well: They saw a chance to get in on the whole Data Science hype that they kept hearing about. But through some weird mix of arrogance and insecurity, team Y refused to collaborate with them or share any of their long term goals with them, even as they went to other parts of the company giving brown bag presentations and tutorials on the new model they created.  Team X got resentful: from what they saw of team Y's model, their approach was hopelessly naive and had little chances of scaling or being sustainable in production, and they knew exactly how to help with that. Deploying the model to production would have taken them a few days, given how comfortable they were with DevOps and continuous delivery (team Y had taken several months to figure out how to deploy a simple R script to production). And despite how old school their own tech was, team X were crafty enough to be able to plug it in to their existing architecture. Moreover, the output of the model was such that it didn't take into account how the business will consume it or how it was going to be fed to downstream systems, and the product owners could have gone a long way in making the model more amenable to adoption by the business stakeholders. But team Y wouldn't listen, and their leads brushed off any attempts at communication, let alone collaboration. The vibe that team Y was giving off was "We are the cutting edge ML team, you guys are the legacy server grunts. We don't need your opinion.", and they seemed to have a complete disregard for domain knowledge, or worse, they thought that all that domain knowledge consisted of was being able to grasp the definitions of a few business metrics.  Team X got frustrated and tried to express their concerns to leadership. But despite owning a vital link in Company A's business process, they were only \~50 people in a large 1000 strong technology and operations org, and they were several layers removed from the C-suite, so it was impossible for them to get their voices heard.  Meanwhile, the unstoppable director was doing what he did best: Playing corporate politics. Despite how little his team had actually delivered, he had convinced the board that all analysis and optimization tasks should now be migrated to his yet to be delivered ML platform. Since most leaders now knew that there was overlap between team Y and team X's objectives, his pitch was no longer that team Y was going to create a new insight, but that they were going to replace (or modernize) the legacy statistics based on-prem tools with more accurate cloud based ML tools. Never mind that there was no support in the academic literature for the idea that Naive Bayes works better than the Econometric approaches used by team X, let alone the additional wacky idea that Bayesian Optimization would definitely outperform the QP solvers that were running in production.  Unbeknownst to team X, the original Bayesian risk analysis project has now grown into a multimillion dollar major overhaul initiative, which included the eventual replacement of all of the tools and functions supported by team X along with the necessary migration to the cloud. The CIO and a couple of business VPs are on now board, and tech leadership is treating it as a done deal. An outside vendor, a startup who nobody had heard of, was contracted to help build the platform, since team Y has no engineering skills. The choice was deliberate, as calling on any of the established consulting or software companies would have eventually led leadership to the conclusion that team X was better suited for a transformation on this scale than team Y.  Team Y has no experience with any major ERP deployments, and no domain knowledge, yet they are being tasked with fundamentally changing the business process that is at the core of Company A's business. Their models actually perform worse than those deployed by team X, and their architecture is hopelessly simplistic, compared to what is necessary for running such a solution in production.  Ironically, using Bayesian thinking and based on all the evidence, the likelihood that team Y succeeds is close to 0%. At best, the project is going to end up being a write off of 50 million dollars or more. Once the !@#$!@hits the fan, a couple of executive heads are going to role, and dozens of people will get laid off. At worst, given how vital risk analysis and portfolio optimization is to Company A's revenue stream, the failure will eventually sink the whole company. It probably won't go bankrupt, but it will lose a significant portion of its business and work force. Failed ERP implementations can and do sink large companies: Just see what happened to National Grid US, SuperValu or Target Canada.  One might argue that this is more about corporate disfunction and bad leadership than about data science and AI. But I disagree. I think the core driver of this debacle is indeed the blind faith in Data Scientists, ML models and the promise of AI, and the overall culture of hype and self promotion that is very common among the ML crowd.  We haven't seen the end of this story: I sincerely hope that this ends well for the sake of my colleagues and all involved. Company A is a good company, and both its customers and its employees deserver better. But the chances of that happening are negligible given all the information available, and this failure will hit my company hard.

12 months ago, I was unemployed. Last week my side hustle got acquired by a $500m fintech company
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12 months ago, I was unemployed. Last week my side hustle got acquired by a $500m fintech company

I’ve learned so much over the years from this subreddit. I thought I’d return the favour and share some of my own learnings. In November 2020 my best friend and I had an idea. “What if we could find out which stocks the Internet is talking about?” This formed the origins of Ticker Nerd. 9 months later we sold Ticker Nerd to Finder (an Australian fintech company valued at around $500m). In this post, I am going to lay out how we got there. How we came up with the idea First off, like other posts have covered - you don’t NEED a revolutionary or original idea to build a business. There are tonnes of “boring” businesses making over 7 figures a year e.g. law firms, marketing agencies, real estate companies etc. If you’re looking for an exact formula to come up with a great business idea I’m sorry, but it doesn’t exist. Finding new business opportunities is more of an art than a science. Although, there are ways you can make it easier to find inspiration. Below are the same resources I use for inspiration. I rarely ever come up with ideas without first searching one of the resources below for inspiration: Starter Story Twitter Startup Ideas My First Million Trends by the Hustle Trends VC To show how you how messy, random and unpredictable it can be to find an idea - let me explain how my co-founder and I came up with the idea for Ticker Nerd: We discovered a new product on Twitter called Exploding Topics. It was a newsletter that uses a bunch of software and algorithms to find trends that are growing quickly before they hit the mainstream. I had recently listened to a podcast episode from My First Million where they spoke about Motley Fool making hundreds of millions from their investment newsletters. We asked ourselves what if we could build a SaaS platform similar to Exploding Topics but it focused on stocks? We built a quick landing page using Carrd + Gumroad that explained what our new idea will do and included a payment option to get early access for $49. We called it Exploding Stock (lol). We shared it around a bunch of Facebook groups and subreddits. We made $1,000 in pre-sales within a couple days. My co-founder and I can’t code so we had to find a developer to build our idea. We interviewed a bunch of potential candidates. Meanwhile, I was trawling through Wall Street Bets and found a bunch of free tools that did roughly what we wanted to build. Instead of building another SaaS tool that did the same thing as these free tools we decided to pivot from our original idea. Our new idea = a paid newsletter that sends a weekly report that summarises 2 of the best stocks that are growing in interest on the Internet. We emailed everyone who pre-ordered access, telling them about the change and offered a full refund if they wanted. tl;dr: We essentially combined two existing businesses (Exploding Topics and Motley Fool) and made it way better. We validated the idea by finding out if people will actually pay money for it BEFORE we decided to build it. The idea we started out with changed over time. How to work out if your idea will actually make money It’s easy to get hung up on designing the logo or choosing the perfect domain name for your new idea. At this stage none of that matters. The most important thing is working out if people will pay money for it. This is where validation comes in. We usually validate ideas using Carrd. It lets you build a simple one page site without having to code. The Ticker Nerd site was actually built using a Carrd template. Here’s how you can do it yourself (at a high level): Create a Carrd pro account (yes it's a $49 one off payment but you’ll get way more value out of it). Buy a cheap template and send it to your Carrd account. You can build your own template but this will save you a lot of time. Once the template reaches your Carrd account, duplicate it. Leave the original so it can be duplicated for other ideas. Jump onto Canva (free) and create a logo using the free logos provided. Import your logo. Add copy to the page that explains your idea. Use the AIDA formula. Sign up to Gumroad (free) and create a pre-sale campaign. Create a discounted lifetime subscription or version of the product. This will be used pre-sales. Add the copy from the site into the pre-sale campaign on Gumroad. Add a ‘widget’ to Carrd and connect it to Gumroad using the existing easy integration feature. Purchase a domain name. Connect it to Carrd. Test the site works. Share your website Now the site is ready you can start promoting it in various places to see how the market reacts. An easy method is to find relevant subreddits using Anvaka (Github tool) or Subreddit Stats. The Anvaka tool provides a spider map of all the connected subreddits that users are active in. The highlighted ones are most relevant. You can post a thread in these subreddits that offer value or can generate discussion. For example: ‘I’m creating a tool that can write all your copy, would anyone actually use this?’ ‘What does everything think of using AI to get our copy written faster?’ ‘It’s time to scratch my own itch, I’m creating a tool that writes marketing copy using GPT-3. What are the biggest problems you face writing marketing copy? I’ll build a solution for it’ Reddit is pretty brutal these days so make sure the post is genuine and only drop your link in the comments or in the post if it seems natural. If people are interested they’ll ask for the link. Another great place to post is r/entrepreuerridealong and r/business_ideas. These subreddits expect people to share their ideas and you’ll likely make some sales straight off the bat. I also suggest posting in some Facebook groups (related to your idea) as well just for good measure. Assess the results If people are paying you for early access you can assume that it’s worth building your idea. The beauty of posting your idea on Reddit or in Facebook groups is you’ll quickly learn why people love/hate your idea. This can help you decide how to tweak the idea or if you should drop it and move on to the next one. How we got our first 100 customers (for free) By validating Ticker Nerd using subreddits and Facebook groups this gave us our first paying customers. But we knew this wouldn’t be sustainable. We sat down and brainstormed every organic strategy we could use to get traction as quickly as possible. The winner: a Product Hunt launch. A successful Product Hunt launch isn’t easy. You need: Someone that has a solid reputation and audience to “hunt” your product (essentially an endorsement). An aged Product Hunt account - you can’t post any products if your account is less than a week old. To be following relevant Product Hunt members - since they get notified when you launch a new product if they’re following you. Relationships with other builders and makers on Product Hunt that also have a solid reputation and following. Although, if you can pull it off you can get your idea in front of tens of thousands of people actively looking for new products. Over the next few weeks, I worked with my co-founder on connecting with different founders, indie hackers and entrepreneurs mainly via Twitter. We explained to them our plans for the Product Hunt launch and managed to get a small army of people ready to upvote our product on launch day. We were both nervous on the day of the launch. We told ourselves to have zero expectations. The worst that could happen was no one signed up and we were in the same position as we’re in now. Luckily, within a couple of hours Ticker Nerd was on the homepage of Product Hunt and in the top 10. The results were instant. After 24 hours we had around 200 people enter their payment details to sign up for our free trial. These signups were equal to around $5,800 in monthly recurring revenue. \-- I hope this post was useful! Drop any questions you have below and I’ll do my best to respond :)

How a Small Startup in Asia Secured a Contract with the US Department of Homeland Security
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How a Small Startup in Asia Secured a Contract with the US Department of Homeland Security

Uzair Javaid, a Ph.D. with a passion for data privacy, co-founded Betterdata to tackle one of AI's most pressing challenges: protecting privacy while enabling innovation. Recently, Betterdata secured a lucrative contract with the US Department of Homeland Security, 1 of only 4 companies worldwide to do so and the only one in Asia. Here's how he did it: The Story So what's your story? I grew up in Peshawar, Pakistan, excelling in coding despite studying electrical engineering. Inspired by my professors, I set my sights on studying abroad and eventually earned a Ph.D. scholarship at NUS Singapore, specializing in data security and privacy. During my research, I ethically hacked Ethereum and published 15 papers—three times the requirement. While wrapping up my Ph.D., I explored startup ideas and joined Entrepreneur First, where I met Kevin Yee. With his expertise in generative models and mine in privacy, we founded Betterdata. Now, nearly three years in, we’ve secured a major contract with the U.S. Department of Homeland Security—one of only four companies globally and the only one from Asia. The Startup In a nutshell, what does your startup do? Betterdata is a startup that uses AI and synthetic data generation to address two major challenges: data privacy and the scarcity of high-quality data for training AI models. By leveraging generative models and privacy-enhancing technologies, Betterdata enables businesses, such as banks, to use customer data without breaching privacy regulations. The platform trains AI on real data, learns its patterns, and generates synthetic data that mimics the real thing without containing any personal or sensitive information. This allows companies to innovate and develop AI solutions safely and ethically, all while tackling the growing need for diverse, high-quality data in AI development. How did you conduct ideation and validation for your startup? The initial idea for Betterdata came from personal experience. During my Ph.D., I ethically hacked Ethereum’s blockchain, exposing flaws in encryption-based data sharing. This led me to explore AI-driven deep synthesis technology—similar to deepfakes but for structured data privacy. With GDPR impacting 28M+ businesses, I saw a massive opportunity to help enterprises securely share data while staying compliant. To validate the idea, I spoke to 50 potential customers—a number that strikes the right balance. Some say 100, but that’s impractical for early-stage founders. At 50, patterns emerge: if 3 out of 10 mention the same problem, and this repeats across 50, you have 10–15 strong signals, making it a solid foundation for an MVP. Instead of outbound sales, which I dislike, we used three key methods: Account-Based Marketing (ABM)—targeting technically savvy users with solutions for niche problems, like scaling synthetic data for banks. Targeted Content Marketing—regular customer conversations shaped our thought leadership and outreach. Raising Awareness Through Partnerships—collaborating with NUS, Singapore’s PDPC, and Plug and Play to build credibility and educate the market. These strategies attracted serious customers willing to pay, guiding Betterdata’s product development and market fit. How did you approach the initial building and ongoing product development? In the early stages, we built synthetic data generation algorithms and a basic UI for proof-of-concept, using open-source datasets to engage with banks. We quickly learned that banks wouldn't share actual customer data due to privacy concerns, so we had to conduct on-site installations and gather feedback to refine our MVP. Through continuous consultation with customers, we discovered real enterprise data posed challenges, such as missing values, which led us to adapt our prototype accordingly. This iterative approach of listening to customer feedback and observing their usage allowed us to improve our product, enhance UX, and address unmet needs while building trust and loyalty. Working closely with our customers also gives us a data advantage. Our solution’s effectiveness depends on customer data, which we can't fully access, but bridging this knowledge gap gives us a competitive edge. The more customers we test on, the more our algorithms adapt to diverse use cases, making it harder for competitors to replicate our insights. My approach to iteration is simple: focus solely on customer feedback and ignore external noise like trends or advice. The key question for the team is: which customer is asking for this feature or solution? As long as there's a clear answer, we move forward. External influences, such as AI hype, often bring more confusion than clarity. True long-term success comes from solving real customer problems, not chasing trends. Customers may not always know exactly what they want, but they understand their problems. Our job is to identify these problems and solve them in innovative ways. While customers may suggest specific features, we stay focused on solving the core issue rather than just fulfilling their exact requests. The idea aligns with the quote often attributed to Henry Ford: "If I asked people what they wanted, they would have said faster horses." The key is understanding their problems, not just taking requests at face value. How do you assess product-market fit? To assess product-market fit, we track two key metrics: Customers' Willingness to Pay: We measure both the quantity and quality of meetings with potential customers. A high number of meetings with key decision-makers signals genuine interest. At Betterdata, we focused on getting meetings with people in banks and large enterprises to gauge our product's resonance with the target market. How Much Customers Are Willing to Pay: We monitor the price customers are willing to pay, especially in the early stages. For us, large enterprises, like banks, were willing to pay a premium for our synthetic data platform due to the growing need for privacy tech. This feedback guided our product refinement and scaling strategy. By focusing on these metrics, we refined our product and positioned it for scaling. What is your business model? We employ a structured, phase-driven approach for out business model, as a B2B startup. I initially struggled with focusing on the core value proposition in sales, often becoming overly educational. Eventually, we developed a product roadmap with models that allowed us to match customer needs to specific offerings and justify our pricing. Our pricing structure includes project-based pilots and annual contracts for successful deployments. At Betterdata, our customer engagement unfolds across three phases: Phase 1: Trial and Benchmarking \- We start with outreach and use open-source datasets to showcase results, offering customers a trial period to evaluate the solution. Phase 2: Pilot or PoC \- After positive trial results, we conduct a PoC or pilot using the customer’s private data, with the understanding that successful pilots lead to an annual contract. Phase 3: Multi-Year Contracts \- Following a successful pilot, we transition to long-term commercial contracts, focusing on multi-year agreements to ensure stability and ongoing partnerships. How do you do marketing for your brand? We take a non-conventional approach to marketing, focusing on answering one key question: Which customers are willing to pay, and how much? This drives our messaging to show how our solution meets their needs. Our strategy centers around two main components: Building a network of lead magnets \- These are influential figures like senior advisors, thought leaders, and strategic partners. Engaging with institutions like IMDA, SUTD, and investors like Plug and Play helps us gain access to the right people and foster warm introductions, which shorten our sales cycle and ensure we’re reaching the right audience. Thought leadership \- We build our brand through customer traction, technology evidence, and regulatory guidelines. This helps us establish credibility in the market and position ourselves as trusted leaders in our field. This holistic approach has enabled us to navigate diverse market conditions in Asia and grow our B2B relationships. By focusing on these areas, we drive business growth and establish strong trust with stakeholders. What's your advice for fundraising? Here are my key takeaways for other founders when it comes to fundraising: Fundraise When You Don’t Need To We closed our seed round in April 2023, a time when we weren't actively raising. Founders should always be in fundraising mode, even when they're not immediately in need of capital. Don’t wait until you have only a few months of runway left. Keep the pipeline open and build relationships. When the timing is right, execution becomes much easier. For us, our investment came through a combination of referrals and inbound interest. Even our lead investor initially rejected us, but after re-engaging, things eventually fell into place. It’s crucial to stay humble, treat everyone with respect, and maintain those relationships for when the time is right. Be Mindful of How You Present Information When fundraising, how you present information matters a lot. We created a comprehensive, easily digestible investment memo, hosted on Notion, which included everything an investor might need—problem, solution, market, team, risks, opportunities, and data. The goal was for investors to be able to get the full picture within 30 minutes without chasing down extra details. We also focused on making our financial model clear and meaningful, even though a 5-year forecast might be overkill at the seed stage. The key was clarity and conciseness, and making it as easy as possible for investors to understand the opportunity. I learned that brevity and simplicity are often the best ways to make a memorable impact. For the pitch itself, keep it simple and focus on 4 things: problem, solution, team, and market. If you can summarize each of these clearly and concisely, you’ll have a compelling pitch. Later on, you can expand into market segments, traction, and other metrics, but for seed-stage, focus on those four areas, and make sure you’re strong in at least three of them. If you do, you'll have a compelling case. How do you run things day-to-day? i.e what's your operational workflow and team structure? Here's an overview of our team structure and process: Internally: Our team is divided into two main areas: backend (internal team) and frontend (market-facing team). There's no formal hierarchy within the backend team. We all operate as equals, defining our goals based on what needs to be developed, assigning tasks, and meeting weekly to share updates and review progress. The focus is on full ownership of tasks and accountability for getting things done. I also contribute to product development, identifying challenges and clearing obstacles to help the team move forward. Backend Team: We approach tasks based on the scope defined by customers, with no blame or hierarchy. It's like a sports team—sometimes someone excels, and other times they struggle, but we support each other and move forward together. Everyone has the creative freedom to work in the way that suits them best, but we establish regular meetings and check-ins to ensure alignment and progress. Frontend Team: For the market-facing side, we implement a hierarchy because the market expects this structure. If I present myself as "CEO," it signals authority and credibility. This distinction affects how we communicate with the market and how we build our brand. The frontend team is split into four main areas: Business Product (Software Engineering) Machine Learning Engineering R&D The C-suite sits at the top, followed by team leads, and then the executors. We distill market expectations into actionable tasks, ensuring that everyone is clear on their role and responsibilities. Process: We start by receiving market expectations and defining tasks based on them. Tasks are assigned to relevant teams, and execution happens with no communication barriers between team members. This ensures seamless collaboration and focused execution. The main goal is always effectiveness—getting things done efficiently while maintaining flexibility in how individuals approach their work. In both teams, there's an emphasis on accountability, collaboration, and clear communication, but the structure varies according to the nature of the work and external expectations.

Turning a Social Media Agency into $1.5 Million in Revenue
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Turning a Social Media Agency into $1.5 Million in Revenue

Steffie here from Founder Folks, with a recent interview I did with Jason Yormark from Socialistics. Here is his story how he started and grew his social media agency. Name: Jason Yormark Company: Socialistics Employee Size: 10 Revenue: $1,500,000/year Year Founded: 2018 Website: www.socialistics.com Technology Tools: ClickUp, Slack, KumoSpace, Google Workspace, Shift, Zapier, Klayvio, Zoom, Gusto, Calendly, Pipedrive Introduction: I am the founder of Socialistics (www.socialistics.com), a leading social media agency that helps businesses turn their social media efforts into real measurable results. I am a 20+ year marketing veteran whose prior work has included launching and managing social media efforts for Microsoft Advertising, Office for Mac, the Air Force, and Habitat for Humanity. I have been recognized as a top B2B social media influencer and thought leader on multiple lists and publications including Forbes, ranking #30 on their 2012 list. I've recently published the book Anti-Agency: A Realistic Path to a $1,000,000 Business, and host the Anti Agency podcast where I share stories of doing business differently. You can learn more about me at www.jasonyormark.com. The Inspiration To Become An Entrepreneur: I’ve been involved with social media marketing since 2007, and have pretty much carved my career out of that. It was a natural progression for me to transition into starting a social media agency. From Idea to Reality: For me realistically, I had to side hustle something long enough to build it up to a point that I could take the leap and risks going full time on my own. For these reasons, I built the company and brand on the side putting out content regularly, and taking on side hustle projects to build out my portfolio and reputation. This went on for about 18 months at which point I had reached the breaking point of my frustrations of working for someone else, and felt I was ready to take the leap since I had the wheels in motion. While balancing a full-time job, I made sure not to overdo it. My main focus was on building out the website/brand and putting out content regularly to gain some traction and work towards some search visibility. I only took on 1-2 clients at a time to make sure I could still meet their needs while balancing a full time job. Attracting Customers: Initially I tapped into my existing network to get my first few clients. Then it was a mix of trade shows, networking events, and throwing a bit of money at paid directories and paid media. This is really a long game. You have to plant seeds over time with people and nurture those relationships over time. A combination of being helpful, likable and a good resource for folks will position you to make asks in the future. If people respect and like you, it makes it much easier to approach for opportunities when the time comes. Overcoming Challenges in Starting the Business: Plenty. Learning when to say no, only hiring the very best, and ultimately the realization that owning a marketing agency is going to have hills and valleys no matter what you do. Costs and Revenue: My largest expense by FAR is personnel, comprising between 50-60% of the business’ expenses, and justifiably so. It’s a people business. Our revenue doubled from the years 2018 through 2021, and we’ve seen between 10-20% growth year over year. A Day in the Life: I’ve successfully removed myself from the day to day of the business and that’s by design. I have a tremendous team, and a rock start Director of Operations who runs the agency day to day. It frees me up to pursue other opportunities, and to mentor, speak and write more. It also allows me to evangelize the book I wrote detailing my journey to a $1M business titled: Anti-Agency: A Realistic Path To A $1,000,000 Business (www.antiagencybook.com). Staying Ahead in a Changing Landscape: You really have to stay on top of technology trends. AI is a huge impact on marketing these days, so making sure we are up to speed on that, and not abusing it or relying on it too much. You also have to embrace that technology and not hide the fact that it’s used. Non-marketers still don’t and can’t do the work regardless of how much AI can help, so we just need to be transparent and smart on how we integrate it, but the fact is, technology will never replace creativity. As an agency, it’s imperative that we operationally allow our account managers to have bandwidth to be creative for clients all the time. It’s how we keep clients and buck the trend of companies changing agencies every year or two. The Vision for Socialistics: Continuing to evolve to cater to our clients through learning, education, and staying on top of the latest tools and technologies. Attracting bigger and more exciting clients, and providing life changing employment opportunities.

How a founder built a B2B AI startup to serve with 65+ global brands (including Fortune500 companies)
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How a founder built a B2B AI startup to serve with 65+ global brands (including Fortune500 companies)

AI Palette is an AI-driven platform that helps food and beverage companies predict emerging product trends. I had the opportunity recently to sit down with the founder to get his advice on building an AI-first startup, which he'll be going through in this post. About AI Palette: Co-founders: >!2 (Somsubhra GanChoudhuri, Himanshu Upreti)!!100+!!$12.7M USD!!AI-powered predictive analytics for the CPG (Consumer Packaged Goods) industry!!Signed first paying customer in the first year!!65+ global brands, including Cargill, Diageo, Ajinomoto, Symrise, Mondelez, and L’Oréal, use AI Palette!!Every new product launched has secured a paying client within months!!Expanded into Beauty & Personal Care (BPC), onboarding one of India’s largest BPC companies within weeks!!Launched multiple new product lines in the last two years, creating a unified suite for brand innovation!Identify the pain points in your industry for ideas* When I was working in the flavour and fragrance industry, I noticed a major issue CPG companies faced: launching a product took at least one to two years. For instance, if a company decided today to launch a new juice, it wouldn’t hit the market until 2027. This long timeline made it difficult to stay relevant and on top of trends. Another big problem I noticed was that companies relied heavily on market research to determine what products to launch. While this might work for current consumer preferences, it was highly inefficient since the product wouldn’t actually reach the market for several years. By the time the product launched, the consumer trends had already shifted, making that research outdated. That’s where AI can play a crucial role. Instead of looking at what consumers like today, we realised that companies should use AI to predict what they will want next. This allows businesses to create products that are ahead of the curve. Right now, the failure rate for new product launches is alarmingly high, with 8 out of 10 products failing. By leveraging AI, companies can avoid wasting resources on products that won’t succeed, leading to better, more successful launches. Start by talking to as many industry experts as possible to identify the real problems When we first had the idea for AI Palette, it was just a hunch, a gut feeling—we had no idea whether people would actually pay for it. To validate the idea, we reached out to as many people as we could within the industry. Since our focus area was all about consumer insights, we spoke to professionals in the CPG sector, particularly those in the insights departments of CPG companies. Through these early conversations, we began to see a common pattern emerge and identified the exact problem we wanted to solve. Don’t tell people what you’re building—listen to their frustrations and challenges first. Going into these early customer conversations, our goal was to listen and understand their challenges without telling them what we were trying to build. This is crucial as it ensures that you can gather as much data about the problem to truly understand it and that you aren't biasing their answers by showing your solution. This process helped us in two key ways: First, it validated that there was a real problem in the industry through the number of people who spoke about experiencing the same problem. Second, it allowed us to understand the exact scale and depth of the problem—e.g., how much money companies were spending on consumer research, what kind of tools they were currently using, etc. Narrow down your focus to a small, actionable area to solve initially. Once we were certain that there was a clear problem worth solving, we didn’t try to tackle everything at once. As a small team of two people, we started by focusing on a specific area of the problem—something big enough to matter but small enough for us to handle. Then, we approached customers with a potential solution and asked them for feedback. We learnt that our solution seemed promising, but we wanted to validate it further. If customers are willing to pay you for the solution, it’s a strong validation signal for market demand. One of our early customer interviewees even asked us to deliver the solution, which we did manually at first. We used machine learning models to analyse the data and presented the results in a slide deck. They paid us for the work, which was a critical moment. It meant we had something with real potential, and we had customers willing to pay us before we had even built the full product. This was the key validation that we needed. By the time we were ready to build the product, we had already gathered crucial insights from our early customers. We understood the specific information they wanted and how they wanted the results to be presented. This input was invaluable in shaping the development of our final product. Building & Product Development Start with a simple concept/design to validate with customers before building When we realised the problem and solution, we began by designing the product, but not by jumping straight into coding. Instead, we created wireframes and user interfaces using tools like InVision and Figma. This allowed us to visually represent the product without the need for backend or frontend development at first. The goal was to showcase how the product would look and feel, helping potential customers understand its value before we even started building. We showed these designs to potential customers and asked for feedback. Would they want to buy this product? Would they pay for it? We didn’t dive into actual development until we found a customer willing to pay a significant amount for the solution. This approach helped us ensure we were on the right track and didn’t waste time or resources building something customers didn’t actually want. Deliver your solution using a manual consulting approach before developing an automated product Initially, we solved problems for customers in a more "consulting" manner, delivering insights manually. Recall how I mentioned that when one of our early customer interviewees asked us to deliver the solution, we initially did it manually by using machine learning models to analyse the data and presenting the results to them in a slide deck. This works for the initial stages of validating your solution, as you don't want to invest too much time into building a full-blown MVP before understanding the exact features and functionalities that your users want. However, after confirming that customers were willing to pay for what we provided, we moved forward with actual product development. This shift from a manual service to product development was key to scaling in a sustainable manner, as our building was guided by real-world feedback and insights rather than intuition. Let ongoing customer feedback drive iteration and the product roadmap Once we built the first version of the product, it was basic, solving only one problem. But as we worked closely with customers, they requested additional features and functionalities to make it more useful. As a result, we continued to evolve the product to handle more complex use cases, gradually developing new modules based on customer feedback. Product development is a continuous process. Our early customers pushed us to expand features and modules, from solving just 20% of their problems to tackling 50–60% of their needs. These demands shaped our product roadmap and guided the development of new features, ultimately resulting in a more complete solution. Revenue and user numbers are key metrics for assessing product-market fit. However, critical mass varies across industries Product-market fit (PMF) can often be gauged by looking at the size of your revenue and the number of customers you're serving. Once you've reached a certain critical mass of customers, you can usually tell that you're starting to hit product-market fit. However, this critical mass varies by industry and the type of customers you're targeting. For example, if you're building an app for a broad consumer market, you may need thousands of users. But for enterprise software, product-market fit may be reached with just a few dozen key customers. Compare customer engagement and retention with other available solutions on the market for product-market fit Revenue and the number of customers alone isn't always enough to determine if you're reaching product-market fit. The type of customer and the use case for your product also matter. The level of engagement with your product—how much time users are spending on the platform—is also an important metric to track. The more time they spend, the more likely it is that your product is meeting a crucial need. Another way to evaluate product-market fit is by assessing retention, i.e whether users are returning to your platform and relying on it consistently, as compared to other solutions available. That's another key indication that your solution is gaining traction in the market. Business Model & Monetisation Prioritise scalability Initially, we started with a consulting-type model where we tailor-made specific solutions for each customer use-case we encountered and delivered the CPG insights manually, but we soon realized that this wasn't scalable. The problem with consulting is that you need to do the same work repeatedly for every new project, which requires a large team to handle the workload. That is not how you sustain a high-growth startup. To solve this, we focused on building a product that would address the most common problems faced by our customers. Once built, this product could be sold to thousands of customers without significant overheads, making the business scalable. With this in mind, we decided on a SaaS (Software as a Service) business model. The benefit of SaaS is that once you create the software, you can sell it to many customers without adding extra overhead. This results in a business with higher margins, where the same product can serve many customers simultaneously, making it much more efficient than the consulting model. Adopt a predictable, simplistic business model for efficiency. Look to industry practices for guidance When it came to monetisation, we considered the needs of our CPG customers, who I knew from experience were already accustomed to paying annual subscriptions for sales databases and other software services. We decided to adopt the same model and charge our customers an annual upfront fee. This model worked well for our target market, aligning with industry standards and ensuring stable, recurring revenue. Moreover, our target CPG customers were already used to this business model and didn't have to choose from a huge variety of payment options, making closing sales a straightforward and efficient process. Marketing & Sales Educate the market to position yourself as a thought leader When we started, AI was not widely understood, especially in the CPG industry. We had to create awareness around both AI and its potential value. Our strategy focused on educating potential users and customers about AI, its relevance, and why they should invest in it. This education was crucial to the success of our marketing efforts. To establish credibility, we adopted a thought leadership approach. We wrote blogs on the importance of AI and how it could solve problems for CPG companies. We also participated in events and conferences to demonstrate our expertise in applying AI to the industry. This helped us build our brand and reputation as leaders in the AI space for CPG, and word-of-mouth spread as customers recognized us as the go-to company for AI solutions. It’s tempting for startups to offer products for free in the hopes of gaining early traction with customers, but this approach doesn't work in the long run. Free offerings don’t establish the value of your product, and customers may not take them seriously. You should always charge for pilots, even if the fee is minimal, to ensure that the customer is serious about potentially working with you, and that they are committed and engaged with the product. Pilots/POCs/Demos should aim to give a "flavour" of what you can deliver A paid pilot/POC trial also gives you the opportunity to provide a “flavour” of what your product can deliver, helping to build confidence and trust with the client. It allows customers to experience a detailed preview of what your product can do, which builds anticipation and desire for the full functionality. During this phase, ensure your product is built to give them a taste of the value you can provide, which sets the stage for a broader, more impactful adoption down the line. Fundraising & Financial Management Leverage PR to generate inbound interest from VCs When it comes to fundraising, our approach was fairly traditional—we reached out to VCs and used connections from existing investors to make introductions. However, looking back, one thing that really helped us build momentum during our fundraising process was getting featured in Tech in Asia. This wasn’t planned; it just so happened that Tech in Asia was doing a series on AI startups in Southeast Asia and they reached out to us for an article. During the interview, they asked if we were fundraising, and we mentioned that we were. As a result, several VCs we hadn’t yet contacted reached out to us. This inbound interest was incredibly valuable, and we found it far more effective than our outbound efforts. So, if you can, try to generate some PR attention—it can help create inbound interest from VCs, and that interest is typically much stronger and more promising than any outbound strategies because they've gone out of their way to reach out to you. Be well-prepared and deliberate about fundraising. Keep trying and don't lose heart When pitching to VCs, it’s crucial to be thoroughly prepared, as you typically only get one shot at making an impression. If you mess up, it’s unlikely they’ll give you a second chance. You need to have key metrics at your fingertips, especially if you're running a SaaS company. Be ready to answer questions like: What’s your retention rate? What are your projections for the year? How much will you close? What’s your average contract value? These numbers should be at the top of your mind. Additionally, fundraising should be treated as a structured process, not something you do on the side while juggling other tasks. When you start, create a clear plan: identify 20 VCs to reach out to each week. By planning ahead, you’ll maintain momentum and speed up the process. Fundraising can be exhausting and disheartening, especially when you face multiple rejections. Remember, you just need one investor to say yes to make it all worthwhile. When using funds, prioritise profitability and grow only when necessary. Don't rely on funding to survive. In the past, the common advice for startups was to raise money, burn through it quickly, and use it to boost revenue numbers, even if that meant operating at a loss. The idea was that profitability wasn’t the main focus, and the goal was to show rapid growth for the next funding round. However, times have changed, especially with the shift from “funding summer” to “funding winter.” My advice now is to aim for profitability as soon as possible and grow only when it's truly needed. For example, it’s tempting to hire a large team when you have substantial funds in the bank, but ask yourself: Do you really need 10 new hires, or could you get by with just four? Growing too quickly can lead to unnecessary expenses, so focus on reaching profitability as soon as possible, rather than just inflating your team or burn rate. The key takeaway is to spend your funds wisely and only when absolutely necessary to reach profitability. You want to avoid becoming dependent on future VC investments to keep your company afloat. Instead, prioritize reaching break-even as quickly as you can, so you're not reliant on external funding to survive in the long run. Team-Building & Leadership Look for complementary skill sets in co-founders When choosing a co-founder, it’s important to find someone with a complementary skill set, not just someone you’re close to. For example, I come from a business and commercial background, so I needed someone with technical expertise. That’s when I found my co-founder, Himanshu, who had experience in machine learning and AI. He was a great match because his technical knowledge complemented my business skills, and together we formed a strong team. It might seem natural to choose your best friend as your co-founder, but this can often lead to conflict. Chances are, you and your best friend share similar interests, skills, and backgrounds, which doesn’t bring diversity to the table. If both of you come from the same industry or have the same strengths, you may end up butting heads on how things should be done. Having diverse skill sets helps avoid this and fosters a more collaborative working relationship. Himanshu (left) and Somsubhra (right) co-founded AI Palette in 2018 Define roles clearly to prevent co-founder conflict To avoid conflict, it’s essential that your roles as co-founders are clearly defined from the beginning. If your co-founder and you have distinct responsibilities, there is no room for overlap or disagreement. This ensures that both of you can work without stepping on each other's toes, and there’s mutual respect for each other’s expertise. This is another reason as to why it helps to have a co-founder with a complementary skillset to yours. Not only is having similar industry backgrounds and skillsets not particularly useful when building out your startup, it's also more likely to lead to conflicts since you both have similar subject expertise. On the other hand, if your co-founder is an expert in something that you're not, you're less likely to argue with them about their decisions regarding that aspect of the business and vice versa when it comes to your decisions. Look for employees who are driven by your mission, not salary For early-stage startups, the first hires are crucial. These employees need to be highly motivated and excited about the mission. Since the salary will likely be low and the work demanding, they must be driven by something beyond just the paycheck. The right employees are the swash-buckling pirates and romantics, i.e those who are genuinely passionate about the startup’s vision and want to be part of something impactful beyond material gains. When employees are motivated by the mission, they are more likely to stick around and help take the startup to greater heights. A litmus test for hiring: Would you be excited to work with them on a Sunday? One of the most important rounds in the hiring process is the culture fit round. This is where you assess whether a candidate shares the same values as you and your team. A key question to ask yourself is: "Would I be excited to work with this person on a Sunday?" If there’s any doubt about your answer, it’s likely not a good fit. The idea is that you want employees who align with the company's culture and values and who you would enjoy collaborating with even outside of regular work hours. How we structure the team at AI Palette We have three broad functions in our organization. The first two are the big ones: Technical Team – This is the core of our product and technology. This team is responsible for product development and incorporating customer feedback into improving the technology Commercial Team – This includes sales, marketing, customer service, account managers, and so on, handling everything related to business growth and customer relations. General and Administrative Team – This smaller team supports functions like finance, HR, and administration. As with almost all businesses, we have teams that address the two core tasks of building (technical team) and selling (commercial team), but given the size we're at now, having the administrative team helps smoothen operations. Set broad goals but let your teams decide on execution What I've done is recruit highly skilled people who don't need me to micromanage them on a day-to-day basis. They're experts in their roles, and as Steve Jobs said, when you hire the right person, you don't have to tell them what to do—they understand the purpose and tell you what to do. So, my job as the CEO is to set the broader goals for them, review the plans they have to achieve those goals, and periodically check in on progress. For example, if our broad goal is to meet a certain revenue target, I break it down across teams: For the sales team, I’ll look at how they plan to hit that target—how many customers they need to sell to, how many salespeople they need, and what tactics and strategies they plan to use. For the technical team, I’ll evaluate our product offerings—whether they think we need to build new products to attract more customers, and whether they think it's scalable for the number of customers we plan to serve. This way, the entire organization's tasks are cascaded in alignment with our overarching goals, with me setting the direction and leaving the details of execution to the skilled team members that I hire.

[Ultimate List] A list of Marketing Tools That I’ve tested over the years and found helpful to do better marketing with less work. More than 50 Tools To Help you with Marketing, Copywriting & Sales!
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[Ultimate List] A list of Marketing Tools That I’ve tested over the years and found helpful to do better marketing with less work. More than 50 Tools To Help you with Marketing, Copywriting & Sales!

Starting to focus on marketing for your business, You will come across the same tools mentioned over and over by marketers. I would like to mention here tools that you might haven’t seen going viral in the community but actually will help you grow faster and efficiently. Starting off with My favourite Marketing Channel! #Email Marketing For SMBs Convertkit / Mailerlite / Mailchimp - These 3 Platforms are the best options for SMBs and entrepreneurs just starting out with email marketing. All 3 have free plans up to 1,000 subscribers. Scribe - Email Signature Tool, Create Great Email signatures for your emails. Liramail - Most Email marketing platforms don’t offer great email templates. This tool will help you build great email templates with drag and drop. Quick mail Auto-Warmer - Most Businesses at the beginning don’t know what to do when open rate drops. You need to use an email warmer like this to keep it up. #Email Marketing For Big Businesses SendGrid - Overall Email Marketing Tools, this tool is best for brands that have huge email lists and email marketing is the key marketing channel. Braze - This tool is leading in email marketing for large Email senders. When I was working for agencies, this was one of the best email marketing tools I had used. NeoCertified - Protect your emails for spammers and threats. To keep your email list healthy, this is a must have! Sparkloop - Referral Marketing For Email Campaigns. Email can generate great huge amount of referrals for you and Sparkloop makes it easier. #Cold Emails & Lead Generation Hunter - A Great Tool to scrape emails from domain names. The tool comes with a green free plan but Pro plan is worth the amount of features it provides. Icyleads - It’s better than Hunter as it’s heavily focused on the sales and prospecting to help you derive great results from your campaigns. Mailshake - Beginner Friend Cold Email Tool with Great features like email list warming. #Communication Tools Twilio - One do the best customer engagement platform used by Companies like Stripe and mine too. Chatlio - Use Live chat feature on your website with slack integration. My favourite easier to catch up on conversations through slack integration. Intercom - Used by Most Marketers, Industry Leading customer communication platform. Great for beginners! Chatwoot - Another Amazing Communication Tool but the best part is they have a great free plan useful for new businesses. Loom - Communicate with your audience through Videos. Loom is great for SaaS and to show human interaction to close new visitors effectively. #CRM Outseta - This tool provides great CRM and their billing system is better than other tools out their which makes it stands out! Hubspot - I don’t think this tool needs an introduction because Hubspot’s CRM is the best in industry. Salesflare - This CRM is a great alternative to hubspot as it’s beginner friendly and helpful for SMBs. #SEO Tools Ahrefs - One of the best SEO tool in the industry. They also just launched a bunch of free tools to help SEO beginners. Screaming frog - The only website crawler I have used since I bought my first domain. It’s the best! Ubersuggest- The Tool by Neil Patel is the best SEO tool for you. (I’m Joking, it’s the worst) Contentking - This tool is good at Real-time SEO Auditing, they do a lot of Marketing work through Newsletters. If you are subscribed to any SEO newsletter. You may have seen this tool. SEOquake & Semrush - SEOquake is a great tool to conduct on-page analysis, SERP, and much more. Great tool but it’s owned by Semrush. You should go for Semrush because that tool will cover all SEO aspects for you. #Content Marketing Buzzsumo - This tool is great for content research and but you may find the regular emails pretty annoying sometimes. Contentrow - Analyse Your Content and find it’s strength. Highly recommended who are weak at content structuring like me. Grammarly - If you are not a native English speaker like me, you might think you need it or not. You need it for sure for grammar corrections. #Graphic Design Tools Visme - At agencies, Infographics can be more effective than usual postscript. Visme is a graphic design tool focused on infographics and designs related to B2B and B2C. It’s great for agencies! Glorify - A Graphic Design Tool focused on E-commerce, filled with Designs useful for E-commerce store owners. Canva - All-in-one Industry leading Graphic Design Tool that everyone knows and every template is overused now. Adobe Creative Cloud ( previously Sparkpost) - It’s a great alternative to Canva filled with Amazing Stock images to use in your visuals but the only backlash is the exports in this tool are not high quality. Snaps - A Canva Alternative that might not have overused templates for your Social Accounts. #Advertising Tools Plai - It’s a great PPC tool to create Ads for Instagram and Tiktok. Wordstream - It’s an industry leading PPC Tool, great for Ad Grading and auditing. AdEspresso - This Is a tool by Hootsuite. They have a lot of Data sourced at the backend, which helps in Ad optimisation through this tool. That’s the reason I recommend this tool. #Video Editing Tools Veed Studio - I have been using Veed from last year. It’s one of the best Video Marketing Tool Optimized for Instagram & Tiktok. Synthesia - It’s a new AI video generation platform. From last few months, if you have seen marketing agencies including Videos in Emails. The chances are that’s not a Agency member taking but AI generated Human. Motionbox - It’s also a great video editing tool focused on video editing for Digital Marketers. Jitter Video - It’s a great motion design tool. Comes with great templates, the only place where other tools I mentioned lacks. It’s great and beginner friendly. #Copywriting Jasper AI - Google’s John Mueller says AI generated content is banned on Search but I think with Jasper AI you can generate SEO optimised Content but you have to put in some efforts like at least give 30 minutes for editing the Copy by yourself. Copy AI - Another AI tool to help you write better copy. This one is more focused on helping you write copy suitable for Ads and Social media campaigns. Hemingway App - To help you write more clearly and Bold. This tool is better than Grammarly if you look for writing perspective and it’s free. #Social Media Management App I’ve used a Lot of SMM Tools and that’s why going to mention all of them with a short review. Sprout social - The Best with deep insights coverage. Hootsuite - Great Scheduling tool just under sprout social. Later - Heavily Focused on Instagram from beginning and Now Tiktok too. SkedSocial - It’s like a Later alternative with great addition features like link-in-bio. Facebook’s Business Manager- Great but sometimes bugs can make a huge issue for you and customer support is like dead. Tweet Hunter & Hypefury- Both are Twitter Scheduling tools growing very fast on platform and are great for growth. Buffer - It’s a great tool but I haven’t seen any new updates to help with management. Zoho Social - It’s a great SMM tool and if you use other marketing solutions from Zoho. It’s a must have! #Market Research Tool • SparkToro - That’s the only one I have ever used. It’s great for audience research and comes with great customer service. Founded by Rand Fishkin, it’s one of the best research tool. #Influencer Marketing & UGC InfluenceGrid - A free search engine To find Tiktok & Instagram Influencers for your campaigns. Tiktok Creative Center- TikTok’s in-built tool called “Creative Center” is the best to find content trends, audience demographics and much more. Archive - Find Instagram Stories and Posts mentioning Your brands and use them as Ads for your business Marketing. #Landing Page Builders Leadpages - Its a great landing page builder because the integration and drag-and-drop features makes it easier to work with! Cardd co - A Great Landing page builder with easy step up but it lacks the copywriting and tracking features. Instapage - It’s one of the best out and I think the overall product is effective enough to help you stand out with your landing page. Unbounce - It’s a great alternative to Instapage due its well polished landing page templates that might be helpful for you. #Community Building Mighty Networks - A Great Community building platform, and you can also sell courses within the platform. Circle so - A great alternative to Mighty networks focused on Communities specifically. We are currently using for small community Of ours. #Sales Tools Drift - You can get much more out of Drift than just sales tools but The Sales solutions provided in Drift are one of the best. Salesforce - It’s the industry Sales solution provider. A go-to and have various pricing plans making it suitable for majority of SMBs. #Social Proof Tools People don’t have enough time to search across internet to decide to trust you after seeing your Ad first time. That’s what you might be facing too. Here are two tools I absolutely love for social proof! Use Proof - Show Recent Activities occurring on your website and build the trust of your visitors. Testimonial to - Gather Testimonials across Social Media platforms related to your business with this tool. Capture tweets and comments mentioning your brands and mention them. #Analytics Tools Plausible Analytics- A privacy friendly Analytics alternative to Google Analytics if you hate Analytics 4 like me. Mixpanel - Product Analytics and funnel reports better than Google Analytics. #Reddit Marketing Gummysearch- This tool will help To find your target audience on Reddit and interact with them with its help and close your new customers. Howitzer- It’s another pretty similar tool to Gummysearch focused on Reddit cold outreach to get clients and new customers. Both are great but Gummysearch provides better customer support while Howtizer is helpful on a large scale Reddit Marketing. #Text Marketing Klaviyo - It’s an email + SMS marketing tool, it’s taking up space in marketing industry very quickly as an industry leader due to its great integrations but you need to learn the platform usage to maximise the outcome. Cartloop - This tool provides great text marketing solutions with integration with Spotify and other e-commerce marketing tools. Attentive Mobile - This is my favourite Text marketing tool due to the interactive dashboard + they have a library of Text marketing examples to help you out with your campaigns. #Other Tools I have used throughout my journey! Triple Whale - It’s a great E-commerce marketing tools with Triple pixel to help you track your campaigns more efficiently. Fastory - To create well optimized Instagram & Tiktok Stories for your business. Jotform - Online Form Builder with integrations with leading marketing tools. Gated - As an entrepreneur and marketer, you may receive a bunch of unwanted emails. Use Gated to get rid of them and receive useful mails only! ClickUp- The main Tool for Project Management, one of the best and highly recommended. Riverside - Forget Zoom or Google Meet, For your Podcast Interviews and Marketing conferences. You need riverside with great video quality and recording features. Manychat- Automate your Instagram DMs and interact with your followers more efficiently + sell out your products/ services when you are offline. Calendy - To schedule meetings with your ideal clients. ServiceProviderPro - It’s a client portal for SEO & Growing Agencies, very helpful in scaling agencies. SendCheckit - Compare your Email Subject Lines with 100,000+ others in the database for free. Otter AI - Using AI track your meetings more effectively, you can easily edit, annotate and share notes from the meetings. Ryte - Optimise your website User experience with this tool focused on UX aspects + SEO too. PhantomBuster - Scrape LinkedIn Profile and Data from Facebook/LinkedIn groups. I clearly love this tool! #Honourable Mentions Zapier - The Only tool you need to integrate your favourite tool with a new effective tool. Elementor - That’s what I use for web design and it’s great! Marketer Hire - To hire world class marketers to work with you. InShot & Capcut - I create Instagram Reels and TikTok’s and life without these tools isn’t possible. Nira - It’s a great tool to Manage your workspace and this tool has launched many marketing templates in-built helpful for marketers and also entrepreneurs. X - The tool you love that wasn’t mentioned here is valuable and I honour that tool and share that if you would like to! I mean thanks for reading what I have curated all over my life as a marketer. I share 5 Marketing Tools, 5 Marketing Resources and 1 Free Resourceevery week in my newsletter, you can subscribe here to receive that for free. Also, You can read an expanded list of email marketing tools in this Reddit post!

12 months ago, I was unemployed. Last week my side hustle got acquired by a $500m fintech company
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12 months ago, I was unemployed. Last week my side hustle got acquired by a $500m fintech company

I’ve learned so much over the years from this subreddit. I thought I’d return the favour and share some of my own learnings. In November 2020 my best friend and I had an idea. “What if we could find out which stocks the Internet is talking about?” This formed the origins of Ticker Nerd. 9 months later we sold Ticker Nerd to Finder (an Australian fintech company valued at around $500m). In this post, I am going to lay out how we got there. How we came up with the idea First off, like other posts have covered - you don’t NEED a revolutionary or original idea to build a business. There are tonnes of “boring” businesses making over 7 figures a year e.g. law firms, marketing agencies, real estate companies etc. If you’re looking for an exact formula to come up with a great business idea I’m sorry, but it doesn’t exist. Finding new business opportunities is more of an art than a science. Although, there are ways you can make it easier to find inspiration. Below are the same resources I use for inspiration. I rarely ever come up with ideas without first searching one of the resources below for inspiration: Starter Story Twitter Startup Ideas My First Million Trends by the Hustle Trends VC To show how you how messy, random and unpredictable it can be to find an idea - let me explain how my co-founder and I came up with the idea for Ticker Nerd: We discovered a new product on Twitter called Exploding Topics. It was a newsletter that uses a bunch of software and algorithms to find trends that are growing quickly before they hit the mainstream. I had recently listened to a podcast episode from My First Million where they spoke about Motley Fool making hundreds of millions from their investment newsletters. We asked ourselves what if we could build a SaaS platform similar to Exploding Topics but it focused on stocks? We built a quick landing page using Carrd + Gumroad that explained what our new idea will do and included a payment option to get early access for $49. We called it Exploding Stock (lol). We shared it around a bunch of Facebook groups and subreddits. We made $1,000 in pre-sales within a couple days. My co-founder and I can’t code so we had to find a developer to build our idea. We interviewed a bunch of potential candidates. Meanwhile, I was trawling through Wall Street Bets and found a bunch of free tools that did roughly what we wanted to build. Instead of building another SaaS tool that did the same thing as these free tools we decided to pivot from our original idea. Our new idea = a paid newsletter that sends a weekly report that summarises 2 of the best stocks that are growing in interest on the Internet. We emailed everyone who pre-ordered access, telling them about the change and offered a full refund if they wanted. tl;dr: We essentially combined two existing businesses (Exploding Topics and Motley Fool) and made it way better. We validated the idea by finding out if people will actually pay money for it BEFORE we decided to build it. The idea we started out with changed over time. How to work out if your idea will actually make money It’s easy to get hung up on designing the logo or choosing the perfect domain name for your new idea. At this stage none of that matters. The most important thing is working out if people will pay money for it. This is where validation comes in. We usually validate ideas using Carrd. It lets you build a simple one page site without having to code. The Ticker Nerd site was actually built using a Carrd template. Here’s how you can do it yourself (at a high level): Create a Carrd pro account (yes it's a $49 one off payment but you’ll get way more value out of it). Buy a cheap template and send it to your Carrd account. You can build your own template but this will save you a lot of time. Once the template reaches your Carrd account, duplicate it. Leave the original so it can be duplicated for other ideas. Jump onto Canva (free) and create a logo using the free logos provided. Import your logo. Add copy to the page that explains your idea. Use the AIDA formula. Sign up to Gumroad (free) and create a pre-sale campaign. Create a discounted lifetime subscription or version of the product. This will be used pre-sales. Add the copy from the site into the pre-sale campaign on Gumroad. Add a ‘widget’ to Carrd and connect it to Gumroad using the existing easy integration feature. Purchase a domain name. Connect it to Carrd. Test the site works. Share your website Now the site is ready you can start promoting it in various places to see how the market reacts. An easy method is to find relevant subreddits using Anvaka (Github tool) or Subreddit Stats. The Anvaka tool provides a spider map of all the connected subreddits that users are active in. The highlighted ones are most relevant. You can post a thread in these subreddits that offer value or can generate discussion. For example: ‘I’m creating a tool that can write all your copy, would anyone actually use this?’ ‘What does everything think of using AI to get our copy written faster?’ ‘It’s time to scratch my own itch, I’m creating a tool that writes marketing copy using GPT-3. What are the biggest problems you face writing marketing copy? I’ll build a solution for it’ Reddit is pretty brutal these days so make sure the post is genuine and only drop your link in the comments or in the post if it seems natural. If people are interested they’ll ask for the link. Another great place to post is r/entrepreuerridealong and r/business_ideas. These subreddits expect people to share their ideas and you’ll likely make some sales straight off the bat. I also suggest posting in some Facebook groups (related to your idea) as well just for good measure. Assess the results If people are paying you for early access you can assume that it’s worth building your idea. The beauty of posting your idea on Reddit or in Facebook groups is you’ll quickly learn why people love/hate your idea. This can help you decide how to tweak the idea or if you should drop it and move on to the next one. How we got our first 100 customers (for free) By validating Ticker Nerd using subreddits and Facebook groups this gave us our first paying customers. But we knew this wouldn’t be sustainable. We sat down and brainstormed every organic strategy we could use to get traction as quickly as possible. The winner: a Product Hunt launch. A successful Product Hunt launch isn’t easy. You need: Someone that has a solid reputation and audience to “hunt” your product (essentially an endorsement). An aged Product Hunt account - you can’t post any products if your account is less than a week old. To be following relevant Product Hunt members - since they get notified when you launch a new product if they’re following you. Relationships with other builders and makers on Product Hunt that also have a solid reputation and following. Although, if you can pull it off you can get your idea in front of tens of thousands of people actively looking for new products. Over the next few weeks, I worked with my co-founder on connecting with different founders, indie hackers and entrepreneurs mainly via Twitter. We explained to them our plans for the Product Hunt launch and managed to get a small army of people ready to upvote our product on launch day. We were both nervous on the day of the launch. We told ourselves to have zero expectations. The worst that could happen was no one signed up and we were in the same position as we’re in now. Luckily, within a couple of hours Ticker Nerd was on the homepage of Product Hunt and in the top 10. The results were instant. After 24 hours we had around 200 people enter their payment details to sign up for our free trial. These signups were equal to around $5,800 in monthly recurring revenue. \-- I hope this post was useful! Drop any questions you have below and I’ll do my best to respond :)

Why Ignoring AI Agents in 2025 Will Kill Your Marketing Strategy
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frankiemuiruriThis week

Why Ignoring AI Agents in 2025 Will Kill Your Marketing Strategy

If you're still focusing solely on grabbing the attention of human beings with your marketing efforts, you're already behind. In 2025, the game will change. Good marketing will demand an in-depth understanding of the AI space, especially the AI Agent space. Why? Your ads and content won’t just be seen by humans anymore. They’ll be analyzed, indexed, and often acted upon by AI agents—automated systems that will be working on behalf of companies and consumers alike. Your New Audience: Humans + AI Agents It’s not just about appealing to people. Companies are employing AI robots to research, negotiate, and make purchasing decisions. These AI agents are fast, thorough, and unrelenting. Unlike humans, they can analyze millions of options in seconds. And if your marketing isn’t optimized for them, you’ll get filtered out before you even reach the human decision-maker. How to Prepare Your Marketing for AI Agents The companies that dominate marketing in 2025 will be the ones that master the art of capturing AI attention. To do this, marketers will need to: Understand the AI agents shaping their industry. Research how AI agents function in your niche. What are they prioritizing? How do they rank options? Create AI-friendly content. Design ads and messaging that are easily understandable and accessible to AI agents. This means clear metadata, structured data, and AI-readable formats. Invest in AI analytics. AI agents leave behind footprints. Tracking and analyzing their behavior is critical. Stay ahead of AI trends. The AI agent space is evolving rapidly. What works today might be obsolete tomorrow. How My Agency Adapted and Thrived in the AI Space At my digital agency, we saw this shift coming and decided to act early. In 2023, we started integrating AI optimization into our marketing strategies. One of our clients—a B2B SaaS company—struggled to get traction because their competitors were drowning them out in Google search rankings and ad platforms. By analyzing the algorithms and behaviors of AI agents in their space, we: Rewrote their website copy with structured data and optimized metadata that was more AI-agent friendly. Created ad campaigns with clear, concise messaging and technical attributes that AI agents could quickly process and index. Implemented predictive analytics to understand what AI agents would prioritize based on past behaviors. The results? Their website traffic doubled in three months, and their lead conversion rate skyrocketed by 40%. Over half of the traffic increase was traced back to AI agents recommending their platform to human users. The Takeaway In 2025, marketing won’t just be about human attention. It’ll be about AI attention—and that requires a completely different mindset. AI agents are not your enemy; they’re your new gatekeepers. Learn to speak their language, and you’ll dominate the marketing game.

Recently hit 6,600,000 monthly organic traffic for a B2C SaaS website. Here's the 40 tips that helped me make that happen.
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DrJigsawThis week

Recently hit 6,600,000 monthly organic traffic for a B2C SaaS website. Here's the 40 tips that helped me make that happen.

Hey guys! So as title says, we recently hit 6,600,000 monthly organic traffic / month for a B2C SaaS website (screenshot. Can't give name publicly, but can show testimonial to a mod). Here's 40 tips that "helped" me make this happen. If you get some value of the post, I write an SEO tip every other day on /r/seogrowth. There's around 10 more tips already up there other than the ones I mention here. If you want to give back for all my walls of text, I'd appreciate a sub <3 Also, there are a bunch of free stuff I mention in the article: content outline, writer guidelines, SEO checklist, and other stuff. Here's the Google Doc with all that! Tip #1. Take SEO With a Grain of Salt A lot of the SEO advice and best practices on the internet are based on 2 things: Personal experiences and case studies of companies that managed to make SEO work for them. Google or John Mueller (Google’s Senior Webmaster Trends Analyst). And, unfortunately, neither of these sources are always accurate. Personal SEO accounts are simply about what worked for specific companies. Sometimes, what worked for others, won’t work for you. For example, you might find a company that managed to rank with zero link-building because their website already had a very strong backlink profile. If you’re starting with a fresh website, chances are, you won’t be able to get the same results. At the same time, information from Google or John Mueller is also not 100% accurate. For example, they’ve said that guest posting is against Google’s guidelines and doesn’t work… But practically, guest posting is a very effective link-building strategy. So the takeaway is this: Take all information you read about SEO with a grain of salt. Analyze the information yourself, and make your conclusions. SEO Tip #2. SEO Takes Time You’ve already heard this one before, but considering how many people keep asking, thought I'd include this anyway. On average, it’s going to take you 6 months to 2 years to get SEO results, depending on the following factors: Your backlink profile. The more quality backlinks you have (or build), the faster you’ll rank. Age of your website. If your website is older (or you purchased an aged website), you can expect your content to rank faster. Amount of content published. The more quality content you publish on your website, the more “authoritative” it is in the eyes of Google, and thus more likely to rank faster. SEO work done on the website. If a lot of your pages are already ranking on Google (page 2-3), it’s easier to get them to page #1 than if you just published the content piece. Local VS global SEO. Ranking locally is (sometimes) easier and faster than ranking globally. That said, some marketing agencies can use “SEO takes time” as an excuse for not driving results. Well, fortunately, there is a way to track SEO results from month #2 - #3 of work. Simply check if your new content pieces/pages are getting more and more impressions on Google Search Console month-to-month. While your content won’t be driving traffic for a while after being published, they’ll still have a growing number of impressions from month #2 or #3 since publication. SEO Tip #3. SEO Might Not Be The Best Channel For You In theory, SEO sounds like the best marketing channel ever. You manage to rank on Google and your marketing seemingly goes on auto-pilot - you’re driving new leads every day from existing content without having to lift a finger… And yet, SEO is not for everyone. Avoid SEO as a marketing channel if: You’re just getting started with your business and need to start driving revenue tomorrow (and not in 1-2 years). If this is you, try Google ads, Facebook ads, or organic marketing. Your target audience is pretty small. If you’re selling enterprise B2B software and have around 2,000 prospects in total worldwide, then it’s simply easier to directly reach out to these prospects. Your product type is brand-new. If customers don’t know your product exists, they probably won’t be Googling it. SEO Tip #4. Traffic Can Be a Vanity Metric I've seen hundreds of websites that drive 6-7 digits of traffic but generate only 200-300 USD per month from those numbers. “What’s the deal?” You might be thinking. “How can you fail to monetize that much traffic?” Well, that brings us to today’s tip: traffic can be a vanity metric. See, not all traffic is created equal. Ranking for “hormone balance supplement” is a lot more valuable than ranking for “Madagascar character names.” The person Googling the first keyword is an adult ready to buy your product. Someone Googling the latter, on the other hand, is a child with zero purchasing power. So, when deciding on which keywords to pursue, always keep in mind the buyer intent behind and don’t go after rankings or traffic just because 6-digit traffic numbers look good. SEO Tip #5. Push Content Fast Whenever you publish a piece of content, you can expect it to rank within 6 months to a year (potentially less if you’re an authority in your niche). So, the faster you publish your content, the faster they’re going to age, and, as such, the faster they’ll rank on Google. On average, I recommend you publish a minimum of 10,000 words of content per month and 20,000 to 30,000 optimally. If you’re not doing link-building for your website, then I’d recommend pushing for even more content. Sometimes, content velocity can compensate for the lack of backlinks. SEO Tip #6. Use Backlink Data to Prioritize Content You might be tempted to go for that juicy, 6-digit traffic cornerstone keyword right from the get-go... But I'd recommend doing the opposite. More often than not, to rank for more competitive, cornerstone keywords, you’ll need to have a ton of supporting content, high-quality backlinks, website authority, and so on. Instead, it’s a lot more reasonable to first focus on the less competitive keywords and then, once you’ve covered those, move on to the rest. Now, as for how to check keyword competitiveness, here are 2 options: Use Mozbar to see the number of backlinks for top-ranking pages, as well as their Domain Authority (DA). If all the pages ranking on page #1 have <5 backlinks and DA of 20 - 40, it’s a good opportunity. Use SEMrush or Ahrefs to sort your keywords by difficulty, and focus on the less difficult keywords first. Now, that said, keep in mind that both of these metrics are third-party, and hence not always accurate. SEO Tip #7. Always Start With Competitive Analysis When doing keyword research, the easiest way to get started is via competitive analysis. Chances are, whatever niche you’re in, there’s a competitor that is doing great with SEO. So, instead of having to do all the work from scratch, run their website through SEMrush or Ahrefs and steal their keyword ideas. But don’t just stop there - once you’ve borrowed keyword ideas from all your competitors, run the seed keywords through a keyword research tool such as UberSuggest or SEMrush Keyword Magic Tool. This should give you dozens of new ideas that your competitors might’ve missed. Finally, don’t just stop at borrowing your competitor’s keyword ideas. You can also borrow some inspiration on: The types of graphics and images you can create to supplement your blog content. The tone and style you can use in your articles. The type of information you can include in specific content pieces. SEO Tip #8. Source a LOT of Writers Content writing is one of those professions that has a very low barrier to entry. Anyone can take a writing course, claim to be a writer, and create an UpWork account… This is why 99% of the writers you’ll have to apply for your gigs are going to be, well, horrible. As such, if you want to produce a lot of content on the reg, you’ll need to source a LOT of writers. Let’s do the math: If, by posting a job ad, you source 100 writers, you’ll see that only 5 of them are a good fit. Out of the 5 writers, 1 has a very high rate, so they drop out. Another doesn’t reply back to your communication, which leaves you with 3 writers. You get the 3 writers to do a trial task, and only one turns out to be a good fit for your team. Now, since the writer is freelance, the best they can do is 4 articles per month for a total of 5,000-words (which, for most niches, ain’t all that much). So, what we’re getting at here is, to hire quality writers, you should source a LOT of them. SEO Tip #9. Create a Process for Filtering Writers If you follow the previous tip, you'll end up with a huge database of hundreds of writers. This creates a whole new problem: You now have a database of 500+ writers waiting for you to sift through them and decide which ones are worth the hire. It would take you 2-3 days of intense work to go through all these writers and vet them yourself. Let’s be real - you don’t have time for that. Here’s what you can do instead: When sourcing writers, always get them to fill in a Google form (instead of DMing or emailing you). In this form, make sure to ask for 3 relevant written samples, a link to the writer’s portfolio page, and the writer’s rate per word. Create a SOP for evaluating writers. The criteria for evaluation should be: Level of English. Does the writer’s sample have any English mistakes? If so, they’re not a good fit. Quality of Samples. Are the samples long-form and engaging content or are they boring 500-word copy-pastes? Technical Knowledge. Has the writer written about a hard-to-explain topic before? Anyone can write about simple topics like traveling—you want to look for someone who knows how to research a new topic and explain it in a simple and easy-to-read way. If someone’s written about how to create a perfect cover letter, they can probably write about traveling, but the opposite isn’t true. Get your VA to evaluate the writer’s samples as per the criteria above and short-list writers that seem competent. If you sourced 500 writers, the end result of this process should be around 50 writers. You or your editor goes through the short-list of 50 writers and invites 5-10 for a (paid) trial task. The trial task is very important - you’ll sometimes find that the samples provided by the writer don’t match their writing level. SEO Tip #10. Use the Right Websites to Find Writers Not sure where to source your writers? Here are some ideas: ProBlogger \- Our #1 choice - a lot of quality writers frequent this website. LinkedIn \- You can headhunt content writers in specific locations. Upwork \- If you post a content gig, most writers are going to be awful. Instead, I recommend headhunting top writers instead. WeWorkRemotely \- Good if you’re looking to make a full-time remote hire. Facebook \- There are a ton of quality Facebook groups for writers. Some of our faves are Cult of Copy Job Board and Content Marketing Lounge. SEO Tip #11. Always Use Content Outlines When giving tasks to your writing team, you need to be very specific about the instructions you give them. Don’t just provide a keyword and tell them to “knock themselves out.” The writer isn’t a SEO expert; chances are, they’re going to mess it up big-time and talk about topics that aren’t related to the keyword you’re targeting. Instead, when giving tasks to writers, do it through content outlines. A content outline, in a nutshell, is a skeleton of the article they’re supposed to write. It includes information on: Target word count (aim for the same or 50% more the word count than that of the competition). Article title. Article structure (which sections should be mentioned and in what order). Related topics of keywords that need to be mentioned in the article. Content outline example in the URL in the post intro. SEO Tip #12. Focus on One Niche at a Time I used to work with this one client that had a SaaS consisting of a mixture of CRM, Accounting Software, and HRS. I had to pick whether we were going to focus on topics for one of these 3 niches or focus on all of them at the same time. I decided to do the former. Here’s why: When evaluating what to rank, Google considers the authority of your website. If you have 60 articles about accounting (most of which link to each other), you’re probably an authority in the niche and are more likely to get good rankings. If you have 20 sales, 20 HR, and 20 accounting articles, though, none of these categories are going to rank as well. It always makes more sense to first focus on a single niche (the one that generates the best ROI for your business), and then move on to the rest. This also makes it easier to hire writers - you hire writers specialized in accounting, instead of having to find writers who can pull off 3 unrelated topics. SEO Tip #13. Just Hire a VA Already It’s 2021 already guys—unless you have a virtual assistant, you’re missing out big-time. Since a lot of SEO tasks are very time-consuming, it really helps to have a VA around to take over. As long as you have solid SOPs in place, you can hire a virtual assistant, train them, and use them to free up your time. Some SEO tasks virtual assistants can help with are: Internal linking. Going through all your blog content and ensuring that they link to each other. Backlink prospecting. Going through hundreds of websites daily to find link opportunities. Uploading content on WordPress and ensuring that the content is optimized well for on-page SEO. SEO Tip #14. Use WordPress (And Make Your Life Easier) Not sure which CMS platform to use? 99% of the time, you’re better off with WordPress. It has a TON of plugins that will make your life easier. Want a drag & drop builder? Use Elementor. It’s cheap, efficient, extremely easy to learn, and comes jam-packed with different plugins and features. Wix, SiteGround, and similar drag & drops are pure meh. SEO Tip #15. Use These Nifty WordPress Plugins There are a lot of really cool WordPress plugins that can make your (SEO) life so much easier. Some of our favorites include: RankMath. A more slick alternative to YoastSEO. Useful for on-page SEO. Smush. App that helps you losslessly compress all images on your website, as well as enables lazy loading. WP Rocket. This plugin helps speed up your website pretty significantly. Elementor. Not a techie? This drag & drop plugin makes it significantly easier to manage your website. WP Forms. Very simple form builder. Akismet Spam Protection. Probably the most popular anti-spam WP plugin. Mammoth Docx. A plugin that uploads your content from a Google doc directly to WordPress. SEO Tip #16. No, Voice Search Is Still Not Relevant Voice search is not and will not be relevant (no matter what sensationalist articles might say). Sure, it does have its application (“Alexa, order me toilet paper please”), but it’s pretty niche and not relevant to most SEOs. After all, you wouldn’t use voice search for bigger purchases (“Alexa, order me a new laptop please”) or informational queries (“Alexa, teach me how to do accounting, thanks”). SEO Tip #17. SEO Is Obviously Not Dead I see these articles every year - “SEO is dead because I failed to make it work.” SEO is not dead and as long as there are people looking up for information/things online, it never will be. And no, SEO is not just for large corporations with huge budgets, either. Some niches are hypercompetitive and require a huge link-building budget (CBD, fitness, VPN, etc.), but they’re more of an exception instead of the rule. SEO Tip #18. Doing Local SEO? Focus on Service Pages If you’re doing local SEO, you’re better off focusing on local service pages than blog content. E.g. if you’re an accounting firm based in Boston, you can make a landing page about /accounting-firm-boston/, /tax-accounting-boston/, /cpa-boston/, and so on. Or alternatively, if you’re a personal injury law firm, you’d want to create pages like /car-accident-law-firm/, /truck-accident-law-firm/, /wrongful-death-law-firm/, and the like. Thing is, you don’t really need to rank on global search terms—you just won’t get leads from there. Even if you ranked on the term “financial accounting,” it wouldn’t really matter for your bottom line that much. SEO Tip #19. Engage With the SEO Community The SEO community is (for the most part) composed of extremely helpful and friendly people. There are a lot of online communities (including this sub) where you can ask for help, tips, case studies, and so on. Some of our faves are: This sub :) SEO Signals Lab (FB Group) Fat Graph Content Ops (FB Group) Proper SEO Group (FB Group) BigSEO Subreddit SEO Tip #20. Test Keywords Before Pursuing Them You can use Google ads to test how profitable any given keyword is before you start trying to rank for it. The process here is: Create a Google Ads account. Pick a keyword you want to test. Create a landing page that corresponds to the search intent behind the keyword. Allocate an appropriate budget. E.g. if you assume a conversion rate of 2%, you’d want to buy 100+ clicks. If the CPC is 2 USD, then the right budget would be 200 USD plus. Run the ads! If you don’t have the budget for this, you can still use the average CPC for the keyword to estimate how well it’s going to convert. If someone is willing to bid 10 USD to rank for a certain keyword, it means that the keyword is most probably generating pretty good revenue/conversions. SEO Tip #21. Test & Improve SEO Headlines Sometimes, you’ll see that you’re ranking in the top 3 positions for your search query, but you’re still not driving that much traffic. “What’s the deal?” you might be asking. Chances are, your headline is not clickable enough. Every 3-4 months, go through your Google Search Console and check for articles that are ranking well but not driving enough traffic. Then, create a Google sheet and include the following data: Targeted keyword Page link CTR (for the last 28 days) Date when you implemented the new title Old title New title New CTR (for the month after the CTR change was implemented) From then on, implement the new headline and track changes in the CTR. If you don’t reach your desired result, you can always test another headline. SEO Tip #22. Longer Content Isn’t Always Better Content You’ve probably heard that long-form content is where it’s at in 2021. Well, this isn’t always the case. Rather, this mostly depends on the keyword you’re targeting. If, for example, you’re targeting the keyword “how to tie a tie,” you don’t need a long-ass 5,000-word mega-guide. In such a case, the reader is looking for something that can be explained in 200-300 words and if your article fails to do this, the reader will bounce off and open a different page. On the other hand, if you’re targeting the keyword “how to write a CV,” you’ll need around 4,000 to 5,000 words to adequately explain the topic and, chances are, you won’t rank with less. SEO Tip #23. SEO is Not All About Written Content More often than not, when people talk about SEO they talk about written blog content creation. It’s very important not to forget, though, that blog content is not end-all-be-all for SEO. Certain keywords do significantly better with video content. For example, if the keyword is “how to do a deadlift,” video content is going to perform significantly better than blog content. Or, if the keyword is “CV template,” you’ll see that a big chunk of the rankings are images of the templates. So, the lesson here is, don’t laser-focus on written content—keep other content mediums in mind, too. SEO Tip #24. Write For Your Audience It’s very important that your content resonates well with your target audience. If, for example, you’re covering the keyword “skateboard tricks,” you can be very casual with your language. Heck, it’s even encouraged! Your readers are Googling the keyword in their free time and are most likely teens or in their early 20s. Meaning, you can use informal language, include pop culture references, and avoid complicated language. Now, on the other hand, if you’re writing about high-level investment advice, your audience probably consists of 40-something suit-and-ties. If you include Rick & Morty references in your article, you'll most likely lose credibility and the Googler, who will go to another website. Some of our best tips on writing for your audience include: Define your audience. Who’s the person you’re writing for? Are they reading the content at work or in their free time? Keep your reader’s level of knowledge in mind. If you’re covering an accounting 101 topic, you want to cover the topic’s basics, as the reader is probably a student. If you’re writing about high-level finance, though, you don’t have to teach the reader what a balance sheet is. More often than not, avoid complicated language. The best practice is to write on a 6th-grade level, as it’s understandable for anyone. Plus, no one wants to read Shakespeare when Googling info online (unless they’re looking for Shakespeare's work, of course). SEO Tip #25. Create Compelling Headlines Want to drive clicks to your articles? You’ll need compelling headlines. Compare the following headline: 101 Productivity Tips \[To Get Things Done in 2021\] With this one: Productivity Tips Guide Which one would you click? Data says it’s the first! To create clickable headlines, I recommend you include the following elements: Keyword. This one’s non-negotiable - you need to include the target keyword in the headline. Numbers. If Buzzfeed taught us anything, it’s that people like to click articles with numbers in their titles. Results. If I read your article, what’s going to be the end result? E.g. “X Resume tips (to land the job)”.* Year (If Relevant). Adding a year to your title shows that the article is recent (which is relevant for some specific topics). E.g. If the keyword is “Marketing Trends,” I want to know marketing trends in 2021, not in 2001. So, adding a year in the title makes the headline more clickable. SEO Tip #26. Make Your Content Visual How good your content looks matters, especially if you're in a competitive niche. Here are some tips on how to make your content as visual as possible: Aim for 2-4 sentences per paragraph. Avoid huge blocks of text. Apply a 60-65% content width to your blog pages. Pick a good-looking font. I’d recommend Montserrat, PT Sans, and Roboto. Alternatively, you can also check out your favorite blogs, see which fonts they’re using, and do the same. Use a reasonable font size. Most top blogs use font sizes ranging from 16 pt to 22 pt. Add images when possible. Avoid stock photos, though. No one wants to see random “office people smiling” scattered around your blog posts. Use content boxes to help convey information better. Content boxes example in the URL in the intro of the post. SEO Tip #27. Ditch the Skyscraper Technique Already Brian Dean’s skyscraper technique is awesome and all, but the following bit really got old: “Hey \[name\], I saw you wrote an article. I, too, wrote an article. Please link to you?” The theory here is, if your content is good, the person will be compelled to link to it. In practice, though, the person really, really doesn’t care. At the end of the day, there’s no real incentive for the person to link to your content. They have to take time out of their day to head over to their website, log in to WordPress, find the article you mentioned, and add a link... Just because some stranger on the internet asked them to. Here’s something that works much better: Instead of fake compliments, be very straightforward about what you can offer them in exchange for that link. Some things you can offer are: A free version of your SaaS. Free product delivered to their doorstep. Backlink exchange. A free backlink from your other website. Sharing their content to your social media following. Money. SEO Tip #28. Get the URL Slug Right for Seasonal Content If you want to rank on a seasonal keyword, there are 2 ways to do this. If you want your article to be evergreen (i.e. you update it every year with new information), then your URL should not contain the year. E.g. your URL would be /saas-trends/, and you simply update the article’s contents+headline each year to keep it timely. If you’re planning on publishing a new trends report annually, though, then you can add a year to the URL. E.g. /saas-trends-2020/ instead of /saas-trends/. SEO Tip #29. AI Content Tools Are a Mixed Bag Lots of people are talking about AI content tools these days. Usually, they’re either saying: “AI content tools are garbage and the output is horrible,” Or: “AI content tools are a game-changer!” So which one is it? The truth is somewhere in-between. In 2021, AI content writing tools are pretty bad. The output you’re going to get is far from something you can publish on your website. That said, some SEOs use such tools to get a very, very rough draft of the article written, and then they do intense surgery on it to make it usable. Should you use AI content writing tools? If you ask me, no - it’s easier to hire a proficient content writer than spend hours salvaging AI-written content. That said, I do believe that such tools are going to get much better years down the line. This one was, clearly, more of a personal opinion than a fact. I’d love to hear YOUR opinion on AI content tools! Are they a fad, or are they the future of content creation? Let me know in the comments. SEO Tip #30. Don’t Overdo it With SEO Tools There are a lot of SEO tools out there for pretty much any SEO function. Keyword research, link-building, on-page, outreach, technical SEO, you name it! If you were to buy most of these tools for your business, you’d easily spend 4-figures on SEO tools per month. Luckily, though, you don’t actually need most of them. At the end of the day, the only must-have SEO tools are: An SEO Suite (Paid). Basically SEMrush or Ahrefs. Both of these tools offer an insane number of features - backlink analysis, keyword research, and a ton of other stuff. Yes, 99 USD a month is expensive for a tool. But then again, if you value your time 20 USD/hour and this tool saves you 6 hours, it's obviously worth it, right? On-Page SEO Tool (Free). RankMath or Yoast. Basically, a tool that's going to help you optimize web pages or blog posts as per SEO best practices. Technical SEO Tool (Freemium). You can use ScreamingFrog to crawl your entire website and find technical SEO problems. There are probably other tools that also do this, but ScreamingFrog is the most popular option. The freemium version of the tool only crawls a limited number of pages (500 URLs, to be exact), so if your website is relatively big, you'll need to pay for the tool. Analytics (Free). Obviously, you'll need Google Analytics (to track website traffic) and Google Search Console (to track organic traffic, specifically) set up on your website. Optionally, you can also use Google Track Manager to better track how your website visitors interact with the site. MozBar (Free). Chrome toolbar that lets you simply track the number of backlinks on Google Search Queries, Domain Authority, and a bunch of other stuff. Website Speed Analysis (Free). You can use Google Page Speed Insights to track how fast your website loads, as well as how mobile-friendly it is. Outreach Tool (Paid). Tool for reaching out to prospects for link-building, guest posting, etc. There are about a dozen good options for this. Personally, I like to use Snov for this. Optimized GMB Profile (Free). Not a tool per se, but if you're a local business, you need to have a well-optimized Google My Business profile. Google Keyword Planner (Free). This gives you the most reliable search volume data of all the tools. So, when doing keyword research, grab the search volume from here. Tool for Storing Keyword Research (Free). You can use Google Sheets or AirTable to store your keyword research and, at the same time, use it as a content calendar. Hemingway App (Free). Helps keep your SEO content easy to read. Spots passive voice, complicated words, etc. Email Finder (Freemium). You can use a tool like Hunter to find the email address of basically anyone on the internet (for link-building or guest posting purposes). Most of the tools that don’t fit into these categories are 100% optional. SEO Tip #31. Hiring an SEO? Here’s How to Vet Them Unless you’re an SEO pro yourself, hiring one is going to be far from easy. There’s a reason there are so many “SEO experts” out there - for the layman, it’s very hard to differentiate between someone who knows their salt and a newbie who took an SEO course, like, last week. Here’s how you can vet both freelance and full-time SEOs: Ask for concrete traffic numbers. The SEO pro should give you the exact numbers on how they’ve grown a website in the past - “100% SEO growth in 1 year” doesn’t mean much if the growth is from 10 monthly traffic to 20. “1,000 to 30,000” traffic, on the other hand, is much better. Ask for client names. While some clients ask their SEOs to sign an NDA and not disclose their collaboration, most don’t. If an SEO can’t name a single client they’ve worked with in the past, that’s a red flag. Make sure they have the right experience. Global and local SEO have very different processes. Make sure that the SEO has experience with the type of SEO you need. Make sure you’re looking for the right candidate. SEO pros can be content writers, link-builders, web developers, or all of the above simultaneously. Make sure you understand which one you need before making the hire. If you’re looking for someone to oversee your content ops, you shouldn’t hire a technical SEO expert. Look for SEO pros in the right places. Conventional job boards are overrated. Post your job ads on SEO communities instead. E.g. this sub, bigseo, SEO Signals Facebook group, etc. SEO Tip #32. Blog Post Not Ranking? Follow This Checklist I wanted to format the post natively for Reddit, but it’s just SO much better on Notion. Tl;dr, the checklist covers every reason your post might not be ranking: Search intent mismatch. Inferior content. Lack of internal linking. Lack of backlinks. And the like. Checklist URL at the intro of the post. SEO Tip #33. Avoid BS Link-Building Tactics The only type of link-building that works is building proper, quality links from websites with a good backlink profile and decent organic traffic. Here’s what DOESN’T work: Blog comment links Forum spam links Drive-by Reddit comment/post links Web 2.0 links Fiverr “100 links for 10 bucks” bs If your “SEO agency” says they’re doing any of the above instead of actually trying to build you links from quality websites, you’re being scammed. SEO Tip #34. Know When to Use 301 and 302 Redirects When doing redirects, it’s very important to know the distinction between these two. 301 is a permanent page redirect and passes on link juice. If you’re killing off a page that has backlinks, it’s better to 301 it to your homepage so that you don’t lose the link juice. If you simply delete a page, it’s going to be a 404, and the backlink juice is lost forever. 302 is a temporary page redirect and doesn’t pass on link juice. If the redirect is temporary, you do a 302. E.g. you want to test how well a new page is going to perform w/ your audience. SEO Tip #35. Social Signals Matter (But Not How You Think) Social signals are NOT a ranking factor. And yet, they can help your content rank on Google’s front page. Wondering what the hell am I talking about? Here’s what’s up: As I said, social signals are not a ranking factor. It’s not something Google takes into consideration to decide whether your article should rank or not. That said, social signals CAN lead to your article ranking better. Let’s say your article goes viral and gets around 20k views within a week. A chunk of these viewers are going to forget your domain/link and they’re going to look up the topic on Google via your chosen keyword + your brand name. The amount of people looking for YOUR keyword and exclusively picking your result over others is going to make Google think that your content is satisfying search intent better than the rest, and thus, reward you with better ranking. SEO Tip #36. Run Remarketing Ads to Lift Organic Traffic Conversions Not satisfied with your conversion rates? You can use Facebook ads to help increase them. Facebook allows you to do something called “remarketing.” This means you can target anyone that visited a certain page (or multiple pages) on your website and serve them ads on Facebook. There are a TON of ways you can take advantage of this. For example, you can target anyone that landed on a high buyer intent page and serve them ads pitching your product or a special offer. Alternatively, you can target people who landed on an educational blog post and offer them something to drive them down the funnel. E.g. free e-book or white paper to teach them more about your product or service. SEO Tip #37. Doing Local SEO? Follow These Tips Local SEO is significantly different from global SEO. Here’s how the two differ (and what you need to do to drive local SEO results): You don’t need to publish content. For 95% of local businesses, you only want to rank for keywords related to your services/products, you don’t actually need to create educational content. You need to focus more on reviews and citation-building. One of Google Maps’ biggest ranking factors is the of reviews your business has. Encourage your customers to leave a review if they enjoyed your product/service through email or real-life communication. You need to create service pages for each location. As a local business, your #1 priority is to rank for keywords around your service. E.g. If you're a personal injury law firm, you want to optimize your homepage for “personal injury law firm” and then create separate pages for each service you provide, e.g. “car accident lawyer,” “motorcycle injury law firm,” etc. Focus on building citations. Being listed on business directories makes your business more trustworthy for Google. BrightLocal is a good service for this. You don’t need to focus as much on link-building. As local SEO is less competitive than global, you don’t have to focus nearly as much on building links. You can, in a lot of cases, rank with the right service pages and citations. SEO Tip #38. Stop Ignoring the Outreach Emails You’re Getting (And Use Them to Build Your Own Links) Got a ton of people emailing you asking for links? You might be tempted to just send them all straight to spam, and I don’t blame you. Outreach messages like “Hey Dr Jigsaw, your article is A+++ amazing! ...can I get a backlink?” can get hella annoying. That said, there IS a better way to deal with these emails: Reply and ask for a link back. Most of the time, people who send such outreach emails are also doing heavy guest posting. So, you can ask for a backlink from a 3rd-party website in exchange for you mentioning their link in your article. Win-win! SEO Tip #39. Doing Internal Linking for a Large Website? This’ll Help Internal linking can get super grueling once you have hundreds of articles on your website. Want to make the process easier? Do this: Pick an article you want to interlink on your website. For the sake of the example, let’s say it’s about “business process improvement.” Go on Google and look up variations of this keyword mentioned on your website. For example: Site:\[yourwebsite\] “improve business process” Site:\[yourwebsite\] “improve process” Site:\[yourwebsite\] “process improvement” The above queries will find you the EXACT articles where these keywords are mentioned. Then, all you have to do is go through them and include the links. SEO Tip #40. Got a Competitor Copying Your Content? File a DMCA Notice Fun fact - if your competitors are copying your website, you can file a DMCA notice with Google. That said, keep in mind that there are consequences for filing a fake notice.

Started a content marketing agency 8 years ago - $0 to $7,863,052 (2025 update)
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mr_t_forhireThis week

Started a content marketing agency 8 years ago - $0 to $7,863,052 (2025 update)

Hey friends, My name is Tyler and for the past 8 years, I’ve been documenting my experience building a content marketing agency called Optimist. Year 1 — 0 to $500k ARR Year 2 — $500k to $1MM ARR Year 3 — $1MM ARR to $1.5MM(ish) ARR Year 4 — $3,333,686 Revenue Year 5 — $4,539,659 Revenue Year 6 — $5,974,324 Revenue Year 7 - $6,815,503 Revenue (Edit: Seems like links are banned now. You can check my post history for all of my previous updates with lessons and learnings.) How Optimist Works First, an overview/recap of the Optimist business model: We operate as a “collective” of full time/professional freelancers Everyone aside from me is a contractor Entirely remote/distributed team We pay freelancers a flat fee for most work, working out to roughly $65-100/hour. Clients pay us a flat monthly fee for full-service content marketing (research, strategy, writing, editing, design/photography, reporting and analytics, targeted linkbuilding, and more)\ Packages range in price from \~$10-20k/mo \This is something we are revisiting now* The Financials In 2024, we posted $1,032,035.34 in revenue. This brings our lifetime revenue to $7,863,052. Here’s our monthly revenue from January 2017 to December of 2024. (Edit: Seems like I'm not allowed to link to the chart.) The good news: Revenue is up 23% YoY. EBITDA in Q4 trending up 1-2 points. We hosted our first retreat in 4 years, going to Ireland with about half the team. The bad news: Our revenue is still historically low. At $1MM for the year, we’re down about 33% from our previous years over $1.5MM. Revenue has been rocky. It doesn’t feel like we’ve really “recovered” from the bumps last year. The trend doesn’t really look great. Even though, anecdotally, it feels like we are moving in a good direction. EBITDA is still hovering at around 7%. Would love to get that closer to 20%. (For those who may ask: I’m calculating EBITDA after paying taxes and W2 portion of my income.) — Almost every year, my update starts the same way: This has been a year of growth and change. Both for my business—and me personally. 2024 was no different. I guess that tells you something about entrepreneurship. It’s a lot more like sailing a ship than driving a car. You’re constantly adapting, tides are shifting, and any blip of calm is usually just a moment before the next storm. As with past years, there’s a lot to unpack from the last 12 months. Here we go again. Everything is Burning In the last 2 years, everything has turned upside down in the world of content and SEO. Back in 2020, we made a big decision to re-position the agency. (See post history) We decided to narrow our focus to our most successful, profitable, and consistent segment of clients and re-work our entire operation to focus on serving them. We defined our ICP as: \~Series A ($10mm+ funding) with 6-12 months runway to scale organic as a channel Product-led company with “simple” sales cycle involving fewer stakeholders Demonstrable opportunity to use SEO to drive business growth Our services: Content focused on growing organic search (SEO) Full-service engagements that included research, planning, writing, design, reporting And our engagement structure: Engaged directly with an executive; ownership over strategy and day-to-day execution 1-2 points of contact or stakeholders Strategic partner that drives business growth (not a service vendor who makes content) Most importantly, we decided that we were no longer going to offer a broader range of content that we used to sell. That included everything from thought leadership content to case studies and ebooks. We doubled-down on “SEO content” for product-led SaaS companies. And this worked phenomenally for us. We started bringing on more clients than ever. We developed a lot of internal system and processes that helped us scale and take on more work than we’ve ever had and drive great outcomes for our ideal clients. But in 2023 and 2024, things started going awry. One big change, of course, was the rise of AI. Many companies and executives (and writers) feel that AI can write content just as well as an agency like ours. That made it a lot harder to sell a $10,000 per month engagement when they feel like the bulk of the work could be “done for free.” (Lots of thoughts on this if you want my opinions.) But it wasn’t just that. Google also started tinkering with their algorithm, introducing new features like AI Overviews, and generally changing the rules of the game. This created 3 big shifts in our world: The perceived value of content (especially “SEO content”) dropped dramatically in many people’s minds because of AI’s writing capabilities SEO became less predictable as a source of traffic and revenue It’s harder than ever for startups and smaller companies to rank for valuable keywords (let alone generate any meaningful traffic or revenue from them) The effect? The middle of the content market has hollowed out. People—like us—providing good, human-crafted content aimed on driving SEO growth saw a dramatic decline in demand. We felt it all year. Fewer and fewer leads. The leads we did see usually scoffed at our prices. They were indexing us against the cost of content mills and mass-produced AI articles. It was a time of soul-searching and looking for a way forward. I spent the first half of the year convinced that the only way to survive was to run toward the fire. We have to build our own AI workflows. We have to cut our rates internally. We have to get faster and cheaper to stay competitive with the agencies offering the same number of deliverables for a fraction of our rates. It’s the only way forward. But then I asked myself a question… Is this the game I actually want to play? As an entrepreneur, do I want to run a business where I’m competing mostly on price and efficiency rather than quality and value? Do I want to hop into a race toward cheaper and cheaper content? Do I want to help people chase a dwindling amount of organic traffic that’s shrinking in value? No. That’s not the game I want to play. That’s not a business I want to run. I don’t want to be in the content mill business. So I decided to turn the wheel—again. Repositioning Part II: Electric Boogaloo What do you do when the whole world shifts around you and the things that used to work aren’t working anymore? You pivot. You re-position the business and move in another direction. So that’s what we decided to do. Again. There was only one problem: I honestly wasn’t sure what opportunities existed in the content marketing industry outside of what we were already doing. We lived in a little echo chamber of startups and SEO. It felt like the whole market was on fire and I had fight through the smoke to find an escape hatch. So I started making calls. Good ol’ fashioned market research. I reached out to a few dozen marketing and content leaders at a bunch of different companies. I got on the phone and just asked lots of questions about their content programs, their goals, and their pain points. I wanted to understand what was happening in the market and how we could be valuable. And, luckily, this process really paid off. I learned a lot about the fragmentation happening across content and how views were shifting. I noticed key trends and how our old target market really wasn’t buying what we were selling. Startups and small companies are no longer willing to invest in an agency like ours. If they were doing content and SEO at all, they were focused entirely on using AI to scale output and minimize costs. VC money is still scarce and venture-backed companies are more focused on profitability than pure growth and raising another round. Larger companies (\~500+ employees) are doing more content than ever and drowning in content production. They want to focus on strategy but can barely tread water keeping up with content requests from sales, demand gen, the CEO, and everyone else. Many of the companies still investing in content are looking at channels and formats outside of SEO. Things like thought leadership, data reports, interview-driven content, and more. They see it as a way to stand out from the crowd of “bland SEO content.” Content needs are constantly in flux. They range from data reports and blog posts to product one-pagers. The idea of a fixed-scope retainer is a total mismatch for the needs of most companies. All of this led to the logical conclusion: We were talking to the wrong people about the wrong things\.\ Many companies came to one of two logical conclusions: SEO is a risky bet, so it’s gotta be a moonshot—super-low cost with a possibility for a big upside (i.e., use AI to crank out lots of content. If it works, great. If it doesn’t, then at least we aren’t out much money.) SEO is a risky bet, so we should diversify into other strategies and channels to drive growth (i.e., shift our budget from SEO and keyword-focused content to video, podcasts, thought leadership, social, etc) Unless we were going to lean into AI and dramatically cut our costs and rates, our old buyers weren’t interested. And the segment of the market that needs our help most are looking primarily for production support across a big range of content types. They’re not looking for a team to run a full-blown program focused entirely on SEO. So we had to go back to the drawing board. I’ve written before about our basic approach to repositioning the business. But, ultimately it comes down to identifying our unique strengths as a team and then connecting them to needs in the market. After reviewing the insights from my discussions and taking another hard look at our business and our strengths, I decided on a new direction: Move upmarket: Serve mid-size to enterprise businesses with \~500-5,000 employees instead of startups Focus on content that supports a broader range of business goals instead of solely on SEO and organic growth (e.g., sales, demand gen, brand, etc) Shift back to our broader playbook of content deliverables, including thought leadership, data studies, and more Focus on content execution and production to support an internally-directed content strategy across multiple functions In a way, it’s sort of a reverse-niche move. Rather than zooming in specifically on driving organic growth for startups, we want to be more of an end-to-end content production partner that solves issues of execution and operations for all kinds of content teams. It’s early days, but the response here has been promising. We’ve seen an uptick in leads through Q4. And more companies in our pipeline fit the new ICP. They’re bigger, often have more budget. (But they move more slowly). We should know by the end of the quarter if this maneuver is truly paying off. Hopefully, this will work out. Hopefully our research and strategy are right and we’ll find a soft landing serving a different type of client. If it doesn’t? Then it will be time to make some harder decisions. As I already mentioned, I’m not interested in the race to the bottom of AI content. And if that’s the only game left in town, then it might be time to think hard about a much bigger change. — To be done: Build new content playbooks for expanded deliverables Build new showcase page for expanded deliverables Retooling the Operation It’s easy to say we’re doing something new. It’s a lot harder to actually do it—and do it well. Beyond just changing our positioning, we have to do open-heart surgery on the entire content operation behind the scenes. We need to create new systems that work for a broader range of content types, formats, and goals. Here’s the first rub: All of our workflows are tooled specifically for SEO-focused content. Every template, worksheet, and process that we’ve built and scaled in the last 5 years assumes that the primary goal of every piece of content is SEO. Even something as simple as requiring a target keyword is a blocker in a world where we’re not entirely focused on SEO. This is relatively easy to fix, but it requires several key changes: Update content calendars to make keywords optional Update workflows to determine whether we need an optimization report for each deliverable Next, we need to break down the deliverables into parts rather than a single line item. In our old system, we would plan content as a single row in a Content Calendar spreadsheet. It was a really wide sheet with lots of fields where we’d define the dimensions of each individual article. This was very efficient and simple to follow. But every article had the same overall scope when it came to the workflow. In Asana (our project management tool), all of the steps in the creation were strung together in a single task. We would create a few basic templates for each client, and then each piece would flow through the same steps: Briefing Writing Editing Design etc. If we had anything that didn’t fit into the “standard” workflow, we’d just tag it in the calendar with an unofficial notation \[USING BRACKETS\]. It worked. But it wasn’t ideal. Now we need the steps to be more modular. Imagine, for example, a client asks us to create a mix of deliverables: 1 article with writing + design 1 content brief 1 long-form ebook with an interview + writing + design Each of these would require its own steps and its own workflow. We need to break down the work to accommodate for a wider variety of workflows and variables. This means we need to update the fields and structure of our calendar to accommodate for the new dimensions—while also keeping the planning process simple and manageable. This leads to the next challenge: The number of “products” that we’re offering could be almost infinite. Just looking at the example scope above, you can mix and match all of these different building blocks to create a huge variety of different types of work, each requiring its own workflow. This is part of the reason we pivoted away from this model to focus on a productized, SEO-focused content service back in 2020. Take something as simple as a case study. On the surface, it seems like one deliverable that can be easily scoped and priced, right? Well, unpack what goes into a case study: Is there already source material from the customer or do we need to conduct an interview? How long is it? Is it a short overview case study or a long-form narrative? Does it need images and graphics? How many? Each of these variables opens up 2-3 possibilities. And when you combine them, we end up with something like 10 possible permutations for this single type of deliverable. It gets a bit messy. But not only do we have to figure out how to scope and price all for all of these variables, we also have to figure out how to account for these variables in the execution. We have to specify—for every deliverable—what type it is, how long, which steps are involved and not involved, the timeline for delivery, and all of the other factors. We’re approaching infinite complexity, here. We have to figure out a system that allows for a high level of flexibility to serve the diverse needs of our clients but is also productized enough that we can build workflows, process, and templates to deliver the work. I’ve spent the last few months designing that system. Failed Attempt #1: Ultra-Productization In my first pass, I tried to make it as straight forward as possible. Just sit down, make a list of all of the possible deliverables we could provide and then assign them specific scopes and services. Want a case study? Okay that’ll include an interview, up to 2,000 words of content, and 5 custom graphics. It costs $X. But this solution quickly fell apart when we started testing it against real-world scenarios. What if the client provided the brief instead of us creating one? What if they didn’t want graphics? What if this particular case study really needs to be 3,000 words but all of the others should be 2,000? In order for this system to work, we’d need to individual scope and price all of these permutations of each productized service. Then we’d need to somehow keep track of all of these and make sure that we accurately scope, price, and deliver them across dozens of clients. It’s sort of like a restaurant handling food allergies by creating separate versions of every single dish to account for every individual type of allergy. Most restaurants have figured out that it makes way more sense to have a “standard” and an “allergy-free” version. Then you only need 2 options to cover 100% of the cases. Onto the next option. Failed Attempt #2: Deliverable-Agnostic Services Next, I sat down with my head of Ops, Katy, to try to map it out. We took a big step back and said: Why does the deliverable itself even matter? At the end of the day, what we’re selling is just a few types of work (research, writing, editing, design, etc) that can be packaged up in an infinite number of ways. Rather than try to define deliverables, shouldn’t we leave it open ended for maximum flexibility? From there, we decided to break down everything into ultra-modular building blocks. We started working on this super complex system of modular deliverables where we would have services like writing, design, editing, etc—plus a sliding scale for different scopes like the length of writing or the number of images. In theory, it would allow us to mix and match any combination of services to create custom deliverables for the client. In fact, we wanted the work to be deliverable-agnostic. That way we could mold it to fit any client’s needs and deliver any type of content, regardless of the format or goal. Want a 5,000-word case study with 15 custom graphics? That’ll be $X. Want a 2,000-word blog post with an interview and no visuals? $Y. Just want us to create 10 briefs, you handle the writing, and we do design? It’s $Z. Again, this feels like a reasonable solution. But it quickly spiraled out of amuck. (That’s an Office reference.) For this to work, we need to have incredibly precise scoping process for every single deliverable. Before we can begin work (or even quote a price), we need to know pretty much the exact word count of the final article, for example. In the real world? This almost never happens. The content is as long as the content needs to be. Clients rarely know if the blog post should be 2,000 words or 3,000 words. They just want good content. We have a general ballpark, but we can rarely dial it in within just 1,000 words until we’ve done enough research to create the brief. Plus, from a packaging and pricing perspective, it introduces all kind of weird scenarios where clients will owe exactly $10,321 for this ultra-specific combination of services. We were building an open system that could accommodate any and all types of potential deliverables. On the face that seems great because it makes us incredibly flexible. In reality, the ambiguity actually works against us. It makes it harder for us to communicate to clients clearly about what they’ll get, how much it will cost, and how long it will take. That, of course, also means that it hurts our client relationships. (This actually kind of goes back to my personal learnings, which I’ll mention in a bit. I tend to be a “let’s leave things vague so we don’t have to limit our options” kind of person. But I’m working on fixing this to be more precise, specific, and clear in everything that we do.) Dialing It In: Building a Closed System We were trying to build an open system. We need to build a closed system. We need to force clarity and get specific about what we do, what we don’t do, and how much it all costs. Then we need a system to expand on that closed system—add new types of deliverables, new content playbooks, and new workflows if and when the need arises. With that in mind, we can start by mapping out the key dimensions of any type of deliverable that we would ever want to deliver. These are the universal dimensions that determine the scope, workflow, and price of any deliverable—regardless of the specific type output. Dimensions are: Brief scope Writing + editing scope Design scope Interview scope Revision (rounds) Scope, essentially, just tells us how many words, graphics, interviews, etc are required for the content we’re creating. In our first crack at the system, we got super granular with these scopes. But to help force a more manageable system, we realized that we didn’t need tiny increments for most of this work. Instead, we just need boundaries—you pay $X for up to Y words. We still need some variability around the scope of these articles. Obviously, most clients won’t be willing to pay the same price for a 1,000-word article as a 10,000-word article. But we can be smarter about the realistic break points. We boiled it down to the most common ranges: (Up to) 250 words 1,000 words 3,000 words 6,000 words 10,000 words This gives us a much more manageable number of variables. But we still haven’t exactly closed the system. We need one final dimension: Deliverable type. This tells us what we’re actually building with these building blocks. This is how we’ll put a cap on the potentially infinite number of combinations we could offer. The deliverable type will define what the final product should look like (e.g., blog post, case study, ebook, etc). And it will also give us a way to put standards and expectations around different types of deliverables that we want to offer. Then we can expand on this list of deliverables to offer new services. In the mean time, only the deliverables that we have already defined are, “on the menu,” so to speak. If a client comes to us and asks for something like a podcast summary article (which we don’t currently offer), we’ll have to either say we can’t provide that work or create a new deliverable type and define the dimensions of that specific piece. But here’s the kicker: No matter the deliverable type, it has to still fit within the scopes we’ve already defined. And the pricing will be the same. This means that if you’re looking for our team to write up to 1,000 words of content, it costs the same amount—whether it’s a blog post, an ebook, a LinkedIn post, or anything else. Rather than trying to retool our entire system to offer this new podcast summary article deliverable, we’ll just create the new deliverable type, add it to the list of options, and it’s ready to sell with the pre-defined dimensions we’ve already identified. To do: Update onboarding workflow Update contracts and scope documents Dial in new briefing process Know Thyself For the last year, I’ve been going through personal therapy. (Huge shout out to my wife, Laura, for her support and encouragement throughout the process.) It’s taught me a lot about myself and my tendencies. It’s helped me find some of my weaknesses and think about how I can improve as a person, as a partner, and as an entrepreneur. And it’s forced me to face a lot of hard truths. For example, consider some of the critical decisions I’ve made for my business: Unconventional freelance “collective” model No formal management structure Open-ended retainers with near-infinite flexibility General contracts without defined scope “Take it or leave it” approach to sales and marketing Over the years, I’ve talked about almost everything on this list as a huge advantage. I saw these things as a reflection of how I wanted to do things differently and better than other companies. But now, I see them more as a reflection of my fears and insecurities. Why did I design my business like this? Why do I want so much “flexibility” and why do I want things left open-ended rather than clearly defined? One reason that could clearly explain it: I’m avoidant. If you’re not steeped in the world of therapy, this basically means that my fight or flight response gets turned all the way to “flight.” If I’m unhappy or uncomfortable, my gut reaction is usually to withdraw from the situation. I see commitment and specificity as a prelude to future conflict. And I avoid conflict whenever possible. So I built my business to minimize it. If I don’t have a specific schedule of work that I’m accountable for delivering, then we can fudge the numbers a bit and hope they even out in the end. If I don’t set a specific standard for the length of an article, then I don’t have to let the client know when their request exceeds that limit. Conflict….avoided? Now, that’s not to say that everything I’ve built was wrong or bad. There is a lot of value in having flexibility in your business. For example, I would say that our flexible retainers are, overall, an advantage. Clients have changing needs. Having flexibility to quickly adapt to those needs can be a huge value add. And not everything can be clearly defined upfront (at least not without a massive amount of time and work just to decide how long to write an article). Overly-rigid structures and processes can be just as problematic as loosey-goosey ones. But, on the whole, I realized that my avoidant tendencies and laissez faire approach to management have left a vacuum in many areas. The places where I avoided specificity were often the places where there was the most confusion, uncertainty, and frustration from the team and from clients. People simply didn’t know what to expect or what was expected of them. Ironically, this often creates the conflict I’m trying to avoid. For example, if I don’t give feedback to people on my team, then they feel uneasy about their work. Or they make assumptions about expectations that don’t match what I’m actually expecting. Then the client might get upset, I might get upset, and our team members may be upset. Conflict definitely not avoided. This happens on the client side, too. If we don’t define a specific timeline when something will be delivered, the client might expect it sooner than we can deliver—creating frustration when we don’t meet their expectation. This conflict actually would have been avoided if we set clearer expectations upfront. But we didn’t do that. I didn’t do that. So it’s time to step up and close the gaps. Stepping Up and Closing the Gaps If I’m going to address these gaps and create more clarity and stability, I have to step up. Both personally and professionally. I have to actually face the fear and uncertainty that drives me to be avoidant. And then apply that to my business in meaningful ways that aren’t cop-out ways of kinda-sorta providing structure without really doing it. I’ve gotta be all in. This means: Fill the gaps where I rely on other people to do things that aren’t really their job but I haven’t put someone in place to do it Set and maintain expectations about our internal work processes, policies, and standards Define clear boundaries on things like roles, timelines, budgets, and scopes Now, this isn’t going to happen overnight. And just because I say that I need to step up to close these gaps doesn’t mean that I need to be the one who’s responsible for them (at least not forever). It just means that, as the business leader, I need to make sure the gaps get filled—by me or by someone else who has been specifically charged with owning that part of the operation. So, this is probably my #1 focus over the coming quarter. And it starts by identifying the gaps that exist. Then, step into those gaps myself, pay someone else to fill that role, or figure out how to eliminate the gap another way. This means going all the way back to the most basic decisions in our business. One of the foundational things about Optimist is being a “different kind” of agency. I always wanted to build something that solved for the bureaucracy, hierarchy, and siloed structure of agencies. If a client has feedback, they should be able to talk directly to the person doing the work rather than going through 3 layers of account management and creative directors. So I tried to be clever. I tried to design all kinds of systems and processes that eliminated these middle rungs. (In retrospect, what I was actually doing was designing a system that played into my avoidant tendencies and made it easy to abdicate responsibility for lots of things.) Since we didn’t want to create hierarchy, we never implemented things like Junior and Senior roles. We never hired someone to manage or direct the individual creatives. We didn’t have Directors or VPs. (Hell, we barely had a project manager for the first several years of existence.) This aversion to hierarchy aligned with our values around elevating ownership and collective contribution. I still believe in the value a flat structure. But a flat structure doesn’t eliminate the complexity of a growing business. No one to review writers and give them 1:1 feedback? I guess I’ll just have to do that….when I have some spare time. No Content Director? Okay, well someone needs to manage our content playbooks and roll out new ones. Just add it to my task list. Our flat structure didn’t eliminate the need for these roles. It just eliminated the people to do them. All of those unfilled roles ultimately fell back on me or our ops person, Katy. Of course, this isn’t the first time we’ve recognized this. We’ve known there were growing holes in our business as it’s gotten bigger and more complex. Over the years, we’ve experimented with different ways to solve for it. The Old Solution: Distributed Ops One system we designed was a “distributed ops” framework. Basically, we had one person who was the head of ops (at the time, we considered anything that was non-client-facing to be “ops”). They’d plan and organize all of the various things that needed to happen around Optimist. Then they’d assign out the work to whoever was able to help. We had a whole system for tying this into the our profit share and even gave people “Partner” status based on their contributions to ops. It worked—kinda. One big downfall is that all of the tasks and projects were ad hoc. People would pick up jobs, but they didn’t have much context or expertise to apply. So the output often varied. Since we were trying to maintain a flat structure, there was minimal oversight or management of the work. In other words, we didn’t always get the best results. But, more importantly, we still didn’t close all of the gaps entirely. Because everything was an ad-hoc list of tasks and projects, we never really had the “big picture” view of everything that needed to be done across the business. This also meant we rarely had clarity on what was important, what was trivial, and what was critical. We need a better system. Stop Reinventing the Wheel (And Create a Damn Org Chart) It’s time to get serious about filling the gaps in our business. It can’t be a half-fix or an ad hoc set of projects and tasks. We need clarity on the roles that need to be filled and then fill them. The first step here is to create an org chart. A real one. Map out all of the jobs that need to be done for Optimist to be successful besides just writers and designers. Roles like: Content director Design director SEO manager Reporting Finance Account management Business development Sales Marketing Project management It feels a bit laughable listing all of these roles. Because most are either empty or have my name attached to them. And that’s the problem. I can’t do everything. And all of the empty roles are gaps in our structure—places where people aren’t getting the direction, feedback, or guidance they need to do their best work. Or where things just aren’t being done consistently. Content director, for example, should be responsible for steering the output of our content strategists, writers, and editors. They’re not micromanaging every deliverable. But they give feedback, set overall policy, and help our team identify opportunities to get better. Right now we don’t have anyone in that role. Which means it’s my job—when I have time. Looking at the org chart (a real org chart that I actually built to help with this), it’s plain as day how many roles look like this. Even if we aren’t going to implement a traditional agency structure and a strict hierarchy, we still need to address these gaps. And the only way for that to happen is face the reality and then create a plan to close the gaps. Now that we have a list of theoretical roles, we need to clearly define the responsibilities and boundaries of those roles to make sure they cover everything that actually needs to happen. Then we can begin the process of delegating, assigning, hiring, and otherwise addressing each one. So that’s what I need to do. To be done: Create job descriptions for all of the roles we need to fill Hire Biz Dev role Hire Account Lead role(s) Hire Head of Content Playing Offense As we move into Q1 of 2025 and I reflect on the tumultuous few years we’ve had, one thought keeps running through my head. We need to play offense. Most of the last 1-2 years was reacting to changes that were happening around us. Trying to make sense and chart a new path forward. Reeling. But what I really want—as a person and as an entrepreneur—is to be proactive. I want to think and plan ahead. Figure out where we want to go before we’re forced to change course by something that’s out of our control. So my overarching focus for Q1 is playing offense. Thinking longer term. Getting ahead of the daily deluge and creating space to be more proactive, innovative, and forward thinking. To do: Pilot new content formats Audit and update our own content strategy Improve feedback workflows Build out long-term roadmap for 1-2 years for Optimist Final Note on Follow-Through and Cadence In my reflection this year, one of the things I’ve realized is how helpful these posts are for me. I process by writing. So I actually end up making a lot of decisions and seeing things more clearly each time I sit down to reflect and write my yearly recap. It also gives me a space to hold myself accountable for the things I said I would do. So, I’m doing two things a bit differently from here on out. First: I’m identifying clear action items that I’m holding myself accountable for getting done in the next 3 months (listed in the above sections). In each future update, I’ll do an accounting of what I got done and what wasn’t finished (and why). Second: I’m going to start writing shorter quarterly updates. This will gives me more chances each year to reflect, process, and make decisions. Plus it gives me a shorter feedback loop for the action items that I identified above. (See—playing offense.) — Okay friends, enemies, and frenemies. This is my first update for 2025. Glad to share with y’all. And thanks to everyone who’s read, commented, reached out, and shared their own experiences over the years. We are all the accumulation of our connections and our experiences. As always, I will pop in to respond to comments and answer questions. Feel free to share your thoughts, questions, and general disdain down below. Cheers, Tyler

How a Small Startup in Asia Secured a Contract with the US Department of Homeland Security
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How a Small Startup in Asia Secured a Contract with the US Department of Homeland Security

Uzair Javaid, a Ph.D. with a passion for data privacy, co-founded Betterdata to tackle one of AI's most pressing challenges: protecting privacy while enabling innovation. Recently, Betterdata secured a lucrative contract with the US Department of Homeland Security, 1 of only 4 companies worldwide to do so and the only one in Asia. Here's how he did it: The Story So what's your story? I grew up in Peshawar, Pakistan, excelling in coding despite studying electrical engineering. Inspired by my professors, I set my sights on studying abroad and eventually earned a Ph.D. scholarship at NUS Singapore, specializing in data security and privacy. During my research, I ethically hacked Ethereum and published 15 papers—three times the requirement. While wrapping up my Ph.D., I explored startup ideas and joined Entrepreneur First, where I met Kevin Yee. With his expertise in generative models and mine in privacy, we founded Betterdata. Now, nearly three years in, we’ve secured a major contract with the U.S. Department of Homeland Security—one of only four companies globally and the only one from Asia. The Startup In a nutshell, what does your startup do? Betterdata is a startup that uses AI and synthetic data generation to address two major challenges: data privacy and the scarcity of high-quality data for training AI models. By leveraging generative models and privacy-enhancing technologies, Betterdata enables businesses, such as banks, to use customer data without breaching privacy regulations. The platform trains AI on real data, learns its patterns, and generates synthetic data that mimics the real thing without containing any personal or sensitive information. This allows companies to innovate and develop AI solutions safely and ethically, all while tackling the growing need for diverse, high-quality data in AI development. How did you conduct ideation and validation for your startup? The initial idea for Betterdata came from personal experience. During my Ph.D., I ethically hacked Ethereum’s blockchain, exposing flaws in encryption-based data sharing. This led me to explore AI-driven deep synthesis technology—similar to deepfakes but for structured data privacy. With GDPR impacting 28M+ businesses, I saw a massive opportunity to help enterprises securely share data while staying compliant. To validate the idea, I spoke to 50 potential customers—a number that strikes the right balance. Some say 100, but that’s impractical for early-stage founders. At 50, patterns emerge: if 3 out of 10 mention the same problem, and this repeats across 50, you have 10–15 strong signals, making it a solid foundation for an MVP. Instead of outbound sales, which I dislike, we used three key methods: Account-Based Marketing (ABM)—targeting technically savvy users with solutions for niche problems, like scaling synthetic data for banks. Targeted Content Marketing—regular customer conversations shaped our thought leadership and outreach. Raising Awareness Through Partnerships—collaborating with NUS, Singapore’s PDPC, and Plug and Play to build credibility and educate the market. These strategies attracted serious customers willing to pay, guiding Betterdata’s product development and market fit. How did you approach the initial building and ongoing product development? In the early stages, we built synthetic data generation algorithms and a basic UI for proof-of-concept, using open-source datasets to engage with banks. We quickly learned that banks wouldn't share actual customer data due to privacy concerns, so we had to conduct on-site installations and gather feedback to refine our MVP. Through continuous consultation with customers, we discovered real enterprise data posed challenges, such as missing values, which led us to adapt our prototype accordingly. This iterative approach of listening to customer feedback and observing their usage allowed us to improve our product, enhance UX, and address unmet needs while building trust and loyalty. Working closely with our customers also gives us a data advantage. Our solution’s effectiveness depends on customer data, which we can't fully access, but bridging this knowledge gap gives us a competitive edge. The more customers we test on, the more our algorithms adapt to diverse use cases, making it harder for competitors to replicate our insights. My approach to iteration is simple: focus solely on customer feedback and ignore external noise like trends or advice. The key question for the team is: which customer is asking for this feature or solution? As long as there's a clear answer, we move forward. External influences, such as AI hype, often bring more confusion than clarity. True long-term success comes from solving real customer problems, not chasing trends. Customers may not always know exactly what they want, but they understand their problems. Our job is to identify these problems and solve them in innovative ways. While customers may suggest specific features, we stay focused on solving the core issue rather than just fulfilling their exact requests. The idea aligns with the quote often attributed to Henry Ford: "If I asked people what they wanted, they would have said faster horses." The key is understanding their problems, not just taking requests at face value. How do you assess product-market fit? To assess product-market fit, we track two key metrics: Customers' Willingness to Pay: We measure both the quantity and quality of meetings with potential customers. A high number of meetings with key decision-makers signals genuine interest. At Betterdata, we focused on getting meetings with people in banks and large enterprises to gauge our product's resonance with the target market. How Much Customers Are Willing to Pay: We monitor the price customers are willing to pay, especially in the early stages. For us, large enterprises, like banks, were willing to pay a premium for our synthetic data platform due to the growing need for privacy tech. This feedback guided our product refinement and scaling strategy. By focusing on these metrics, we refined our product and positioned it for scaling. What is your business model? We employ a structured, phase-driven approach for out business model, as a B2B startup. I initially struggled with focusing on the core value proposition in sales, often becoming overly educational. Eventually, we developed a product roadmap with models that allowed us to match customer needs to specific offerings and justify our pricing. Our pricing structure includes project-based pilots and annual contracts for successful deployments. At Betterdata, our customer engagement unfolds across three phases: Phase 1: Trial and Benchmarking \- We start with outreach and use open-source datasets to showcase results, offering customers a trial period to evaluate the solution. Phase 2: Pilot or PoC \- After positive trial results, we conduct a PoC or pilot using the customer’s private data, with the understanding that successful pilots lead to an annual contract. Phase 3: Multi-Year Contracts \- Following a successful pilot, we transition to long-term commercial contracts, focusing on multi-year agreements to ensure stability and ongoing partnerships. How do you do marketing for your brand? We take a non-conventional approach to marketing, focusing on answering one key question: Which customers are willing to pay, and how much? This drives our messaging to show how our solution meets their needs. Our strategy centers around two main components: Building a network of lead magnets \- These are influential figures like senior advisors, thought leaders, and strategic partners. Engaging with institutions like IMDA, SUTD, and investors like Plug and Play helps us gain access to the right people and foster warm introductions, which shorten our sales cycle and ensure we’re reaching the right audience. Thought leadership \- We build our brand through customer traction, technology evidence, and regulatory guidelines. This helps us establish credibility in the market and position ourselves as trusted leaders in our field. This holistic approach has enabled us to navigate diverse market conditions in Asia and grow our B2B relationships. By focusing on these areas, we drive business growth and establish strong trust with stakeholders. What's your advice for fundraising? Here are my key takeaways for other founders when it comes to fundraising: Fundraise When You Don’t Need To We closed our seed round in April 2023, a time when we weren't actively raising. Founders should always be in fundraising mode, even when they're not immediately in need of capital. Don’t wait until you have only a few months of runway left. Keep the pipeline open and build relationships. When the timing is right, execution becomes much easier. For us, our investment came through a combination of referrals and inbound interest. Even our lead investor initially rejected us, but after re-engaging, things eventually fell into place. It’s crucial to stay humble, treat everyone with respect, and maintain those relationships for when the time is right. Be Mindful of How You Present Information When fundraising, how you present information matters a lot. We created a comprehensive, easily digestible investment memo, hosted on Notion, which included everything an investor might need—problem, solution, market, team, risks, opportunities, and data. The goal was for investors to be able to get the full picture within 30 minutes without chasing down extra details. We also focused on making our financial model clear and meaningful, even though a 5-year forecast might be overkill at the seed stage. The key was clarity and conciseness, and making it as easy as possible for investors to understand the opportunity. I learned that brevity and simplicity are often the best ways to make a memorable impact. For the pitch itself, keep it simple and focus on 4 things: problem, solution, team, and market. If you can summarize each of these clearly and concisely, you’ll have a compelling pitch. Later on, you can expand into market segments, traction, and other metrics, but for seed-stage, focus on those four areas, and make sure you’re strong in at least three of them. If you do, you'll have a compelling case. How do you run things day-to-day? i.e what's your operational workflow and team structure? Here's an overview of our team structure and process: Internally: Our team is divided into two main areas: backend (internal team) and frontend (market-facing team). There's no formal hierarchy within the backend team. We all operate as equals, defining our goals based on what needs to be developed, assigning tasks, and meeting weekly to share updates and review progress. The focus is on full ownership of tasks and accountability for getting things done. I also contribute to product development, identifying challenges and clearing obstacles to help the team move forward. Backend Team: We approach tasks based on the scope defined by customers, with no blame or hierarchy. It's like a sports team—sometimes someone excels, and other times they struggle, but we support each other and move forward together. Everyone has the creative freedom to work in the way that suits them best, but we establish regular meetings and check-ins to ensure alignment and progress. Frontend Team: For the market-facing side, we implement a hierarchy because the market expects this structure. If I present myself as "CEO," it signals authority and credibility. This distinction affects how we communicate with the market and how we build our brand. The frontend team is split into four main areas: Business Product (Software Engineering) Machine Learning Engineering R&D The C-suite sits at the top, followed by team leads, and then the executors. We distill market expectations into actionable tasks, ensuring that everyone is clear on their role and responsibilities. Process: We start by receiving market expectations and defining tasks based on them. Tasks are assigned to relevant teams, and execution happens with no communication barriers between team members. This ensures seamless collaboration and focused execution. The main goal is always effectiveness—getting things done efficiently while maintaining flexibility in how individuals approach their work. In both teams, there's an emphasis on accountability, collaboration, and clear communication, but the structure varies according to the nature of the work and external expectations.

101 best SEO tips to help you drive traffic in 2k21
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101 best SEO tips to help you drive traffic in 2k21

Hey guys! I don't have to tell you how SEO can be good for your business - you can drive leads to your SaaS on autopilot, drive traffic to your store/gym/bar/whatever, etc. The thing with SEO, though, is that most SEO tips on the internet are just not that good. Most of the said tips: Are way too simple & basic (“add meta descriptions to your images”*) Are not impactful. Sure, adding that meta tag to an image is important, but that’s not what’s going to drive traffic to your website Don’t talk much about SEO strategy (which is ultimately the most important thing for SEO). Sure, on-page SEO is great, but you sure as hell won't drive much traffic if you can't hire the right writers to scale your content. And to drive serious SEO traffic, you'll need a LOT more than that. Over the past few years, my and my co-founder have helped grow websites to over 200k+ monthly traffic (check out our older Reddit post if you want to learn more about us, our process, and what we do), and we compiled all our most important SEO tips and tricks, as well as case studies, research, and experiments from the web, into this article. Hope you like it ;) If you think we missed something super important, let us know and we'll add it to the list. And btw, we also published this article on our own blog with images, smart filters, and all that good stuff. If you want to check it out, click here. That said, grab some coffee (or beer) & let's dive in - this is going to be a long one. SEO Strategy Tips Tip #1. A Lot of SEO Tips On The Internet Are NOT Necessarily Factual A lot of the SEO content you’ll read on the internet will be based on personal experiences and hearsay. Unfortunately, Google is a bit vague about SEO advice, so you have to rely more on experiments conducted by SEO pros in the community. So, sometimes, a lot of this information is questionable, wrong, or simply based on inaccurate data.  What we’re getting at here is, whenever you hear some new SEO advice, take it with a grain of salt. Google it to double-check other sources, and really understand what this SEO advice is based on (instead of just taking it at face value). Tip #2. SEO Takes Time - Get Used to It Any way you spin it, SEO takes time.  It can take around 6 months to 2 years (depending on the competition in your niche) before you start seeing some serious results.  So, don’t get disappointed if you don’t see any results within 3 months of publishing content. Tip #3. SEO Isn’t The Best Channel for Everyone That said, if you need results for your business tomorrow, you might want to reconsider SEO altogether.  If you just started your business, for example, and are trying to get to break-even ASAP, SEO is a bad idea - you’ll quit before you even start seeing any results.  If that’s the case, focus on other marketing channels that can have faster results like content marketing, PPC, outreach, etc. Tip #4. Use PPC to Validate Keywords Not sure if SEO is right for your business? Do this: set up Google Search ads for the most high-intent keywords in your niche. See how well the traffic converts and then decide if it’s worthwhile to focus on SEO (and rank on these keywords organically). Tip #5. Use GSC to See If SEO Is Working While it takes a while to see SEO results, it IS possible to see if you’re going in the right direction. On a monthly basis, you can use Search Console to check if your articles are indexed by Google and if their average position is improving over time. Tip #6. Publish a TON of Content The more content you publish on your blog, the better. We recommend a minimum of 10,000 words per month and optimally 20,000 - 30,000 (especially if your website is fresh). If an agency offers you the typical “4 500-word articles per month” deal, stay away. No one’s ever gotten results in SEO with short, once-per-week articles. Tip #7. Upgrade Your Writers Got a writer that’s performing well? Hire them as an editor and get them to oversee content operations / edit other writers’ content. Then, upgrade your best editor to Head of Content and get them to manage the entire editor / writer ops. Tip #8. Use Backlink Data to Prioritize Content When doing keyword research, gather the backlink data of the top 3 ranking articles and add it to your sheet. Then, use this data to help you prioritize which keywords to focus on first. We usually prioritize keywords that have lower competition, high traffic, and a medium to high buyer intent. Tip #9. Conduct In-Depth Keyword Research Make your initial keyword research as comprehensive as possible. This will give you a much more realistic view of your niche and allow you to prioritize content the right way. We usually aim for 100 to 300 keywords (depending on the niche) for the initial keyword research when we start working with a client. Tip #10. Start With Competitive Analysis Start every keyword research with competitive analysis. Extract the keywords your top 3 competitors are ranking on.  Then, use them as inspiration and build upon it. Use tools like UberSuggest to help generate new keyword ideas. Tip #11. Get SEMrush of Ahrefs You NEED SEMrush or Ahrefs, there’s no doubt about it. While they might seem expensive at a glance (99 USD per month billed annually), they’re going to save you a lot of manpower doing menial SEO tasks. Tip #12. Don’t Overdo It With SEO Tools Don’t overdo it with SEO tools. There are hundreds of those out there, and if you’re the type that’s into SaaS, you might be tempted to play around with dozens at a time. And yes, to be fair, most of these tools ARE helpful one way or another. To effectively do organic SEO, though, you don’t really need that many tools. In most cases, you just need the following: SEMrush/Ahrefs Screaming Frog RankMath/Yoast SEO Whichever outreach tool you prefer (our favorite is snov.io). Tip #13. Try Some of the Optional Tools In addition to the tools we mentioned before, you can also try the following 2 which are pretty useful & popular in the SEO community: Surfer SEO - helps with on-page SEO and creating content briefs for writers. ClusterAI - tool that helps simplify keyword research & save time. Tip #14. Constantly Source Writers Want to take your content production to the next level? You’ll need to hire more writers.  There is, however, one thing that makes this really, really difficult: 95 - 99% of writers applying for your gigs won’t be relevant. Up to 80% will be awful at writing, and the remainder just won’t be relevant for your niche. So, in order to scale your writing team, we recommend sourcing constantly, and not just once every few months. Tip #15. Create a Process for Writer Filtering As we just mentioned, when sourcing writers, you’ll be getting a ton of applicants, but most won’t be qualified. Fun fact \- every single time we post a job ad on ProBlogger, we get around 300 - 500 applications (most of which are totally not relevant). Trust us, you don’t want to spend your time going through such a huge list and checking out the writer samples. So, instead, we recommend you do this: Hire a virtual assistant to own the process of evaluating and short-listing writers. Create a process for evaluating writers. We recommend evaluating writers by: Level of English. If their samples aren’t fluent, they’re not relevant. Quality of Samples. Are the samples engaging / long-form content, or are they boring 500-word copy-pastes? Technical Knowledge. Has the writer written about a hard-to-explain topic before? Anyone can write about simple topics like traveling - you want to look for someone who knows how to research a new topic and explain it in a simple and easy to read way. If someone’s written about how to create a perfect cover letter, they can probably write about traveling, but the opposite isn’t true. The VA constantly evaluates new applicants and forwards the relevant ones to the editor. The editor goes through the short-listed writers and gives them trial tasks and hires the ones that perform well. Tip #16. Use The Right Websites to Source Writers “Is UpWork any good?” This question pops up on social media time and time again. If you ask us, no, UpWork is not good at all. Of course, there are qualified writers there (just like anywhere else), but from our experience, those writers are few and far in-between. Instead, here are some of our favorite ways to source writers: Cult of Copy Job Board ProBlogger Headhunting on LinkedIn If you really want to use UpWork, use it for headhunting (instead of posting a job ad) Tip #17. Hire Writers the Right Way If you want to seriously scale your content production, hire your writers full-time. This (especially) makes sense if you’re a content marketing agency that creates a TON of content for clients all the time. If you’re doing SEO just for your own blog, though, it usually makes more sense to use freelancers. Tip #18. Topic Authority Matters Google keeps your website's authoritativeness in mind. Meaning, if you have 100 articles on digital marketing, you’re probably more of an authority on the topic than someone that has just 10. Hence, Google is a lot more likely to reward you with better rankings. This is also partially why content volume really matters: the more frequently you publish content, the sooner Google will view you as an authority. Tip #19. Focus on One Niche at a Time Let’s say your blog covers the following topics: sales, accounting, and business management.  You’re more likely to rank if you have 30 articles on a single topic (e.g. accounting) than if you have 10 articles on each. So, we recommend you double-down on one niche instead of spreading your content team thin with different topics. Tip #20. Don’t Fret on the Details While technical SEO is important, you shouldn’t get too hung up on it.  Sure, there are thousands of technical tips you can find on the internet, and most of them DO matter. The truth, though, is that Google won’t punish you just because your website doesn’t load in 3 milliseconds or there’s a meta description missing on a single page. Especially if you have SEO fundamentals done right: Get your website to run as fast as possible. Create a ton of good SEO content. Get backlinks for your website on a regular basis. You’ll still rank, even if your website isn’t 100% optimized. Tip #21. Do Yourself a Favor and Hire a VA There are a TON of boring SEO tasks that your team should really not be wasting time with. So, hire a full-time VA to help with all that. Some tasks you want to outsource include gathering contacts to reach out to for link-building, uploading articles on WordPress, etc. Tip #22. Google Isn’t Everything While Google IS the dominant search engine in most parts of the world, there ARE countries with other popular search engines.  If you want to improve your SEO in China, for example, you should be more concerned with ranking on Baidu. Targeting Russia? Focus on Yandex. Tip #23. No, Voice Search is Still Not Relevant Voice search is not and will not be relevant (no matter what sensationalist articles might say). It’s just too impractical for most search queries to use voice (as opposed to traditional search). Tip #24. SEO Is Not Dead SEO is not dead and will still be relevant decades down the line. Every year, there’s a sensationalist article talking about this.  Ignore those. Tip #25. Doing Local SEO? Focus on Service Pages If you’re doing local SEO, focus on creating service-based landing pages instead of content.  E.g. if you’re an accounting firm based in Boston, you can make a landing page about /accounting-firm-boston/, /tax-accounting-boston/, /cpa-boston/, and so on. Thing is, you don’t really need to rank on global search terms - you just won’t get leads from there. Even if you ranked on the term “financial accounting,” it wouldn’t really matter for your bottom line that much. Tip #26. Learn More on Local SEO Speaking of local SEO, we definitely don’t do the topic justice in this guide. There’s a lot more you need to know to do local SEO effectively and some of it goes against the general SEO advice we talk about in this article (e.g. you don't necessarily need blog content for local SEO). We're going to publish an article on that soon enough, so if you want to check it out, DM me and I'll hit you up when it's up. Tip #27. Avoid Vanity Metrics Don’t get side-tracked by vanity metrics.  At the end of the day, you should care about how your traffic impacts your bottom line. Fat graphs and lots of traffic are nice and all, but none of it matters if the traffic doesn’t have the right search intent to convert to your product/service. Tip #28. Struggling With SEO? Hire an Expert Failing to make SEO work for your business? When in doubt, hire an organic SEO consultant or an SEO agency.  The #1 benefit of hiring an SEO agency or consultant is that they’ve been there and done that - more than once. They might be able to catch issues an inexperienced SEO can’t. Tip #29. Engage With the Community Need a couple of SEO questions answered?  SEO pros are super helpful & easy to reach! Join these Facebook groups and ask your question - you’ll get about a dozen helpful answers! SEO Signals Lab SEO & Content Marketing The Proper SEO Group. Tip #30. Stay Up to Date With SEO Trends SEO is always changing - Google is constantly pumping out new updates that have a significant impact on how the game is played.  Make sure to stay up to date with the latest SEO trends and Google updates by following the Google Search Central blog. Tip #31. Increase Organic CTR With PPC Want to get the most out of your rankings? Run PPC ads for your best keywords. Googlers who first see your ad are more likely to click your organic listing. Content & On-Page SEO Tips Tip #32. Create 50% Longer Content On average, we recommend you create an article that’s around 50% longer than the best article ranking on the keyword.  One small exception, though, is if you’re in a super competitive niche and all top-ranking articles are already as comprehensive as they can be. For example, in the VPN niche, all articles ranking for the keyword “best VPN” are around 10,000 - 11,000 words long. And that’s the optimal word count - even if you go beyond, you won’t be able to deliver that much value for the reader to make it worth the effort of creating the content. Tip #33. Longer Is Not Always Better Sometimes, a short-form article can get the job done much better.  For example, let’s say you’re targeting the keyword “how to tie a tie.”  The reader expects a short and simple guide, something under 500 words, and not “The Ultimate Guide to Tie Tying for 2021 \[11 Best Tips and Tricks\]” Tip #34. SEO is Not Just About Written Content Written content is not always best. Sometimes, videos can perform significantly better. E.g. If the Googler is looking to learn how to get a deadlift form right, they’re most likely going to be looking for a video. Tip #35. Don’t Forget to Follow Basic Optimization Tips For all your web pages (articles included), follow basic SEO optimization tips. E.g. include the keyword in the URL, use the right headings etc.  Just use RankMath or YoastSEO for this and you’re in the clear! Tip #36. Hire Specialized Writers When hiring content writers, try to look for ones that specialize in creating SEO content.  There are a LOT of writers on the internet, plenty of which are really good.  However, if they haven’t written SEO content before, chances are, they won’t do that good of a job. Tip #37. Use Content Outlines Speaking of writers - when working with writers, create a content outline that summarizes what the article should be about and what kind of topics it needs to cover instead of giving them a keyword and asking them to “knock themselves out.”   This makes it a lot more likely for the writer to create something that ranks. When creating content outlines, we recommend you include the following information: Target keyword Related keywords that should be mentioned in the article Article structure - which headings should the writer use? In what order? Article title Tip #38. Find Writers With Niche Knowledge Try to find a SEO content writer with some experience or past knowledge about your niche. Otherwise, they’re going to take around a month or two to become an expert. Alternatively, if you’re having difficulty finding a writer with niche knowledge, try to find someone with experience in technical or hard to explain topics. Writers who’ve written about cybersecurity in the past, for example, are a lot more likely to successfully cover other complicated topics (as opposed to, for example, a food or travel blogger). Tip #39. Keep Your Audience’s Knowledge in Mind When creating SEO content, always keep your audience’s knowledge in mind. If you’re writing about advanced finance, for example, you don’t need to teach your reader what an income statement is. If you’re writing about income statements, on the other hand, you’d want to start from the very barebone basics. Tip #40. Write for Your Audience If your readers are suit-and-tie lawyers, they’re going to expect professionally written content. 20-something hipsters? You can get away with throwing a Rick and Morty reference here and there. Tip #41. Use Grammarly Trust us, it’ll seriously make your life easier! Keep in mind, though, that the app is not a replacement for a professional editor. Tip #42. Use Hemingway Online content should be very easy to read & follow for everyone, whether they’re a senior profession with a Ph.D. or a college kid looking to learn a new topic. As such, your content should be written in a simple manner - and that’s where Hemingway comes in. It helps you keep your blog content simple. Tip #43. Create Compelling Headlines Want to drive clicks to your articles? You’ll need compelling headlines. Compare the two headlines below; which one would you click? 101 Productivity Tips \[To Get Things Done in 2021\] VS Productivity Tips Guide Exactly! To create clickable headlines, we recommend you include the following elements: Keyword Numbers Results Year (If Relevant) Tip #44. Nail Your Blog Content Formatting Format your blog posts well and avoid overly long walls of text. There’s a reason Backlinko content is so popular - it’s extremely easy to read and follow. Tip #45. Use Relevant Images In Your SEO Content Key here - relevant. Don’t just spray random stock photos of “office people smiling” around your posts; no one likes those.  Instead, add graphs, charts, screenshots, quote blocks, CSS boxes, and other engaging elements. Tip #46. Implement the Skyscraper Technique (The Right Way) Want to implement Backlinko’s skyscraper technique?  Keep this in mind before you do: not all content is meant to be promoted.  Pick a topic that fits the following criteria if you want the internet to care: It’s on an important topic. “Mega-Guide to SaaS Marketing” is good, “top 5 benefits of SaaS marketing” is not. You’re creating something significantly better than the original material. The internet is filled with mediocre content - strive to do better. Tip #47. Get The URL Slug Right for Seasonal Content If you want to rank on a seasonal keyword with one piece of content (e.g. you want to rank on “saas trends 2020, 2021, etc.”), don’t mention the year in the URL slug - keep it /saas-trends/ and just change the headline every year instead.  If you want to rank with separate articles, on the other hand (e.g. you publish a new trends report every year), include the year in the URL. Tip #48. Avoid content cannibalization.  Meaning, don’t write 2+ articles on one topic. This will confuse Google on which article it should rank. Tip #49. Don’t Overdo Outbound Links Don’t include too many outbound links in your content. Yes, including sources is good, but there is such a thing as overdoing it.  If your 1,000 word article has 20 outbound links, Google might consider it as spam (even if all those links are relevant). Tip #50. Consider “People Also Ask” To get the most out of SERP, you want to grab as many spots on the search result as possible, and this includes “people also ask (PAA):” Make a list of the topic’s PAA questions and ensure that your article answers them.  If you can’t fit the questions & answers within the article, though, you can also add an FAQ section at the end where you directly pose these questions and provide the answers. Tip #51. Optimize For Google Snippet Optimize your content for the Google Snippet. Check what’s currently ranking as the snippet. Then, try to do something similar (or even better) in terms of content and formatting. Tip #52. Get Inspired by Viral Content Want to create content that gets insane shares & links?  Reverse-engineer what has worked in the past. Look up content in your niche that went viral on Reddit, Hacker News, Facebook groups, Buzzsumo, etc. and create something similar, but significantly better. Tip #53. Avoid AI Content Tools No, robots can’t write SEO content.  If you’ve seen any of those “AI generated content tools,” you should know to stay away. The only thing those tools are (currently) good for is creating news content. Tip #54. Avoid Bad Content You will never, ever, ever rank with one 500-word article per week.  There are some SEO agencies (even the more reputable ones) that offer this as part of their service. Trust us, this is a waste of time. Tip #55. Update Your Content Regularly Check your top-performing articles annually and see if there’s anything you can do to improve them.  When most companies finally get the #1 ranking for a keyword, they leave the article alone and never touch it again… ...Until they get outranked, of course, by someone who one-upped their original article. Want to prevent this from happening? Analyze your top-performing content once a year and improve it when possible. Tip #56. Experiment With CTR Do your articles have low CTR? Experiment with different headlines and see if you can improve it.  Keep in mind, though, that what a “good CTR” is really depends on the keyword.  In some cases, the first ranking will drive 50% of the traffic. In others, it’s going to be less than 15%. Link-Building Tips Tip #57. Yes, Links Matter. Here’s What You Need to Know “Do I need backlinks to rank?” is probably one of the most common SEO questions.  The answer to the question (alongside all other SEO-related questions) is that it depends on the niche.  If your competitors don’t have a lot of backlinks, chances are, you can rank solely by creating superior content. If you’re in an extremely competitive niche (e.g. VPN, insurance, etc.), though, everyone has amazing, quality content - that’s just the baseline.  What sets top-ranking content apart from the rest is backlinks. Tip #58. Sometimes, You’ll Have to Pay For Links Unfortunately, in some niches, paying for links is unavoidable - e.g. gambling, CBD, and others. In such cases, you either need a hefty link-building budget, or a very creative link-building campaign (create a viral infographic, news-worthy story based on interesting data, etc.). Tip #59. Build Relationships, Not Links The very best link-building is actually relationship building.  Make a list of websites in your niche and build a relationship with them - don’t just spam them with the standard “hey, I have this amazing article, can you link to it?”.  If you spam, you risk ruining your reputation (and this is going to make further outreach much harder). Tip #60. Stick With The Classics At the end of the day, the most effective link-building tactics are the most straightforward ones:  Direct Outreach Broken Link-Building Guest Posting Skyscraper Technique Creating Viral Content Guestposting With Infographics Tip #61. Give, Don’t Just Take! If you’re doing link-building outreach, don’t just ask for links - give something in return.  This will significantly improve the reply rate from your outreach email. If you own a SaaS tool, for example, you can offer the bloggers you’re reaching out to free access to your software. Or, alternatively, if you’re doing a lot of guest posting, you can offer the website owner a link from the guest post in exchange for the link to your website. Tip #62. Avoid Link Resellers That guy DMing you on LinkedIn, trying to sell you links from a Google Sheet?  Don’t fall for it - most of those links are PBNs and are likely to backfire on you. Tip #63. Avoid Fiverr Like The Plague Speaking of spammy links, don’t touch anything that’s sold on Fiverr - pretty much all of the links there are useless. Tip #64. Focus on Quality Links Not all links are created equal. A link is of higher quality if it’s linked from a page that: Is NOT a PBN. Doesn’t have a lot of outbound links. If the page links to 20 other websites, each of them gets less link juice. Has a lot of (quality) backlinks. Is part of a website with a high domain authority. Is about a topic relevant to the page it’s linking to. If your article about pets has a link from an accounting blog, Google will consider it a bit suspicious. Tip #65. Data-Backed Content Just Works Data-backed content can get insane results for link-building.  For example, OKCupid used to publish interesting data & research based on how people interacted with their platform and it never failed to go viral. Each of their reports ended up being covered by dozens of news media (which got them a ton of easy links). Tip #66. Be Creative - SEO Is Marketing, After All Be novel & creative with your link-building initiatives.  Here’s the thing: the very best link-builders are not going to write about the tactics they’re using.  If they did, you’d see half the internet using the exact same tactic as them in less than a week! Which, as you can guess, would make the tactic cliche and significantly less effective. In order to get superior results with your link-building, you’ll need to be creative - think about how you can make your outreach different from what everyone does. Experiment it, measure it, and improve it till it works! Tip #67. Try HARO HARO, or Help a Reporter Out, is a platform that matches journalists with sources. You get an email every day with journalists looking for experts in specific niches, and if you pitch them right, they might feature you in their article or link to your website. Tip #68. No-Follow Links Aren’t That Bad Contrary to what you might’ve heard, no-follow links are not useless. Google uses no-follow as more of a suggestion than anything else.  There have been case studies that prove Google can disregard the no-follow tag and still reward you with increased rankings. Tip #69. Start Fresh With an Expired Domain Starting a new website? It might make sense to buy an expired one with existing backlinks (that’s in a similar niche as yours). The right domain can give you a serious boost to how fast you can rank. Tip #70. Don’t Overspend on Useless Links “Rel=sponsored” links don’t pass pagerank and hence, won’t help increase your website rankings.  So, avoid buying links from media websites like Forbes, Entrepreneur, etc. Tip #71. Promote Your Content Other than link-building, focus on organic content promotion. For example, you can repost your content on Facebook groups, LinkedIn, Reddit, etc. and focus on driving traffic.  This will actually lead to you getting links, too. We got around 95 backlinks to our SEO case study article just because of our successful content promotion. Tons of people saw the article on the net, liked it, and linked to it from their website. Tip #72. Do Expert Roundups Want to build relationships with influencers in your niche, but don’t know where to start?  Create an expert roundup article. If you’re in the sales niche, for example, you can write about Top 21 Sales Influencers in 2021 and reach out to the said influencers letting them know that they got featured. Trust us, they’ll love you for this! Tip #73. .Edu Links are Overhyped .edu links are overrated. According to John Mueller, .edu domains tend to have a ton of outbound links, and as such, Google ignores a big chunk of them. Tip #74. Build Relationships With Your Customers Little-known link-building hack: if you’re a SaaS company doing SEO, you can build relationships with your customers (the ones that are in the same topical niche as you are) and help each other build links! Tip #75. Reciprocal Links Aren’t That Bad Reciprocal links are not nearly as bad as Google makes them out to be. Sure, they can be bad at scale (if trading links is all you’re doing). Exchanging a link or two with another website / blog, though, is completely harmless in 99% of cases. Tip #76. Don’t Overspam Don’t do outreach for every single post you publish - just the big ones.  Most people already don’t care about your outreach email. Chances are, they’re going to care even less if you’re asking them to link to this new amazing article you wrote (which is about the top 5 benefits of adopting a puppy). Technical SEO Tips Tip #77. Use PageSpeed Insights If your website is extremely slow, it’s definitely going to impact your rankings. Use PageSpeed Insights to see how your website is currently performing. Tip #78. Load Speed Matters While load speed doesn’t impact rankings directly, it DOES impact your user experience. Chances are, if your page takes 5 seconds to load, but your competition’s loads instantly, the average Googler will drop off and pick them over you. Tip #79. Stick to a Low Crawl Depth Crawl depth of any page on your website should be lower than 4 (meaning, any given page should be possible to reach in no more than 3 clicks from the homepage).  Tip #80. Use Next-Gen Image Formats Next-gen image formats such as JPEG 2000, JPEG XR, and WebP can be compressed a lot better than PNG or JPG. So, when possible, use next-get formats for images on your website. Tip #81. De-Index Irrelevant Pages Hide the pages you don’t want Google to index (e.g: non-public, or unimportant pages) via your Robots.txt. If you’re a SaaS, for example, this would include most of your in-app pages or your internal knowledge base pages. Tip #82. Make Your Website Mobile-Friendly Make sure that your website is mobile-friendly. Google uses “mobile-first indexing.” Meaning, unless you have a working mobile version of your website, your rankings will seriously suffer. Tip #83. Lazy-Load Images Lazy-load your images. If your pages contain a lot of images, you MUST activate lazy-loading. This allows images that are below the screen, to be loaded only once the visitor scrolls down enough to see the image. Tip #84. Enable Gzip Compression Enable Gzip compression to allow your HTML, CSS and JS files to load faster. Tip #85. Clean Up Your Code If your website loads slowly because you have 100+ external javascript files and stylesheets being requested from the server, you can try minifying, aggregating, and inlining some of those files. Tip 86. Use Rel-Canonical Have duplicate content on your website? Use rel-canonical to show Google which version is the original (and should be prioritized for search results). Tip #87. Install an SSL Certificate Not only does an SSL certificate help keep your website safe, but it’s also a direct ranking factor. Google prioritizes websites that have SSL certificates over the ones that don’t. Tip #88. Use Correct Anchor Texts for Internal Links When linking to an internal page, mention the keyword you’re trying to rank for on that page in the anchor text. This helps Google understand that the page is, indeed, about the keyword you’re associating it with. Tip #89. Use GSC to Make Sure Your Content is Interlinked Internal links can have a serious impact on your rankings. So, make sure that all your blog posts (especially the new ones) are properly linked to/from your past content.  You can check how many links any given page has via Google Search Console. Tip #90. Bounce rate is NOT a Google ranking factor. Meaning, you can still rank high-up even with a high bounce rate. Tip #91. Don’t Fret About a High Bounce Rate Speaking of the bounce rate, you’ll see that some of your web pages have a higher-than-average bounce rate (70%+).  While this can sometimes be a cause for alarm, it’s not necessarily so. Sometimes, the search intent behind a given keyword means that you WILL have a high bounce rate even if your article is the most amazing thing ever.  E.g. if it’s a recipe page, the reader gets the recipe and bounces off (since they don’t need anything else). Tip #92. Google Will Ignore Your Meta Description More often than not, Google won’t use the meta description you provide - that’s normal. It will, instead, automatically pick a part of the text that it thinks is most relevant and use it as a meta description. Despite this, you should always add a meta description to all pages. Tip #93. Disavow Spammy & PBN Links Keep track of your backlinks and disavow anything that’s obviously spammy or PBNy. In most cases, Google will ignore these links anyway. However, you never know when a competitor is deliberately targeting you with too many spammy or PBN links (which might put you at risk for being penalized). Tip #94. Use The Correct Redirect  When permanently migrating your pages, use 301 redirect to pass on the link juice from the old page to the new one. If the redirect is temporary, use a 302 redirect instead. Tip #95. When A/B Testing, Do This A/B testing two pages? Use rel-canonical to show Google which page is the original. Tip #96. Avoid Amp DON’T use Amp.  Unless you’re a media company, Amp will negatively impact your website. Tip #97. Get Your URL Slugs Right Keep your blog URLs short and to-the-point. Good Example: apollodigital.io/blog/seo-case-study Bad Example: apollodigital.io/blog/seo-case-study-2021-0-to-200,000/ Tip #98. Avoid Dates in URLs An outdated date in your URL can hurt your CTR. Readers are more likely to click / read articles published recently than the ones written years back. Tip #99. Social Signals Matter Social signals impact your Google rankings, just not in the way you think. No, your number of shares and likes does NOT impact your ranking at all.  However, if your article goes viral and people use Google to find your article, click it, and read it, then yes, it will impact your rankings.  E.g. you read our SaaS marketing guide on Facebook, then look up “SaaS marketing” on Google, click it, and read it from there. Tip #100. Audit Your Website Frequently Every other month, crawl your website with ScreamingFrog and see if you have any broken links, 404s, etc. Tip #101. Use WordPress Not sure which CMS platform to use?  99% of the time, you’re better off with WordPress.  It has a TON of plugins that will make your life easier.  Want a drag & drop builder? Use Elementor. Wix, SiteGround and similar drag & drops are bad for SEO. Tip #102. Check Rankings the Right Way When checking on how well a post is ranking on Google Search Console, make sure to check Page AND Query to get the accurate number.  If you check just the page, it’s going to give you the average ranking on all keywords the page is ranking for (which is almost always going to be useless data). Conclusion Aaand that's about it - thanks for the read! Now, let's circle back to Tip #1 for a sec. Remember when we said a big chunk of what you read on SEO is based on personal experiences, experiments, and the like? Well, the tips we've mentioned are part of OUR experience. Chances are, you've done something that might be different (or completely goes against) our advice in this article. If that's the case, we'd love it if you let us know down in the comments. If you mention something extra-spicy, we'll even include it in this article.

Why Ignoring AI Agents in 2025 Will Kill Your Marketing Strategy
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frankiemuiruriThis week

Why Ignoring AI Agents in 2025 Will Kill Your Marketing Strategy

If you're still focusing solely on grabbing the attention of human beings with your marketing efforts, you're already behind. In 2025, the game will change. Good marketing will demand an in-depth understanding of the AI space, especially the AI Agent space. Why? Your ads and content won’t just be seen by humans anymore. They’ll be analyzed, indexed, and often acted upon by AI agents—automated systems that will be working on behalf of companies and consumers alike. Your New Audience: Humans + AI Agents It’s not just about appealing to people. Companies are employing AI robots to research, negotiate, and make purchasing decisions. These AI agents are fast, thorough, and unrelenting. Unlike humans, they can analyze millions of options in seconds. And if your marketing isn’t optimized for them, you’ll get filtered out before you even reach the human decision-maker. How to Prepare Your Marketing for AI Agents The companies that dominate marketing in 2025 will be the ones that master the art of capturing AI attention. To do this, marketers will need to: Understand the AI agents shaping their industry. Research how AI agents function in your niche. What are they prioritizing? How do they rank options? Create AI-friendly content. Design ads and messaging that are easily understandable and accessible to AI agents. This means clear metadata, structured data, and AI-readable formats. Invest in AI analytics. AI agents leave behind footprints. Tracking and analyzing their behavior is critical. Stay ahead of AI trends. The AI agent space is evolving rapidly. What works today might be obsolete tomorrow. How My Agency Adapted and Thrived in the AI Space At my digital agency, we saw this shift coming and decided to act early. In 2023, we started integrating AI optimization into our marketing strategies. One of our clients—a B2B SaaS company—struggled to get traction because their competitors were drowning them out in Google search rankings and ad platforms. By analyzing the algorithms and behaviors of AI agents in their space, we: Rewrote their website copy with structured data and optimized metadata that was more AI-agent friendly. Created ad campaigns with clear, concise messaging and technical attributes that AI agents could quickly process and index. Implemented predictive analytics to understand what AI agents would prioritize based on past behaviors. The results? Their website traffic doubled in three months, and their lead conversion rate skyrocketed by 40%. Over half of the traffic increase was traced back to AI agents recommending their platform to human users. The Takeaway In 2025, marketing won’t just be about human attention. It’ll be about AI attention—and that requires a completely different mindset. AI agents are not your enemy; they’re your new gatekeepers. Learn to speak their language, and you’ll dominate the marketing game.

Ai C-Level team
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thestoicdesignerThis week

Ai C-Level team

I've been exploring ways to run a company where I'm essentially the only internal team member, relying entirely on a suite of specialized AIs for executive roles, supported occasionally by external consultants for niche expertise. My goal is to stay lean, agile, and highly creative, especially in a fashion / tech brand context. Essentially, I'm building an AI-driven C-Level team, or what I like to call a "C-Level AI Wallet." Here's what I'm thinking for the key executive roles I'd need to cover with AI: CEO AI – Responsible for overall strategy, decision-making, trend analysis, and guiding the company's vision. I'd probably lean on something advanced like Gemini, GPT-4, or similar models, fine-tuned with market-specific data. COO AI (Operations): I'd need tools that streamline and automate logistics, supply chain management, and day-to-day operations (think something along the lines of Zapier AI integrations or Make). CMO AI (Marketing & Content): For branding, content creation, digital marketing, and consumer insights, I'd use Jasper or Copy . ai, combined with predictive analytics tools like Google Vertex AI to understand trends better. Additionally, for generating engaging visual and multimedia content, tools like Midjourney, DALL·E, Adobe Firefly, and Runway ML would be perfect. CFO AI (Financial Management): For financial management, cash flow control, and investment decisions, I'd probably leverage AI tools like Bloomberg GPT, combined with AI-powered forecasting platforms. CHRO AI (Human Resources & Culture): Although the internal team is minimal (just myself!), I'd still rely on AI for tasks like project management, freelancer hiring, and performance tracking—tools like HireVue AI, Motion, or even Notion's AI could be beneficial here. CSO AI (Sustainability & Compliance): Since sustainability and ethical sourcing are critical, I'd integrate ESG-focused AI tools to ensure transparency and responsible sourcing. My idea is that, with the right AI tools seamlessly integrated, I can manage the strategic vision and creative direction personally, leveraging external consultants only when necessary. This setup would ideally allow me to operate as a one-person internal team supported by a robust "wallet" of AI executives. Has anyone tried a similar approach? What AI tools would you recommend for a truly lean, innovative brand structure? I'm very curious about your experiences or suggestions—let me know your thoughts!

[Ultimate List] A list of Marketing Tools That I’ve tested over the years and found helpful to do better marketing with less work. More than 50 Tools To Help you with Marketing, Copywriting & Sales!
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lazymentorsThis week

[Ultimate List] A list of Marketing Tools That I’ve tested over the years and found helpful to do better marketing with less work. More than 50 Tools To Help you with Marketing, Copywriting & Sales!

Starting to focus on marketing for your business, You will come across the same tools mentioned over and over by marketers. I would like to mention here tools that you might haven’t seen going viral in the community but actually will help you grow faster and efficiently. Starting off with My favourite Marketing Channel! #Email Marketing For SMBs Convertkit / Mailerlite / Mailchimp - These 3 Platforms are the best options for SMBs and entrepreneurs just starting out with email marketing. All 3 have free plans up to 1,000 subscribers. Scribe - Email Signature Tool, Create Great Email signatures for your emails. Liramail - Most Email marketing platforms don’t offer great email templates. This tool will help you build great email templates with drag and drop. Quick mail Auto-Warmer - Most Businesses at the beginning don’t know what to do when open rate drops. You need to use an email warmer like this to keep it up. #Email Marketing For Big Businesses SendGrid - Overall Email Marketing Tools, this tool is best for brands that have huge email lists and email marketing is the key marketing channel. Braze - This tool is leading in email marketing for large Email senders. When I was working for agencies, this was one of the best email marketing tools I had used. NeoCertified - Protect your emails for spammers and threats. To keep your email list healthy, this is a must have! Sparkloop - Referral Marketing For Email Campaigns. Email can generate great huge amount of referrals for you and Sparkloop makes it easier. #Cold Emails & Lead Generation Hunter - A Great Tool to scrape emails from domain names. The tool comes with a green free plan but Pro plan is worth the amount of features it provides. Icyleads - It’s better than Hunter as it’s heavily focused on the sales and prospecting to help you derive great results from your campaigns. Mailshake - Beginner Friend Cold Email Tool with Great features like email list warming. #Communication Tools Twilio - One do the best customer engagement platform used by Companies like Stripe and mine too. Chatlio - Use Live chat feature on your website with slack integration. My favourite easier to catch up on conversations through slack integration. Intercom - Used by Most Marketers, Industry Leading customer communication platform. Great for beginners! Chatwoot - Another Amazing Communication Tool but the best part is they have a great free plan useful for new businesses. Loom - Communicate with your audience through Videos. Loom is great for SaaS and to show human interaction to close new visitors effectively. #CRM Outseta - This tool provides great CRM and their billing system is better than other tools out their which makes it stands out! Hubspot - I don’t think this tool needs an introduction because Hubspot’s CRM is the best in industry. Salesflare - This CRM is a great alternative to hubspot as it’s beginner friendly and helpful for SMBs. #SEO Tools Ahrefs - One of the best SEO tool in the industry. They also just launched a bunch of free tools to help SEO beginners. Screaming frog - The only website crawler I have used since I bought my first domain. It’s the best! Ubersuggest- The Tool by Neil Patel is the best SEO tool for you. (I’m Joking, it’s the worst) Contentking - This tool is good at Real-time SEO Auditing, they do a lot of Marketing work through Newsletters. If you are subscribed to any SEO newsletter. You may have seen this tool. SEOquake & Semrush - SEOquake is a great tool to conduct on-page analysis, SERP, and much more. Great tool but it’s owned by Semrush. You should go for Semrush because that tool will cover all SEO aspects for you. #Content Marketing Buzzsumo - This tool is great for content research and but you may find the regular emails pretty annoying sometimes. Contentrow - Analyse Your Content and find it’s strength. Highly recommended who are weak at content structuring like me. Grammarly - If you are not a native English speaker like me, you might think you need it or not. You need it for sure for grammar corrections. #Graphic Design Tools Visme - At agencies, Infographics can be more effective than usual postscript. Visme is a graphic design tool focused on infographics and designs related to B2B and B2C. It’s great for agencies! Glorify - A Graphic Design Tool focused on E-commerce, filled with Designs useful for E-commerce store owners. Canva - All-in-one Industry leading Graphic Design Tool that everyone knows and every template is overused now. Adobe Creative Cloud ( previously Sparkpost) - It’s a great alternative to Canva filled with Amazing Stock images to use in your visuals but the only backlash is the exports in this tool are not high quality. Snaps - A Canva Alternative that might not have overused templates for your Social Accounts. #Advertising Tools Plai - It’s a great PPC tool to create Ads for Instagram and Tiktok. Wordstream - It’s an industry leading PPC Tool, great for Ad Grading and auditing. AdEspresso - This Is a tool by Hootsuite. They have a lot of Data sourced at the backend, which helps in Ad optimisation through this tool. That’s the reason I recommend this tool. #Video Editing Tools Veed Studio - I have been using Veed from last year. It’s one of the best Video Marketing Tool Optimized for Instagram & Tiktok. Synthesia - It’s a new AI video generation platform. From last few months, if you have seen marketing agencies including Videos in Emails. The chances are that’s not a Agency member taking but AI generated Human. Motionbox - It’s also a great video editing tool focused on video editing for Digital Marketers. Jitter Video - It’s a great motion design tool. Comes with great templates, the only place where other tools I mentioned lacks. It’s great and beginner friendly. #Copywriting Jasper AI - Google’s John Mueller says AI generated content is banned on Search but I think with Jasper AI you can generate SEO optimised Content but you have to put in some efforts like at least give 30 minutes for editing the Copy by yourself. Copy AI - Another AI tool to help you write better copy. This one is more focused on helping you write copy suitable for Ads and Social media campaigns. Hemingway App - To help you write more clearly and Bold. This tool is better than Grammarly if you look for writing perspective and it’s free. #Social Media Management App I’ve used a Lot of SMM Tools and that’s why going to mention all of them with a short review. Sprout social - The Best with deep insights coverage. Hootsuite - Great Scheduling tool just under sprout social. Later - Heavily Focused on Instagram from beginning and Now Tiktok too. SkedSocial - It’s like a Later alternative with great addition features like link-in-bio. Facebook’s Business Manager- Great but sometimes bugs can make a huge issue for you and customer support is like dead. Tweet Hunter & Hypefury- Both are Twitter Scheduling tools growing very fast on platform and are great for growth. Buffer - It’s a great tool but I haven’t seen any new updates to help with management. Zoho Social - It’s a great SMM tool and if you use other marketing solutions from Zoho. It’s a must have! #Market Research Tool • SparkToro - That’s the only one I have ever used. It’s great for audience research and comes with great customer service. Founded by Rand Fishkin, it’s one of the best research tool. #Influencer Marketing & UGC InfluenceGrid - A free search engine To find Tiktok & Instagram Influencers for your campaigns. Tiktok Creative Center- TikTok’s in-built tool called “Creative Center” is the best to find content trends, audience demographics and much more. Archive - Find Instagram Stories and Posts mentioning Your brands and use them as Ads for your business Marketing. #Landing Page Builders Leadpages - Its a great landing page builder because the integration and drag-and-drop features makes it easier to work with! Cardd co - A Great Landing page builder with easy step up but it lacks the copywriting and tracking features. Instapage - It’s one of the best out and I think the overall product is effective enough to help you stand out with your landing page. Unbounce - It’s a great alternative to Instapage due its well polished landing page templates that might be helpful for you. #Community Building Mighty Networks - A Great Community building platform, and you can also sell courses within the platform. Circle so - A great alternative to Mighty networks focused on Communities specifically. We are currently using for small community Of ours. #Sales Tools Drift - You can get much more out of Drift than just sales tools but The Sales solutions provided in Drift are one of the best. Salesforce - It’s the industry Sales solution provider. A go-to and have various pricing plans making it suitable for majority of SMBs. #Social Proof Tools People don’t have enough time to search across internet to decide to trust you after seeing your Ad first time. That’s what you might be facing too. Here are two tools I absolutely love for social proof! Use Proof - Show Recent Activities occurring on your website and build the trust of your visitors. Testimonial to - Gather Testimonials across Social Media platforms related to your business with this tool. Capture tweets and comments mentioning your brands and mention them. #Analytics Tools Plausible Analytics- A privacy friendly Analytics alternative to Google Analytics if you hate Analytics 4 like me. Mixpanel - Product Analytics and funnel reports better than Google Analytics. #Reddit Marketing Gummysearch- This tool will help To find your target audience on Reddit and interact with them with its help and close your new customers. Howitzer- It’s another pretty similar tool to Gummysearch focused on Reddit cold outreach to get clients and new customers. Both are great but Gummysearch provides better customer support while Howtizer is helpful on a large scale Reddit Marketing. #Text Marketing Klaviyo - It’s an email + SMS marketing tool, it’s taking up space in marketing industry very quickly as an industry leader due to its great integrations but you need to learn the platform usage to maximise the outcome. Cartloop - This tool provides great text marketing solutions with integration with Spotify and other e-commerce marketing tools. Attentive Mobile - This is my favourite Text marketing tool due to the interactive dashboard + they have a library of Text marketing examples to help you out with your campaigns. #Other Tools I have used throughout my journey! Triple Whale - It’s a great E-commerce marketing tools with Triple pixel to help you track your campaigns more efficiently. Fastory - To create well optimized Instagram & Tiktok Stories for your business. Jotform - Online Form Builder with integrations with leading marketing tools. Gated - As an entrepreneur and marketer, you may receive a bunch of unwanted emails. Use Gated to get rid of them and receive useful mails only! ClickUp- The main Tool for Project Management, one of the best and highly recommended. Riverside - Forget Zoom or Google Meet, For your Podcast Interviews and Marketing conferences. You need riverside with great video quality and recording features. Manychat- Automate your Instagram DMs and interact with your followers more efficiently + sell out your products/ services when you are offline. Calendy - To schedule meetings with your ideal clients. ServiceProviderPro - It’s a client portal for SEO & Growing Agencies, very helpful in scaling agencies. SendCheckit - Compare your Email Subject Lines with 100,000+ others in the database for free. Otter AI - Using AI track your meetings more effectively, you can easily edit, annotate and share notes from the meetings. Ryte - Optimise your website User experience with this tool focused on UX aspects + SEO too. PhantomBuster - Scrape LinkedIn Profile and Data from Facebook/LinkedIn groups. I clearly love this tool! #Honourable Mentions Zapier - The Only tool you need to integrate your favourite tool with a new effective tool. Elementor - That’s what I use for web design and it’s great! Marketer Hire - To hire world class marketers to work with you. InShot & Capcut - I create Instagram Reels and TikTok’s and life without these tools isn’t possible. Nira - It’s a great tool to Manage your workspace and this tool has launched many marketing templates in-built helpful for marketers and also entrepreneurs. X - The tool you love that wasn’t mentioned here is valuable and I honour that tool and share that if you would like to! I mean thanks for reading what I have curated all over my life as a marketer. I share 5 Marketing Tools, 5 Marketing Resources and 1 Free Resourceevery week in my newsletter, you can subscribe here to receive that for free. Also, You can read an expanded list of email marketing tools in this Reddit post!

We create AI software and provide AI automation for companies. Here is a list of the best AI tools for sales IMHO
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We create AI software and provide AI automation for companies. Here is a list of the best AI tools for sales IMHO

Here are some AI tools that are useful for sales. I tried to touch as many different parts of the sales process so the tools are all quite different but all useful for sales. I tried to include some of the best and underrated AI tools. Most of them are free so check them out if you want. I did not include ChatGPT as it can basically be used for anything with the right prompts. So these tools will be more research-oriented. A quick disclaimer – I work for the company Idealink where we create custom ChatGPT for businesses and other AI products. Apollo AI Seamless AI CoPilot AI Lavender AI Regie AI Gemini Plusdocs Make Midjourney Fireflies AI Apollo AI - Find potential customers Apollo is a platform for sales and business development. It offers a range of tools to find and engage with ideal customers. The platform has an extensive B2B database and features that streamline the sales process from prospecting to closing deals. Key Features: Extensive B2B Database: Apollo boasts a large, accurate database of over 275 million contacts, providing a wealth of potential leads and opportunities for sales teams. Data Enrichment and Lead Insights: The platform offers data enrichment capabilities, ensuring CRM systems are continuously updated with detailed and actionable lead information. AI-Driven Sales Engagement: Apollo's AI technology assists in crafting effective communication and prioritizing high-value leads, enhancing the overall sales engagement process. Comprehensive Sales Tools: The platform provides an integrated suite of tools for email, call, and social media engagement, combined with analytics and automation features to streamline the sales cycle. Tailored Solutions for Teams: Apollo offers customized solutions for different team types, including sales and business development, founders, and marketing teams, addressing specific needs and goals. Seamless AI - Sale process made easier Seamless.AI is an innovative B2B sales lead generation solution that allows sales teams to efficiently connect with their ideal customers. The platform's features provide accurate and up-to-date contact information and integrate easily with existing sales and marketing tools. Key Features: Real-Time Search Engine: Seamless.AI uses AI to scour the web in real time, ensuring the contact information for sales leads is current and accurate. Comprehensive Integration: Easily integrates with popular CRMs and sales tools like Salesforce, HubSpot, and LinkedIn Sales Navigator, enhancing productivity and eliminating manual data entry. Chrome Extension: Enhances web browsing experience for sales teams, allowing them to build lead lists directly from their browser. Pitch Intelligence and Writer: Tools for crafting effective sales messages and marketing content, personalized for each potential customer. Data Enrichment and Autopilot: Keeps customer data current and automates lead-building, supporting consistent lead generation. Buyer Intent Data and Job Changes: Offers insights into potential customers' buying intentions and keeps track of significant job changes within key accounts. CoPilot AI - Helps sales reps manage leads CoPilot AI is an advanced AI-powered sales support platform designed for B2B sales teams and agencies to drive consistent revenue growth. The tool focuses on using LinkedIn for sales prospecting, engagement, and conversion. Key Features: LinkedIn Lead Generation: Targets and automates outreach to high-intent LinkedIn leads, enhancing efficiency and scalability in lead generation. Personalized Messaging Automation: Facilitates sending of personalized, one-click messages at scale, maintaining a human touch in digital interactions. Sales Conversion Insights: Offers tools to understand and adapt to prospects' communication styles, improving the likelihood of conversion. Sales Process Optimization: Provides analytics to evaluate and refine sales strategies, identifying opportunities for improvement in the sales funnel. Industry Versatility: Adapts to diverse industries, offering tailored solutions for B2B sales, marketing, HR, and financial services sectors. Collaborative Team Tools: Enables team synchronization and collaboration, boosting productivity and synergy in sales teams Lavender AI - Email AI assistant Lavender AI is an AI-powered email tool that helps users write better emails. It provides real-time feedback and personalized suggestions to optimize email communication efficiency. Key Features: Email Coaching and Scoring: Lavender evaluates emails using AI and a vast database of email interactions, offering a score and tips for improvement. It identifies factors that might reduce the likelihood of receiving a reply, helping users refine their email content. Personalization Assistant: This feature integrates prospect data directly into the user's email platform, suggesting personalization strategies based on recipient data and personality insights to foster deeper connections. Adaptive Improvement: Lavender's scoring and recommendations evolve in real-time with changing email behaviors and practices, thanks to its generative AI and extensive data analysis, ensuring users always follow the best practices. Data-Driven Managerial Insights: The platform provides managers with valuable insights derived from actual email interactions, aiding them in coaching their teams more effectively based on real performance and communication trends. Broad Integration Capability: Lavender integrates with various email and sales platforms including Gmail, Outlook, and others, making it versatile for different user preferences and workflows. Regie AI - Great for business intelligence Regie.ai simplifies the sales prospecting process for businesses, using GenAI and automation to improve interactions with prospects. The platform offers tools like Auto-Pilot for automatic prospecting and meeting scheduling, Co-Pilot for sales rep support, and integrations with various CRM and sales engagement platforms. It also includes a Chrome Extension and CMS for content management and customization. Key Features: Automated Prospecting with Auto-Pilot: Regie.ai's Auto-Pilot feature autonomously prospects and schedules meetings, using Generative AI for Sales Agents to enhance outbound sales efforts. Audience Discovery and Content Generation: The platform identifies target accounts not in the CRM, generating relevant, on-brand content for each message, thus ensuring efficiency in list building and message personalization. Outbound Prioritization and Dynamic Engagement: It utilizes engagement and intent data to prioritize outreach to in-market prospects and adjust engagement strategies based on buyer responsiveness. Full Funnel Brand Protection and Analytics: Regie.ai ensures consistent use of marketing-approved language in all sales outreach and provides insights into campaign and document performance, thereby safeguarding brand integrity throughout the sales funnel. Gemini - AI powered conversational platform Gemini is a large language model chatbot developed by Google AI. It can generate text, translate languages, write different creative text formats, and answer your questions in an informative way. It is still under development but has learned to perform many kinds of tasks. Key features: Generate different creative text formats of text content (poems, code, scripts, musical pieces, email, letters, etc.) Answer your questions in an informative way, even if they are open ended, challenging, or strange. Translate languages Follow your instructions and complete your requests thoughtfully. Plusdocs (Plus AI) - AI tool for presentations Plus AI is a versatile tool that helps improve presentations and integrates with Slides in a simple and intuitive way. It simplifies slide creation and customization by converting text into slides and utilizing AI for various languages. Key Features: Text-to-Slide Conversion: Plus AI excels in transforming textual content into visually appealing slides, streamlining the presentation creation process. Multilingual AI Support: The tool is equipped to handle various languages, making it adaptable for a global user base. Professional Design Options: Users have access to professionally designed slide layouts, enabling the creation of polished presentations with ease. Customization and AI Design: Plus AI allows for extensive customization, including the use of AI for designing and editing slides, ensuring unique and personalized presentations. Live Snapshots and Templates: The tool offers live snapshots for real-time updates and a wide range of templates for quick and effective slide creation. Make - AI automation Make is a powerful visual platform that allows users to build and automate tasks, workflows, apps, and systems. It offers an intuitive, no-code interface that empowers users across various business functions to design and implement complex processes without the need for developer resources. Key Features: No-Code Visual Workflow Builder: Make's core feature is its user-friendly interface that allows for the creation of intricate workflows without coding expertise, making it accessible to a wide range of users. Extensive App Integration: The platform boasts compatibility with over 1000 apps, facilitating seamless connections and data sharing across diverse tools and systems. Custom Automation Solutions: Make enables personalized automation strategies, fitting various business needs from marketing automation to IT workflow control. Template Library: Users can jumpstart their automation projects with a vast collection of pre-built templates, which are customizable to fit specific workflow requirements. Enterprise-Level Solutions: Make offers advanced options for larger organizations, including enhanced security, single sign-on, custom functions, and dedicated support. Midjourney - Making sales content Midjourney is an AI-based image generation tool that changes the way we visualise and create digital art. It offers a lot of artistic possibilities, allowing users to create stunning images from text prompts. This innovative service caters to artists, designers, and anyone seeking to bring their creative visions to life. Key Features: Advanced AI Image Generation: Midjourney's core strength lies in its powerful AI algorithms, which interpret text prompts to generate detailed, high-quality images. This feature allows users to explore an endless array of visual concepts and styles. User-driven Customization: The tool offers significant control over the image creation process, enabling users to guide the AI with specific instructions, ensuring that the final output aligns closely with their vision. Diverse Artistic Styles: Midjourney can mimic various artistic styles, from classical to contemporary, providing users with a wide range of aesthetic options for their creations. Collaboration and Community Features: The platform fosters a community of users who can share, critique, and collaborate on artistic projects, enriching the creative experience. Fireflies AI - Sales meeting assistant Fireflies.ai is a powerful tool for improving team productivity and efficiency in managing meetings and voice conversations. It offers a range of features to simplify the process of capturing, organizing, and analyzing meeting content. Key Features: Automatic Meeting Transcription: Fireflies.ai can transcribe meetings held on various video-conferencing platforms and dialers. The tool captures both video and audio, providing transcripts quickly and efficiently. AI-Powered Search and Summarization: It allows users to review long meetings in a fraction of the time, highlighting key action items, tasks, and questions. Users can filter and focus on specific topics discussed in meetings. Improved Collaboration: The tool enables adding comments, pins, and reactions to specific conversation parts. Users can create and share soundbites and integrate meeting notes with popular collaboration apps such as Slack, Notion, and Asana. Conversation Intelligence: Fireflies.ai offers insights into meetings by tracking metrics like speaker talk time and sentiment. It helps in coaching team members and improving performance in sales, recruiting, and other internal processes. Workflow Automation: The AI assistant from Fireflies.ai can log call notes and activities in CRMs, create tasks through voice commands, and share meeting recaps instantly across various platforms. Comprehensive Knowledge Base: It compiles all voice conversations into an easily accessible and updatable knowledge base, with features to organize meetings into channels and set custom privacy controls. I’ll keep updating this little guide, so add your comments and I’ll try to add more tools. This is all just a personal opinion, so it’s completely cool if you disagree with it. Btw here is the link to the full blog post about all the AI tools in a bit more depth.

Founder Pitch: AI Agent for Simplifying Public Cloud Management
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rasvi786This week

Founder Pitch: AI Agent for Simplifying Public Cloud Management

Video to understand : https://youtu.be/9ocUjlUrU\w?si=S0ETDbKSdJqlVDyg Are You Ready to Redefine Cloud Management with AI? Imagine an intelligent AI agent that transforms the complexity of managing public cloud infrastructure into simple, natural language commands. No more navigating through endless configurations or deciphering technical documentation—our AI agent is here to revolutionize the way organizations interact with cloud platforms. About the Project We’re building an AI-powered agent designed to handle public cloud management tasks seamlessly. Whether you’re setting up your organization’s cloud foundation or deploying complex workloads, this AI agent makes it as easy as having a conversation. What Can the AI Agent Do? Cloud Foundation Setup: Example: “Please set up a cloud foundation blueprint for my organization on Google Cloud.”* The AI agent will ask key questions (e.g., organization ID) and guide you through authentication. Once authorized, it sets up the foundation using GCP APIs. Workload Deployment: Example: “Spin up a GKE cluster for me.”* The agent will ask for necessary details (e.g., number of nodes, VPC info), authenticate, and deploy the cluster in minutes. Security and Compliance Validation: Example: “Validate my organization’s cloud setup and check for security vulnerabilities.”* The agent audits your setup, identifies potential risks, and provides actionable insights. Current Progress We’ve developed a working prototype that integrates with major cloud providers like Google Cloud. The AI agent can already: Authenticate with cloud APIs Execute foundational tasks such as setting up organizations and spinning up clusters Perform initial security validations Who I’m Looking For I’m searching for a co-founder with enterprise sales experience and a strategic vision to grow our user base. You will be instrumental in helping us: Build relationships with companies willing to pilot our product Develop go-to-market strategies for enterprise adoption Identify opportunities for partnerships with cloud service providers Your Role As a co-founder, you’ll lead efforts to: Secure Pilot Programs: Identify and onboard enterprises for product trials to gather feedback and refine the solution. Drive Growth: Develop scalable strategies to grow our user base across industries. Market Positioning: Work with me to define our unique value proposition and establish thought leadership in the cloud management space. My Background I bring over a decade of experience in tech, with a strong focus on software engineering and infrastructure. My contributions so far include: Developing the core AI engine and cloud integrations Designing workflows that simplify complex cloud tasks Why Join This Project? Revolutionize Cloud Management: Be part of a project that will redefine how organizations interact with public clouds. Tackle Challenging Problems: Work at the cutting edge of AI and cloud computing. High Growth Potential: Join an industry projected to grow exponentially as enterprises embrace AI-driven automation. Build a Company from Scratch: Shape the product, team, and culture as we grow together. What’s Next? Our immediate priorities include: Expanding the AI agent’s capabilities to support multi-cloud setups. Conducting pilot programs with enterprise clients. Iterating on the product based on real-world feedback. What We Need to Succeed Expertise in enterprise sales and partnerships A deep understanding of enterprise challenges and cloud adoption trends A shared passion for leveraging AI to solve complex problems Let’s work together to build the future of cloud management. If you’re excited about this vision and bring the expertise we need, I’d love to connect and discuss how we can take this project to the next level.

Please, help me to narrow down the list of ideas to pursuit
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SpiritedSecond4791This week

Please, help me to narrow down the list of ideas to pursuit

Hi guys, I need help to narrow down the possible problems to solve. How do you do it? What do you think about these ideas? All came from real-life problems. Break-It-Down Problem-Solving Assistant Problem: Large, complex projects can feel overwhelming and difficult to tackle. Solution: An AI-guided assistant that analyzes your project goals and automatically breaks them into smaller, manageable tasks. It provides suggested resources and real-time collaboration with team members for smoother task delegation. Personalized Sleep Solutions Problem: Poor sleep quality affects health, productivity, and overall well-being. Solution: An adaptive app that tracks sleep patterns through wearable data and adjusts sleep routines, room settings, and audio cues based on real-time sleep stages for optimal rest. Skill Analysis & Development Tool Problem: It’s challenging to identify valuable skills for career growth and keep up with future demands. Solution: AI-driven skill analysis with a personalized career roadmap that maps out high-demand skills for your specific industry, combined with real-time market trend analysis to suggest learning resources and certifications. Innovator’s Problem Discovery Platform Problem: Innovators struggle to identify real industry problems that need innovative solutions. Solution: An AI-powered platform that gathers and analyzes challenges from different industries, crowdsources ideas, and uses machine learning to highlight innovation opportunities tailored to your skills and interests. High-Earning Career Strategy Platform Problem: Many professionals face challenges in maximizing their earning potential and advancing their careers. Solution: A dynamic career advancement platform that analyzes your skill set, tracks job market trends, and offers personalized mentorship sessions with high-earning professionals in your field, along with salary benchmarking and negotiation tips.

How I Made $250.000+ in a Year: A Case Study of My AI Influencer Journey
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How I Made $250.000+ in a Year: A Case Study of My AI Influencer Journey

Update on February 22th: I changed my AI influencer's names because it caused some problems on my business. One year, two AI-powered influencers, and $250K in revenue. Sounds unreal? It’s not. Today, I’m pulling back the curtain on the strategies, tools, and hard-won lessons that took me from concept to a six-figure success story in the AI influencer space. Hey, I'm Ben—a 32-year-old designer who spent the past year navigating the world of AI influencers. Let me clear up any confusion right from the start: I’m not here to sell you anything. This is purely a case study to share what worked, what didn’t, and what I’ve learned along the way. I’ll also make sure to answer all your questions in the comments for free whenever I can, so don’t hesitate to ask. Links to Past Topics: If you're curious about some of the groundwork I covered, check out a few of my earlier posts here: How I Make $10,000 Monthly | AI Influencer Management How I Earned $7000+ in 15 Days | AI Influencer Business Update These earlier posts cover a lot of the backstory, so feel free to explore them before diving into this one. So if you're ready, here is the full story: \---- The idea of creating an AI influencer was one of those “what if” moments that wouldn’t leave my mind. At first, it sounded futuristic—even a bit too ambitious. It all started when I stumbled upon an AI influencer on Instagram with the handle AnnaMaes2000. Her content blew me away—the quality, the detail, and just how real everything looked. I was instantly hooked and ended up going through every post, just trying to figure out how she was pulling this off. That’s when I knew I had to learn how this was done. The next step? YouTube. I dived into videos on Stable Diffusion, soaking up everything I could about creating AI-generated images. Those tutorials taught me the basics and got me up to speed. Then, I created my first AI influencer, let's call her Mel for now. Right after that, to complete the storyline and boost engagement, I introduced Mel's “mother,” Jess. Adding Jess gave the whole project depth and a narrative that drew people in, creating a unique family dynamic that instantly elevated traffic and interest. After thousands of bad photos, hundreds of deleted posts, and months of trial and error, you can now see the quality that defines my current accounts. Here’s a rundown of the tools and checkpoints I’ve used from day one, in order: Fooocus on RunDiffusion — Juggernaut V8 Fooocus on RunDiffusion — Juggernaut V9 Fooocus on PC (locally) — Juggernaut V9 Fooocus on PC (locally) —Lyuyang Mix + Juggernaut V9 Flux on PC (couple of photos only since it's so slow even on RTX 4090) Flux on Fal.ai. \---- There’s no magic Instagram hack that guarantees success, despite what everyone thinks and keeps asking me. Quality content, consistent uploads, and solid craftsmanship are what actually help your photos hit trends and show up on the Explore page. Unlike 95% of low-quality AI accounts out there, I don’t rely on faceswap videos, spam Reels, or go around liking comments on other accounts. My approach is fully organic, focused solely on creating my own unique content. By following Instagram's guidelines to the letter, I've managed to direct some of Mel and Jess' fans over to Patreon and Fanvue. There, for a small subscription fee, fans can access exclusive lingerie content. For those looking for more, higher-tier subscriptions give access to even more premium content. Some possible questions and their answers: No, you can't share hardcore NSFW content on Patreon. You can do that on Fanvue. Yes, you can create AI creators on Fanvue — OnlyFans doesn't allow it. Yes, you can use your own ID to get KYC. Yes, we're telling both Mel and Jess is (or use) AI to generate content. And yes, some people leave and some people still have fun with chatting, having a good time and get perfect content for their needs. And yes, we have a chatter team to work on these accounts. \---- This journey wasn’t all smooth sailing. I faced unexpected roadblocks, like platform restrictions that limited certain types of content, and managing fan expectations was more challenging than anticipated. Staying within guidelines while keeping fans engaged required constant adaptation. These hurdles forced me to get creative, adjust my approach, and learn fast. Once I saw Mel and Jess gaining traction, I knew it was time to scale up. Expanding meant finding new ways to keep content fresh, creating deeper narratives, and considering how to bring even more followers into the fold. My focus turned to building a sustainable model that could grow without sacrificing quality or authenticity. If you’re thinking about diving into AI content creation, here’s my advice: patience, consistency, and a focus on quality are key. Don’t cut corners or rely on quick-fix hacks. Invest time in learning the right tools, creating engaging stories, and building an audience that values what you bring to the table. This approach took me from zero to six figures, and it’s what makes the journey worth it. \---- And finally, here’s the income breakdown that everyone’s curious about: Mel on Fanvue: $82,331.58 (Gross earnings because we have chatter cuts like 15%) Mel on Patreon: $50,865.98 (Net earnings) Jess on Fanvue: $89,068.26 (Gross earnings because we have chatter cuts like 15%) Jess on Patreon: $39,040.70 And thanks to Reddit and my old posts, I got a perfect investor like after 5 months, so this is a "payback" for that. Like I said, I'll answer every question in the comments — take care and let me know.

How I Made $250.000+ in a Year: A Case Study of My AI Influencer Journey
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benfromwhereThis week

How I Made $250.000+ in a Year: A Case Study of My AI Influencer Journey

Update on February 22th: I changed my AI influencer's names because it caused some problems on my business. One year, two AI-powered influencers, and $250K in revenue. Sounds unreal? It’s not. Today, I’m pulling back the curtain on the strategies, tools, and hard-won lessons that took me from concept to a six-figure success story in the AI influencer space. Hey, I'm Ben—a 32-year-old designer who spent the past year navigating the world of AI influencers. Let me clear up any confusion right from the start: I’m not here to sell you anything. This is purely a case study to share what worked, what didn’t, and what I’ve learned along the way. I’ll also make sure to answer all your questions in the comments for free whenever I can, so don’t hesitate to ask. Links to Past Topics: If you're curious about some of the groundwork I covered, check out a few of my earlier posts here: How I Make $10,000 Monthly | AI Influencer Management How I Earned $7000+ in 15 Days | AI Influencer Business Update These earlier posts cover a lot of the backstory, so feel free to explore them before diving into this one. So if you're ready, here is the full story: \---- The idea of creating an AI influencer was one of those “what if” moments that wouldn’t leave my mind. At first, it sounded futuristic—even a bit too ambitious. It all started when I stumbled upon an AI influencer on Instagram with the handle AnnaMaes2000. Her content blew me away—the quality, the detail, and just how real everything looked. I was instantly hooked and ended up going through every post, just trying to figure out how she was pulling this off. That’s when I knew I had to learn how this was done. The next step? YouTube. I dived into videos on Stable Diffusion, soaking up everything I could about creating AI-generated images. Those tutorials taught me the basics and got me up to speed. Then, I created my first AI influencer, let's call her Mel for now. Right after that, to complete the storyline and boost engagement, I introduced Mel's “mother,” Jess. Adding Jess gave the whole project depth and a narrative that drew people in, creating a unique family dynamic that instantly elevated traffic and interest. After thousands of bad photos, hundreds of deleted posts, and months of trial and error, you can now see the quality that defines my current accounts. Here’s a rundown of the tools and checkpoints I’ve used from day one, in order: Fooocus on RunDiffusion — Juggernaut V8 Fooocus on RunDiffusion — Juggernaut V9 Fooocus on PC (locally) — Juggernaut V9 Fooocus on PC (locally) —Lyuyang Mix + Juggernaut V9 Flux on PC (couple of photos only since it's so slow even on RTX 4090) Flux on Fal.ai. \---- There’s no magic Instagram hack that guarantees success, despite what everyone thinks and keeps asking me. Quality content, consistent uploads, and solid craftsmanship are what actually help your photos hit trends and show up on the Explore page. Unlike 95% of low-quality AI accounts out there, I don’t rely on faceswap videos, spam Reels, or go around liking comments on other accounts. My approach is fully organic, focused solely on creating my own unique content. By following Instagram's guidelines to the letter, I've managed to direct some of Mel and Jess' fans over to Patreon and Fanvue. There, for a small subscription fee, fans can access exclusive lingerie content. For those looking for more, higher-tier subscriptions give access to even more premium content. Some possible questions and their answers: No, you can't share hardcore NSFW content on Patreon. You can do that on Fanvue. Yes, you can create AI creators on Fanvue — OnlyFans doesn't allow it. Yes, you can use your own ID to get KYC. Yes, we're telling both Mel and Jess is (or use) AI to generate content. And yes, some people leave and some people still have fun with chatting, having a good time and get perfect content for their needs. And yes, we have a chatter team to work on these accounts. \---- This journey wasn’t all smooth sailing. I faced unexpected roadblocks, like platform restrictions that limited certain types of content, and managing fan expectations was more challenging than anticipated. Staying within guidelines while keeping fans engaged required constant adaptation. These hurdles forced me to get creative, adjust my approach, and learn fast. Once I saw Mel and Jess gaining traction, I knew it was time to scale up. Expanding meant finding new ways to keep content fresh, creating deeper narratives, and considering how to bring even more followers into the fold. My focus turned to building a sustainable model that could grow without sacrificing quality or authenticity. If you’re thinking about diving into AI content creation, here’s my advice: patience, consistency, and a focus on quality are key. Don’t cut corners or rely on quick-fix hacks. Invest time in learning the right tools, creating engaging stories, and building an audience that values what you bring to the table. This approach took me from zero to six figures, and it’s what makes the journey worth it. \---- And finally, here’s the income breakdown that everyone’s curious about: Mel on Fanvue: $82,331.58 (Gross earnings because we have chatter cuts like 15%) Mel on Patreon: $50,865.98 (Net earnings) Jess on Fanvue: $89,068.26 (Gross earnings because we have chatter cuts like 15%) Jess on Patreon: $39,040.70 And thanks to Reddit and my old posts, I got a perfect investor like after 5 months, so this is a "payback" for that. Like I said, I'll answer every question in the comments — take care and let me know.

Founder Pitch: AI Agent for Simplifying Public Cloud Management
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rasvi786This week

Founder Pitch: AI Agent for Simplifying Public Cloud Management

Video to understand : https://youtu.be/9ocUjlUrU\w?si=S0ETDbKSdJqlVDyg Are You Ready to Redefine Cloud Management with AI? Imagine an intelligent AI agent that transforms the complexity of managing public cloud infrastructure into simple, natural language commands. No more navigating through endless configurations or deciphering technical documentation—our AI agent is here to revolutionize the way organizations interact with cloud platforms. About the Project We’re building an AI-powered agent designed to handle public cloud management tasks seamlessly. Whether you’re setting up your organization’s cloud foundation or deploying complex workloads, this AI agent makes it as easy as having a conversation. What Can the AI Agent Do? Cloud Foundation Setup: Example: “Please set up a cloud foundation blueprint for my organization on Google Cloud.”* The AI agent will ask key questions (e.g., organization ID) and guide you through authentication. Once authorized, it sets up the foundation using GCP APIs. Workload Deployment: Example: “Spin up a GKE cluster for me.”* The agent will ask for necessary details (e.g., number of nodes, VPC info), authenticate, and deploy the cluster in minutes. Security and Compliance Validation: Example: “Validate my organization’s cloud setup and check for security vulnerabilities.”* The agent audits your setup, identifies potential risks, and provides actionable insights. Current Progress We’ve developed a working prototype that integrates with major cloud providers like Google Cloud. The AI agent can already: Authenticate with cloud APIs Execute foundational tasks such as setting up organizations and spinning up clusters Perform initial security validations Who I’m Looking For I’m searching for a co-founder with enterprise sales experience and a strategic vision to grow our user base. You will be instrumental in helping us: Build relationships with companies willing to pilot our product Develop go-to-market strategies for enterprise adoption Identify opportunities for partnerships with cloud service providers Your Role As a co-founder, you’ll lead efforts to: Secure Pilot Programs: Identify and onboard enterprises for product trials to gather feedback and refine the solution. Drive Growth: Develop scalable strategies to grow our user base across industries. Market Positioning: Work with me to define our unique value proposition and establish thought leadership in the cloud management space. My Background I bring over a decade of experience in tech, with a strong focus on software engineering and infrastructure. My contributions so far include: Developing the core AI engine and cloud integrations Designing workflows that simplify complex cloud tasks Why Join This Project? Revolutionize Cloud Management: Be part of a project that will redefine how organizations interact with public clouds. Tackle Challenging Problems: Work at the cutting edge of AI and cloud computing. High Growth Potential: Join an industry projected to grow exponentially as enterprises embrace AI-driven automation. Build a Company from Scratch: Shape the product, team, and culture as we grow together. What’s Next? Our immediate priorities include: Expanding the AI agent’s capabilities to support multi-cloud setups. Conducting pilot programs with enterprise clients. Iterating on the product based on real-world feedback. What We Need to Succeed Expertise in enterprise sales and partnerships A deep understanding of enterprise challenges and cloud adoption trends A shared passion for leveraging AI to solve complex problems Let’s work together to build the future of cloud management. If you’re excited about this vision and bring the expertise we need, I’d love to connect and discuss how we can take this project to the next level.

🛒7 Strategies to Increase Retail Store Footfall post-COVID | Ultimate Blueprint & Guide 📈
reddit
LLM Vibe Score0
Human Vibe Score1
bnk3r_This week

🛒7 Strategies to Increase Retail Store Footfall post-COVID | Ultimate Blueprint & Guide 📈

Hello fellow marketers/entrepreneurs! Covid has had a gobsmacking effect on all retail promotions and marketing efforts. For people with retail businesses that thrive on footfall, it has been an uphill battle, but markets of the world are slowly resuming action. Knowing the footfall to your retail store can help you decide how many products you need to stock, which days of the week are best for promotions, and what type of promotional offers work well. The pandemic has drastically impacted customer behavior and customer loyalty is plunging. People prefer shopping online to brick-and-mortar purchases, and consumers are limiting their spending on a range of items - investing only in essentials is the norm now (McKinsey). We found some companies like Target having programs like Cartwheel that offer 5% to 50% off specific items when customers shop in-store to increase foot traffic. Strategies like these ultimately add up, an ICSC report cites that 69% of customers who went to collect their orders eventually bought additional items. I've put together a detailed list of 7 strategies to boost footfall to stores post COVID, I hope they come in handy! Abide by COVID-19 Protocols for a Safer Environment Be well-informed of the COVID-19 protocols. Don't implement this merely under the government norms, instead take extra measures to show customers that you care! Have an automated entrance Deploy hygiene counters Fix thermal sensors in the entrance Have an isolation space for those showing symptoms of the coronavirus To see more check this link for the entire list! Run Catchy In-Store Promotions Discounts are a perfect way to attract new customers and retain existing ones. When you want to increase customer traffic in a brick-and-mortar store, give customers an offer that only works inside the store. Surprise your consumers with free samples of your products. This would allow them to try some new brands and products. If you’d want to reduce your excess stock post the quarantine time, try running a multi-buy campaign. Digital Signages - Enhance In-store Shopping Experience Digital signage is a type of advertising that uses a video screen to display marketing messages. They can be used for attracting customers, conveying information, and promoting merchandise. Retail outlets in malls that have fashion sections can display the latest trends on their screens so customers know what’s new. This helps them pick out something they might like quickly. Some restaurants showcase menus on screens while others even project live cooking shows! These displays help with menu navigation too; helping a diner decide between chicken tikka masala or steak tartare by showing pictures of both dishes at once. Leverage Beacon Notification to Attract Customers to Your Store The beacon technology is a way to implement a tracking system indoors. A beacon is an inaudible signal that can be tracked and act as the trigger for other events like sending notifications about deals, discounts, or new products. Beacon technology helps with driving footfalls by giving customers an indoor mapping experience of your store's inventory. This ensures they always know where they are going and what’s around them. The navigation reminds them of their proximity to items on display so there’s never any confusion over whether something is nearby or farther off. Train your Salespeople to Become the Shopper's Friend Educating your salespersons on how to be consumers’ friends is important. They should be knowledgeable about what products are popular and in-demand so that they can help the customers find exactly what they want while at the same time giving guidance on how to save money by telling them where discounts and deals can be found. Reconceptualize Checkout Counters Customers abandon their purchases because of long lines at the checkout. With the pandemic out there, this could be one of the reasons why the retail foot traffic is diminishing. Include contactless payments that can be automated or replace your existing POS setup. Encourage BOPIS (Buy Online Pick-up In-store) To implement BOPIS for your retail store, you need to have a centralized platform that allows you to manage orders, sales, and customers. This helps you to deliver a personalized customer experience. In combination with BOPIS, another way to promote footfall into the store and drive sales in retail is by bringing your website in-store. And this will be a good move if you have multiple stores and not all the stock in one place. This is because, when you know how to calculate footfall in retail it can help you with many retail metrics like: How to plan your store for peak footfall times? How much stock you need in the store and how often you'll need to restock it? What products are selling well on an hourly basis? This is so crucial information for retailers that will help inform decisions about where to place certain items or which ones may be more popular than others etc. When stores should have promotions (if they want), discounts, and raise weekend sales? We've put together an elaborate, research-based White Paper that covers these segments: How have pandemics catalyzed technological innovations Customer sentiment and behavior during COVID-19 An omnichannel customer engagement strategy to drive sales in retail and footfall The ultimate roadmap to increase retail footfalls How to build the perfect loyalty program to turn foot traffic into brand ambassadors? You can find the same over here, hope my team's effort comes in handy to some of y'all that could improve your store visits, cheers!

How I Made $250.000+ in a Year: A Case Study of My AI Influencer Journey
reddit
LLM Vibe Score0
Human Vibe Score0.778
benfromwhereThis week

How I Made $250.000+ in a Year: A Case Study of My AI Influencer Journey

Update on February 22th: I changed my AI influencer's names because it caused some problems on my business. One year, two AI-powered influencers, and $250K in revenue. Sounds unreal? It’s not. Today, I’m pulling back the curtain on the strategies, tools, and hard-won lessons that took me from concept to a six-figure success story in the AI influencer space. Hey, I'm Ben—a 32-year-old designer who spent the past year navigating the world of AI influencers. Let me clear up any confusion right from the start: I’m not here to sell you anything. This is purely a case study to share what worked, what didn’t, and what I’ve learned along the way. I’ll also make sure to answer all your questions in the comments for free whenever I can, so don’t hesitate to ask. Links to Past Topics: If you're curious about some of the groundwork I covered, check out a few of my earlier posts here: How I Make $10,000 Monthly | AI Influencer Management How I Earned $7000+ in 15 Days | AI Influencer Business Update These earlier posts cover a lot of the backstory, so feel free to explore them before diving into this one. So if you're ready, here is the full story: \---- The idea of creating an AI influencer was one of those “what if” moments that wouldn’t leave my mind. At first, it sounded futuristic—even a bit too ambitious. It all started when I stumbled upon an AI influencer on Instagram with the handle AnnaMaes2000. Her content blew me away—the quality, the detail, and just how real everything looked. I was instantly hooked and ended up going through every post, just trying to figure out how she was pulling this off. That’s when I knew I had to learn how this was done. The next step? YouTube. I dived into videos on Stable Diffusion, soaking up everything I could about creating AI-generated images. Those tutorials taught me the basics and got me up to speed. Then, I created my first AI influencer, let's call her Mel for now. Right after that, to complete the storyline and boost engagement, I introduced Mel's “mother,” Jess. Adding Jess gave the whole project depth and a narrative that drew people in, creating a unique family dynamic that instantly elevated traffic and interest. After thousands of bad photos, hundreds of deleted posts, and months of trial and error, you can now see the quality that defines my current accounts. Here’s a rundown of the tools and checkpoints I’ve used from day one, in order: Fooocus on RunDiffusion — Juggernaut V8 Fooocus on RunDiffusion — Juggernaut V9 Fooocus on PC (locally) — Juggernaut V9 Fooocus on PC (locally) —Lyuyang Mix + Juggernaut V9 Flux on PC (couple of photos only since it's so slow even on RTX 4090) Flux on Fal.ai. \---- There’s no magic Instagram hack that guarantees success, despite what everyone thinks and keeps asking me. Quality content, consistent uploads, and solid craftsmanship are what actually help your photos hit trends and show up on the Explore page. Unlike 95% of low-quality AI accounts out there, I don’t rely on faceswap videos, spam Reels, or go around liking comments on other accounts. My approach is fully organic, focused solely on creating my own unique content. By following Instagram's guidelines to the letter, I've managed to direct some of Mel and Jess' fans over to Patreon and Fanvue. There, for a small subscription fee, fans can access exclusive lingerie content. For those looking for more, higher-tier subscriptions give access to even more premium content. Some possible questions and their answers: No, you can't share hardcore NSFW content on Patreon. You can do that on Fanvue. Yes, you can create AI creators on Fanvue — OnlyFans doesn't allow it. Yes, you can use your own ID to get KYC. Yes, we're telling both Mel and Jess is (or use) AI to generate content. And yes, some people leave and some people still have fun with chatting, having a good time and get perfect content for their needs. And yes, we have a chatter team to work on these accounts. \---- This journey wasn’t all smooth sailing. I faced unexpected roadblocks, like platform restrictions that limited certain types of content, and managing fan expectations was more challenging than anticipated. Staying within guidelines while keeping fans engaged required constant adaptation. These hurdles forced me to get creative, adjust my approach, and learn fast. Once I saw Mel and Jess gaining traction, I knew it was time to scale up. Expanding meant finding new ways to keep content fresh, creating deeper narratives, and considering how to bring even more followers into the fold. My focus turned to building a sustainable model that could grow without sacrificing quality or authenticity. If you’re thinking about diving into AI content creation, here’s my advice: patience, consistency, and a focus on quality are key. Don’t cut corners or rely on quick-fix hacks. Invest time in learning the right tools, creating engaging stories, and building an audience that values what you bring to the table. This approach took me from zero to six figures, and it’s what makes the journey worth it. \---- And finally, here’s the income breakdown that everyone’s curious about: Mel on Fanvue: $82,331.58 (Gross earnings because we have chatter cuts like 15%) Mel on Patreon: $50,865.98 (Net earnings) Jess on Fanvue: $89,068.26 (Gross earnings because we have chatter cuts like 15%) Jess on Patreon: $39,040.70 And thanks to Reddit and my old posts, I got a perfect investor like after 5 months, so this is a "payback" for that. Like I said, I'll answer every question in the comments — take care and let me know.

better-genshin-impact
github
LLM Vibe Score0.58
Human Vibe Score0.5281045668197327
babalaeMar 28, 2025

better-genshin-impact

BetterGI 🌟 点一下右上角的 Star,Github 主页就能收到软件更新通知了哦~ BetterGI · 更好的原神, 一个基于计算机视觉技术,意图让原神变的更好的项目。 功能 实时任务 自动拾取:遇到可交互/拾取内容时自动按 F,支持黑白名单配置 自动剧情:快速点击过剧情、自动选择选项、自动提交物品、关闭弹出书页等 与凯瑟琳对话时有橙色选项会 自动领取「每日委托」奖励、自动重新派遣 自动邀约:自动剧情开启的情况下此功能才会生效,自动选择邀约选项 快速传送:在地图上点击传送点,或者点击后出现的列表中存在传送点,会自动点击传送点并传送 半自动钓鱼:AI 识别自动抛竿,鱼上钩时自动收杆,并自动完成钓鱼进度 自动烹饪:自动在完美区域完成食物烹饪,暂不支持“仙跳墙” 独立任务 全自动七圣召唤:帮助你轻松完成七圣召唤角色邀请、每周来客挑战等 PVE 内容 自动伐木:自动 Z 键使用「王树瑞佑」,利用上下线可以刷新木材的原理,挂机刷满一背包的木材 自动秘境:全自动秘境挂机刷体力,自动循环进入秘境开启钥匙、战斗、走到古树并领取奖励 自动音游:一键自动完成千音雅集的专辑,快速获取成就 全自动钓鱼:在出现钓鱼F按钮的位置面向鱼塘,然后启动全自动钓鱼,启动后程序会自动完成钓鱼,并切换白天和晚上 全自动 一条龙:一键完成日常(使用历练点),并领取奖励 自动采集/挖矿/锄地:通过左上角小地图的识别,完成自动采集、挖矿、锄地等功能 键鼠录制:可以录制回放当前的键鼠操作,建议配合调度器使用 操控辅助 那维莱特转圈:设置快捷键后,长按可以不断水平旋转视角(当然你也可以用来转草神) 快速圣遗物强化:通过快速切换“详情”、“强化”页跳过圣遗物强化结果展示,快速+20 商店一键购买:可以快速以满数量购买商店中的物品,适合快速清空活动兑换,尘歌壶商店兑换等 …… 自带一个遮罩窗口覆盖在游戏界面上,用于显示日志和图像识别结果 截图 !0 39 1 下载 [!NOTE] 下载地址:⚡Github 下载 不知道下载哪个?第一次使用?请看:快速上手 , 遇到问题请先看:常见问题 最新编译版本可以从自动构建中获取: 使用方法 由于图像识别比较吃性能,低配置电脑可能无法正常使用部分功能。 推荐的电脑配置至少能够中画质60帧流畅游玩原神,否则部分功能的使用体验会较差。 你的系统需要满足以下条件: Windows 10 或更高版本的64位系统 .NET 8 运行时 (没有的话,启动程序,系统会提示下载安装) ⚠️注意: 窗口大小变化、切换游戏分辨率、切换显示器的时候请重启本软件。 不支持任何画面滤镜(HDR、N卡滤镜等)。游戏亮度请保持默认。 当前只支持 16:9 的分辨率,推荐在 1920x1080 窗口化游戏下使用。 模拟操作部分可能被部分安全软件拦截,请加入白名单。已知360或者自定义规则WD会拦截部分类型的模拟点击 打开软件以后,在“启动”页选择好截图方式,点击启动按钮就可以享受 BetterGI 带来的便利了! 详细使用指南请看:快速上手 具体功能效果与使用方式见:文档 FAQ 为什么需要管理员权限? 因为游戏是以管理员权限启动的,软件不以管理员权限启动的话没有权限模拟鼠标点击。 会不会封号? 理论上不会被封。 BetterGI 不会做出任何修改游戏文件、读写游戏内存等任何危害游戏本体的行为,单纯依靠视觉算法和模拟操作实现。 但是mhy是自由的,用户条款上明确说明第三方软件/模拟操作是封号理由之一。当前方案还是存在被检测的可能。只能说请低调使用,请不要跳脸官方。 更多常见问题... 致谢 本项目的完成离不开以下项目: Yap genshin-woodmen Fischless MicaSetup cvAutoTrack genshinimpactassistant HutaoFisher minimap kachina-installer 另外特别感谢 @Lightczx 和 @emako 对本项目的指导与贡献 开发者 格式化:CodeMaid.config、Settings.XamlStyler; 如何编译项目? 许可证 !GPL-v3 问题反馈 提 Issue 或 QQ群1029539994

anything-llm
github
LLM Vibe Score0.572
Human Vibe Score0.4703504093656464
Mintplex-LabsMar 28, 2025

anything-llm

AnythingLLM: The all-in-one AI app you were looking for. Chat with your docs, use AI Agents, hyper-configurable, multi-user, & no frustrating set up required. | | Docs | Hosted Instance English · 简体中文 · 日本語 👉 AnythingLLM for desktop (Mac, Windows, & Linux)! Download Now A full-stack application that enables you to turn any document, resource, or piece of content into context that any LLM can use as references during chatting. This application allows you to pick and choose which LLM or Vector Database you want to use as well as supporting multi-user management and permissions. !Chatting Watch the demo! Product Overview AnythingLLM is a full-stack application where you can use commercial off-the-shelf LLMs or popular open source LLMs and vectorDB solutions to build a private ChatGPT with no compromises that you can run locally as well as host remotely and be able to chat intelligently with any documents you provide it. AnythingLLM divides your documents into objects called workspaces. A Workspace functions a lot like a thread, but with the addition of containerization of your documents. Workspaces can share documents, but they do not talk to each other so you can keep your context for each workspace clean. Cool features of AnythingLLM 🆕 Custom AI Agents 🆕 No-code AI Agent builder 🖼️ Multi-modal support (both closed and open-source LLMs!) 👤 Multi-user instance support and permissioning Docker version only 🦾 Agents inside your workspace (browse the web, etc) 💬 Custom Embeddable Chat widget for your website Docker version only 📖 Multiple document type support (PDF, TXT, DOCX, etc) Simple chat UI with Drag-n-Drop funcitonality and clear citations. 100% Cloud deployment ready. Works with all popular closed and open-source LLM providers. Built-in cost & time-saving measures for managing very large documents compared to any other chat UI. Full Developer API for custom integrations! Much more...install and find out! Supported LLMs, Embedder Models, Speech models, and Vector Databases Large Language Models (LLMs): Any open-source llama.cpp compatible model OpenAI OpenAI (Generic) Azure OpenAI AWS Bedrock Anthropic NVIDIA NIM (chat models) Google Gemini Pro Hugging Face (chat models) Ollama (chat models) LM Studio (all models) LocalAi (all models) Together AI (chat models) Fireworks AI (chat models) Perplexity (chat models) OpenRouter (chat models) DeepSeek (chat models) Mistral Groq Cohere KoboldCPP LiteLLM Text Generation Web UI Apipie xAI Novita AI (chat models) PPIO Embedder models: AnythingLLM Native Embedder (default) OpenAI Azure OpenAI LocalAi (all) Ollama (all) LM Studio (all) Cohere Audio Transcription models: AnythingLLM Built-in (default) OpenAI TTS (text-to-speech) support: Native Browser Built-in (default) PiperTTSLocal - runs in browser OpenAI TTS ElevenLabs Any OpenAI Compatible TTS service. STT (speech-to-text) support: Native Browser Built-in (default) Vector Databases: LanceDB (default) Astra DB Pinecone Chroma Weaviate Qdrant Milvus Zilliz Technical Overview This monorepo consists of three main sections: frontend: A viteJS + React frontend that you can run to easily create and manage all your content the LLM can use. server: A NodeJS express server to handle all the interactions and do all the vectorDB management and LLM interactions. collector: NodeJS express server that process and parses documents from the UI. docker: Docker instructions and build process + information for building from source. embed: Submodule for generation & creation of the web embed widget. browser-extension: Submodule for the chrome browser extension. 🛳 Self Hosting Mintplex Labs & the community maintain a number of deployment methods, scripts, and templates that you can use to run AnythingLLM locally. Refer to the table below to read how to deploy on your preferred environment or to automatically deploy. | Docker | AWS | GCP | Digital Ocean | Render.com | |----------------------------------------|----|-----|---------------|------------| | [![Deploy on Docker][docker-btn]][docker-deploy] | [![Deploy on AWS][aws-btn]][aws-deploy] | [![Deploy on GCP][gcp-btn]][gcp-deploy] | [![Deploy on DigitalOcean][do-btn]][do-deploy] | [![Deploy on Render.com][render-btn]][render-deploy] | | Railway | RepoCloud | Elestio | | --- | --- | --- | | [![Deploy on Railway][railway-btn]][railway-deploy] | [![Deploy on RepoCloud][repocloud-btn]][repocloud-deploy] | [![Deploy on Elestio][elestio-btn]][elestio-deploy] | or set up a production AnythingLLM instance without Docker → How to setup for development yarn setup To fill in the required .env files you'll need in each of the application sections (from root of repo). Go fill those out before proceeding. Ensure server/.env.development is filled or else things won't work right. yarn dev:server To boot the server locally (from root of repo). yarn dev:frontend To boot the frontend locally (from root of repo). yarn dev:collector To then run the document collector (from root of repo). Learn about documents Learn about vector caching External Apps & Integrations These are apps that are not maintained by Mintplex Labs, but are compatible with AnythingLLM. A listing here is not an endorsement. Midori AI Subsystem Manager - A streamlined and efficient way to deploy AI systems using Docker container technology. Coolify - Deploy AnythingLLM with a single click. GPTLocalhost for Microsoft Word - A local Word Add-in for you to use AnythingLLM in Microsoft Word. Telemetry & Privacy AnythingLLM by Mintplex Labs Inc contains a telemetry feature that collects anonymous usage information. More about Telemetry & Privacy for AnythingLLM Why? We use this information to help us understand how AnythingLLM is used, to help us prioritize work on new features and bug fixes, and to help us improve AnythingLLM's performance and stability. Opting out Set DISABLE_TELEMETRY in your server or docker .env settings to "true" to opt out of telemetry. You can also do this in-app by going to the sidebar > Privacy and disabling telemetry. What do you explicitly track? We will only track usage details that help us make product and roadmap decisions, specifically: Type of your installation (Docker or Desktop) When a document is added or removed. No information about the document. Just that the event occurred. This gives us an idea of use. Type of vector database in use. Let's us know which vector database provider is the most used to prioritize changes when updates arrive for that provider. Type of LLM in use. Let's us know the most popular choice and prioritize changes when updates arrive for that provider. Chat is sent. This is the most regular "event" and gives us an idea of the daily-activity of this project across all installations. Again, only the event is sent - we have no information on the nature or content of the chat itself. You can verify these claims by finding all locations Telemetry.sendTelemetry is called. Additionally these events are written to the output log so you can also see the specific data which was sent - if enabled. No IP or other identifying information is collected. The Telemetry provider is PostHog - an open-source telemetry collection service. View all telemetry events in source code 👋 Contributing create issue create PR with branch name format of - LGTM from core-team 🌟 Contributors 🔗 More Products [VectorAdmin][vector-admin]: An all-in-one GUI & tool-suite for managing vector databases. [OpenAI Assistant Swarm][assistant-swarm]: Turn your entire library of OpenAI assistants into one single army commanded from a single agent. [![][back-to-top]](#readme-top) Copyright © 2025 [Mintplex Labs][profile-link]. This project is MIT licensed. [back-to-top]: https://img.shields.io/badge/-BACKTOTOP-222628?style=flat-square [profile-link]: https://github.com/mintplex-labs [vector-admin]: https://github.com/mintplex-labs/vector-admin [assistant-swarm]: https://github.com/Mintplex-Labs/openai-assistant-swarm [docker-btn]: ./images/deployBtns/docker.png [docker-deploy]: ./docker/HOWTOUSE_DOCKER.md [aws-btn]: ./images/deployBtns/aws.png [aws-deploy]: ./cloud-deployments/aws/cloudformation/DEPLOY.md [gcp-btn]: https://deploy.cloud.run/button.svg [gcp-deploy]: ./cloud-deployments/gcp/deployment/DEPLOY.md [do-btn]: https://www.deploytodo.com/do-btn-blue.svg [do-deploy]: ./cloud-deployments/digitalocean/terraform/DEPLOY.md [render-btn]: https://render.com/images/deploy-to-render-button.svg [render-deploy]: https://render.com/deploy?repo=https://github.com/Mintplex-Labs/anything-llm&branch=render [render-btn]: https://render.com/images/deploy-to-render-button.svg [render-deploy]: https://render.com/deploy?repo=https://github.com/Mintplex-Labs/anything-llm&branch=render [railway-btn]: https://railway.app/button.svg [railway-deploy]: https://railway.app/template/HNSCS1?referralCode=WFgJkn [repocloud-btn]: https://d16t0pc4846x52.cloudfront.net/deploylobe.svg [repocloud-deploy]: https://repocloud.io/details/?app_id=276 [elestio-btn]: https://elest.io/images/logos/deploy-to-elestio-btn.png [elestio-deploy]: https://elest.io/open-source/anythingllm

awesome-ai-in-finance
github
LLM Vibe Score0.58
Human Vibe Score1
georgezouqMar 28, 2025

awesome-ai-in-finance

Awesome AI in Finance There are millions of trades made in the global financial market every day. Data grows very quickly and people are hard to understand. With the power of the latest artificial intelligence research, people analyze & trade automatically and intelligently. This list contains the research, tools and code that people use to beat the market. [中文资源] Contents LLMs Papers Courses & Books Strategies & Research Time Series Data Portfolio Management High Frequency Trading Event Drive Crypto Currencies Strategies Technical Analysis Lottery & Gamble Arbitrage Data Sources Research Tools Trading System TA Lib Exchange API Articles Others LLMs 🌟🌟 MarS - A Financial Market Simulation Engine Powered by Generative Foundation Model. 🌟🌟 Financial Statement Analysis with Large Language Models - GPT-4 can outperform professional financial analysts in predicting future earnings changes, generating useful narrative insights, and resulting in superior trading strategies with higher Sharpe ratios and alphas, thereby suggesting a potential central role for LLMs in financial decision-making. PIXIU - An open-source resource providing a financial large language model, a dataset with 136K instruction samples, and a comprehensive evaluation benchmark. FinGPT - Provides a playground for all people interested in LLMs and NLP in Finance. MACD + RSI + ADX Strategy (ChatGPT-powered) by TradeSmart - Asked ChatGPT on which indicators are the most popular for trading. We used all of the recommendations given. A ChatGPT trading algorithm delivered 500% returns in stock market. My breakdown on what this means for hedge funds and retail investors Use chatgpt to adjust strategy parameters Hands-on LLMs: Train and Deploy a Real-time Financial Advisor - Train and deploy a real-time financial advisor chatbot with Falcon 7B and CometLLM. ChatGPT Strategy by OctoBot - Use ChatGPT to determine which cryptocurrency to trade based on technical indicators. Papers The Theory of Speculation L. Bachelier, 1900 - The influences which determine the movements of the Stock Exchange are. Brownian Motion in the Stock Market Osborne, 1959 - The common-stock prices can be regarded as an ensemble of decisions in statistical equilibrium. An Investigation into the Use of Reinforcement Learning Techniques within the Algorithmic Trading Domain, 2015 A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem Reinforcement Learning for Trading, 1994 Dragon-Kings, Black Swans and the Prediction of Crises Didier Sornette - The power laws in the distributions of event sizes under a broad range of conditions in a large variety of systems. Financial Trading as a Game: A Deep Reinforcement Learning Approach - Deep reinforcement learning provides a framework toward end-to-end training of such trading agent. Machine Learning for Trading - With an appropriate choice of the reward function, reinforcement learning techniques can successfully handle the risk-averse case. Ten Financial Applications of Machine Learning, 2018 - Slides review few important financial ML applications. FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, 2020 - Introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy, 2020 - Propose an ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return. Courses & Books & Blogs 🌟 QuantResearch - Quantitative analysis, strategies and backtests https://letianzj.github.io/ NYU: Overview of Advanced Methods of Reinforcement Learning in Finance Udacity: Artificial Intelligence for Trading AI in Finance - Learn Fintech Online. Advanced-Deep-Trading - Experiments based on "Advances in financial machine learning" book. Advances in Financial Machine Learning - Using advanced ML solutions to overcome real-world investment problems. Build Financial Software with Generative AI - Book about how to build financial software hands-on using generative AI tools like ChatGPT and Copilot. Mastering Python for Finance - Sources codes for: Mastering Python for Finance, Second Edition. MLSys-NYU-2022 - Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2022. Train and Deploy a Serverless API to predict crypto prices - In this tutorial you won't build an ML system that will make you rich. But you will master the MLOps frameworks and tools you need to build ML systems that, together with tons of experimentation, can take you there. Strategies & Research Time Series Data Price and Volume process with Technology Analysis Indices 🌟🌟 stockpredictionai - A complete process for predicting stock price movements. 🌟 Personae - Implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading. 🌟 Ensemble-Strategy - Deep Reinforcement Learning for Automated Stock Trading. FinRL - A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance. AutomatedStockTrading-DeepQ-Learning - Build a Deep Q-learning reinforcement agent model as automated trading robot. tfdeeprltrader - Trading environment(OpenAI Gym) + PPO(TensorForce). trading-gym - Trading agent to train with episode of short term trading itself. trading-rl - Deep Reinforcement Learning for Financial Trading using Price Trailing. deeprltrader - Trading environment(OpenAI Gym) + DDQN (Keras-RL). Quantitative-Trading - Papers and code implementing Quantitative-Trading. gym-trading - Environment for reinforcement-learning algorithmic trading models. zenbrain - A framework for machine-learning bots. DeepLearningNotes - Machine learning in quant analysis. stockmarketreinforcementlearning - Stock market trading OpenAI Gym environment with Deep Reinforcement Learning using Keras. Chaos Genius - ML powered analytics engine for outlier/anomaly detection and root cause analysis.. mlforecast - Scalable machine learning based time series forecasting. Portfolio Management Deep-Reinforcement-Stock-Trading - A light-weight deep reinforcement learning framework for portfolio management. qtrader - Reinforcement Learning for portfolio management. PGPortfolio - A Deep Reinforcement Learning framework for the financial portfolio management problem. DeepDow - Portfolio optimization with deep learning. skfolio - Python library for portfolio optimization built on top of scikit-learn. High Frequency Trading High-Frequency-Trading-Model-with-IB - A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion. 🌟 SGX-Full-OrderBook-Tick-Data-Trading-Strategy - Solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data. HFTBitcoin - Analysis of High Frequency Trading on Bitcoin exchanges. Event Drive 🌟🌟 stockpredictionai - Complete process for predicting stock price movements. 🌟 trump2cash - A stock trading bot powered by Trump tweets. Crypto Currencies Strategies LSTM-Crypto-Price-Prediction - Predicting price trends in crypto markets using an LSTM-RNN for trading. tforcebtctrader - TensorForce Bitcoin trading bot. Tensorflow-NeuroEvolution-Trading-Bot - A population model that trade cyrpto and breed and mutate iteratively. gekkoga - Genetic algorithm for solving optimization of trading strategies using Gekko. GekkoANNStrategies - ANN trading strategies for the Gekko trading bot. gekko-neuralnet - Neural network strategy for Gekko. bitcoinprediction - Code for "Bitcoin Prediction" by Siraj Raval on YouTube. Technical Analysis quant-trading - Python quantitative trading strategies. Gekko-Bot-Resources - Gekko bot resources. gekkotools - Gekko strategies, tools etc. gekko RSIWR - Gekko RSIWR strategies. gekko HL - Calculate down peak and trade on. EthTradingAlgorithm - Ethereum trading algorithm using Python 3.5 and the library ZipLine. gekkotradingstuff - Awesome crypto currency trading platform. forex.analytics - Node.js native library performing technical analysis over an OHLC dataset with use of genetic algorithmv. BitcoinMACDStrategy - Bitcoin MACD crossover trading strategy backtest. crypto-signal - Automated crypto trading & technical analysis (TA) bot for Bittrex, Binance, GDAX, and more. Gekko-Strategies - Strategies to Gekko trading bot with backtests results and some useful tools. gekko-gannswing - Gann's Swing trade strategy for Gekko trade bot. Lottery & Gamble LotteryPredict - Use LSTM to predict lottery. Arbitrage ArbitrageBot - Arbitrage bot that currently works on bittrex & poloniex. r2 - Automatic arbitrage trading system powered by Node.js + TypeScript. cryptocurrency-arbitrage - A crypto currency arbitrage opportunity calculator. Over 800 currencies and 50 markets. bitcoin-arbitrage - Bitcoin arbitrage opportunity detector. blackbird - Long / short market-neutral strategy. Data Sources Traditional Markets 🌟 Quandl - Get millions of financial and economic dataset from hundreds of publishers via a single free API. yahoo-finance - Python module to get stock data from Yahoo! Finance. Tushare - Crawling historical data of Chinese stocks. Financial Data - Stock Market and Financial Data API. Crypto Currencies CryptoInscriber - A live crypto currency historical trade data blotter. Download live historical trade data from any crypto exchange. Gekko-Datasets - Gekko trading bot dataset dumps. Download and use history files in SQLite format. Research Tools Synthical - AI-powered collaborative environment for Research. 🌟🌟 TensorTrade - Trade efficiently with reinforcement learning. ML-Quant - Quant resources from ArXiv (sanity), SSRN, RePec, Journals, Podcasts, Videos, and Blogs. JAQS - An open source quant strategies research platform. pyfolio - Portfolio and risk analytics in Python. alphalens - Performance analysis of predictive (alpha) stock factors. empyrical - Common financial risk and performance metrics. Used by Zipline and pyfolio. zvt - Zero vector trader. Trading System For Back Test & Live trading Traditional Market System 🌟🌟🌟 OpenBB - AI-powered opensource research and analytics workspace. 🌟🌟 zipline - A python algorithmic trading library. 🌟 TradingView - Get real-time information and market insights. rqalpha - A extendable, replaceable Python algorithmic backtest & trading framework. backtrader - Python backtesting library for trading strategies. kungfu - Kungfu Master trading system. lean - Algorithmic trading engine built for easy strategy research, backtesting and live trading. Combine & Rebuild pylivetrader - Python live trade execution library with zipline interface. CoinMarketCapBacktesting - As backtest frameworks for coin trading strategy. Crypto Currencies zenbot - Command-line crypto currency trading bot using Node.js and MongoDB. bot18 - High-frequency crypto currency trading bot developed by Zenbot. magic8bot - Crypto currency trading bot using Node.js and MongoDB. catalyst - An algorithmic trading library for Crypto-Assets in python. QuantResearchDev - Quant Research dev & Traders open source project. MACD - Zenbot MACD Auto-Trader. abu - A quant trading system base on python. Plugins CoinMarketCapBacktesting - Tests bt and Quantopian Zipline as backtesting frameworks for coin trading strategy. Gekko-BacktestTool - Batch backtest, import and strategy params optimalization for Gekko Trading Bot. TA Lib pandastalib - A Python Pandas implementation of technical analysis indicators. finta - Common financial technical indicators implemented in Python-Pandas (70+ indicators). tulipnode - Official Node.js wrapper for Tulip Indicators. Provides over 100 technical analysis overlay and indicator functions. techan.js - A visual, technical analysis and charting (Candlestick, OHLC, indicators) library built on D3. Exchange API Do it in real world! IbPy - Python API for the Interactive Brokers on-line trading system. HuobiFeeder - Connect HUOBIPRO exchange, get market/historical data for ABAT trading platform backtest analysis and live trading. ctpwrapper - Shanghai future exchange CTP api. PENDAX - Javascript SDK for Trading/Data API and Websockets for cryptocurrency exchanges like FTX, FTXUS, OKX, Bybit, & More Framework tf-quant-finance - High-performance TensorFlow library for quantitative finance. Visualizing playground - Play with neural networks. netron - Visualizer for deep learning and machine learning models. KLineChart - Highly customizable professional lightweight financial charts GYM Environment 🌟 TradingGym - Trading and Backtesting environment for training reinforcement learning agent. TradzQAI - Trading environment for RL agents, backtesting and training. btgym - Scalable, event-driven, deep-learning-friendly backtesting library. Articles The-Economist - The Economist. nyu-mlif-notes - NYU machine learning in finance notes. Using LSTMs to Turn Feelings Into Trades Others zipline-tensorboard - TensorBoard as a Zipline dashboard. gekko-quasar-ui - An UI port for gekko trading bot using Quasar framework. Floom AI gateway and marketplace for developers, enables streamlined integration and least volatile approach of AI features into products Other Resource 🌟🌟🌟 Stock-Prediction-Models - Stock-Prediction-Models, Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations. 🌟🌟 Financial Machine Learning - A curated list of practical financial machine learning (FinML) tools and applications. This collection is primarily in Python. 🌟 Awesome-Quant-Machine-Learning-Trading - Quant / Algorithm trading resources with an emphasis on Machine Learning. awesome-quant - A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance). FinancePy - A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives. Explore Finance Service Libraries & Projects - Explore a curated list of Fintech popular & new libraries, top authors, trending project kits, discussions, tutorials & learning resources on kandi.

AI-PhD-S24
github
LLM Vibe Score0.472
Human Vibe Score0.0922477795435268
rphilipzhangMar 25, 2025

AI-PhD-S24

Artificial Intelligence for Business Research (Spring 2024) Scribed Lecture Notes Class Recordings (You need to apply for access.) Teaching Team Instructor*: Renyu (Philip) Zhang, Associate Professor, Department of Decisions, Operations and Technology, CUHK Business School, philipzhang@cuhk.edu.hk, @911 Cheng Yu Tung Building. Teaching Assistant*: Leo Cao, Full-time TA, Department of Decisions, Operations and Technology, CUHK Business School, yinglyucao@cuhk.edu.hk. Please be noted that Leo will help with any issues related to the logistics, but not the content, of this course. Tutorial Instructor*: Qiansiqi Hu, MSBA Student, Department of Decisions, Operations and Technology, CUHK Business School, 1155208353@link.cuhk.edu.hk. BS in ECE, Shanghai Jiaotong University Michigan Institute. Basic Information Website: https://github.com/rphilipzhang/AI-PhD-S24 Time: Tuesday, 12:30pm-3:15pm, from Jan 9, 2024 to Apr 16, 2024, except for Feb 13 (Chinese New Year) and Mar 5 (Final Project Discussion) Location: Cheng Yu Tung Building (CYT) LT5 About Welcome to the mono-repo of the PhD course AI for Business Research (DSME 6635) at CUHK Business School in Spring 2024. You may download the Syllabus of this course first. The purpose of this course is to learn the following: Have a basic understanding of the fundamental concepts/methods in machine learning (ML) and artificial intelligence (AI) that are used (or potentially useful) in business research. Understand how business researchers have utilized ML/AI and what managerial questions have been addressed by ML/AI in the recent decade. Nurture a taste of what the state-of-the-art AI/ML technologies can do in the ML/AI community and, potentially, in your own research field. We will meet each Tuesday at 12:30pm in Cheng Yu Tung Building (CYT) LT5 (please pay attention to this room change). Please ask for my approval if you need to join us via the following Zoom links: Zoom link, Meeting ID 996 4239 3764, Passcode 386119. Most of the code in this course will be distributed through the Google CoLab cloud computing environment to avoid the incompatibility and version control issues on your local individual computer. On the other hand, you can always download the Jupyter Notebook from CoLab and run it your own computer. The CoLab files of this course can be found at this folder. The Google Sheet to sign up for groups and group tasks can be found here. The overleaf template for scribing the lecture notes of this course can be found here. If you have any feedback on this course, please directly contact Philip at philipzhang@cuhk.edu.hk and we will try our best to address it. Brief Schedule Subject to modifications. All classes start at 12:30pm and end at 3:15pm. |Session|Date |Topic|Key Words| |:-------:|:-------------:|:----:|:-:| |1|1.09|AI/ML in a Nutshell|Course Intro, ML Models, Model Evaluations| |2|1.16|Intro to DL|DL Intro, Neural Nets, Computational Issues in DL| |3|1.23|Prediction and Traditional NLP|Prediction in Biz Research, Pre-processing| |4|1.30|NLP (II): Traditional NLP|$N$-gram, NLP Performance Evaluations, Naïve Bayes| |5|2.06|NLP (III): Word2Vec|CBOW, Skip Gram| |6|2.20|NLP (IV): RNN|Glove, Language Model Evaluation, RNN| |7|2.27|NLP (V): Seq2Seq|LSTM, Seq2Seq, Attention Mechanism| |7.5|3.05|NLP (V.V): Transformer|The Bitter Lesson, Attention is All You Need| |8|3.12|NLP (VI): Pre-training|Computational Tricks in DL, BERT, GPT| |9|3.19|NLP (VII): LLM|Emergent Abilities, Chain-of-Thought, In-context Learning, GenAI in Business Research| |10|3.26|CV (I): Image Classification|CNN, AlexNet, ResNet, ViT| |11|4.02|CV (II): Image Segmentation and Video Analysis|R-CNN, YOLO, 3D-CNN| |12|4.09|Unsupervised Learning (I): Clustering & Topic Modeling|GMM, EM Algorithm, LDA| |13|4.16|Unsupervised Learning (II): Diffusion Models|VAE, DDPM, LDM, DiT| Important Dates All problem sets are due at 12:30pm right before class. |Date| Time|Event|Note| |:--:|:-:|:---:|:--:| |1.10| 11:59pm|Group Sign-Ups|Each group has at most two students.| |1.12| 7:00pm-9:00pm|Python Tutorial|Given by Qiansiqi Hu, Python Tutorial CoLab| |1.19| 7:00pm-9:00pm|PyTorch Tutorial|Given by Qiansiqi Hu, PyTorch Tutorial CoLab| |3.05|9:00am-6:00pm|Final Project Discussion|Please schedule a meeting with Philip.| |3.12| 12:30pm|Final Project Proposal|1-page maximum| |4.30| 11:59pm|Scribed Lecture Notes|Overleaf link| |5.12|11:59pm|Project Paper, Slides, and Code|Paper page limit: 10| Useful Resources Find more on the Syllabus. Books: ESL, Deep Learning, Dive into Deep Learning, ML Fairness, Applied Causal Inference Powered by ML and AI Courses: ML Intro by Andrew Ng, DL Intro by Andrew Ng, NLP (CS224N) by Chris Manning, CV (CS231N) by Fei-Fei Li, Deep Unsupervised Learning by Pieter Abbeel, DLR by Sergey Levine, DL Theory by Matus Telgarsky, LLM by Danqi Chen, Generative AI by Andrew Ng, Machine Learning and Big Data by Melissa Dell and Matthew Harding, Digital Economics and the Economics of AI by Martin Beraja, Chiara Farronato, Avi Goldfarb, and Catherine Tucker Detailed Schedule The following schedule is tentative and subject to changes. Session 1. Artificial Intelligence and Machine Learning in a Nutshell (Jan/09/2024) Keywords: Course Introduction, Machine Learning Basics, Bias-Variance Trade-off, Cross Validation, $k$-Nearest Neighbors, Decision Tree, Ensemble Methods Slides: Course Introduction, Machine Learning Basics CoLab Notebook Demos: k-Nearest Neighbors, Decision Tree Homework: Problem Set 1: Bias-Variance Trade-Off Online Python Tutorial: Python Tutorial CoLab, 7:00pm-9:00pm, Jan/12/2024 (Friday), given by Qiansiqi Hu, 1155208353@link.cuhk.edu.hk. Zoom Link, Meeting ID: 923 4642 4433, Pass code: 178146 References: The Elements of Statistical Learning (2nd Edition), 2009, by Trevor Hastie, Robert Tibshirani, Jerome Friedman, https://hastie.su.domains/ElemStatLearn/. Probabilistic Machine Learning: An Introduction, 2022, by Kevin Murphy, https://probml.github.io/pml-book/book1.html. Mullainathan, Sendhil, and Jann Spiess. 2017. Machine learning: an applied econometric approach. Journal of Economic Perspectives 31(2): 87-106. Athey, Susan, and Guido W. Imbens. 2019. Machine learning methods that economists should know about. Annual Review of Economics 11: 685-725. Hofman, Jake M., et al. 2021. Integrating explanation and prediction in computational social science. Nature 595.7866: 181-188. Bastani, Hamsa, Dennis Zhang, and Heng Zhang. 2022. Applied machine learning in operations management. Innovative Technology at the Interface of Finance and Operations. Springer: 189-222. Kelly, Brian, and Dacheng Xiu. 2023. Financial machine learning, SSRN, https://ssrn.com/abstract=4501707. The Bitter Lesson, by Rich Sutton, which develops so far the most critical insight of AI: "The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin." Session 2. Introduction to Deep Learning (Jan/16/2024) Keywords: Random Forests, eXtreme Gradient Boosting Trees, Deep Learning Basics, Neural Nets Models, Computational Issues of Deep Learning Slides: Machine Learning Basics, Deep Learning Basics CoLab Notebook Demos: Random Forest, Extreme Gradient Boosting Tree, Gradient Descent, Chain Rule Presentation: By Xinyu Li and Qingyu Xu. Gu, Shihao, Brian Kelly, and Dacheng Xiu. 2020. Empirical asset pricing via machine learning. Review of Financial Studies 33: 2223-2273. Link to the paper. Homework: Problem Set 2: Implementing Neural Nets Online PyTorch Tutorial: PyTorch Tutorial CoLab, 7:00pm-9:00pm, Jan/19/2024 (Friday), given by Qiansiqi Hu, 1155208353@link.cuhk.edu.hk. Zoom Link, Meeting ID: 923 4642 4433, Pass code: 178146 References: Deep Learning, 2016, by Ian Goodfellow, Yoshua Bengio and Aaron Courville, https://www.deeplearningbook.org/. Dive into Deep Learning (2nd Edition), 2023, by Aston Zhang, Zack Lipton, Mu Li, and Alex J. Smola, https://d2l.ai/. Probabilistic Machine Learning: Advanced Topics, 2023, by Kevin Murphy, https://probml.github.io/pml-book/book2.html. Deep Learning with PyTorch, 2020, by Eli Stevens, Luca Antiga, and Thomas Viehmann. Gu, Shihao, Brian Kelly, and Dacheng Xiu. 2020. Empirical asset pricing with machine learning. Review of Financial Studies 33: 2223-2273. Session 3. DL Basics, Predictions in Business Research, and Traditonal NLP (Jan/23/2024) Keywords: Optimization and Computational Issues of Deep Learning, Prediction Problems in Business Research, Pre-processing and Word Representations in Traditional Natural Language Processing Slides: Deep Learning Basics, Prediction Problems in Business Research, NLP(I): Pre-processing and Word Representations.pdf) CoLab Notebook Demos: He Initialization, Dropout, Micrograd, NLP Pre-processing Presentation: By Letian Kong and Liheng Tan. Mullainathan, Sendhil, and Jann Spiess. 2017. Machine learning: an applied econometric approach. Journal of Economic Perspectives 31(2): 87-106. Link to the paper. Homework: Problem Set 2: Implementing Neural Nets, due at 12:30pm, Jan/30/2024 (Tuesday). References: Kleinberg, Jon, Jens Ludwig, Sendhil Mullainathan, and Ziad Obermeyer. 2015. Prediction policy problems. American Economic Review 105(5): 491-495. Mullainathan, Sendhil, and Jann Spiess. 2017. Machine learning: an applied econometric approach. Journal of Economic Perspectives 31(2): 87-106. Kleinberg, Jon, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. 2018. Human decisions and machine predictions. Quarterly Journal of Economics 133(1): 237-293. Bajari, Patrick, Denis Nekipelov, Stephen P. Ryan, and Miaoyu Yang. 2015. Machine learning methods for demand estimation. American Economic Review, 105(5): 481-485. Farias, Vivek F., and Andrew A. Li. 2019. Learning preferences with side information. Management Science 65(7): 3131-3149. Cui, Ruomeng, Santiago Gallino, Antonio Moreno, and Dennis J. Zhang. 2018. The operational value of social media information. Production and Operations Management, 27(10): 1749-1769. Gentzkow, Matthew, Bryan Kelly, and Matt Taddy. 2019. Text as data. Journal of Economic Literature, 57(3): 535-574. Chapter 2, Introduction to Information Retrieval, 2008, Cambridge University Press, by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schutze, https://nlp.stanford.edu/IR-book/information-retrieval-book.html. Chapter 2, Speech and Language Processing (3rd ed. draft), 2023, by Dan Jurafsky and James H. Martin, https://web.stanford.edu/~jurafsky/slp3/. Parameter Initialization and Batch Normalization (in Chinese) GPU Comparisons-vs-NVIDIA-H100-(PCIe)-vs-NVIDIA-RTX-6000-Ada/624vs632vs640) GitHub Repo for Micrograd, by Andrej Karpathy. Hand Written Notes Session 4. Traditonal NLP (Jan/30/2024) Keywords: Pre-processing and Word Representations in NLP, N-Gram, Naïve Bayes, Language Model Evaluation, Traditional NLP Applied to Business/Econ Research Slides: NLP(I): Pre-processing and Word Representations.pdf), NLP(II): N-Gram, Naïve Bayes, and Language Model Evaluation.pdf) CoLab Notebook Demos: NLP Pre-processing, N-Gram, Naïve Bayes Presentation: By Zhi Li and Boya Peng. Hansen, Stephen, Michael McMahon, and Andrea Prat. 2018. Transparency and deliberation within the FOMC: A computational linguistics approach. Quarterly Journal of Economics, 133(2): 801-870. Link to the paper. Homework: Problem Set 3: Implementing Traditional NLP Techniques, due at 12:30pm, Feb/6/2024 (Tuesday). References: Gentzkow, Matthew, Bryan Kelly, and Matt Taddy. 2019. Text as data. Journal of Economic Literature, 57(3): 535-574. Hansen, Stephen, Michael McMahon, and Andrea Prat. 2018. Transparency and deliberation within the FOMC: A computational linguistics approach. Quarterly Journal of Economics, 133(2): 801-870. Chapters 2, 12, & 13, Introduction to Information Retrieval, 2008, Cambridge University Press, by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schutze, https://nlp.stanford.edu/IR-book/information-retrieval-book.html. Chapter 2, 3 & 4, Speech and Language Processing (3rd ed. draft), 2023, by Dan Jurafsky and James H. Martin, https://web.stanford.edu/~jurafsky/slp3/. Natural Language Tool Kit (NLTK) Documentation Hand Written Notes Session 5. Deep-Learning-Based NLP: Word2Vec (Feb/06/2024) Keywords: Traditional NLP Applied to Business/Econ Research, Word2Vec: Continuous Bag of Words and Skip-Gram Slides: NLP(II): N-Gram, Naïve Bayes, and Language Model Evaluation.pdf), NLP(III): Word2Vec.pdf) CoLab Notebook Demos: Word2Vec: CBOW, Word2Vec: Skip-Gram Presentation: By Xinyu Xu and Shu Zhang. Timoshenko, Artem, and John R. Hauser. 2019. Identifying customer needs from user-generated content. Marketing Science, 38(1): 1-20. Link to the paper. Homework: No homework this week. Probably you should think about your final project when enjoying your Lunar New Year Holiday. References: Gentzkow, Matthew, Bryan Kelly, and Matt Taddy. 2019. Text as data. Journal of Economic Literature, 57(3): 535-574. Tetlock, Paul. 2007. Giving content to investor sentiment: The role of media in the stock market. Journal of Finance, 62(3): 1139-1168. Baker, Scott, Nicholas Bloom, and Steven Davis, 2016. Measuring economic policy uncertainty. Quarterly Journal of Economics, 131(4): 1593-1636. Gentzkow, Matthew, and Jesse Shapiro. 2010. What drives media slant? Evidence from US daily newspapers. Econometrica, 78(1): 35-71. Timoshenko, Artem, and John R. Hauser. 2019. Identifying customer needs from user-generated content. Marketing Science, 38(1): 1-20. Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeff Dean. 2013. Efficient estimation of word representations in vector space. ArXiv Preprint, arXiv:1301.3781. Mikolov, Tomas, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems (NeurIPS) 26. Parts I - II, Lecture Notes and Slides for CS224n: Natural Language Processing with Deep Learning, by Christopher D. Manning, Diyi Yang, and Tatsunori Hashimoto, https://web.stanford.edu/class/cs224n/. Word Embeddings Trained on Google News Corpus Hand Written Notes Session 6. Deep-Learning-Based NLP: RNN and Seq2Seq (Feb/20/2024) Keywords: Word2Vec: GloVe, Word Embedding and Language Model Evaluations, Word2Vec and RNN Applied to Business/Econ Research, RNN Slides: Guest Lecture Announcement, NLP(III): Word2Vec.pdf), NLP(IV): RNN & Seq2Seq.pdf) CoLab Notebook Demos: Word2Vec: CBOW, Word2Vec: Skip-Gram Presentation: By Qiyu Dai and Yifan Ren. Huang, Allen H., Hui Wang, and Yi Yang. 2023. FinBERT: A large language model for extracting information from financial text. Contemporary Accounting Research, 40(2): 806-841. Link to the paper. Link to GitHub Repo. Homework: Problem Set 4 - Word2Vec & LSTM for Sentiment Analysis References: Ash, Elliot, and Stephen Hansen. 2023. Text algorithms in economics. Annual Review of Economics, 15: 659-688. Associated GitHub with Code Demonstrations. Li, Kai, Feng Mai, Rui Shen, and Xinyan Yan. 2021. Measuring corporate culture using machine learning. Review of Financial Studies, 34(7): 3265-3315. Chen, Fanglin, Xiao Liu, Davide Proserpio, and Isamar Troncoso. 2022. Product2Vec: Leveraging representation learning to model consumer product choice in large assortments. Available at SSRN 3519358. Pennington, Jeffrey, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543). Parts 2 and 5, Lecture Notes and Slides for CS224n: Natural Language Processing with Deep Learning, by Christopher D. Manning, Diyi Yang, and Tatsunori Hashimoto, https://web.stanford.edu/class/cs224n/. Chapters 9 and 10, Dive into Deep Learning (2nd Edition), 2023, by Aston Zhang, Zack Lipton, Mu Li, and Alex J. Smola, https://d2l.ai/. RNN and LSTM Visualizations Hand Written Notes Session 7. Deep-Learning-Based NLP: Attention and Transformer (Feb/27/2024) Keywords: RNN and its Applications to Business/Econ Research, LSTM, Seq2Seq, Attention Mechanism Slides: Final Project, NLP(IV): RNN & Seq2Seq.pdf), NLP(V): Attention & Transformer.pdf) CoLab Notebook Demos: RNN & LSTM, Attention Mechanism Presentation: By Qinghe Gui and Chaoyuan Jiang. Zhang, Mengxia and Lan Luo. 2023. Can consumer-posted photos serve as a leading indicator of restaurant survival? Evidence from Yelp. Management Science 69(1): 25-50. Link to the paper. Homework: Problem Set 4 - Word2Vec & LSTM for Sentiment Analysis References: Qi, Meng, Yuanyuan Shi, Yongzhi Qi, Chenxin Ma, Rong Yuan, Di Wu, Zuo-Jun (Max) Shen. 2023. A Practical End-to-End Inventory Management Model with Deep Learning. Management Science, 69(2): 759-773. Sarzynska-Wawer, Justyna, Aleksander Wawer, Aleksandra Pawlak, Julia Szymanowska, Izabela Stefaniak, Michal Jarkiewicz, and Lukasz Okruszek. 2021. Detecting formal thought disorder by deep contextualized word representations. Psychiatry Research, 304, 114135. Hansen, Stephen, Peter J. Lambert, Nicholas Bloom, Steven J. Davis, Raffaella Sadun, and Bledi Taska. 2023. Remote work across jobs, companies, and space (No. w31007). National Bureau of Economic Research. Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. Advances in neural information processing systems, 27. Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. ICLR Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... and Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. Parts 5, 6, and 8, Lecture Notes and Slides for CS224n: Natural Language Processing with Deep Learning, by Christopher D. Manning, Diyi Yang, and Tatsunori Hashimoto, https://web.stanford.edu/class/cs224n/. Chapters 9, 10, and 11, Dive into Deep Learning (2nd Edition), 2023, by Aston Zhang, Zack Lipton, Mu Li, and Alex J. Smola, https://d2l.ai/. RNN and LSTM Visualizations PyTorch's Tutorial of Seq2Seq for Machine Translation Illustrated Transformer Transformer from Scratch, with the Code on GitHub Hand Written Notes Session 7.5. Deep-Learning-Based NLP: Attention is All You Need (Mar/05/2024) Keywords: Bitter Lesson: Power of Computation in AI, Attention Mechanism, Transformer Slides: The Bitter Lesson, NLP(V): Attention & Transformer.pdf) CoLab Notebook Demos: Attention Mechanism, Transformer Homework: One-page Proposal for Your Final Project References: The Bitter Lesson, by Rich Sutton Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. ICLR Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... and Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. Part 8, Lecture Notes and Slides for CS224n: Natural Language Processing with Deep Learning, by Christopher D. Manning, Diyi Yang, and Tatsunori Hashimoto, https://web.stanford.edu/class/cs224n/. Chapter 11, Dive into Deep Learning (2nd Edition), 2023, by Aston Zhang, Zack Lipton, Mu Li, and Alex J. Smola, https://d2l.ai/. Illustrated Transformer Transformer from Scratch, with the Code on GitHub Andrej Karpathy's Lecture to Build Transformers Hand Written Notes Session 8. Deep-Learning-Based NLP: Pretraining (Mar/12/2024) Keywords: Computations in AI, BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pretrained Transformers) Slides: Guest Lecture by Dr. Liubo Li on Deep Learning Computation, Pretraining.pdf) CoLab Notebook Demos: Crafting Intelligence: The Art of Deep Learning Modeling, BERT API @ Hugging Face Presentation: By Zhankun Chen and Yiyi Zhao. Noy, Shakked and Whitney Zhang. 2023. Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381: 187-192. Link to the Paper Homework: Problem Set 5 - Sentiment Analysis with Hugging Face, due at 12:30pm, March 26, Tuesday. References: Devlin, Jacob, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. ArXiv preprint arXiv:1810.04805. GitHub Repo Radford, Alec, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving language understanding by generative pre-training, (GPT-1) PDF link, GitHub Repo Radford, Alec, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9. (GPT-2) PDF Link, GitHub Repo Brown, Tom, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901. (GPT-3) GitHub Repo Huang, Allen H., Hui Wang, and Yi Yang. 2023. FinBERT: A large language model for extracting information from financial text. Contemporary Accounting Research, 40(2): 806-841. GitHub Repo Part 9, Lecture Notes and Slides for CS 224N: Natural Language Processing with Deep Learning, by Christopher D. Manning, Diyi Yang, and Tatsunori Hashimoto. Link to CS 224N Part 2 & 4, Slides for COS 597G: Understanding Large Language Models, by Danqi Chen. Link to COS 597G A Visual Guide to BERT, How GPT-3 Works Andrej Karpathy's Lecture to Build GPT-2 (124M) from Scratch Hand Written Notes Session 9. Deep-Learning-Based NLP: Large Language Models (Mar/19/2024) Keywords: Large Language Models, Generative AI, Emergent Ababilities, Instruction Fine-Tuning (IFT), Reinforcement Learning with Human Feedback (RLHF), In-Context Learning, Chain-of-Thought (CoT) Slides: What's Next, Pretraining.pdf), Large Language Models.pdf) CoLab Notebook Demos: BERT API @ Hugging Face Presentation: By Jia Liu. Liu, Liu, Dzyabura, Daria, Mizik, Natalie. 2020. Visual listening in: Extracting brand image portrayed on social media. Marketing Science, 39(4): 669-686. Link to the Paper Homework: Problem Set 5 - Sentiment Analysis with Hugging Face, due at 12:30pm, March 26, Tuesday (soft-deadline). References: Wei, Jason, et al. 2021. Finetuned language models are zero-shot learners. ArXiv preprint arXiv:2109.01652, link to the paper. Wei, Jason, et al. 2022. Emergent abilities of large language models. ArXiv preprint arXiv:2206.07682, link to the paper. Ouyang, Long, et al. 2022. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730-27744. Wei, Jason, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824-24837. Kaplan, Jared. 2020. Scaling laws for neural language models. ArXiv preprint arXiv:2001.08361, link to the paper. Hoffmann, Jordan, et al. 2022. Training compute-optimal large language models. ArXiv preprint arXiv:2203.15556, link to the paper. Shinn, Noah, et al. 2023. Reflexion: Language agents with verbal reinforcement learning. ArXiv preprint arXiv:2303.11366, link to the paper. Reisenbichler, Martin, Thomas Reutterer, David A. Schweidel, and Daniel Dan. 2022. Frontiers: Supporting content marketing with natural language generation. Marketing Science, 41(3): 441-452. Romera-Paredes, B., Barekatain, M., Novikov, A. et al. 2023. Mathematical discoveries from program search with large language models. Nature, link to the paper. Part 10, Lecture Notes and Slides for CS224N: Natural Language Processing with Deep Learning, by Christopher D. Manning, Diyi Yang, and Tatsunori Hashimoto. Link to CS 224N COS 597G: Understanding Large Language Models, by Danqi Chen. Link to COS 597G Andrej Karpathy's 1-hour Talk on LLM CS224n, Hugging Face Tutorial Session 10. Deep-Learning-Based CV: Image Classification (Mar/26/2024) Keywords: Large Language Models Applications, Convolution Neural Nets (CNN), LeNet, AlexNet, VGG, ResNet, ViT Slides: What's Next, Large Language Models.pdf), Image Classification.pdf) CoLab Notebook Demos: CNN, LeNet, & AlexNet, VGG, ResNet, ViT Presentation: By Yingxin Lin and Zeshen Ye. Netzer, Oded, Alain Lemaire, and Michal Herzenstein. 2019. When words sweat: Identifying signals for loan default in the text of loan applications. Journal of Marketing Research, 56(6): 960-980. Link to the Paper Homework: Problem Set 6 - AlexNet and ResNet, due at 12:30pm, April 9, Tuesday. References: Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25. He, Kaiming, Xiangyu Zhang, Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778. Dosovitskiy, Alexey, et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. ArXiv preprint, arXiv:2010.11929, link to the paper, link to the GitHub repo. Jean, Neal, Marshall Burke, Michael Xie, Matthew W. Davis, David B. Lobell, and Stefand Ermon. 2016. Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794. Zhang, Mengxia and Lan Luo. 2023. Can consumer-posted photos serve as a leading indicator of restaurant survival? Evidence from Yelp. Management Science 69(1): 25-50. Course Notes (Lectures 5 & 6) for CS231n: Deep Learning for Computer Vision, by Fei-Fei Li, Ruohan Gao, & Yunzhu Li. Link to CS231n. Chapters 7 and 8, Dive into Deep Learning (2nd Edition), 2023, by Aston Zhang, Zack Lipton, Mu Li, and Alex J. Smola. Link to the book. Fine-Tune ViT for Image Classification with Hugging Face 🤗 Transformers Hugging Face 🤗 ViT CoLab Tutorial Session 11. Deep-Learning-Based CV (II): Object Detection & Video Analysis (Apr/2/2024) Keywords: Image Processing Applications, Localization, R-CNNs, YOLOs, Semantic Segmentation, 3D CNN, Video Analysis Applications Slides: What's Next, Image Classification.pdf), Object Detection and Video Analysis.pdf) CoLab Notebook Demos: Data Augmentation, Faster R-CNN & YOLO v5 Presentation: By Qinlu Hu and Yilin Shi. Yang, Jeremy, Juanjuan Zhang, and Yuhan Zhang. 2023. Engagement that sells: Influencer video advertising on TikTok. Available at SSRN Link to the Paper Homework: Problem Set 6 - AlexNet and ResNet, due at 12:30pm, April 9, Tuesday. References: Girshick, R., Donahue, J., Darrell, T. and Malik, J., 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587). Redmon, Joseph, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788). Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R. and Fei-Fei, L., 2014. Large-scale video classification with convolutional neural networks. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (pp. 1725-1732). Glaeser, Edward L., Scott D. Kominers, Michael Luca, and Nikhil Naik. 2018. Big data and big cities: The promises and limitations of improved measures of urban life. Economic Inquiry, 56(1): 114-137. Zhang, S., Xu, K. and Srinivasan, K., 2023. Frontiers: Unmasking Social Compliance Behavior During the Pandemic. Marketing Science, 42(3), pp.440-450. Course Notes (Lectures 10 & 11) for CS231n: Deep Learning for Computer Vision, by Fei-Fei Li, Ruohan Gao, & Yunzhu Li. Link to CS231n. Chapter 14, Dive into Deep Learning (2nd Edition), 2023, by Aston Zhang, Zack Lipton, Mu Li, and Alex J. Smola. Link to the book. Hand Written Notes Session 12. Unsupervised Learning: Clustering, Topic Modeling & VAE (Apr/9/2024) Keywords: K-Means, Gaussian Mixture Models, EM-Algorithm, Latent Dirichlet Allocation, Variational Auto-Encoder Slides: What's Next, Clustering, Topic Modeling & VAE.pdf) CoLab Notebook Demos: K-Means, LDA, VAE Homework: Problem Set 7 - Unsupervised Learning (EM & LDA), due at 12:30pm, April 23, Tuesday. References: Blei, David M., Ng, Andrew Y., and Jordan, Michael I. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research, 3(Jan): 993-1022. Kingma, D.P. and Welling, M., 2013. Auto-encoding Variational Bayes. arXiv preprint arXiv:1312.6114. Kingma, D.P. and Welling, M., 2019. An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4), pp.307-392. Bandiera, O., Prat, A., Hansen, S., & Sadun, R. 2020. CEO behavior and firm performance. Journal of Political Economy, 128(4), 1325-1369. Liu, Jia and Olivier Toubia. 2018. A semantic approach for estimating consumer content preferences from online search queries. Marketing Science, 37(6): 930-952. Mueller, Hannes, and Christopher Rauh. 2018. Reading between the lines: Prediction of political violence using newspaper text. American Political Science Review, 112(2): 358-375. Tian, Z., Dew, R. and Iyengar, R., 2023. Mega or Micro? Influencer Selection Using Follower Elasticity. Journal of Marketing Research. Chapters 8.5 and 14, The Elements of Statistical Learning (2nd Edition), 2009, by Trevor Hastie, Robert Tibshirani, Jerome Friedman, Link to Book. Course Notes (Lectures 1 & 4) for CS294-158-SP24: Deep Unsupervised Learning, taught by Pieter Abbeel, Wilson Yan, Kevin Frans, Philipp Wu. Link to CS294-158-SP24. Hand Written Notes Session 13. Unsupervised Learning: Diffusion Models (Apr/16/2024) Keywords: VAE, Denoised Diffusion Probabilistic Models, Latent Diffusion Models, CLIP, Imagen, Diffusion Transformers Slides: Clustering, Topic Modeling & VAE.pdf), Diffusion Models.pdf), Course Summary CoLab Notebook Demos: VAE, DDPM, DiT Homework: Problem Set 7 - Unsupervised Learning (EM & LDA), due at 12:30pm, April 23, Tuesday. References: Kingma, D.P. and Welling, M., 2013. Auto-encoding Variational Bayes. arXiv preprint arXiv:1312.6114. Kingma, D.P. and Welling, M., 2019. An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4), pp.307-392. Ho, J., Jain, A. and Abbeel, P., 2020. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33, 6840-6851. Chan, S.H., 2024. Tutorial on Diffusion Models for Imaging and Vision. arXiv preprint arXiv:2403.18103. Peebles, W. and Xie, S., 2023. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 4195-4205. Link to GitHub Repo. Tian, Z., Dew, R. and Iyengar, R., 2023. Mega or Micro? Influencer Selection Using Follower Elasticity. Journal of Marketing Research. Ludwig, J. and Mullainathan, S., 2024. Machine learning as a tool for hypothesis generation. Quarterly Journal of Economics, 139(2), 751-827. Burnap, A., Hauser, J.R. and Timoshenko, A., 2023. Product aesthetic design: A machine learning augmentation. Marketing Science, 42(6), 1029-1056. Course Notes (Lecture 6) for CS294-158-SP24: Deep Unsupervised Learning, taught by Pieter Abbeel, Wilson Yan, Kevin Frans, Philipp Wu. Link to CS294-158-SP24. CVPR 2022 Tutorial: Denoising Diffusion-based Generative Modeling: Foundations and Applications, by Karsten Kreis, Ruiqi Gao, and Arash Vahdat Link to the Tutorial Lilian Weng (OpenAI)'s Blog on Diffusion Models Lilian Weng (OpenAI)'s Blog on Diffusion Models for Video Generation Hugging Face Diffusers 🤗 Library Hand Written Notes

Awesome-Ai-Tools
github
LLM Vibe Score0.385
Human Vibe Score0.0020930582944730723
aliammari1Feb 21, 2025

Awesome-Ai-Tools

Awesome-Ai-Tools This repo contains AI tools that will help you achieve your goals. The tools are categorized into different sections based on their functionality. Contents Awesome-Ai-Tools Contents Productivity Time Management Task Management Email Management Creativity Art Music Writing Communication Writing Personality Analysis Translation Data Science Machine Learning Data Analysis Data Visualization Natural Language Processing Text Classification Named Entity Recognition Computer Vision Image Classification Object Detection Robotics Robot Simulation Robot Control Miscellaneous Language Models Generative Models Productivity If you're looking to boost your productivity, there are a number of AI tools that can help. Time Management RescueTime - RescueTime is an AI-powered time tracking tool that helps you understand how you're spending your time on your computer. It can help you identify areas where you're wasting time and make adjustments to your workflow to be more productive. Focus@Will - Focus@Will is an AI-powered music service that helps you stay focused and productive while you work. It uses neuroscience to create music that is scientifically optimized to help you concentrate. Clockify - Clockify is an AI-powered time tracking tool that helps you track your time across different projects and tasks. It can help you identify areas where you're spending too much time and make adjustments to your workflow to be more productive. Trello - Trello is an AI-powered task management tool that helps you stay organized and on top of your to-do list. It can help you prioritize tasks, set deadlines, and even collaborate with others on projects. Motion - Motion is an AI-powered calendar and task management tool that automatically schedules your tasks and meetings for optimal productivity. Reclaim.ai - Reclaim is an intelligent calendar assistant that helps you protect your time by automatically scheduling meetings and tasks. Task Management Todoist - Todoist is an AI-powered task management tool that helps you stay organized and on top of your to-do list. It can help you prioritize tasks, set deadlines, and even suggest tasks based on your previous activity. Asana - Asana is an AI-powered task management tool that helps you stay organized and on top of your to-do list. It can help you prioritize tasks, set deadlines, and even collaborate with others on projects. Notion - Notion is an AI-powered productivity tool that can help you manage tasks, take notes, and collaborate with others on projects. It can also be used to create wikis, databases, and other types of content. Taskade - Taskade is an AI-powered productivity tool that can manage tasks and notes for individuals and teams. ClickUp - ClickUp is an AI-enhanced project management tool that helps teams organize work with automated task distributions and smart notifications. Monday.com - Monday.com uses AI to streamline workflow management and automate routine tasks. Email Management Boomerang - Boomerang is an AI-powered email management tool that helps you manage your inbox more efficiently. It can help you schedule emails to be sent later, remind you to follow up on emails, and even suggest responses to emails. SaneBox - SaneBox is an AI-powered email management tool that helps you manage your inbox more efficiently. It can help you prioritize emails, unsubscribe from unwanted emails, and even snooze emails to be dealt with later. Mailstrom - Mailstrom is an AI-powered email management tool that helps you clean up your inbox. It can help you quickly identify and delete unwanted emails, and even unsubscribe from newsletters and other types of email subscriptions. Creativity If you're looking to get more creative, there are a number of AI tools that can help. Art Artbreeder - Artbreeder is an AI-powered tool that allows you to create unique digital art by combining different images and styles. Runway ML - Runway is an AI-powered tool that allows users to edit and generate videos using natural language descriptions. Prisma - Prisma is an AI-powered tool that allows you to transform your photos into works of art using neural networks. Music AIVA - AIVA is an AI-powered music composition tool that can help you create original music for your projects. Writing monica - Monica is a chrome extension powered by ChatGPT API. It is designed to be your personal AI assistant for effortless chatting and copywriting. CopyAI - CopyAI is an AI-powered writing assistant that can help you generate high-quality marketing copy, product descriptions, and more. Grammarly - Grammarly is an AI-powered writing assistant that helps you catch grammar and spelling errors in your writing. It can also suggest improvements to your writing style to help you communicate more effectively. Jasper - Jasper is an AI writing assistant that helps create marketing copy, blog posts, and social media content. Rytr - Rytr is an AI writing tool that helps generate content in different tones and styles. Communication If you're looking to improve your communication skills, there are a number of AI tools that can help. Writing Linguix - Linguix is an AI-powered writing assistant that can help you improve your writing skills. It can catch grammar and spelling errors, suggest improvements to your writing style, and even help you avoid plagiarism. Hemingway Editor - Hemingway Editor is an AI-powered writing tool that helps you simplify your writing and make it more readable. It can help you identify complex sentences, passive voice, and other issues that can make your writing difficult to understand. Personality Analysis Crystal - Crystal is an AI-powered tool that helps you understand the personality of the people you're communicating with. It can provide insights into their communication style and suggest ways to communicate more effectively with them. IBM Watson Personality Insights - IBM Watson Personality Insights is a tool that uses natural language processing and machine learning algorithms to analyze text and provide insights into the personality traits of the author. Translation DeepL - DeepL is an AI-powered translation tool that provides high-quality translations in multiple languages. It uses neural network algorithms to provide more accurate translations than traditional translation tools. Google Translate - Google Translate is a free online translation tool that uses machine learning algorithms to provide translations in over 100 languages. Data Science If you're working with data, there are a number of AI tools that can help you analyze and make sense of it. Machine Learning DataRobot - DataRobot is an AI-powered platform that helps you build and deploy machine learning models. It can help you automate the process of building models and make predictions based on your data. TensorFlow - TensorFlow is an open-source machine learning framework developed by Google. It can help you build and train machine learning models for a variety of applications. PyTorch - PyTorch is another open-source machine learning framework that is popular among researchers and developers. It is known for its ease of use and flexibility. H2O.ai - H2O.ai is an open-source machine learning platform that allows you to build and deploy machine learning models at scale. PyTorch3d - Pytorch 3d is an open-source library for deep learning with 3d data. Auto-sklearn - Auto-sklearn is an automated machine learning toolkit that helps find the best machine learning pipeline for your dataset. Ludwig - Ludwig is a declarative machine learning framework that makes it easy to build and train models without writing code. Data Analysis Pandas - Pandas is an open-source data analysis library for Python. It can help you manipulate and analyze data in a variety of formats, including CSV, Excel, and SQL databases. RapidMiner - RapidMiner is an AI-powered data science platform that allows you to build and deploy predictive models without writing any code. Apache Spark - Apache Spark is an open-source big data processing framework that can help you analyze large datasets in a distributed computing environment. Data Visualization Tableau - Tableau is a data visualization tool that uses AI to help you explore and understand your data. It can help you identify patterns and trends in your data that might not be immediately obvious. Plotly - Plotly is an open-source data visualization library for Python. It can help you create interactive charts and graphs that can be embedded in web pages and other applications. D3.js - D3.js is a JavaScript library for data visualization that allows you to create dynamic and interactive visualizations using web standards like HTML, CSS, and SVG. Natural Language Processing If you're interested in natural language processing, there are a number of AI tools that can help you get started. Text Classification TextBlob - TextBlob is an open-source library for processing textual data in Python. It can help you perform tasks like sentiment analysis, part-of-speech tagging, and text classification. NLTK - NLTK (Natural Language Toolkit) is another open-source library for natural language processing in Python. It can help you perform tasks like tokenization, stemming, and named entity recognition. Amazon Comprehend - Amazon Comprehend is a natural language processing service that uses machine learning to analyze text and provide insights into the content and sentiment of the text. Named Entity Recognition spaCy - spaCy is an open-source library for advanced natural language processing in Python. It can help you build applications that can understand and analyze human language. One of its key features is named entity recognition, which can identify and classify entities like people, organizations, and locations. Google Cloud Natural Language API - Google Cloud Natural Language API is a natural language processing service that can analyze text and provide insights into the sentiment, entities, and syntax of the text. Computer Vision If you're interested in computer vision, there are a number of AI tools that can help you get started. Image Classification Clarifai - Clarifai is an AI-powered image recognition tool that can help you classify images based on their content. It can recognize objects, scenes, and even specific concepts like emotions and colors. Google Cloud Vision API - Google Cloud Vision API is a computer vision service that can analyze images and provide insights into the content of the images, including objects, faces, and text. Object Detection YOLO - YOLO (You Only Look Once) is an open-source object detection system that can detect objects in real-time video streams. It is known for its speed and accuracy. Amazon Rekognition - Amazon Rekognition is a computer vision service that can analyze images and videos and provide insights into the content of the media, including objects, faces, and text. Robotics If you're interested in robotics, there are a number of AI tools that can help you get started. Robot Simulation Gazebo - Gazebo is an open-source robot simulation tool that allows you to simulate robots in a virtual environment. It can help you test and debug your robot control algorithms before deploying them on a physical robot. Webots - Webots is another open-source robot simulation tool that allows you to simulate robots in a virtual environment. It supports a wide range of robots and sensors, and can be used for both research and education. Robot Control ROS - ROS (Robot Operating System) is an open-source framework for building robotics software. It can help you build and control robots using a variety of programming languages. Miscellaneous If you're looking for AI tools that don't fit into any of the above categories, here are a few to check out: Language Models GPT-3 - GPT-3 is an AI-powered language model developed by OpenAI. It can generate human-like text, answer questions, and even write code. BERT - BERT is a language model developed by Google AI. It is trained on a massive dataset of text and code, and can be used for a variety of tasks, including natural language understanding, question answering, and text classification. LLama 2 - LLama 2 models are a collection of pretrained and fine-tuned large language models developed and released by Meta AI . These models are built upon the success of LLama 1 and provide significant improvements, including a larger scale and more extensive context. Claude - Claude is an AI assistant developed by Anthropic that excels at analysis, writing, and coding tasks. PaLM 2 - PaLM 2 is Google's next-generation language model with improved multilingual, reasoning, and coding capabilities. Generative Models StyleGAN - StyleGAN is an AI-powered generative model that can create high-quality images of faces, animals, and other objects. It is known for its ability to create realistic and diverse images. Generative Pre-trained Transformer 3 (GPT-3) - GPT-3 is an AI-powered language model developed by OpenAI. It can generate human-like text, answer questions, and even write code.

internet-tools-collection
github
LLM Vibe Score0.236
Human Vibe Score0.009333333333333334
bogdanmosicaJan 23, 2025

internet-tools-collection

Internet Tools Collection A collection of tools, website and AI for entrepreneurs, web designers, programmers and for everyone else. Content by category Artificial Intelligence Developers Design Entrepreneur Video Editing Stock videos Stock Photos Stock music Search Engine Optimization Blog Posts Resume Interviews No code website builder No code game builder Side Hustle Browser Extensions Other Students Artificial Intelligence Jasper - The Best AI Writing Assistant [](https://www.jasper.ai/) Create content 5x faster with artificial intelligence. Jasper is the highest quality AI copywriting tool with over 3,000 5-star reviews. Best for writing blog posts, social media content, and marketing copy. AutoDraw [](https://www.autodraw.com/) Fast drawing for everyone. AutoDraw pairs machine learning with drawings from talented artists to help you draw stuff fast. Rytr - Best AI Writer, Content Generator & Writing Assistant [](https://rytr.me/) Rytr is an AI writing assistant that helps you create high-quality content, in just a few seconds, at a fraction of the cost! Neevo - Neevo [](https://www.neevo.ai/) Kinetix Tech [](https://kinetix.tech/) Kinetix is a no-code 3D creation tool powered by Artificial Intelligence. The web-based platform leverages AI motion capture to convert a video into a 3D animation and lets you customize your avatars and environments. We make 3D animation accessible to every creator so they can create engaging stories. LALAL.AI: 100% AI-Powered Vocal and Instrumental Tracks Remover [](https://www.lalal.ai/) Split vocal and instrumental tracks quickly and accurately with LALAL.AI. Upload any audio file and receive high-quality extracted tracks in a few seconds. Copy.ai: Write better marketing copy and content with AI [](https://www.copy.ai/) Get great copy that sells. Copy.ai is an AI-powered copywriter that generates high-quality copy for your business. Get started for free, no credit card required! Marketing simplified! OpenAI [](https://openai.com/) OpenAI is an AI research and deployment company. Our mission is to ensure that artificial general intelligence benefits all of humanity. DALL·E 2 [](https://openai.com/dall-e-2/) DALL·E 2 is a new AI system that can create realistic images and art from a description in natural language. Steve.ai - World’s fastest way to create Videos [](https://www.steve.ai/) Steve.AI is an online Video making software that helps anyone to create Videos and animations in seconds. Octie.ai - Your A.I. ecommerce marketing assistant [](https://octie.ai/) Write emails, product descriptions, and more, with A.I. Created by Octane AI. hypnogram.xyz [](https://hypnogram.xyz/) Generate images from text descriptions using AI FakeYou. Deep Fake Text to Speech. [](https://fakeyou.com/) FakeYou is a text to speech wonderland where all of your dreams come true. Craiyon, formerly DALL-E mini [](https://www.craiyon.com/) Craiyon, formerly DALL-E mini, is an AI model that can draw images from any text prompt! Deck Rocks - Create Pictch Decks [](https://www.deck.rocks/) Writely | Using AI to Improve Your Writing [](https://www.writelyai.com/) Making the art of writing accessible to all Writesonic AI Writer - Best AI Writing Assistant [](https://writesonic.com/) Writesonic is an AI writer that's been trained on top-performing SEO content, high-performing ads, and converting sales copy to help you supercharge your writing and marketing efforts. Smart Copy - AI Copywriting Assistant | Unbounce [](https://unbounce.com/product/smart-copy/) Generate creative AI copy on-the-spot across your favourite tools Synthesia | #1 AI Video Generation Platform [](https://www.synthesia.io/) Create AI videos by simply typing in text. Easy to use, cheap and scalable. Make engaging videos with human presenters — directly from your browser. Free demo. NVIDIA Canvas: Turn Simple Brushstrokes into Realistic Images [](https://www.nvidia.com/en-us/studio/canvas/) Create backgrounds quickly, or speed up your concept exploration so you can spend more time visualizing ideas with the help of NVIDIA Canvas. Hotpot.ai - Hotpot.ai [](https://hotpot.ai/) Hotpot.ai makes graphic design and image editing easy. AI tools allow experts and non-designers to automate tedious tasks while attractive, easy-to-edit templates allow anyone to create device mockups, social media posts, marketing images, app icons, and other work graphics. Klaviyo: Marketing Automation Platform for Email & SMS [](https://www.klaviyo.com/) Klaviyo, an ecommerce marketing automation platform for email marketing and sms syncs your tech stack with your website store to scale your business. Search listening tool for market, customer & content research - AnswerThePublic [](https://answerthepublic.com/) Use our free tool to get instant, raw search insights, direct from the minds of your customers. Upgrade to a paid plan to monitor for new ways that people talk & ask questions about your brand, product or topic. Topic Mojo [](https://topicmojo.com/) Discover unique & newest queries around any topic and find what your customers are searching for. Pulling data from 50+ sources to enhance your topic research. AI Image Enlarger | Enlarge Image Without Losing Quality! [](https://imglarger.com/) AI Image Enlarger is a FREE online image enlarger that could upscale and enhance small images automatically. Make jpg/png pictures big without losing quality. Midjourney [](https://www.midjourney.com/app/) Kaedim - AI for turning 2D images to 3D models [](https://www.kaedim3d.com/webapp) AI for turning 2D images, sketches and photos to 3D models in seconds. Overdub: Ultra realistic text to speech voice cloning - Descript [](https://www.descript.com/overdub) Create a text to speech model of your voice. Try a live demo. Getting Started [](https://magenta.tensorflow.org/get-started) Resources to learn about Magenta Photosonic AI Art Generator | Create Unique Images with AI [](https://photosonic.writesonic.com/) Transform your imagination into stunning digital art with Photosonic - the AI art generator. With its creative suggestions, this Writesonic's AI image generator can help unleash your inner artist and share your creations with the world. Image Computer [](https://image.computer/) Most downloaded Instagram Captions App (+more creator tools) [](https://captionplus.app/) Join 3 Million+ Instagram Creators who use CaptionPlus to find Instagram Captions, Hashtags, Feed Planning, Reel Ideas, IG Story Design and more. Writecream - Best AI Writer & Content Generator - Writecream [](https://www.writecream.com/) Sentence Rewriter is a free tool to reword a sentence, paragraph and even entire essays in a short amount of time. Hypotenuse AI: AI Writing Assistant and Text Generator [](https://www.hypotenuse.ai/) Turn a few keywords into original, insightful articles, product descriptions and social media copy with AI copywriting—all in just minutes. Try it free today. Text to Speach Listnr: Generate realistic Text to Speech voiceovers in seconds [](https://www.listnr.tech/) AI Voiceover Generator with over 600+ voiceovers in 80+ languages, go from Text to Voice in seconds. Get started for Free! Free Text to Speech: Online, App, Software, Commercial license with Natural Sounding Voices. [](https://www.naturalreaders.com/) Free text to speech online app with natural voices, convert text to audio and mp3, for personal and commercial use Developers OverAPI.com | Collecting all the cheat sheets [](https://overapi.com/) OverAPI.com is a site collecting all the cheatsheets,all! Search Engine For Devs [](https://you.com/) Spline - Design tool for 3D web browser experiences [](https://spline.design/) Create web-based 3D browser experiences Image to HTML CSS converter. Convert image to HTML CSS with AI: Fronty [](https://fronty.com/) Fronty - Image to HTML CSS code converter. Convert image to HTML powered by AI. Sketchfab - The best 3D viewer on the web [](https://sketchfab.com/) With a community of over one million creators, we are the world’s largest platform to publish, share, and discover 3D content on web, mobile, AR, and VR. Railway [](https://railway.app/) Railway is an infrastructure platform where you can provision infrastructure, develop with that infrastructure locally, and then deploy to the cloud. JSON Crack - Crack your data into pieces [](https://jsoncrack.com/) Simple visualization tool for your JSON data. No forced structure, paste your JSON and view it instantly. Locofy.ai - ship your products 3-4x faster — with low code [](https://www.locofy.ai/) Turn your designs into production-ready frontend code for mobile apps and web. Ship products 3-4x faster with your existing design tools, tech stacks & workflows. Oh Shit, Git!?! [](https://ohshitgit.com/) Carbon | Create and share beautiful images of your source code [](https://carbon.now.sh/) Carbon is the easiest way to create and share beautiful images of your source code. GPRM : GitHub Profile ReadMe Maker [](https://gprm.itsvg.in/) Best Profile Generator, Create your perfect GitHub Profile ReadMe in the best possible way. Lots of features and tools included, all for free ! HubSpot | Software, Tools, and Resources to Help Your Business Grow Better [](https://www.hubspot.com/) HubSpot’s integrated CRM platform contains the marketing, sales, service, operations, and website-building software you need to grow your business. QuickRef.ME - Quick Reference Cheat Sheet [](https://quickref.me/) Share quick reference and cheat sheet for developers massCode | A free and open source code snippets manager for developers [](https://masscode.io/) Code snippets manager for developers, developed using web technologies. Snyk | Developer security | Develop fast. Stay secure. [](https://snyk.io/) Snyk helps software-driven businesses develop fast and stay secure. Continuously find and fix vulnerabilities for npm, Maven, NuGet, RubyGems, PyPI and more. Developer Roadmaps [](https://roadmap.sh/) Community driven roadmaps, articles, guides, quizzes, tips and resources for developers to learn from, identify their career paths, know what they don't know, find out the knowledge gaps, learn and improve. CSS Generators Get Waves – Create SVG waves for your next design [](https://getwaves.io/) A free SVG wave generator to make unique SVG waves for your next web design. Choose a curve, adjust complexity, randomize! Box Shadows [](https://box-shadow.dev/) Tridiv | CSS 3D Editor [](http://tridiv.com/) Tridiv is a web-based editor for creating 3D shapes in CSS Glassmorphism CSS Generator - Glass UI [](https://ui.glass/generator/) Generate CSS and HTML components using the glassmorphism design specifications based on the Glass UI library. Blobmaker - Make organic SVG shapes for your next design [](https://www.blobmaker.app/) Make organic SVG shapes for your next design. Modify the complexity, contrast, and color, to generate unique SVG blobs every time. Keyframes.app [](https://keyframes.app/) cssFilters.co - Custom and Instagram like photo filters for CSS [](https://www.cssfilters.co/) Visual playground for generating CSS for custom and Instagram like photo filters. Experiment with your own uploaded photo or select one from the Unsplash collection. CSS Animations Animista - CSS Animations on Demand [](https://animista.net/) Animista is a CSS animation library and a place where you can play with a collection of ready-made CSS animations and download only those you will use. Build Internal apps Superblocks | Save 100s of developer hours on internal tools [](https://www.superblocks.com/) Superblocks is the fast, easy and secure way for developers to build custom internal tools fast. Connect your databases & APIs. Drag and drop UI components. Extend with Python or Javascript. Deploy in 1-click. Secure and Monitor using your favorite tools Budibase | Build internal tools in minutes, the easy way [](https://budibase.com/) Budibase is a modern, open source low-code platform for building modern internal applications in minutes. Retool | Build internal tools, remarkably fast. [](https://retool.com/) Retool is the fast way to build internal tools. Drag-and-drop our building blocks and connect them to your databases and APIs to build your own tools, instantly. Connects with Postgres, REST APIs, GraphQL, Firebase, Google Sheets, and more. Built by developers, for developers. Trusted by startups and Fortune 500s. Sign up for free. GitHub Repositories GitHub - vasanthk/how-web-works: What happens behind the scenes when we type www.google.com in a browser? [](https://github.com/vasanthk/how-web-works) What happens behind the scenes when we type www.google.com in a browser? - GitHub - vasanthk/how-web-works: What happens behind the scenes when we type www.google.com in a browser? GitHub - kamranahmedse/developer-roadmap: Interactive roadmaps, guides and other educational content to help developers grow in their careers. [](https://github.com/kamranahmedse/developer-roadmap) Interactive roadmaps, guides and other educational content to help developers grow in their careers. - GitHub - kamranahmedse/developer-roadmap: Interactive roadmaps, guides and other educational content to help developers grow in their careers. GitHub - apptension/developer-handbook: An opinionated guide on how to become a professional Web/Mobile App Developer. [](https://github.com/apptension/developer-handbook) An opinionated guide on how to become a professional Web/Mobile App Developer. - GitHub - apptension/developer-handbook: An opinionated guide on how to become a professional Web/Mobile App Developer. ProfileMe.dev | Create an amazing GitHub profile in minutes [](https://www.profileme.dev/) ProfileMe.dev | Create an amazing GitHub profile in minutes GitHub - Kristories/awesome-guidelines: A curated list of high quality coding style conventions and standards. [](https://github.com/Kristories/awesome-guidelines) A curated list of high quality coding style conventions and standards. - GitHub - Kristories/awesome-guidelines: A curated list of high quality coding style conventions and standards. GitHub - tiimgreen/github-cheat-sheet: A list of cool features of Git and GitHub. [](https://github.com/tiimgreen/github-cheat-sheet) A list of cool features of Git and GitHub. Contribute to tiimgreen/github-cheat-sheet development by creating an account on GitHub. GitHub - andreasbm/web-skills: A visual overview of useful skills to learn as a web developer [](https://github.com/andreasbm/web-skills) A visual overview of useful skills to learn as a web developer - GitHub - andreasbm/web-skills: A visual overview of useful skills to learn as a web developer GitHub - Ebazhanov/linkedin-skill-assessments-quizzes: Full reference of LinkedIn answers 2022 for skill assessments (aws-lambda, rest-api, javascript, react, git, html, jquery, mongodb, java, Go, python, machine-learning, power-point) linkedin excel test lösungen, linkedin machine learning test LinkedIn test questions and answers [](https://github.com/Ebazhanov/linkedin-skill-assessments-quizzes) Full reference of LinkedIn answers 2022 for skill assessments (aws-lambda, rest-api, javascript, react, git, html, jquery, mongodb, java, Go, python, machine-learning, power-point) linkedin excel test lösungen, linkedin machine learning test LinkedIn test questions and answers - GitHub - Ebazhanov/linkedin-skill-assessments-quizzes: Full reference of LinkedIn answers 2022 for skill assessments (aws-lambda, rest-api, javascript, react, git, html, jquery, mongodb, java, Go, python, machine-learning, power-point) linkedin excel test lösungen, linkedin machine learning test LinkedIn test questions and answers Blockchain/Crypto Dashboards [](https://dune.com/) Blockchain ecosystem analytics by and for the community. Explore and share data from Ethereum, xDai, Polygon, Optimism, BSC and Solana for free. Introduction - The Anchor Book v0.24.0 [](https://book.anchor-lang.com/introduction/introduction.html) Crypto & Fiat Exchange Super App | Trade, Save & Spend | hi [](https://hi.com/) Buy, Trade, Send and Earn Crypto & Fiat. Deposit Bitcoin, ETH, USDT and other cryptos and start earning. Get the hi Debit Card and Multi-Currency IBAN Account. Moralis Web3 - Enterprise-Grade Web3 APIs [](https://moralis.io/) Bridge the development gap between Web2 and Web3 with Moralis’ powerful Web3 APIs. Mirror [](https://mirror.xyz/) Built on web3 for web3, Mirror’s robust publishing platform pushes the boundaries of writing online—whether it’s the next big white paper or a weekly community update. Makerdao [](https://blog.makerdao.com/) Sholi — software for Investors & Traders / Sholi MetriX [](https://sholi.io/) Sholi — software for Investors & Traders / Sholi MetriX Stock Trading Quiver Quantitative [](https://www.quiverquant.com/) Quiver Quantitative Chart Prime - The only tool you'll need for trading assets across all markets [](https://chartprime.com/) ChartPrime offers a toolkit that will take your trading game to the next level. Visit our site for a full rundown of features and helpful tutorials. Learning Hacker Rank [](https://www.hackerrank.com/) Coderbyte | Code Screening, Challenges, & Interview Prep [](https://coderbyte.com/) Improve your coding skills with our library of 300+ challenges and prepare for coding interviews with content from leading technology companies. Competitive Programming | Participate & Learn | CodeChef [](https://www.codechef.com/) Learn competitive programming with the help of CodeChef's coding competitions. Take part in these online coding contests to level up your skills Learn to Code - for Free | Codecademy [](https://www.codecademy.com/) Learn the technical skills to get the job you want. Join over 50 million people choosing Codecademy to start a new career (or advance in their current one). Free Code Camp [](https://www.freecodecamp.org/) Learn to Code — For Free Sololearn: Learn to Code [](https://www.sololearn.com/home) Join Now to learn the basics or advance your existing skills Mimo: The coding app you need to learn to code! Python, HTML, JavaScript [](https://getmimo.com/) Join more than 17 million learners worldwide. Learn to code for free. Learn Python, JavaScript, CSS, SQL, HTML, and more with our free code learning app. Free for developers [](https://free-for.dev/#/) Your Career in Web Development Starts Here | The Odin Project [](https://www.theodinproject.com/) The Odin Project empowers aspiring web developers to learn together for free Code Learning Games CheckiO - coding games and programming challenges for beginner and advanced [](https://checkio.org/) CheckiO - coding websites and programming games. Improve your coding skills by solving coding challenges and exercises online with your friends in a fun way. Exchanges experience with other users online through fun coding activities Coding for Kids | Game-Based Programming | CodeMonkey [](https://www.codemonkey.com/) CodeMonkey is a leading coding for kids program. Through its award-winning courses, millions of students learn how to code in real programming languages. Coding Games and Programming Challenges to Code Better [](https://www.codingame.com/) CodinGame is a challenge-based training platform for programmers where you can play with the hottest programming topics. Solve games, code AI bots, learn from your peers, have fun. Learn VIM while playing a game - VIM Adventures [](https://vim-adventures.com/) VIM Adventures is an online game based on VIM's keyboard shortcuts. It's the "Zelda meets text editing" game. So come have some fun and learn some VIM! CodeCombat - Coding games to learn Python and JavaScript [](https://codecombat.com/) Learn typed code through a programming game. Learn Python, JavaScript, and HTML as you solve puzzles and learn to make your own coding games and websites. Design Useberry - Codeless prototype analytics [](https://www.useberry.com/) User testing feedback & rich insights in minutes, not months! Figma: the collaborative interface design tool. [](https://www.figma.com/) Build better products as a team. Design, prototype, and gather feedback all in one place with Figma. Dribbble - Discover the World’s Top Designers & Creative Professionals [](https://dribbble.com/) Find Top Designers & Creative Professionals on Dribbble. We are where designers gain inspiration, feedback, community, and jobs. Your best resource to discover and connect with designers worldwide. Photopea | Online Photo Editor [](https://www.photopea.com/) Photopea Online Photo Editor lets you edit photos, apply effects, filters, add text, crop or resize pictures. Do Online Photo Editing in your browser for free! Toools.design – An archive of 1000+ Design Resources [](https://www.toools.design/) A growing archive of over a thousand design resources, weekly updated for the community. Discover highly useful design tools you never thought existed. All Online Tools in One Box | 10015 Tools [](https://10015.io/) All online tools you need in one box for free. Build anything online with “all-in-one toolbox”. All tools are easy-to-use, blazing fast & free. Phase - Digital Design Reinvented| Phase [](https://phase.com/) Design and prototype websites and apps visually and intuitively, in a new powerful product reworked for the digital age. Animated Backgrounds [](https://animatedbackgrounds.me/) A Collection of 30+ animated backgrounds for websites and blogs.With Animated Backgrounds, set a simple, elegant background animations on your websites and blogs. Trianglify.io · Low Poly Pattern Generator [](https://trianglify.io/) Trianglify.io is a tool for generating low poly triangle patterns that can be used as wallpapers and website assets. Cool Backgrounds [](https://coolbackgrounds.io/) Explore a beautifully curated selection of cool backgrounds that you can add to blogs, websites, or as desktop and phone wallpapers. SVG Repo - Free SVG Vectors and Icons [](https://www.svgrepo.com/) Free Vectors and Icons in SVG format. ✅ Download free mono or multi color vectors for commercial use. Search in 300.000+ Free SVG Vectors and Icons. Microcopy - Short copy text for your website. [](https://www.microcopy.me/) Search micro UX copy text: slogans, headlines, notifications, CTA, error messages, email, account preferences, and much more. 3D icons and icon paks - Free3Dicon [](https://free3dicon.com/) All 3D icons you need in one place. This is a collection of free, beautiful, trending 3D icons, that you can use in any project. Love 3D Icon [](https://free3dicons.com/) Downloads free 3D icons GIMP - GNU Image Manipulation Program [](https://www.gimp.org/) GIMP - The GNU Image Manipulation Program: The Free and Open Source Image Editor blender.org - Home of the Blender project - Free and Open 3D Creation Software [](https://www.blender.org/) The Freedom to Create 3D Design Software | 3D Modeling on the Web | SketchUp [](https://www.sketchup.com/) SketchUp is a premier 3D design software that truly makes 3D modeling for everyone, with a simple to learn yet robust toolset that empowers you to create whatever you can imagine. Free Logo Maker - Create a Logo in Seconds - Shopify [](https://www.shopify.com/tools/logo-maker) Free logo maker tool to generate custom design logos in seconds. This logo creator is built for entrepreneurs on the go with hundreds of templates, free vectors, fonts and icons to design your own logo. The easiest way to create business logos online. All your design tools in one place | Renderforest [](https://www.renderforest.com/) Time to get your brand noticed. Create professional videos, logos, mockups, websites, and graphics — all in one place. Get started now! Prompt Hero [](https://prompthero.com/) Type Scale - A Visual Calculator [](https://type-scale.com/) Preview and choose the right type scale for your project. Experiment with font size, scale and different webfonts. DreamFusion: Text-to-3D using 2D Diffusion [](https://dreamfusion3d.github.io/) DreamFusion: Text-to-3D using 2D Diffusion, 2022. The branding style guidelines documents archive [](https://brandingstyleguides.com/) Welcome to the brand design manual documents directory. Search over our worldwide style assets handpicked collection, access to PDF documents for inspiration. Super designer | Create beautiful designs with a few clicks [](https://superdesigner.co/) Create beautiful designs with a few clicks. Simple design tools to generate unique patterns, backgrounds, 3D shapes, colors & images for social media, websites and more Readymag—a design tool to create websites without coding [](https://readymag.com/) Meet the most elegant, simple and powerful web-tool for designing websites, presentations, portfolios and all kinds of digital publications. ffflux: Online SVG Fluid Gradient Background Generator | fffuel [](https://fffuel.co/ffflux/) SVG generator to make fluid gradient backgrounds that feel organic and motion-like. Perfect to add a feeling of motion and fluidity to your web designs. Generate unique SVG design assets | Haikei [](https://haikei.app/) A web-based design tool to generate unique SVG design assets for websites, social media, blog posts, desktop and mobile wallpapers, posters, and more! Our generators let you discover, customize, randomize, and export generative SVG design assets ready to use with your favorite design tools. UI/UX - Inspirational Free Website Builder Software | 10,000+ Free Templates [](https://nicepage.com/) Nicepage is your website builder software breaking limitations common for website builders with revolutionary freehand positioning. 7000+ Free Templates. Easy Drag-n-Drop. No coding. Mobile-friendly. Clean HTML. Super designer | Create beautiful designs with a few clicks [](https://superdesigner.co/) Create beautiful designs with a few clicks. Simple design tools to generate unique patterns, backgrounds, 3D shapes, colors & images for social media, websites and more Pika – Create beautiful mockups from screenshots [](https://pika.style/) Quickly create beautiful website and device mockup from screenshot. Pika lets you capture website screenshots form URL, add device and browser frames, customize background and more LiveTerm [](https://liveterm.vercel.app/) Minimal Gallery – Web design inspiration [](https://minimal.gallery/) For the love of beautiful, clean and functional websites. Awwwards - Website Awards - Best Web Design Trends [](https://www.awwwards.com/) Awwwards are the Website Awards that recognize and promote the talent and effort of the best developers, designers and web agencies in the world. Design Systems For Figma [](https://www.designsystemsforfigma.com/) A collection of Design Systems for Figma from all over the globe. Superside: Design At Scale For Ambitious Brands [](https://www.superside.com/) We are an always-on design company. Get a team of dedicated designers, speedy turnarounds, magical creative collaboration tech and the top 1% of global talent. UXArchive - Made by Waldo [](https://uxarchive.com/) UXArchive the world's largest library of mobile user flows. Be inspired to design the best user experiences. Search by Muzli [](https://search.muz.li/) Search, discover, test and create beautiful color palettes for your projects Siteinspire | Web Design Inspiration [](https://www.siteinspire.com/) SAVEE [](https://savee.it/) The best way to save and share inspiration. A little corner of the internet to find good landing page copywriting examples [](https://greatlandingpagecopy.com/) A little corner of the internet to find great landing page copywriting examples. The Best Landing Page Examples For Design Inspiration - SaaS Landing Page [](https://saaslandingpage.com/) SaaS Landing Page showcases the best landing page examples created by top-class SaaS companies. Get ideas and inspirations for your next design project. Websites Free templates Premium Bootstrap Themes and Templates: Download @ Creative Tim [](https://www.creative-tim.com/) UI Kits, Templates and Dashboards built on top of Bootstrap, Vue.js, React, Angular, Node.js and Laravel. Join over 2,014,387+ creatives to access all our products! Free Bootstrap Themes, Templates, Snippets, and Guides - Start Bootstrap [](https://startbootstrap.com/) Start Bootstrap develops free to download, open source Bootstrap 5 themes, templates, and snippets and creates guides and tutorials to help you learn more about designing and developing with Bootstrap. Free Website Templates [](https://freewebsitetemplates.com/) Get your free website templates here and use them on your website without needing to link back to us. One Page Love - One Page Website Inspiration and Templates [](https://onepagelove.com/) One Page Love is a One Page website design gallery showcasing the best Single Page websites, templates and resources. Free CSS | 3400 Free Website Templates, CSS Templates and Open Source Templates [](https://www.free-css.com/) Free CSS has 3400 free website templates, all templates are free CSS templates, open source templates or creative commons templates. Free Bootstrap Themes and Website Templates | BootstrapMade [](https://bootstrapmade.com/) At BootstrapMade, we create beautiful website templates and bootstrap themes using Bootstrap, the most popular HTML, CSS and JavaScript framework. Free and Premium Bootstrap Themes, Templates by Themesberg [](https://themesberg.com/) Free and Premium Bootstrap themes, templates, admin dashboards and UI kits used by over 38820 web developers and software companies HTML, Vue.js and React templates for startup landing pages - Cruip [](https://cruip.com/) Cruip is a gallery of premium and free HTML, Vue.js and React templates for startups and SaaS. Free Website Templates Download | WordPress Themes - W3Layouts [](https://w3layouts.com/) Want to download free website templates? W3Layouts WordPress themes and website templates are built with responsive web design techniques. Download now! Free HTML Landing Page Templates and UI Kits | UIdeck [](https://uideck.com/) Free HTML Landing Page Templates, Bootstrap Themes, React Templates, HTML Templates, Tailwind Templates, and UI Kits. Create Online Graphics Snappa - Quick & Easy Graphic Design Software [](https://snappa.com/) Snappa makes it easy to create any type of online graphic. Create & publish images for social media, blogs, ads, and more! Canva [](https://www.canva.com/) Polotno Studio - Make graphical designs [](https://studio.polotno.com) Free online design editor. Create images for social media, youtube previews, facebook covers Free Logo Maker: Design Custom Logos | Adobe Express [](https://www.adobe.com/express/create/logo) The Adobe Express logo maker is instant, intuitive, and intelligent. Use it to generate a wide range of possibilities for your own logo. Photo Editor: Fotor – Free Online Photo Editing & Image Editor [](https://www.fotor.com/) Fotor's online photo editor helps you edit photos with free online photo editing tools. Crop photos, resize images, and add effects/filters, text, and graphics in just a few clicks. Photoshop online has never been easier with Fotor's free online photo editor. VistaCreate – Free Graphic Design Software with 70,000+ Free Templates [](https://create.vista.com/) Looking for free graphic design software? Easily create professional designs with VistaCreate, a free design tool with powerful features and 50K+ ready-made templates Draw Freely | Inkscape [](https://inkscape.org/) Inkscape is professional quality vector graphics software which runs on Linux, Mac OS X and Windows desktop computers. Visual & Video Maker Trusted By 11 Million Users - Piktochart [](https://piktochart.com/) With Piktochart, you can create professional-looking infographics, flyers, posters, charts, videos, and more. No design experience needed. Start for free. The Web's Favorite Online Graphic Design Tool | Stencil [](https://getstencil.com/) Stencil is a fantastically easy-to-use online graphic design tool and image editor built for business owners, social media marketers, and bloggers. Pablo by Buffer - Design engaging images for your social media posts in under 30 seconds [](https://pablo.buffer.com/) Buffer makes it super easy to share any page you're reading. Keep your Buffer topped up and we automagically share them for you through the day. Free Online Graphic Design Software | Create stunning designs in seconds. [](https://desygner.com/) Easy drag and drop graphic design tool for anyone to use with 1000's of ready made templates. Create & print professional business cards, flyers, social posts and more. Color Pallet Color Palettes for Designers and Artists - Color Hunt [](https://colorhunt.co/) Discover the newest hand-picked color palettes of Color Hunt. Get color inspiration for your design and art projects. Coolors - The super fast color palettes generator! [](https://coolors.co/) Generate or browse beautiful color combinations for your designs. Get color palette inspiration from nature - colorpalettes.earth [](https://colorpalettes.earth/) Color palettes inspired by beautiful nature photos Color Palette Generator - Create Beautiful Color Schemes [](https://colors.muz.li/) Search, discover, test and create beautiful color palettes for your projects A Most Useful Color Picker | 0to255 [](https://0to255.com/) Find lighter and darker colors based on any color. Discover why over two million people have used 0to255 to choose colors for their website, logo, room interior, and print design projects. Colour Contrast Checker [](https://colourcontrast.cc/) Check the contrast between different colour combinations against WCAG standards Fonts Google Fonts [](https://fonts.google.com/) Making the web more beautiful, fast, and open through great typography Fonts In Use – Type at work in the real world. [](https://fontsinuse.com/) A searchable archive of typographic design, indexed by typeface, format, and topic. Wordmark - Helps you choose fonts! [](https://wordmark.it/) Wordmark helps you choose fonts by quickly displaying your text with your fonts. OH no Type Company [](https://ohnotype.co/) OH no Type Co. Retail and custom typefaces. Life’s a thrill, fonts are chill! Illustrations Illustrations | unDraw [](https://undraw.co/illustrations) The design project with open-source illustrations for any idea you can imagine and create. Create beautiful websites, products and applications with your color, for free. Design Junction [](https://designjunction.xyz/) Design Junction is a one-stop resource library for Designers and Creatives with curated list of best resources handpicked from around the web Humaaans: Mix-&-Match illustration library [](https://www.humaaans.com/) Mix-&-match illustrations of people with a design library for InVIsion Studio and Sketch. Stubborn - Free Illustrations Generator [](https://stubborn.fun/) Free illustrations generator for Figma and Sketch. Get the opportunity to design your characters using symbols and styles. Open Peeps, Hand-Drawn Illustration Library [](https://www.openpeeps.com/) Open Peeps is a hand-drawn illustration library to create scenes of people. You can use them in product illustration, marketing, comics, product states, user flows, personas, storyboarding, quinceañera invitations, or whatever you want! ⠀ Reshot | Free icons & illustrations [](https://www.reshot.com/) Design freely with instant downloads of curated SVG icons and vector illustrations. All free with commercial licensing. No attribution required. Blush: Illustrations for everyone [](https://blush.design/) Blush makes it easy to add free illustrations to your designs. Play with fully customizable graphics made by artists across the globe. Mockups Angle 4 - 5000+ Device Mockups for Figma, Sketch and XD [](https://angle.sh/) Vector mockups for iPhone, iPad, Android and Mac devices, including the new iPhone 13, Pro, Pro Max and Mini. Perfect for presenting your apps. Huge library of components, compositions, wallpapers and plugins made for Figma, Sketch and XD. Make Mockups, Logos, Videos and Designs in Seconds [](https://placeit.net/) Get unlimited downloads on all our 100K templates! You can make a logo, video, mockup, flyer, business card and social media image in seconds right from your browser. Free and premium tools for graphic designers | Lstore Graphics [](https://www.ls.graphics/) Free and premium mockups, UI/UX tools, scene creators for busy designers Logo Design & Brand Identity Platform for Entrepreneurs | Looka [](https://looka.com/) Logojoy is now Looka! Design a Logo, make a website, and create a Brand Identity you’ll love with the power of Artificial Intelligence. 100% free to use. Create stunning product mockups easily and online - Smartmockups [](https://smartmockups.com/) Smartmockups enables you to create stunning high-resolution mockups right inside your browser within one interface across multiple devices. Previewed - Free mockup generator for your app [](https://previewed.app/) Join Previewed to create stunning 3D image shots and animations for your app. Choose from hundreds of ready made mockups, or create your own. Free Design Software - Graphic Online Maker - Glorify [](https://www.glorify.com/) Create professional and high converting social media posts, ads, infographics, presentations, and more with Glorify, a free design software & graphic maker. Other BuiltWith Technology Lookup [](https://builtwith.com/) Web technology information profiler tool. Find out what a website is built with. Compress JPEG Images Online [](https://compressjpeg.com/) Compress JPEG images and photos for displaying on web pages, sharing on social networks or sending by email. PhotoRoom - Remove Background and Create Product Pictures [](https://www.photoroom.com/) Create product and portrait pictures using only your phone. Remove background, change background and showcase products. Magic Eraser - Remove unwanted things from images in seconds [](https://www.magiceraser.io/) Magic Eraser - Use AI to remove unwanted things from images in seconds. Upload an image, mark the bit you need removed, download the fixed up image. Compressor.io - optimize and compress JPEG photos and PNG images [](https://compressor.io/) Optimize and compress JPEG, PNG, SVG, GIF and WEBP images online. Compress, resize and rename your photos for free. Remove Video Background – Unscreen [](https://www.unscreen.com/) Remove the background of any video - 100% automatically, online & free! Goodbye Greenscreen. Hello Unscreen. Noun Project: Free Icons & Stock Photos for Everything [](https://thenounproject.com/) Noun Project features the most diverse collection of icons and stock photos ever. Download SVG and PNG. Browse over 5 million art-quality icons and photos. Design Principles [](https://principles.design/) An Open Source collection of Design Principles and methods Shapefest™ - A massive library of free 3D shapes [](https://www.shapefest.com/) A massive free library of beautifully rendered 3D shapes. 160,000+ high resolution PNG images in one cohesive library. Learning UX Degreeless.design - Everything I Learned in Design School [](https://degreeless.design/) This is a list of everything I've found useful in my journey of learning design, and an ongoing list of things I think you should read. For budding UX, UI, Interaction, or whatever other title designers. UX Tools | Practical UX skills and tools [](https://uxtools.co/) Lessons and resources from two full-time product designers. Built For Mars [](https://builtformars.com/) On a mission to help the world build better user experiences by demystifying UX. Thousands of hours of research packed into UX case studies. Case Study Club – Curated UX Case Study Gallery [](https://www.casestudy.club/) Case Study Club is the biggest curated gallery of the best UI/UX design case studies. Get inspired by industry-leading designers, openly sharing their UX process. The Guide to Design [](https://start.uxdesign.cc/) A self-guided class to help you get started in UX and answer key questions about craft, design, and career Uxcel - Where design careers are built [](https://app.uxcel.com/explore) Available on any device anywhere in the world, Uxcel is the best way to improve and learn UX design online in just 5 minutes per day. UI & UX Design Tips by Jim Raptis. [](https://www.uidesign.tips/) Learn UI & UX Design with practical byte-sized tips and in-depth articles from Jim Raptis. Entrepreneur Instant Username Search [](https://instantusername.com/#/) Instant Username Search checks out if your username is available on more than 100 social media sites. Results appear instantly as you type. Flourish | Data Visualization & Storytelling [](https://flourish.studio/) Beautiful, easy data visualization and storytelling PiPiADS - #1 TikTok Ads Spy Tool [](https://www.pipiads.com/) PiPiADS is the best tiktok ads spy tool .We provide tiktok advertising,advertising on tiktok,tiktok ads examples,tiktok ads library,tiktok ads best practices,so you can understand the tiktok ads cost and master the tiktok ads 2021 and tiktok ads manager. Minea - The best adspy for product search in ecommerce and dropshipping [](https://en.minea.com/) Minea is the ultimate e-commerce product search tool. Minea tracks all ads on all networks. Facebook Ads, influencer product placements, Snapspy, all networks are tracked. Stop paying adspy 149€ for one network and discover Minea. AdSpy [](https://adspy.com/) Google Trends [](https://trends.google.com/) ScoreApp: Advanced Quiz Funnel Marketing | Make a Quiz Today [](https://www.scoreapp.com/) ScoreApp makes quiz funnel marketing easy, so you can attract relevant warm leads, insightful data and increase your sales. Try for free today Mailmodo - Send Interactive Emails That Drive Conversions [](https://www.mailmodo.com/) Use Mailmodo to create and send interactive emails your customers love. Drive conversions and get better email ROI. Sign up for a free trial now. 185 Top E-Commerce Sites Ranked by User Experience Performance – Baymard Institute [](https://baymard.com/ux-benchmark) See the ranked UX performance of the 185 largest e-commerce sites in the US and Europe. The chart summarizes 50,000+ UX performance ratings. Metricool - Analyze, manage and measure your digital content [](https://metricool.com/) Social media scheduling, web analytics, link in bio and reporting. Metricool is free per live for one brand. START HERE Visualping: #1 Website change detection, monitoring and alerts [](https://visualping.io/) More than 1.5 millions users monitor changes in websites with Visualping, the No1 website change detection, website checker, webpage change monitoring and webpage change detection tool. Gumroad – Sell what you know and see what sticks [](https://gumroad.com/) Gumroad is a powerful, but simple, e-commerce platform. We make it easy to earn your first dollar online by selling digital products, memberships and more. Product Hunt – The best new products in tech. [](https://www.producthunt.com/) Product Hunt is a curation of the best new products, every day. Discover the latest mobile apps, websites, and technology products that everyone's talking about. 12ft Ladder [](https://12ft.io/) Show me a 10ft paywall, I’ll show you a 12ft ladder. namecheckr | Social and Domain Name Availability Search For Brand Professionals [](https://www.namecheckr.com/) Social and Domain Name Availability Search For Brand Professionals Excel AI Formula Generator - Excelformulabot.com [](https://excelformulabot.com/) Transform your text instructions into Excel formulas in seconds with the help of AI. Z-Library [](https://z-lib.org/) Global Print On Demand Platform | Gelato [](https://www.gelato.com/) Create and sell custom products online. With local production in 33 countries, easy integration, and 24/7 customer support, Gelato is an all-in-one platform. Freecycle: Front Door [](https://freecycle.org/) Free eBooks | Project Gutenberg [](https://www.gutenberg.org/) Project Gutenberg is a library of free eBooks. Convertio — File Converter [](https://convertio.co/) Convertio - Easy tool to convert files online. More than 309 different document, image, spreadsheet, ebook, archive, presentation, audio and video formats supported. Namechk [](https://namechk.com/) Crazy Egg Website — Optimization | Heatmaps, Recordings, Surveys & A/B Testing [](https://www.crazyegg.com/) Use Crazy Egg to see what's hot and what's not, and to know what your web visitors are doing with tools, such as heatmaps, recordings, surveys, A/B testing & more. Ifttt [](https://ifttt.com/) Also Asked [](https://alsoasked.com/) Business Name Generator - Easily create Brandable Business Names - Namelix [](https://namelix.com/) Namelix uses artificial intelligence to create a short, brandable business name. Search for domain availability, and instantly generate a logo for your new business Merch Informer [](https://merchinformer.com/) Headline Generator [](https://www.title-generator.com/) Title Generator: create 700 headlines with ONE CLICK: Content Ideas + Catchy Headlines + Ad Campaign E-mail Subject Lines + Emotional Titles. Simple - Efficient - One Click Make [](https://www.make.com/en) Create and add calculator widgets to your website | CALCONIC_ [](https://www.calconic.com/) Web calculator builder empowers you to choose from a pre-made templates or build your own calculator widgets from a scratch without any need of programming knowledge Boost Your Views And Subscribers On YouTube - vidIQ [](https://vidiq.com/) vidIQ helps you acquire the tools and knowledge needed to grow your audience faster on YouTube and beyond. Learn More Last Pass [](https://www.lastpass.com/) Starter Story: Learn How People Are Starting Successful Businesses [](https://www.starterstory.com/) Starter Story interviews successful entrepreneurs and shares the stories behind their businesses. In each interview, we ask how they got started, how they grew, and how they run their business today. How To Say No [](https://www.starterstory.com/how-to-say-no) Saying no is hard, but it's also essential for your sanity. Here are some templates for how to say no - so you can take back your life. Think with Google - Discover Marketing Research & Digital Trends [](https://www.thinkwithgoogle.com/) Uncover the latest marketing research and digital trends with data reports, guides, infographics, and articles from Think with Google. ClickUp™ | One app to replace them all [](https://clickup.com/) Our mission is to make the world more productive. To do this, we built one app to replace them all - Tasks, Docs, Goals, and Chat. The Manual [](https://manual.withcompound.com/) Wealth-planning resources for founders and startup employees Software for Amazon FBA Sellers & Walmart Sellers | Helium 10 [](https://www.helium10.com/) If you're looking for the best software for Amazon FBA & Walmart sellers on the market, check out Helium 10's capabilities online today! Buffer: All-you-need social media toolkit for small businesses [](https://buffer.com/) Use Buffer to manage your social media so that you have more time for your business. Join 160,000+ small businesses today. CPGD — The Consumer Packaged Goods Directory [](https://www.cpgd.xyz/) The Consumer Packaged Goods Directory is a platform to discover new brands and resources. We share weekly trends in our newsletter and partner with services to provide vetted, recommended platforms for our Directory brands. Jungle Scout [](https://www.junglescout.com/) BuzzSumo | The World's #1 Content Marketing Platform [](https://buzzsumo.com/) BuzzSumo powers the strategies of 500k+ marketers, with content marketing data on 8b articles, 42m websites, 300t engagements, 500k journalists & 492m questions. Login - Capital [](https://app.capital.xyz/) Raise, hold, spend, and send funds — all in one place. Marketing Pictory – Video Marketing Made Easy - Pictory.ai [](https://pictory.ai/) Pictory's powerful AI enables you to create and edit professional quality videos using text, no technical skills required or software to download. Tolstoy | Communicate with interactive videos [](https://www.gotolstoy.com/) Start having face-to-face conversations with your customers. Create Email Marketing Your Audience Will Love - MailerLite [](https://www.mailerlite.com/) Email marketing tools to grow your audience faster and drive revenue smarter. Get free access to premium features with a 30-day trial! Sign up now! Hypefury - Schedule & Automate Social Media Marketing [](https://hypefury.com/) Save time on social media while creating more value, and growing your audience faster. Schedule & automate your social media experience! Klaviyo: Marketing Automation Platform for Email & SMS [](https://www.klaviyo.com/) Klaviyo, an ecommerce marketing automation platform for email marketing and sms syncs your tech stack with your website store to scale your business. Online Email & Lead Scraper | Klean Leads [](https://www.kleanleads.com/) Klean Leads is an online email scraper & email address finder. Use it to book more appointments, get more replies, and close more sales. PhantomBuster [](https://phantombuster.com/) Call to Action Examples - 300+ CTA Phrases [](https://ctaexamples.com/) See the best CTA example in every situation covered by the library of 300+ CTA goals. Use the examples to create your own CTAs in minutes. Creative Center: one-stop creative solution for TikTok [](https://ads.tiktok.com/business/creativecenter/pc/en?from=001010) Come to get your next great idea for TikTok. Here you can find the best performing ads, viral videos, and trending hashtags across regions and verticals. Groove.cm GrooveFunnels, GrooveMail with CRM and Digital Marketing Automation Platform - Groove.cm with GrooveFunnels, GroovePages, GrooveKart [](https://groove.cm/) Groove is a website creator, page builder, sales funnel maker, membership site platform, email autoresponder, blog tool, shopping cart system, ecommerce store solution, affiliate manager, video marketing software and more apps to help build your online business. SurveyMonkey: The World’s Most Popular Free Online Survey Tool [](https://www.surveymonkey.com/) Use SurveyMonkey to drive your business forward by using our free online survey tool to capture the voices and opinions of the people who matter most to you. Video Maker | Create Videos Online | Promo.com [](https://promo.com/) Free customizable video maker to help boost your business. Video creator for ads, social media, product and explainer videos, and for anything else you need! beehiiv — The newsletter platform built for growth [](https://www.beehiiv.com/) Access the best tools available in email, helping your newsletter scale and monetize like never before. GetResponse | Professional Email Marketing for Everyone [](https://www.getresponse.com/) No matter your level of expertise, we have a solution for you. At GetResponse, it's email marketing done right. Start your free account today! Search Email Newsletter Archives : Email Tuna [](https://emailtuna.com/) Explore newsletters without subscribing. Get email design ideas, discount coupon codes and exclusive newsletters deals. Database of email newsletters archived from all over the internet. Other Tools Simplescraper — Scrape Websites and turn them into APIs [](https://simplescraper.io/) Web scraping made easy — a powerful and free Chrome extension for scraping websites in your browser, automated in the cloud, or via API. No code required. Exploding Topics - Discover the hottest new trends. [](https://explodingtopics.com/) See new market opportunities, trending topics, emerging technology, hot startups and more on Exploding Topics. Scribe | Visual step-by-step guides [](https://scribehow.com/) By capturing your process while you work, Scribe automatically generates a visual guide, ready to share with the click of a button. Get It Free – The internet's BEST place to find free stuff! [](https://getitfree.us/) The internet's BEST place to find free stuff! Inflact by Ingramer – Marketing toolkit for Instagram [](https://inflact.com/) Sell on Instagram, build your audience, curate content with the right set of tools. Free Online Form Builder & Form Creator | Jotform [](https://www.jotform.com/) We believe the right form makes all the difference. Go from busywork to less work with powerful forms that use conditional logic, accept payments, generate reports, and automate workflows. Manage Your Team’s Projects From Anywhere | Trello [](https://trello.com/en) Trello is the ultimate project management tool. Start up a board in seconds, automate tedious tasks, and collaborate anywhere, even on mobile. TikTok hashtag generator - tiktokhashtags.com [](https://tiktokhashtags.com/) Find out which are the best hashtags for your TikTok post. Create Infographics, Reports and Maps - Infogram [](https://infogram.com/) Infogram is an easy to use infographic and chart maker. Create and share beautiful infographics, online reports, and interactive maps. Make your own here. Confetto - Create Instagram content in minutes [](https://www.confet.to/) Confetto is an all-in-one social media marketing tool built for SMBs and Social Media Managers. Confetto helps you create high-quality content for your audience that maximizes your reach and engagement on social media. Design, copy-write, plan and schedule content all in one place. Find email addresses in seconds • Hunter (Email Hunter) [](https://hunter.io/) Hunter is the leading solution to find and verify professional email addresses. Start using Hunter and connect with the people that matter for your business. PlayPhrase.me: Site for cinema archaeologists. [](https://playphrase.me/) Travel and explore the world of cinema. Largest collection of video quotes from movies on the web. #1 Free SEO Tools → SEO Review Tools [](https://www.seoreviewtools.com/) SEO Review Tools: 42+ Free Online SEO Tools build with ❤! → Rank checker → Domain Authority Checker → Keyword Tool → Backlink Checker Podcastle: Seamless Podcast Recording & Editing [](https://podcastle.ai/) Podcastle is the simplest way to create professional-quality podcasts. Record, edit, transcribe, and export your content with the power of AI, in an intuitive web-based platform. Save Ads from TikTok & Facebook Ad Library - Foreplay [](https://www.foreplay.co/) The best way to save ads from TikTok Creative Center and Facebook Ad Library, Organize them into boards and share ad inspiration with your team. Supercharge your creative strategy. SiteRight - Automate Your Business [](https://www.siteright.co/) SiteRight combines the abilities of multiple online resources into a single dashboard allowing you to have full control over how you manage your business. Diffchecker - Compare text online to find the difference between two text files [](https://www.diffchecker.com/) Diffchecker will compare text to find the difference between two text files. Just paste your files and click Find Difference! Yout.com [](https://yout.com/) Yout.com allows you to record videos from YouTube, FaceBook, SoundCloud, VK and others too many formats with clipping. Intuitively easy to use, with Yout the Internet DVR, with a bit of extra. AI Content Generation | Competitor Analysis - Predis.ai [](https://predis.ai/) Predis helps brands and influencers communicate better on social media by providing AI-powered content strategy analysis, content and hashtag recommendations. Castr | #1 Live Video Streaming Solution With Video Hosting [](https://castr.io/) Castr is a live video streaming solution platform that delivers enterprise-grade live videos globally with CDN. Live event streaming, video hosting, pre-recorded live, multi stream – all in one place using Castr. Headliner - Promote your podcast, radio show or blog with video [](https://www.headliner.app/) Easily create videos to promote your podcast, radio show or blog. Share to Instagram, Facebook, Twitter, YouTube, Linkedin and anywhere video lives Create Presentations, Infographics, Design & Video | Visme [](https://www.visme.co/) Create professional presentations, interactive infographics, beautiful design and engaging videos, all in one place. Start using Visme today. Designrr - Create eBooks, Kindle books, Leadmagnets, Flipbooks and Blog posts from your content in 2 minutes [](https://designrr.io/) Upload any web page, MS Word, Video, Podcast or YouTube and it will create a stunning ebook and convert it to pdf, epub, Kindle or Flipbook. Quick and Easy to use. Full Training, 24x7 Support and Facebook Group Included. SwipeWell | Swipe File Software [](https://www.swipewell.app/) The only Chrome extension dedicated to helping you save, organize, and reference marketing examples (so you never feel stumped). Tango | Create how-to guides, in seconds [](https://www.tango.us/) Tango takes the pain out of documenting processes by automatically generating how-to guides while you work. Empower your team to do their best work. Ad Creative Bank [](https://www.theadcreativebank.com/) Get inspired by ads from across industries, learn new best practices, and start thinking creatively about your brand’s digital creative. Signature Hound • Free Email Signature and Template Generator [](https://signaturehound.com/) Our email signature generator is free and easy to use. Our customizable templates work with Gmail, Outlook, Office 365, Apple Mail and more. Organize All Of Your Marketing In One Place - CoSchedule [](https://coschedule.com/) Get more done in less time with the only work management software for marketers. B Ok - Books [](https://b-ok.xyz/categories) OmmWriter [](https://ommwriter.com/) Ommwriter Rebrandly | Custom URL Shortener, Branded Link Management, API [](https://www.rebrandly.com/) URL Shortener with custom domains. Shorten, brand and track URLs with the industry-leading link management platform. Free to try. API, Short URL, Custom Domains. Common Tools [](https://www.commontools.org/) Book Bolt [](https://bookbolt.io/) Zazzle [](https://www.zazzle.com/) InspiroBot [](https://inspirobot.me/) Download Free Cheat Sheets or Create Your Own! - Cheatography.com: Cheat Sheets For Every Occasion [](https://cheatography.com/) Find thousands of incredible, original programming cheat sheets, all free to download. No Code Chatbot Platform | Free Chatbot Platform | WotNot [](https://wotnot.io/) WotNot is the best no code chatbot platform to build AI bot easily without coding. Deploy bots and live chat on the Website, Messenger, WhatsApp, and more. SpyFu - Competitor Keyword Research Tools for Google Ads PPC & SEO [](https://www.spyfu.com/) Systeme.io - The only tool you need to launch your online business [](https://systeme.io/) Systeme.io has all the tools you need to grow your online business. Click here to create your FREE account! Productivity Temp Mail [](https://temp-mail.org/en/) The Visual Collaboration Platform for Every Team | Miro [](https://miro.com/) Scalable, secure, cross-device and enterprise-ready team collaboration whiteboard for distributed teams. Join 35M+ users from around the world. Grammarly: Free Online Writing Assistant [](https://www.grammarly.com/) Millions trust Grammarly’s free writing app to make their online writing clear and effective. Getting started is simple — download Grammarly’s extension today. Rize · Maximize Your Productivity [](https://rize.io/) Rize is a smart time tracker that improves your focus and helps you build better work habits. Motion | Manage calendars, meetings, projects & tasks in one app [](https://www.usemotion.com/) Automatically prioritize tasks, schedule meetings, and resolve calendar conflicts. Used by over 10k CEOs and professionals to improve focus, get more done, and streamline workday. Notion – One workspace. Every team. [](https://www.notion.so/) We’re more than a doc. Or a table. Customize Notion to work the way you do. Loom: Async Video Messaging for Work | Loom [](https://www.loom.com/) Record your screen, share your thoughts, and get things done faster with async video. Zapier | Automation that moves you forward [](https://zapier.com/) Workflow automation for everyone. Zapier automates your work across 5,000+ app integrations, so you can focus on what matters. Rows — The spreadsheet with superpowers [](https://rows.com/) Combine the power of a spreadsheet with built-in integrations from your business apps. Automate workflows and build tools that make work simpler. Free Online Form Builder | Tally [](https://tally.so/) Tally is the simplest way to create free forms & surveys. Create any type of form in seconds, without knowing how to code, and for free. Highbrow | Learn Something New Every Day. Join for Free! [](https://gohighbrow.com/) Highbrow helps you learn something new every day with 5-minute lessons delivered to your inbox every morning. Join over 400,000 lifelong learners today! Slick Write | Check your grammar. Proofread online. [](https://www.slickwrite.com/#!home) Slick Write is a powerful, FREE application that makes it easy to check your writing for grammar errors, potential stylistic mistakes, and other features of interest. Whether you're a blogger, novelist, SEO professional, or student writing an essay for school, Slick Write can help take your writing to the next level. Reverso [](https://www.reverso.net) Hemingway Editor [](https://hemingwayapp.com/) Web Apps by 123apps - Edit, Convert, Create [](https://123apps.com/) Splitbee – Your all-in-one analytics and conversion platform [](https://splitbee.io/) Track and optimize your online business with Splitbee. Analytics, Funnels, Automations, A/B Testing and more. PDF Tools Free PDF, Video, Image & Other Online Tools - TinyWow [](https://tinywow.com/) Smallpdf.com - A Free Solution to all your PDF Problems [](https://smallpdf.com/) Smallpdf - the platform that makes it super easy to convert and edit all your PDF files. Solving all your PDF problems in one place - and yes, free. Sejda helps with your PDF tasks [](https://www.sejda.com/) Sejda helps with your PDF tasks. Quick and simple online service, no installation required! Split, merge or convert PDF to images, alternate mix or split scans and many other. iLovePDF | Online PDF tools for PDF lovers [](https://www.ilovepdf.com/) iLovePDF is an online service to work with PDF files completely free and easy to use. Merge PDF, split PDF, compress PDF, office to PDF, PDF to JPG and more! Text rewrite QuillBot [](https://quillbot.com/) Pre Post SEO : Online SEO Tools [](https://www.prepostseo.com/) Free Online SEO Tools: plagiarism checker, grammar checker, image compressor, website seo checker, article rewriter, back link checker Wordtune | Your personal writing assistant & editor [](https://www.wordtune.com/) Wordtune is the ultimate AI writing tool that rewrites, rephrases, and rewords your writing! Trusted by over 1,000,000 users, Wordtune strengthens articles, academic papers, essays, emails and any other online content. Aliexpress alternatives CJdropshipping - Dropshipping from Worldwide to Worldwide! [](https://cjdropshipping.com/) China's reliable eCommerce dropshipping fulfillment supplier, helps small businesses ship worldwide, dropship and fulfillment services that are friendly to start-ups and small businesses, Shopify dropshipping. SaleHoo [](https://www.salehoo.com/) Alibaba.com: Manufacturers, Suppliers, Exporters & Importers from the world's largest online B2B marketplace [](https://www.alibaba.com/) Find quality Manufacturers, Suppliers, Exporters, Importers, Buyers, Wholesalers, Products and Trade Leads from our award-winning International Trade Site. Import & Export on alibaba.com Best Dropshipping Suppliers for US + EU Products | Spocket [](https://www.spocket.co/) Spocket allows you to easily start dropshipping top products from US and EU suppliers. Get started for free and see why Spocket consistently gets 5 stars. Best dropshipping supplier to the US [](https://www.usadrop.com/) THE ONLY AMERICAN-MADE FULFILLMENT CENTER IN CHINA. Our knowledge of the Worldwide dropshipping market and the Chinese Supply-Chain can't be beat! 阿里1688 [](https://www.1688.com/) 阿里巴巴(1688.com)是全球企业间(B2B)电子商务的著名品牌,为数千万网商提供海量商机信息和便捷安全的在线交易市场,也是商人们以商会友、真实互动的社区平台。目前1688.com已覆盖原材料、工业品、服装服饰、家居百货、小商品等12个行业大类,提供从原料--生产--加工--现货等一系列的供应产品和服务 Dropshipping Tools Oberlo | Where Self Made is Made [](https://www.oberlo.com/) Start selling online now with Shopify. All the videos, podcasts, ebooks, and dropshipping tools you'll need to build your online empire. Klaviyo: Marketing Automation Platform for Email & SMS [](https://www.klaviyo.com/) Klaviyo, an ecommerce marketing automation platform for email marketing and sms syncs your tech stack with your website store to scale your business. SMSBump | SMS Marketing E-Commerce App for Shopify [](https://smsbump.com/) SMSBump is an SMS marketing & automation app for Shopify. Segment customers, recover orders, send campaign text messages with a 35%+ click through rate. 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Generate number sequences, draw fractals, do quick matrix and numerical calculations and more! edX | Free Online Courses by Harvard, MIT, & more | edX [](https://www.edx.org/) Access 2000 free online courses from 140 leading institutions worldwide. Gain new skills and earn a certificate of completion. Join today. Sci-Hub [](https://sci-hub.hkvisa.net/) Sci-Hub,mg.scihub.ltd,sci-hub.tw,The project is supported by user donations. Imagine the world with free access to knowledge for everyone ‐ a world without any paywalls. DigitalDefynd - Find the Best + Free Courses Online [](https://digitaldefynd.com/) 4 Million+ Learners | 96,000+ Courses | 45,000+ Free Courses | 1200+ Free Certificates Learn Anything [](https://learn-anything.xyz/) Search Interactive Mind Maps to learn anything HubSpot Academy - Homepage [](https://academy.hubspot.com/) HubSpot Academy is the worldwide leader in inbound marketing, sales, and customer service/support training.

teach-AI-in-business
github
LLM Vibe Score0.443
Human Vibe Score0.018525334165293606
aenyneJan 9, 2025

teach-AI-in-business

Teaching AI in Business ![HitCount] I am collecting material for teaching AI-related issues to non-tech people. The links should provide for a general understanding of AI without going too deep into technical issues. Please contribute! Make this Issue your First Issue I am collecting material for teaching AI-related issues to non-tech people. The links should have provide for a general understanding of AI without going too deep into technical issues. Please contribute! Kindly use only those Resources with NO CODE NEW Check out also the AI Wiki NEW Online Videos & Courses | Link to Issue | Description | |---|---| | Top Trending Technologies | Youtube Channel to master top trending technologyies including artificial intelligence | | AI4All | AI 4 All is a resource for AI facilitators to bring AI to scholars and students | | Elements of AI | Elements of AI is a free open online course to teach AI principles | | Visual Introduction to Machine Learning | Visual introduction to Machine Learning is a beautiful website that gives a comprehensive introduction and easily understood first encounter with machine learning | | CS50's Introduction to Artificial Intelligence with Python | Learn to use machine learning in Python in this introductory course on artificial intelligence.| | Crash course for AI | This is a fun video series that introduces students and educators to Artificial Intelligence and also offers additional more advanced videos. Learn about the basics, neural networks, algorithms, and more. | Youtuber Channel Machine Learning Tutorial | Youtube Channel Turorial Teachable Machine for beginner | | Artificial Intelligence (AI) |Learn the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems | | AI For Everyone by Andrew Ng | AI For Everyone is a course especially for people from a non-technical background to understand AI strategies | | How far is too far? The age of AI| This is a Youtube Orignals series by Robert Downey| | Fundamentals of Artificial Intelligence|This course is for absolute beginners with no technical knowledge.| | Bandit Algorithm (Online Machine Learning)|No requirement of technical knowledge, but a basic understending of Probability Ttheory would help| | An Executive's Guide to AI|This is an interactive guide to teaching business professionals how they might employ artificial intelligence in their business| | AI Business School|Series of videos that teach how AI may be incorporated in various business industries| | Artificial Intelligence Tutorial for Beginners | This video will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples. | | Indonesian Machine Learning Tutorial | Turorial Teachable Machine to train a computer for beginner | | Indonesian Youtube Playlist AI Tutorial | Youtube Playlist AI Tutorial For Beginner | | Artificial Intelligence Search Methods For Problem Solving By Prof. Deepak Khemani|These video lectures are for absolute beginners with no technical knowledge| | AI Basics Tutorial | This video starts from the very basics of AI and ML, and finally has a hands-on demo of the standard MNIST Dataset Number Detection model using Keras and Tensorflow.| | Simple brain.js Tutorial | This video explains a very simple javascript AI library called brain.js so you can easily run AI in the browser.| | Google AI| A complete kit for by google official for non-tech guy to start all over from basics, till advanced | | Microsoft AI for Beginners| A self-driven curriculum by Microsoft, which includes 24 lessons on AI. | Train Your Own AI | Link to Issue | Description | |---|---| | Teachable Machine | Use Teachable Machine to train a computer to recognize your own images, sounds, & poses | | eCraft2Learn | Resource and interactive space (Snap, a visual programming environment like Scratch) to learn how to create AI programs | | Google Quick Draw | Train an AI to guess from drawings| | Deepdream Generator| Merge Pictures to Deep Dreams using the Deepdream Generator| | Create ML|Quickly build and train Core ML models on your Mac with no code.| | What-If Tool|Visually probe the behavior of trained machine learning models, with minimal coding.| | Metaranx|Use and build artificial intelligence tools to analyze and make decisions about your data. Drag-and-drop. No code.| | obviously.ai|The total process of building ML algorithms, explaining results, and predicting outcomes in one single click.| Articles | By & Title | Description | |---|---| | Artificial Intelligence | Wikipedia Page of AI | | The Non-Technical AI Guide | One of the good blog post that could help AI more understandable for people without technical background | | LIAI | A detailed introduction to AI and neural networks | | Layman's Intro | A layman's introduction to AI | | AI and Machine Learning: A Nontechnical Overview | AI and Machine Learning: A Nontechnical Overview from OREILLY themselves is a guide to learn anyone everything they need to know about AI, focussed on non-tech people | | What business leaders need to know about artifical intelligence|Short article that summarizes the essential aspects of AI that business leaders need to understand| | How Will No-Code Impact the Future of Conversational AI | A humble explanation to the current state of converstational AI i.e.Chatbots and how it coul evolve with the current trend of no coding. | | Investopedia | Basic explanation of what AI is in a very basic and comprehensive way | | Packtpub | A non programmer’s guide to learning Machine learning | | Builtin | Artificial Intelligence.What is Artificial Intelligence? How Does AI Work? | | Future Of Life | Benefits & Risks of Artificial Intelligence | | NSDM India -Arpit | 100+ AI Tools For Non-Coders That Will Make Your Marketing Better. | | AI in Marketing for Startups & Non-technical Marketers | A practical guide for non-technical people | | Blog - Machine Learning MAstery | Blogs and Articles by Jason Browniee on ML | | AI Chatbots without programming| Chatbots are increasingly in demand among global businesses. This course will teach you how to build, analyze, deploy and monetize chatbots - with the help of IBM Watson and the power of AI.| Book Resources for Further Reading | Author | Book | Description & Notes | |---|---|---| | Ethem Alpaydin|Machine Learning: The New AI | Graph Theory with Applications to Engineering & Computer Science. A concise overview of machine learning—computer programs that learn from data—which underlies applications that include recommendation systems, face recognition, and driverless cars. | | Charu C. Aggarwal| Neural Networks and Deep Learning | This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. | | Hal Daumé III | A Course in Machine Learning | The purpose of this book is to provide a gentle and pedagogically organized introduction to the field. A second goal of this book is to provide a view of machine learning that focuses on ideas and models, not on math. | | Ian Goodfellow and Yoshua Bengio and Aaron Courville| Deep Learning | The book starts with a discussion on machine learning basics, including the applied mathematics and algorithms needed to effectively study deep learning from an academic perspective. There is no code covered in the book, making it perfect for a non-technical AI enthusiast. | | Peter Harrington|Machine Learning in Action| (Source: https://github.com/kerasking/book-1/blob/master/ML%20Machine%20Learning%20in%20Action.pdf) This book acts as a guide to walk newcomers through the techniques needed for machine learning as well as the concepts behind the practices.| | Jeff Heaton| Artificial Intelligence for Humans |This book helps its readers get an overview and understanding of AI algorithms. It is meant to teach AI for those who don’t have an extensive mathematical background. The readers need to have only a basic knowledge of computer programming and college algebra.| | John D. Kelleher, Brian Mac Namee and Aoife D'Arcy|Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)|This book covers all the fundamentals of machine learning, diving into the theory of the subject and using practical applications, working examples, and case studies to drive the knowledge home.| | Deepak Khemani| [A First Course in Artificial Intelligence] | It is an introductory course on Artificial Intelligence, a knowledge-based approach using agents all across and detailed, well-structured algorithms with proofs. This book mainly follows a bottom-up approach exploring the basic strategies needed problem-solving on the intelligence part. | | Maxim Lapan | Deep Reinforcement Learning Hands-On - Second Edition | Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. | | Tom M Mitchell | Machine Learning | This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning. | | John Paul Mueller and Luca Massaron|Machine Learning For Dummies|This book aims to get readers familiar with the basic concepts and theories of machine learning and how it applies to the real world. And "Dummies" here refers to absolute beginners with no technical background.The book introduces a little coding in Python and R used to teach machines to find patterns and analyze results. From those small tasks and patterns, we can extrapolate how machine learning is useful in daily lives through web searches, internet ads, email filters, fraud detection, and so on. With this book, you can take a small step into the realm of machine learning and we can learn some basic coding in Pyton and R (if interested)| | Michael Nielsen| Neural Networks and Deep Learning |Introduction to the core principles of Neural Networks and Deep Learning in AI| | Simon Rogers and Mark Girolami| A Course in Machine Learning |A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC.| |Peter Norvig| Paradigm of Artificial Intelligence Programming |Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems. By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts.| | Stuart Russel & Peter Norvig | Artificial Intelligence: A Modern Approach, 3rd Edition | This is the prescribed text book for my Introduction to AI university course. It starts off explaining all the basics and definitions of what AI is, before launching into agents, algorithms, and how to apply them. Russel is from the University of California at Berkeley. Norvig is from Google.| | Richard S. Sutton and Andrew G. Barto| Reinforcement Learning: An Introduction |Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment.| | Alex Smola and S.V.N. Vishwanathan | Introduction to Machine Learning | Provides the reader with an overview of the vast applications of ML, including some basic tools of statistics and probability theory. Also includes discussions on sophisticated ideas and concepts. | | Shai Shalev-Shwartz and Shai Ben-David | Understanding Machine Learning From Theory to Algorithms |The primary goal of this book is to provide a rigorous, yet easy to follow, introduction to the main concepts underlying machine learning. | | Chandra S.S.V | Artificial Intelligence and Machine Learning | This book is primarily intended for undergraduate and postgraduate students of computer science and engineering. This textbook covers the gap between the difficult contexts of Artificial Intelligence and Machine Learning. It provides the most number of case studies and worked-out examples. In addition to Artificial Intelligence and Machine Learning, it also covers various types of learning like reinforced, supervised, unsupervised and statistical learning. It features well-explained algorithms and pseudo-codes for each topic which makes this book very useful for students. | | Oliver Theobald|Machine Learning For Absolute Beginners: A Plain English Introduction|This is an absolute beginners ML guide.No mathematical background is needed, nor coding experience — this is the most basic introduction to the topic for anyone interested in machine learning.“Plain” language is highly valued here to prevent beginners from being overwhelmed by technical jargon. Clear, accessible explanations and visual examples accompany the various algorithms to make sure things are easy to follow.| | Tom Taulli | Artificial Intelligence Basics: A Non-Technical Introduction | This book equips you with a fundamental grasp of Artificial Intelligence and its impact. It provides a non-technical introduction to important concepts such as Machine Learning, Deep Learning, Natural Language Processing, Robotics and more. Further the author expands on the questions surrounding the future impact of AI on aspects that include societal trends, ethics, governments, company structures and daily life. | |Cornelius Weber, Mark Elshaw, N. Michael Mayer| Reinforcement Learning |Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning.| |John D. Kelleher, Brian Mac Namee, Aoife D'arcy| Algorithms, Worked Examples, and Case Studies | A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. |

10 Best AI Business Ideas 2024
youtube
LLM Vibe Score0.408
Human Vibe Score0.48
AI UncoveredMar 3, 2024

10 Best AI Business Ideas 2024

10 Best AI Business Ideas 2024 🔒 Keep Your Digital Life Private and Be Safe Online: https://nordvpn.com/safetyfirst Are you curious about the future of business in the exciting realm of Artificial Intelligence (AI)? Look no further! In this captivating video, we unveil the top 10 AI business ideas that are set to revolutionize the entrepreneurial landscape in 2024. From cutting-edge technology to innovative solutions, we delve into the most promising ventures that harness the power of AI to drive success and growth. Discover how AI is reshaping traditional business models and opening up endless possibilities for aspiring entrepreneurs. Whether you're a seasoned professional or a budding visionary, these handpicked AI business ideas offer a gateway to prosperity in the ever-evolving digital age. Join us as we explore groundbreaking concepts that blend creativity with computational intelligence, paving the way for unprecedented innovation and profitability. From automated customer service to personalized marketing strategies, AI is poised to transform every aspect of modern business operations. Dive deep into the realm of AI-powered startups and witness firsthand how these groundbreaking ideas are shaping the future of commerce. With our expert insights and comprehensive analysis, you'll gain invaluable knowledge to embark on your own AI-driven entrepreneurial journey. Don't miss out on the opportunity to stay ahead of the curve and capitalize on the transformative potential of AI in business. Join us as we unveil the 10 best AI business ideas for 2024 and embark on a journey towards success in the dynamic world of artificial intelligence. Subscribe now and stay tuned for more cutting-edge content that empowers you to thrive in the digital economy of tomorrow! #ai #artificialintelligence #aibusiness Subscribe for more! Welcome to AI Uncovered, your ultimate destination for exploring the fascinating world of artificial intelligence! Our channel delves deep into the latest AI trends and technology, providing insights into cutting-edge AI tools, AI news, and breakthroughs in artificial general intelligence (AGI). We simplify complex concepts, making AI explained in a way that is accessible to everyone. At AI Uncovered, we're passionate about uncovering the most captivating stories in AI, including the marvels of ChatGPT and advancements by organizations like OpenAI. Our content spans a wide range of topics, from science news and AI innovations to in-depth discussions on the ethical implications of artificial intelligence. Our mission is to enlighten, inspire, and inform our audience about the rapidly evolving technology landscape. Whether you're a tech enthusiast, a professional seeking to stay ahead of AI trends, or someone curious about the future of artificial intelligence, AI Uncovered is the perfect place to expand your knowledge. Join us as we uncover the secrets behind AI tools and their potential to revolutionize our world. Subscribe to AI Uncovered and stay tuned for enlightening content that bridges the gap between AI novices and experts, covering AI news, AGI, ChatGPT, OpenAI, artificial intelligence, and more. Together, let's explore the limitless possibilities of technology and AI. Disclaimer: Some links included in this description might be affiliate links. If you purchase a product or service through the links that we provide, we may receive a small commission. There is no additional charge for you. Thank you for supporting AI Uncovered so we can continue to provide you with free, high-quality content. _ 🌟 Contact: ai.uncovered.ai@gmail.com

13 Best AI Tools For Startups & Entrepreneurs [2024]
youtube
LLM Vibe Score0.401
Human Vibe Score0.33
Business SolutionDec 15, 2023

13 Best AI Tools For Startups & Entrepreneurs [2024]

Here are the best AI tools for startups and entrepreneurs: Bubble ▶ Bubble free plan: https://businessolution.org/get/bubble/ Taskade ▶ Taskade free plan : https://businessolution.org/get/taskade/ Process Street ▶ Process Street free trial: https://businessolution.org/get/process-street/ CustomGPT ▶ Try CustomGPT for free: https://businessolution.org/get/customgpt/ MeetGeek ▶ MeetGeek free plan: https://businessolution.org/get/meetgeek-ai/ Mixo ▶ Try Mixo for free: https://businessolution.org/get/mixo/ Tidio ▶ Tidio free plan (+20% OFF): https://businessolution.org/get/tidio/ AdCreative.ai ▶ AdCreative.ai 25% OFF: https://businessolution.org/get/adcreative/ LeadFuze ▶ LeadFuze free trial: https://businessolution.org/get/leadfuze/ HubSpot ▶ HubSpot free plan: https://businessolution.org/get/hubspot/ ClickFunnels 2.0 ▶ ClickFunnels 2.0 free trial: https://businessolution.org/get/clickfunnels-2-0/ GoHire ▶ GoHire free trial: https://businessolution.org/get/gohire-2/ DeepBrain ▶ Try DeepBrain for free: https://businessolution.org/get/deepbrain/ Timestamps: 0:00 – AI Tools for Startups 0:17 – Bubble.io 2:26 – Taskade 4:35 – Process Street 6:20 – CustomGPT 7:44 – MeetGeek 8:31 – Mixo 9:09 – Tidio 10:15 – AdCreative.ai 11:34 – LeadFuze 12:51 – HubSpot 14:48 – ClickFunnels 2.0 16:10 – GoHire 17:25 – DeepBrain 👉‍ See all 17 AI tools for startups in this article: https://businessolution.org/ai-tools-for-startups/ In today's fast-paced and competitive business landscape, startups are constantly seeking innovative ways to gain a competitive edge and drive growth. Enter the realm of artificial intelligence (AI) tools for startups – a game-changing technology that holds the potential to revolutionize how new businesses operate, strategize, and scale. From automating repetitive tasks to unlocking valuable insights from data, AI tools offer startups an unprecedented opportunity to streamline operations, enhance decision-making, and deliver exceptional customer experiences. Imagine having access to intelligent algorithms that can analyze market trends, predict consumer behavior, and optimize resource allocation with unparalleled accuracy. These AI tools can empower startups to make data-driven decisions with confidence while freeing up valuable time and resources for creative problem-solving and strategic planning. By harnessing the power of AI technology, startups can navigate the complexities of today's business environment with agility, precision, and scalability like never before. Join us as we delve into the world of AI tools for startups and explore how this transformative technology is poised to reshape the entrepreneurial landscape in profound ways.

The 9 AI Skills You Need NOW to Stay Ahead of 97% of People
youtube
LLM Vibe Score0.289
Human Vibe Score0.91
AI UncoveredMay 14, 2023

The 9 AI Skills You Need NOW to Stay Ahead of 97% of People

The 9 AI Skills You Need NOW to Stay Ahead of 97% of People 🔒 Keep Your Digital Life Private: Stay Safe & Secure Online with NordVPN: https://nordvpn.com/safetyfirst Welcome to our latest educational video, "The 9 AI Skills You Need NOW to Stay Ahead of 97% of People." This video is designed for anyone eager to take a deep dive into the world of artificial intelligence and machine learning. Our goal is to provide you with the most essential AI skills needed to excel in this rapidly evolving field, keeping you ahead of the curve and well-positioned in the job market. In this comprehensive guide, we explore nine fundamental AI skills, ranging from understanding algorithms to deep learning, data science, natural language processing, computer vision, robotics, and more. We also provide practical tips on how to apply these skills in real-world scenarios, whether you're an AI enthusiast or a seasoned professional. AI is not just the future; it's here NOW. By acquiring these nine essential AI skills, you can position yourself among the top 3% of people who are ready to shape the future. Don't be left behind as AI transforms industries, from healthcare and finance to entertainment and transportation. This video is a must-watch for anyone interested in AI, machine learning, data analysis, robotics, or any related field. Whether you're just starting out, looking to upskill, or aiming to stay ahead in your career, these nine AI skills are your key to success. Remember to subscribe to our channel for more valuable content and hit the notification bell so you never miss an update. Join the conversation in the comments section - we'd love to hear your thoughts on AI and how you plan to incorporate these skills into your career or studies. So get ready, click play, and let's take a step towards the future together, learning the 9 AI skills you need NOW to stay ahead of 97% of people. Your AI journey starts here. Enjoy the video! #artificialintelligence #ai #airevolution Subscribe for more! Welcome to AI Uncovered, your ultimate destination for exploring the fascinating world of artificial intelligence! Our channel delves deep into the latest AI trends and technology, providing insights into cutting-edge AI tools, AI news, and breakthroughs in artificial general intelligence (AGI). We simplify complex concepts, making AI explained in a way that is accessible to everyone. At AI Uncovered, we're passionate about uncovering the most captivating stories in AI, including the marvels of ChatGPT and advancements by organizations like OpenAI. Our content spans a wide range of topics, from science news and AI innovations to in-depth discussions on the ethical implications of artificial intelligence. Our mission is to enlighten, inspire, and inform our audience about the rapidly evolving technology landscape. Whether you're a tech enthusiast, a professional seeking to stay ahead of AI trends, or someone curious about the future of artificial intelligence, AI Uncovered is the perfect place to expand your knowledge. Join us as we uncover the secrets behind AI tools and their potential to revolutionize our world. Subscribe to AI Uncovered and stay tuned for enlightening content that bridges the gap between AI novices and experts, covering AI news, AGI, ChatGPT, OpenAI, artificial intelligence, and more. Together, let's explore the limitless possibilities of technology and AI. Disclaimer: Some links included in this description might be affiliate links. If you purchase a product or service through the links that we provide, we may receive a small commission. There is no additional charge for you. Thank you for supporting AI Uncovered so we can continue to provide you with free, high-quality content.

The future of AI
youtube
LLM Vibe Score0.471
Human Vibe Score0.61
GaryVeeMay 9, 2023

The future of AI

When voice and ai hit scale … shits gonna get interesting… — Thanks for watching! Join My Discord!: https://www.garyvee.com/discord Check out another series on my channel: Keynotes: https://www.youtube.com/watch?v=6vCDlmhRmBo&list=PLfA33-E9P7FCEF1izpctGGoak841XYzrJ NFTs: https://www.youtube.com/watch?v=AwMJ6bScB2s&list=PLfA33-E9P7FAcvsVSFqzSuJhHu3SkW2Ma Business Meetings: https://www.youtube.com/watch?v=wILI_VV6z4Y&list=PLfA33-E9P7FCTIY62wkqZ-E1cwpc2hxBJ Gary Vaynerchuk Original Films: https://youtube.com/playlist?list=PLfA33-E9P7FAvnrOcgy4MvIcCXxoyjuku Trash Talk: https://youtube.com/playlist?list=PLfA33-E9P7FDelN4bXFgtJuczC9HHmm2- WeeklyVee: https://youtube.com/playlist?list=PLfA33-E9P7FBPjdQcF6uedz9fdk8XKn-b — Gary Vaynerchuk is a serial entrepreneur, and serves as the Chairman of VaynerX, the CEO of VaynerMedia and the Creator & CEO of VeeFriends. Gary is considered one of the leading global minds on what’s next in culture, relevance and the internet. Known as “GaryVee” he is described as one of the most forward thinkers in business – he acutely recognizes trends and patterns early to help others understand how these shifts impact markets and consumer behavior. Whether its emerging artists, esports, NFT investing or digital communications, Gary understands how to bring brand relevance to the forefront. He is a prolific angel investor with early investments in companies such as Facebook, Twitter, Tumblr, Venmo, Snapchat, Coinbase and Uber. Gary is an entrepreneur at heart — he builds businesses. Today, he helps Fortune 1000 brands leverage consumer attention through his full service advertising agency, VaynerMedia which has offices in NY, LA, London, Mexico City, LATAM and Singapore. VaynerMedia is part of the VaynerX holding company which also includes VaynerProductions, VaynerNFT, Gallery Media Group, The Sasha Group, Tracer, VaynerSpeakers, VaynerTalent, and VaynerCommerce. Gary is also the Co-Founder of VaynerSports, Resy and Empathy Wines. Gary guided both Resy and Empathy to successful exits — both were sold respectively to American Express and Constellation Brands. He’s also a Board Member at Candy Digital, Co-Founder of VCR Group, Co-Founder of ArtOfficial, and Creator & CEO of VeeFriends. Gary was recently named to the Fortune list of the Top 50 Influential people in the NFT industry. In addition to running multiple businesses, Gary documents his life daily as a CEO through his social media channels which has more than 34 million followers and garnishes over 272 million monthly impressions/views across all platforms. His podcast ‘The GaryVee Audio Experience’ ranks among the top podcasts globally. He is a five-time New York Times Best-Selling Author and one of the most highly sought after public speakers. Gary serves on the board of MikMak, Bojangles Restaurants, and Pencils of Promise. He is also a longtime Well Member of Charity:Water.