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How I went from $27 to $3K as a solopreneur still in a 9-5
reddit
LLM Vibe Score0
Human Vibe Score1
jottrledThis week

How I went from $27 to $3K as a solopreneur still in a 9-5

My journey started back in November 2023. I was scrolling through Twitter and YouTube and saw a word that I had never come across before. Solopreneur. The word caught my eye. Mainly because I was pretty sure I knew what it meant even though it's not a word you'll find in the dictionary. I liked what it was describing. A solo entrepreneur. A one man business. It completely resonated with me. As a software engineer by trade I'm used to working alone, especially since the pandemic hit and we were forced to work remotely. See, I always wanted to ditch the 9-5 thing but thought that was too big and too scary for a single person to do. Surely you would need a lot of money to get started, right? Surely you would need investors? The whole concept seemed impossible to me. That was until I found all the success stories. I became obsessed with the concept of solopreneurship. As I went further down the rabbit hole I found people like Justin Welsh, Kieran Drew and Marc Louvion to name a few. All of whom have one person businesses making huge money every year. So I thought, if they can do it, why can't I? People like this have cleared the pathway for those looking to escape the 9-5 grind. I decided 2024 would be the year I try this out. My main goal for the year? Build a one man business, earn my first $ online and learn a sh\*t ton along the way. My main goal in general? Build my business to $100K per year, quit my 9-5 and live with freedom. From December 2023 to February 2024 I began brainstorming ideas. I was like a lost puppy looking for his ball. How on earth did people find good ideas? I began writing everything and anything that came to mind down in my notes app on my phone. By February I would have approximately 70 ideas. Each as weird and whacky as the other. I was skeptical though. If I went through all the trouble of building a product for one of these ideas how would I know if anyone would even be interested in using it? I got scared and took a break for a week. All these ideas seemed too big and the chance that they would take off into the atmosphere was slim (in my mind anyways). I was learning more and more about solopreneurship as the weeks went on so I decided to build a product centered around everything I was learning about. The idea was simple. Enter a business idea and use AI to give the user details about how to market it, who their target customers were, what to write on their landing page, etc. All for a measly $27 per use. I quickly built it and launched on March 3rd 2024. I posted about it on Indie Hackers, Reddit and Hacker News. I was so excited about the prospect of earning my first internet $! Surely everyone wanted to use my product! Nope...all I got was crickets. I was quickly brought back down to earth. That was until 5 days later. I looked at my phone and had a new Stripe notification! Cha-ching! My first internet $. What a feeling! That was goal number 1 complete. It would be another 6 days before I would get my second sale...and then another 15 days to get my third. It was an emotional rollercoaster. I went from feeling like quitting the 9-5 was actually possible to thinking that maybe the ups and downs aren't worth it. On one hand I had made my first internet dollar so I should my ecstatic, and don't get me wrong, I was but I wanted more. More validation that I could do this long term. By May I was starting to give up on the product. I had learned so much in the past few months about marketing, SEO, building an audience, etc. and I wanted to build something that I thought could have more success so I focused on one critical thing that I had learned about. What was it? Building a product that had SEO potential. A product that I knew hundreds of people were looking for. See this was my thinking - If I could find a keyword that people were searching for on Google hundreds/thousands of times every month and it was easy to rank high on search engines then I would go all in (in SEO land this equates to a Keyword that has a Keyword Difficulty of = 500). I began researching and found that the keyword "micro saas ideas" was being searched for around 600 times each month. Micro Saas was something that really interested me. It was perfect for solopreneurs. Small software products that 1 person could build. What's not to like if you're in the game of software and solopreneurship? Researching keywords like this became like a game for me. I was hooked. I was doing it every day, finding gems that were being searched for hundreds and thousands of times every month that still had potential. That's when I came up with my next product idea. I decided to create a database of Micro Saas Ideas all with this sort of SEO potential. See if you can build a product that you know people are looking for then that's all the validation you need. So I put this theory to the test. I created a database of Micro Saas Ideas with SEO Potential and launched it in June 2024. This time it was different. I made $700 in the first week of launching. A large contrast to my previous failed attempt at becoming the worlds greatest solopreneur. Since launch I have grown the product to $3K and I couldn't be happier. I know what you're saying, $3K isn't a lot. But it's validation. It's validation that I can earn $ online. Validation that I can grow a business and it gives me hope that one day I'll be able to quit that 9-5 grind. My plan is to keep growing the business. I expect there to be a few challenges up ahead but I'll tackle them as I go and learn from the failures and successes. I have a newsletter where I share Micro Saas Ideas with SEO potential every week which I'll leave below in the first comment. Feel free to come along for the ride. If not I hope this post brings you some value If you're thinking about starting as a solopreneur, stop thinking and start doing, you won't regret it.

How I went from $27 to $3K as a solopreneur still in a 9-5
reddit
LLM Vibe Score0
Human Vibe Score1
jottrledThis week

How I went from $27 to $3K as a solopreneur still in a 9-5

My journey started back in November 2023. I was scrolling through Twitter and YouTube and saw a word that I had never come across before. Solopreneur. The word caught my eye. Mainly because I was pretty sure I knew what it meant even though it's not a word you'll find in the dictionary. I liked what it was describing. A solo entrepreneur. A one man business. It completely resonated with me. As a software engineer by trade I'm used to working alone, especially since the pandemic hit and we were forced to work remotely. See, I always wanted to ditch the 9-5 thing but thought that was too big and too scary for a single person to do. Surely you would need a lot of money to get started, right? Surely you would need investors? The whole concept seemed impossible to me. That was until I found all the success stories. I became obsessed with the concept of solopreneurship. As I went further down the rabbit hole I found people like Justin Welsh, Kieran Drew and Marc Louvion to name a few. All of whom have one person businesses making huge money every year. So I thought, if they can do it, why can't I? People like this have cleared the pathway for those looking to escape the 9-5 grind. I decided 2024 would be the year I try this out. My main goal for the year? Build a one man business, earn my first $ online and learn a sh\*t ton along the way. My main goal in general? Build my business to $100K per year, quit my 9-5 and live with freedom. From December 2023 to February 2024 I began brainstorming ideas. I was like a lost puppy looking for his ball. How on earth did people find good ideas? I began writing everything and anything that came to mind down in my notes app on my phone. By February I would have approximately 70 ideas. Each as weird and whacky as the other. I was skeptical though. If I went through all the trouble of building a product for one of these ideas how would I know if anyone would even be interested in using it? I got scared and took a break for a week. All these ideas seemed too big and the chance that they would take off into the atmosphere was slim (in my mind anyways). I was learning more and more about solopreneurship as the weeks went on so I decided to build a product centered around everything I was learning about. The idea was simple. Enter a business idea and use AI to give the user details about how to market it, who their target customers were, what to write on their landing page, etc. All for a measly $27 per use. I quickly built it and launched on March 3rd 2024. I posted about it on Indie Hackers, Reddit and Hacker News. I was so excited about the prospect of earning my first internet $! Surely everyone wanted to use my product! Nope...all I got was crickets. I was quickly brought back down to earth. That was until 5 days later. I looked at my phone and had a new Stripe notification! Cha-ching! My first internet $. What a feeling! That was goal number 1 complete. It would be another 6 days before I would get my second sale...and then another 15 days to get my third. It was an emotional rollercoaster. I went from feeling like quitting the 9-5 was actually possible to thinking that maybe the ups and downs aren't worth it. On one hand I had made my first internet dollar so I should my ecstatic, and don't get me wrong, I was but I wanted more. More validation that I could do this long term. By May I was starting to give up on the product. I had learned so much in the past few months about marketing, SEO, building an audience, etc. and I wanted to build something that I thought could have more success so I focused on one critical thing that I had learned about. What was it? Building a product that had SEO potential. A product that I knew hundreds of people were looking for. See this was my thinking - If I could find a keyword that people were searching for on Google hundreds/thousands of times every month and it was easy to rank high on search engines then I would go all in (in SEO land this equates to a Keyword that has a Keyword Difficulty of = 500). I began researching and found that the keyword "micro saas ideas" was being searched for around 600 times each month. Micro Saas was something that really interested me. It was perfect for solopreneurs. Small software products that 1 person could build. What's not to like if you're in the game of software and solopreneurship? Researching keywords like this became like a game for me. I was hooked. I was doing it every day, finding gems that were being searched for hundreds and thousands of times every month that still had potential. That's when I came up with my next product idea. I decided to create a database of Micro Saas Ideas all with this sort of SEO potential. See if you can build a product that you know people are looking for then that's all the validation you need. So I put this theory to the test. I created a database of Micro Saas Ideas with SEO Potential and launched it in June 2024. This time it was different. I made $700 in the first week of launching. A large contrast to my previous failed attempt at becoming the worlds greatest solopreneur. Since launch I have grown the product to $3K and I couldn't be happier. I know what you're saying, $3K isn't a lot. But it's validation. It's validation that I can earn $ online. Validation that I can grow a business and it gives me hope that one day I'll be able to quit that 9-5 grind. My plan is to keep growing the business. I expect there to be a few challenges up ahead but I'll tackle them as I go and learn from the failures and successes. I have a newsletter where I share Micro Saas Ideas with SEO potential every week which I'll leave below in the first comment. Feel free to come along for the ride. If not I hope this post brings you some value If you're thinking about starting as a solopreneur, stop thinking and start doing, you won't regret it.

How I went from $27 to $3K as a solopreneur still in a 9-5
reddit
LLM Vibe Score0
Human Vibe Score1
jottrledThis week

How I went from $27 to $3K as a solopreneur still in a 9-5

My journey started back in November 2023. I was scrolling through Twitter and YouTube and saw a word that I had never come across before. Solopreneur. The word caught my eye. Mainly because I was pretty sure I knew what it meant even though it's not a word you'll find in the dictionary. I liked what it was describing. A solo entrepreneur. A one man business. It completely resonated with me. As a software engineer by trade I'm used to working alone, especially since the pandemic hit and we were forced to work remotely. See, I always wanted to ditch the 9-5 thing but thought that was too big and too scary for a single person to do. Surely you would need a lot of money to get started, right? Surely you would need investors? The whole concept seemed impossible to me. That was until I found all the success stories. I became obsessed with the concept of solopreneurship. As I went further down the rabbit hole I found people like Justin Welsh, Kieran Drew and Marc Louvion to name a few. All of whom have one person businesses making huge money every year. So I thought, if they can do it, why can't I? People like this have cleared the pathway for those looking to escape the 9-5 grind. I decided 2024 would be the year I try this out. My main goal for the year? Build a one man business, earn my first $ online and learn a sh\*t ton along the way. My main goal in general? Build my business to $100K per year, quit my 9-5 and live with freedom. From December 2023 to February 2024 I began brainstorming ideas. I was like a lost puppy looking for his ball. How on earth did people find good ideas? I began writing everything and anything that came to mind down in my notes app on my phone. By February I would have approximately 70 ideas. Each as weird and whacky as the other. I was skeptical though. If I went through all the trouble of building a product for one of these ideas how would I know if anyone would even be interested in using it? I got scared and took a break for a week. All these ideas seemed too big and the chance that they would take off into the atmosphere was slim (in my mind anyways). I was learning more and more about solopreneurship as the weeks went on so I decided to build a product centered around everything I was learning about. The idea was simple. Enter a business idea and use AI to give the user details about how to market it, who their target customers were, what to write on their landing page, etc. All for a measly $27 per use. I quickly built it and launched on March 3rd 2024. I posted about it on Indie Hackers, Reddit and Hacker News. I was so excited about the prospect of earning my first internet $! Surely everyone wanted to use my product! Nope...all I got was crickets. I was quickly brought back down to earth. That was until 5 days later. I looked at my phone and had a new Stripe notification! Cha-ching! My first internet $. What a feeling! That was goal number 1 complete. It would be another 6 days before I would get my second sale...and then another 15 days to get my third. It was an emotional rollercoaster. I went from feeling like quitting the 9-5 was actually possible to thinking that maybe the ups and downs aren't worth it. On one hand I had made my first internet dollar so I should my ecstatic, and don't get me wrong, I was but I wanted more. More validation that I could do this long term. By May I was starting to give up on the product. I had learned so much in the past few months about marketing, SEO, building an audience, etc. and I wanted to build something that I thought could have more success so I focused on one critical thing that I had learned about. What was it? Building a product that had SEO potential. A product that I knew hundreds of people were looking for. See this was my thinking - If I could find a keyword that people were searching for on Google hundreds/thousands of times every month and it was easy to rank high on search engines then I would go all in (in SEO land this equates to a Keyword that has a Keyword Difficulty of = 500). I began researching and found that the keyword "micro saas ideas" was being searched for around 600 times each month. Micro Saas was something that really interested me. It was perfect for solopreneurs. Small software products that 1 person could build. What's not to like if you're in the game of software and solopreneurship? Researching keywords like this became like a game for me. I was hooked. I was doing it every day, finding gems that were being searched for hundreds and thousands of times every month that still had potential. That's when I came up with my next product idea. I decided to create a database of Micro Saas Ideas all with this sort of SEO potential. See if you can build a product that you know people are looking for then that's all the validation you need. So I put this theory to the test. I created a database of Micro Saas Ideas with SEO Potential and launched it in June 2024. This time it was different. I made $700 in the first week of launching. A large contrast to my previous failed attempt at becoming the worlds greatest solopreneur. Since launch I have grown the product to $3K and I couldn't be happier. I know what you're saying, $3K isn't a lot. But it's validation. It's validation that I can earn $ online. Validation that I can grow a business and it gives me hope that one day I'll be able to quit that 9-5 grind. My plan is to keep growing the business. I expect there to be a few challenges up ahead but I'll tackle them as I go and learn from the failures and successes. I have a newsletter where I share Micro Saas Ideas with SEO potential every week which I'll leave below in the first comment. Feel free to come along for the ride. If not I hope this post brings you some value If you're thinking about starting as a solopreneur, stop thinking and start doing, you won't regret it.

Finally Launched My First App Without Any Coding Experience
reddit
LLM Vibe Score0
Human Vibe Score1
Consistent_Access844This week

Finally Launched My First App Without Any Coding Experience

About Myself I am a structural engineer that are taught to design buildings in the day and I have been dreaming forever to build a SaaS business to get out of the rat race. However, as a structural engineer, coding is definitely not something I am capable of doing (I have some simple knowledge, but its no way close to building an app) The Journey As I've mentioned, I always wanted to build a SaaS business because in my mind the business model is most attractive to me, where you only need to build once and can sell to millions. So I started off searching and exploring on the internet and my first ever "SaaS" was from Wordpress. I am buying plugin from other user and then pluggin into my own Wordpress website. It was a project management tool SaaS. I was so excited about the website and can't even sleep well at night because I'm just so hype about it. But, the reality is because this is my first ever business, I totally didn't realise about the importance of UI UX or my business differentiation, thinking that everyone will be as excited as I am. Then, I went deeper and deeper into the journey (I can write more about this in another post if anyone is interested) and finally landed on Flutterflow to create my first ever app. No Code Journey Thanks to no code builder, I never thought that a non-coder like me can ever create an app and got accepted by the App Store/Play Store. Since that I am using a low-code builder, for any specific requirement that I need that are not covered natively, I will just talk to ChatGPT and boom I pretty much got most of the answer I needed. About The App As someone that always try to keep track of my expenses, I never able to find an app that are simple and interesting enough for me to continue on the journey. I realise that I could have incorporate AI into this journey and hence there go, I created an AI Money Tracker. Let me introduce Rolly: AI Money Tracker - a new AI expense tracker where you can easily record your transactions just by chatting with our bot Rolly and it will automatically record and categorise the transaction into the most suitable category (you can also create any of your own category and it will also take care of it in consideration). I am not sharing the app link here to avoid getting ban, but feel free to search up Rolly: AI Money Tracker on either App Store on Play Store. My Learnings As someone that can't code and never imagine that I could create a production app by myself and publish it on to the App Store and Play Store. Since I am not making any money yet and just at the beginning of my entrepreneur journey, I can't give any substantial advice, all I can say is just my own learnings and feelings. My advice is if you have a dream of building a business, just go for it, don't worry about all the problems that you can think of to convince yourself not making the start at all. From my point of view, as long as you're not giving up everything (eg, putting yourself in huge debt etc), why don't just go for it and you've got nothing much to lose. You'll only lose if you never even get started. And also, I believe that creating an app is always the easiest step out of the entreprenuership journey, marketing and distribution is the key to success. Even though you've spent days and nights on it and it might mean everything to you, the truth is people don't really cares and you'll need to market for it. I am still in journey to learn how to do marketing, content, building a business and everything. I think this is just a very beginning of my journey and hopefully there's more interesting one to share further down the road.

Behind the scene : fundraising pre-seed of an AI startup
reddit
LLM Vibe Score0
Human Vibe Score1
Consistent-Wafer7325This week

Behind the scene : fundraising pre-seed of an AI startup

A bit of feedback from our journey at our AI startup. We started prototyping stuff around agentic AI last winter with very cool underlying tech research based on some academic papers (I can send you links if you're interested in LLM orchestration). I'm a serial entrepreneur with 2x exits, nothing went fancy but enough to keep going into the next topic. This time, running an AI project has been a bit different and unique due to the huge interest around the topic. Here are a few insights. Jan \~ Mar: Research Nothing was serious, just a side project with a friend on weekends (the guy became our lead SWE). Market was promising and we had the convinction that our tech can be game changer in computer systems workflows. March \~ April: Market Waking Up Devin published their pre-seed $20m fundraising led by Founders Fund; they paved the market with legitimacy. I decided to launch some coffee meetings with a few angels in my network. Interest confirmed. Back to work on some more serious early prototyping; hard work started here. April \~ May: YC S24 (Fail) Pumped up by our prospective angels and the market waking up on the agentic topic, I applied to YC as a solo founder (was still looking for funds and co-founders). Eventually got rejected (no co-founder and not US-based). May \~ July: VC Dance (Momentum 1) Almost randomly at the same time we got rejected from YC, I got introduced to key members of the VC community by one of our prospective angels. Interest went crazy... tons of calls. Brace yourself here, we probably met 30\~40 funds (+ angels). Got strong interests from 4\~5 of them (3 to 5 meetings each), ultimately closed 1 and some interests which might convert later in the next stage. The legend of AI being hype is true. Majority of our calls went only by word of mouth, lots of inbounds, people even not having the deck would book us a call in the next 48h after saying hi. Also lots of "tourists," just looking because of AI but with no strong opinion on the subject to move further. The hearsay about 90% rejection is true. You'll have a lot of nos, ending some days exhausted and unmotivated. End July: Closing, the Hard Part The VC roadshow is kind of an art you need to master. You need to keep momentum high enough and looking over-subscribed. Good pre-seed VC deals are over-competitive, and good funds only focus on them; they will have opportunities to catch up on lost chances at the seed stage later. We succeeded (arduously) to close our 18\~24mo budget with 1 VC, a few angels, and some state-guaranteed debt. Cash in bank just on time for payday in August (don't under-estimate time of processing) Now: Launching and Prepping the Seed Round We're now in our first weeks of go-to-market with a lot of uncertainty but a very ambitious plan ahead. The good part of having met TONS of VCs during the pre-seed roadshow is that we met probably our future lead investors in these. What would look like a loss of time in the initial pre-seed VC meetings has been finally very prolific, helping us to refine our strategy, assessing more in-depth the market (investors have a lot of insights, they meet a lot of people... that's their full-time job). We now have clear milestones and are heading to raise our seed round by end of year/Q1 if stars stay aligned :) Don't give up, the show must go on.

Upselling from $8/mo to $2k/mo
reddit
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Human Vibe Score1
Afraid-Astronomer130This week

Upselling from $8/mo to $2k/mo

I just closed a client for $1947/mo. But 5 months ago he was spending only $8/mo. Most customers have way more purchasing power than you think. Unlock it with the power of stacking. Here's my 3-steps stacking formula: Step 1 - Build trust with a low-ticket product In a world full of scams and deceit, building trust is damn hard. The best way to combat skepticism is through a free or low-ticket product, where you can go above and beyond to demonstrate your credibility. When I first onboarded this client onto my SaaS, an AI to help you with HARO link-building, my product was at a very early stage with many rough edges. He gave me lots of great feedback. I implemented his suggestions the same day and got more feedback from him. After a couple of back-and-forths, I established myself as a trustworthy hustler, instead of just a stranger online. This is easy to do for an agile startup but impossible for big companies, so make good use of opportunities like this to build long-term relationships. Turn your customers into raving fans. Step 2 - Validate a mid-ticket offer Three months into his subscription, he told me he wanted to cancel. When digging into the why, he suggested a performance-based DFY service to remove all the work on his end. Inspired by his suggestion, I took on him and 6 other clients for $237, a one-time package for 1 backlink. It's sold through my newsletter email blast to 300 subscribers, with a total CAC of $0. I wrote about the details of this launch in another long form. At this price range, impulsive purchases can still happen if you have a strong offer and good copywriting. Use this mid-ticket offer to validate your offer and positioning, build out a team, and establish trust. We went beyond the 1 link for almost all our clients, including this one in particular. For $237, we got him on Forbes, HubSpot, 2 DR50+ sites, and a few other smaller media outlets. By doing this, we further built trust into the relationship and established authority in what we do. Step 3 - Create a high-ticket subscription-based offer By now, you'll hopefully have built enough trust to get through the skepticism filter for something high-ticket. Now, it's time to develop an offer that amplifies your previous one. Something that allows you to let your clients achieve their goals to the maximum extent. For me, this is pitching every relevant media query on every platform for this client every day, to leverage HARO link-building to its full extent, all for a fixed price of $1947/mo. This customized offer is based on direct client feedback, isn't publicized on our website, but we're confident it will directly contribute to achieving this client's goal. A subscription-based offer is much superior because it allows you to create a stable source of revenue, especially at the early stage. That's how I created 3 different offers to solve the same problem for one client. By stacking each offer on top of the previous one, I was able to guide clients from one option to the next. This formula isn't some new rocket science I came up with. It's proven over and over again by other agency owners building in public, like Nick from Baked Design who started with a $9 design kit and now sells $9k/mo design subscriptions at $1M ARR. By stacking offers, you position yourself as a committed partner in your client's long-term success. Lastly, I want to address a common objection: "My customers can't afford $2k/month." But consider this: most people are reading your site on their $3000 MacBook or $1000 iPhone. It's not that they lack the funds, it's more likely that your service isn't meeting their expectations. Talk to them to discover the irresistible offer they'll gladly pay for. Update: lots of DM asking about more specifics so I wrote about it here. https://coldstartblueprint.com/p/ai-agent-email-list-building

Finally Launched My First App Without Any Coding Experience
reddit
LLM Vibe Score0
Human Vibe Score1
Consistent_Access844This week

Finally Launched My First App Without Any Coding Experience

About Myself I am a structural engineer that are taught to design buildings in the day and I have been dreaming forever to build a SaaS business to get out of the rat race. However, as a structural engineer, coding is definitely not something I am capable of doing (I have some simple knowledge, but its no way close to building an app) The Journey As I've mentioned, I always wanted to build a SaaS business because in my mind the business model is most attractive to me, where you only need to build once and can sell to millions. So I started off searching and exploring on the internet and my first ever "SaaS" was from Wordpress. I am buying plugin from other user and then pluggin into my own Wordpress website. It was a project management tool SaaS. I was so excited about the website and can't even sleep well at night because I'm just so hype about it. But, the reality is because this is my first ever business, I totally didn't realise about the importance of UI UX or my business differentiation, thinking that everyone will be as excited as I am. Then, I went deeper and deeper into the journey (I can write more about this in another post if anyone is interested) and finally landed on Flutterflow to create my first ever app. No Code Journey Thanks to no code builder, I never thought that a non-coder like me can ever create an app and got accepted by the App Store/Play Store. Since that I am using a low-code builder, for any specific requirement that I need that are not covered natively, I will just talk to ChatGPT and boom I pretty much got most of the answer I needed. About The App As someone that always try to keep track of my expenses, I never able to find an app that are simple and interesting enough for me to continue on the journey. I realise that I could have incorporate AI into this journey and hence there go, I created an AI Money Tracker. Let me introduce Rolly: AI Money Tracker - a new AI expense tracker where you can easily record your transactions just by chatting with our bot Rolly and it will automatically record and categorise the transaction into the most suitable category (you can also create any of your own category and it will also take care of it in consideration). I am not sharing the app link here to avoid getting ban, but feel free to search up Rolly: AI Money Tracker on either App Store on Play Store. My Learnings As someone that can't code and never imagine that I could create a production app by myself and publish it on to the App Store and Play Store. Since I am not making any money yet and just at the beginning of my entrepreneur journey, I can't give any substantial advice, all I can say is just my own learnings and feelings. My advice is if you have a dream of building a business, just go for it, don't worry about all the problems that you can think of to convince yourself not making the start at all. From my point of view, as long as you're not giving up everything (eg, putting yourself in huge debt etc), why don't just go for it and you've got nothing much to lose. You'll only lose if you never even get started. And also, I believe that creating an app is always the easiest step out of the entreprenuership journey, marketing and distribution is the key to success. Even though you've spent days and nights on it and it might mean everything to you, the truth is people don't really cares and you'll need to market for it. I am still in journey to learn how to do marketing, content, building a business and everything. I think this is just a very beginning of my journey and hopefully there's more interesting one to share further down the road.

Behind the scene : fundraising pre-seed of an AI startup
reddit
LLM Vibe Score0
Human Vibe Score1
Consistent-Wafer7325This week

Behind the scene : fundraising pre-seed of an AI startup

A bit of feedback from our journey at our AI startup. We started prototyping stuff around agentic AI last winter with very cool underlying tech research based on some academic papers (I can send you links if you're interested in LLM orchestration). I'm a serial entrepreneur with 2x exits, nothing went fancy but enough to keep going into the next topic. This time, running an AI project has been a bit different and unique due to the huge interest around the topic. Here are a few insights. Jan \~ Mar: Research Nothing was serious, just a side project with a friend on weekends (the guy became our lead SWE). Market was promising and we had the convinction that our tech can be game changer in computer systems workflows. March \~ April: Market Waking Up Devin published their pre-seed $20m fundraising led by Founders Fund; they paved the market with legitimacy. I decided to launch some coffee meetings with a few angels in my network. Interest confirmed. Back to work on some more serious early prototyping; hard work started here. April \~ May: YC S24 (Fail) Pumped up by our prospective angels and the market waking up on the agentic topic, I applied to YC as a solo founder (was still looking for funds and co-founders). Eventually got rejected (no co-founder and not US-based). May \~ July: VC Dance (Momentum 1) Almost randomly at the same time we got rejected from YC, I got introduced to key members of the VC community by one of our prospective angels. Interest went crazy... tons of calls. Brace yourself here, we probably met 30\~40 funds (+ angels). Got strong interests from 4\~5 of them (3 to 5 meetings each), ultimately closed 1 and some interests which might convert later in the next stage. The legend of AI being hype is true. Majority of our calls went only by word of mouth, lots of inbounds, people even not having the deck would book us a call in the next 48h after saying hi. Also lots of "tourists," just looking because of AI but with no strong opinion on the subject to move further. The hearsay about 90% rejection is true. You'll have a lot of nos, ending some days exhausted and unmotivated. End July: Closing, the Hard Part The VC roadshow is kind of an art you need to master. You need to keep momentum high enough and looking over-subscribed. Good pre-seed VC deals are over-competitive, and good funds only focus on them; they will have opportunities to catch up on lost chances at the seed stage later. We succeeded (arduously) to close our 18\~24mo budget with 1 VC, a few angels, and some state-guaranteed debt. Cash in bank just on time for payday in August (don't under-estimate time of processing) Now: Launching and Prepping the Seed Round We're now in our first weeks of go-to-market with a lot of uncertainty but a very ambitious plan ahead. The good part of having met TONS of VCs during the pre-seed roadshow is that we met probably our future lead investors in these. What would look like a loss of time in the initial pre-seed VC meetings has been finally very prolific, helping us to refine our strategy, assessing more in-depth the market (investors have a lot of insights, they meet a lot of people... that's their full-time job). We now have clear milestones and are heading to raise our seed round by end of year/Q1 if stars stay aligned :) Don't give up, the show must go on.

We built a tool to help you find relevant grants. Would you pay for it?
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CliznitchThis week

We built a tool to help you find relevant grants. Would you pay for it?

Hi everyone, About a year ago, I asked you guys whether it would make sense to develop a tool to help entrepreneurs find relevant grants. Many of you provided incredibly valuable feedback, which we used to refine the concept. With this concept, we went through Techstars and finally launched a beta version of our grant scan tool last week! Along the way, we realized something interesting: when you ask a grant advisor which grants might be a great fit for you, they almost always recommend the ones they know well. This makes sense since most work on a success fee basis, and referring you to lesser-known grants (which take more time to write and have lower success rates) isn’t worth it for them. Plus, memorizing the details of 20,000+ grants is, understandably, pretty tough. Our platform uses AI to scan and analyze thousands of grants. It identifies the best matches, estimates your chances of success, and calculates how much time you might need for the application and reporting phases. We can then match you with a grant advisor with relevant expertise—whether to write the application for you or provide feedback on your draft. We’re considering launching both a free and a paid version. The free version would provide basic insights, while the paid version would include more comprehensive results, expert comments (such as explaining why certain grants are a good fit), and updates when new relevant grants become available. Both versions will allow you to connect with relevant experts. Would you pay for the paid version? And if so, which features should it include? Also, any general feedback is much appreciated! Thanks!

Am I on the right track?
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ayezee33This week

Am I on the right track?

This might be a little long for the average reader. But i'll do my best to format it so it's skimmable. Context I left my SaaS company 2 months ago. I was employee number 4 and helped them grow to 8 figures. I had a seat at the executive table and equity in the business. Burnt out and wanted to start my own thing. I forgot how hard it is to go from 0 👉 1 📚 Two schools of thought Build a product that solves your pain point and find others with that pain point Perform customer discovery calls until you get signal and start building + follow up with them 🥇 First approach For the last 45 days I built the product I wished I had when leading a 10 person marketing/sales team for the SaaS I was previously at. It checked all the boxes, pulled data, automated specific steps, showed the conversion tracking, data, etc. I launched it as a beta to my close network and the crowd went MILD. 😒 After some follow up - I realized I built something that already kind of exists and it's hard to convince others (even those who personally know me) that it's different or better. Undiscouraged, I am going to go back to the drawing board and try approach #2 above and schedule some customer discovery calls. 🥈 Second approach After trying and failing to turn the marketing numbers around at my last role I am convicted of 4 brutal truths about digital marketing today Truth #1 – AI-generated content is flooding the internet and ANYONE can and will be creating content with AI. Truth #2 – Ranking for high-volume keywords is harder than ever and probably not worth it anymore. Truth #3 – AI-driven efficiency is non-negotiable. If you haven’t installed AI in your business - you are WAY behind. Truth #4 – Most businesses are thinking about AI completely wrong. Easy button vs quality stair step. I have some early thoughts on how I would like to solve this (backed by data and some user stories). But my main question and the entire point of this post is.... ⁉️ Questions Before I schedule these product discovery calls should I make it clear where I am convicted and find those who want to talk (agree or disagree) with the above. Or just keep that out of the mix and ask them my product discovery questions regardless? I am probably overthinking it - but I just hit up my personal network with a beta launch, feels silly to go back with product discovery questions for them. Is there a good place (besides reddit) to pay people for product discovery calls? A quick Google Search and it's unclear to me.

10y of product development, 2 bankruptcies, and 1 Exit — what next? [Extended Story]
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Slight-Explanation29This week

10y of product development, 2 bankruptcies, and 1 Exit — what next? [Extended Story]

10 years of obsessive pursuit from the bottom to impressive product-market fit and exit. Bootstrapping tech products as Software Developer and 3x Startup Founder (2 bankruptcies and 1 exit). Hi everyone, your motivation has inspired me to delve deeper into my story. So, as promised to some of you, I've expanded on it a bit more, along with my brief reflections. There are many founders, product creators, and proactive individuals, I’ve read many of your crazy stories and lessons so I decided to share mine and the lessons I learned from the bottom to impressive product-market fit and exit. I've spent almost the past 10 years building tech products as a Corporate Team Leader, Senior Software Developer, Online Course Creator, Programming Tutor, Head of Development/CTO, and 3x Startup Founder (2 bankruptcies, and 1 exit). And what next? good question... A brief summary of my journey: Chapter 1: Software Developer / Team Leader / Senior Software Developer I’ve always wanted to create products that win over users’ hearts, carry value, and influence users. Ever since my school days, I’ve loved the tech part of building digital products. At the beginning of school, I started hosting servers for games, blogs and internet forums, and other things that did not require much programming knowledge. My classmates and later even over 100 people played on servers that I hosted on my home PC. Later, as the only person in school, I passed the final exam in computer science. During my computer science studies, I started my first job as a software developer. It was crazy, I was spending 200–300 hours a month in the office attending also to daily classes. Yes, I didn’t have a life, but it truly was the fulfillment of my dreams. I was able to earn good money doing what I love, and I devoted fully myself to it. My key to effectively studying IT and growing my knowledge at rocket speed was learning day by day reading guides, building products to the portfolio, watching youtube channels and attending conferences, and even watching them online, even if I didn’t understand everything at the beginning. In one year we’ve been to every possible event within 400km. We were building healthcare products that were actually used in hospitals and medical facilities. It was a beautiful adventure and tons of knowledge I took from this place. That time I built my first product teams, hired many great people, and over the years became a senior developer and team leader. Even I convinced my study mates to apply to this company and we studied together and worked as well. Finally, there were 4 of us, when I left a friend of mine took over my position and still works there. If you’re reading this, I’m sending you a flood of love and appreciation. I joined as the 8th person, and after around 4 years, when I left hungry for change, there were already over 30 of us, now around 100. It was a good time, greetings to everyone. I finished my Master’s and Engineering degrees in Computer Science, and it was time for changes. Chapter 2: 1st time as a Co-founder — Marketplace In the meantime, there was also my first startup (a marketplace) with four of my friends. We all worked on the product, each of us spent thousands of hours, after hours, entire weekends… and I think finally over a year of work. As you might guess, we lacked the most important things: sales, marketing, and product-market fit. We thought users think like us. We all also worked commercially, so the work went very smoothly, but we didn’t know what we should do next with it… Finally, we didn’t have any customers, but you know what, I don’t regret it, a lot of learning things which I used many times later. The first attempts at validating the idea with the market and business activities. In the end, the product was Airbnb-sized. Landing pages, listings, user panels, customer panels, admin site, notifications, caches, queues, load balancing, and much more. We wanted to publish the fully ready product to the market. It was a marketplace, so if you can guess, we had to attract both sides to be valuable. “Marketplace” — You can imagine something like Uber, if you don’t have passengers it was difficult to convince taxi drivers, if you don’t have a large number of taxi drivers you cannot attract passengers. After a year of development, we were overloaded, and without business, marketing, sales knowledge, and budget. Chapter 3: Corp Team Lead / Programming Tutor / Programming Architecture Workshop Leader Working in a corporation, a totally different environment, an international fintech, another learning experience, large products, and workmates who were waiting for 5 pm to finish — it wasn’t for me. Very slow product development, huge hierarchy, being an ant at the bottom, and low impact on the final product. At that time I understood that being a software developer is not anything special and I compared my work to factory worker. Sorry for that. High rates have been pumped only by high demand. Friends of mine from another industry do more difficult things and have a bigger responsibility for lower rates. That’s how the market works. This lower responsibility time allowed for building the first online course after hours, my own course platform, individual teaching newbies programming, and my first huge success — my first B2C customers, and B2B clients for workshops. I pivoted to full focus on sales, marketing, funnels, advertisements, demand, understanding the market, etc. It was 10x easier than startups but allowed me to learn and validate my conceptions and ideas on an easier market and showed me that it’s much easier to locate their problem/need/want and create a service/product that responds to it than to convince people of your innovative ideas. It’s just supply and demand, such a simple and basic statement, in reality, is very deep and difficult to understand without personal experience. If you’re inexperienced and you think you understand, you don’t. To this day, I love to analyze this catchword in relation to various industries / services / products and rediscover it again and again... While writing this sentence, I’m wondering if I’m not obsessed. Chapter 4: Next try — 2nd time as a founder — Edtech Drawing upon my experiences in selling services, offering trainings, and teaching programming, I wanted to broaden my horizons, delve into various fields of knowledge, involve more teachers, and so on. We started with simple services in different fields of knowledge, mainly relying on teaching in the local area (without online lessons). As I had already gathered some knowledge and experience in marketing and sales, things were going well and were moving in the right direction. The number of teachers in various fields was growing, as was the number of students. I don’t remember the exact statistics anymore, but it was another significant achievement that brought me a lot of satisfaction and new experiences. As you know, I’m a technology lover and couldn’t bear to look at manual processes — I wanted to automate everything: lessons, payments, invoices, customer service, etc. That’s when I hired our first developers (if you’re reading this, I’m sending you a flood of love — we spent a lot of time together and I remember it as a very fruitful and great year) and we began the process of tool and automation development. After a year we had really extended tools for students, teachers, franchise owners, etc. We had really big goals, we wanted to climb higher and higher. Maybe I wouldn’t even fully call it Startup, as the client was paying for the lessons, not for the software. But it gave us positive income, bootstrap financing, and tool development for services provided. Scaling this model was not as costless as SaaS because customer satisfaction was mainly on the side of the teacher, not the quality of the product (software). Finally, we grew to nearly 10 people and dozens of teachers, with zero external funding, and almost $50k monthly revenue. We worked very hard, day and night, and by November 2019, we were packed with clients to the brim. And as you know, that’s when the pandemic hit. It turned everything upside down by 180 degrees. Probably no one was ready for it. With a drastic drop in revenues, society started to save. Tired from the previous months, we had to work even harder. We had to reduce the team, change the model, and save what we had built. We stopped the tool’s development and sales, and with the developers, we started supporting other product teams to not fire them in difficult times. The tool worked passively for the next two years, reducing incomes month by month. With a smaller team providing programming services, we had full stability and earned more than relying only on educational services. At the peak of the pandemic, I promised myself that it was the last digital product I built… Never say never… Chapter 5: Time for fintech — Senior Software Developer / Team Lead / Head of Development I worked for small startups and companies. Building products from scratch, having a significant impact on the product, and complete fulfillment. Thousands of hours and sacrifices. This article mainly talks about startups that I built, so I don’t want to list all the companies, products, and applications that I supported as a technology consultant. These were mainly start-ups with a couple of people up to around 100 people on board. Some of the products were just a rescue mission, others were building an entire tech team. I was fully involved in all of them with the hope that we would work together for a long time, but I wasn’t the only one who made mistakes when looking for a product-market fit. One thing I fully understood: You can’t spend 8–15 hours a day writing code, managing a tech team, and still be able to help build an audience. In marketing and sales, you need to be rested and very creative to bring results and achieve further results and goals. If you have too many responsibilities related to technology, it becomes ineffective. I noticed that when I have more free time, more time to think, and more time to bounce the ball against the wall, I come up with really working marketing/sales strategies and solutions. It’s impossible when you are focused on code all day. You must know that this chapter of my life was long and has continued until now. Chapter 6: 3rd time as a founder — sold Never say never… right?\\ It was a time when the crypto market was really high and it was really trending topic. You know that I love technology right? So I cannot miss the blockchain world. I had experience in blockchain topics by learning on my own and from startups where I worked before. I was involved in crypto communities and I noticed a “starving crowd”. People who did things manually and earned money(crypto) on it.I found potential for building a small product that solves a technological problem. I said a few years before that I don’t want to start from scratch. I decided to share my observations and possibilities with my good friend. He said, “If you gonna built it, I’m in”. I couldn’t stop thinking about it. I had thought and planned every aspect of marketing and sales. And you know what. On this huge mindmap “product” was only one block. 90% of the mindmap was focused on marketing and sales. Now, writing this article, I understood what path I went from my first startup to this one. In the first (described earlier) 90% was the product, but in the last one 90% was sales and marketing. Many years later, I did this approach automatically. What has changed in my head over the years and so many mistakes? At that time, the company for which I provided services was acquired. The next day I got a thank you for my hard work and all my accounts were blocked. Life… I was shocked. We were simply replaced by their trusted technology managers. They wanted to get full control. They acted a bit unkindly, but I knew that they had all my knowledge about the product in the documentation, because I’m used to drawing everything so that in the moment of my weakness (illness, whatever) the team could handle it. That’s what solid leaders do, right? After a time, I know that these are normal procedures in financial companies, the point is that under the influence of emotions, do not do anything inappropriate. I quickly forgot about it, that I was brutally fired. All that mattered was to bring my plan to life. And it has been started, 15–20 hours a day every day. You have to believe me, getting back into the game was incredibly satisfying for me. I didn’t even know that I would be so excited. Then we also noticed that someone was starting to think about the same product as me. So the race began a game against time and the market. I assume that if you have reached this point, you are interested in product-market fit, marketing, and sales, so let me explain my assumptions to you: Product: A very very small tool that allowed you to automate proper tracking and creation of on-chain transactions. Literally, the whole app for the user was located on only three subpages. Starving Crowd: We tapped into an underserved market. The crypto market primarily operates via communities on platforms like Discord, Reddit, Twitter, Telegram, and so on. Therefore, our main strategy was directly communicating with users and demonstrating our tool. This was essentially “free marketing” (excluding the time we invested), as we did not need to invest in ads, promotional materials, or convince people about the efficacy of our tool. The community could directly observe on-chain transactions executed by our algorithms, which were processed at an exceptionally fast rate. This was something they couldn’t accomplish manually, so whenever someone conducted transactions using our algorithm, it was immediately noticeable and stirred a curiosity within the community (how did they do that!). Tests: I conducted the initial tests of the application on myself — we had already invested significantly in developing the product, but I preferred risking my own resources over that of the users. I provided the tool access to my wallet, containing 0.3ETH, and went to sleep. Upon waking up, I discovered that the transactions were successful and my wallet had grown to 0.99ETH. My excitement knew no bounds, it felt like a windfall. But, of course, there was a fair chance I could have lost it too. It worked. As we progressed, some users achieved higher results, but it largely hinged on the parameters set by them. As you can surmise, the strategy was simple — buy low, sell high. There was considerable risk involved. Churn: For those versed in marketing, the significance of repeat visitors cannot be overstated. Access to our tool was granted only after email verification and a special technique that I’d prefer to keep confidential. And this was all provided for free. While we had zero followers on social media, we saw an explosion in our email subscriber base and amassed a substantial number of users and advocates. Revenue Generation: Our product quickly gained popularity as we were effectively helping users earn — an undeniable value proposition. Now, it was time to capitalize on our efforts. We introduced a subscription model charging $300 per week or $1,000 per month — seemingly high rates, but the demand was so intense that it wasn’t an issue. Being a subscriber meant you were prioritized in the queue, ensuring you were among the first to reap benefits — thus adding more “value”. Marketing: The quality of our product and its ability to continually engage users contributed to it achieving what can best be described as viral. It was both a source of pride and astonishment to witness users sharing charts and analyses derived from our tool in forum discussions. They weren’t actively promoting our product but rather using screenshots from our application to illustrate certain aspects of the crypto world. By that stage, we had already assembled a team to assist with marketing, and programming, and to provide round-the-clock helpdesk support. Unforgettable Time: Despite the hype, my focus remained steadfast on monitoring our servers, their capacity, and speed. Considering we had only been on the market for a few weeks, we were yet to implement alerts, server scaling, etc. Our active user base spanned from Japan to the West Coast of the United States. Primarily, our application was used daily during the evenings, but considering the variety of time zones, the only time I could afford to sleep was during the evening hours in Far Eastern Europe, where we had the least users. However, someone always needed to be on guard, and as such, my phone was constantly by my side. After all, we couldn’t afford to let our users down. We found ourselves working 20 hours a day, catering to thousands of users, enduring physical fatigue, engaging in talks with VCs, and participating in conferences. Sudden Downturn: Our pinnacle was abruptly interrupted by the war in Ukraine (next macroeconomic shot straight in the face, lucky guy), a precipitous drop in cryptocurrency value, and swiftly emerging competition. By this time, there were 5–8 comparable tools had infiltrated the market. It was a challenging period as we continually stumbled upon new rivals. They immediately embarked on swift fundraising endeavors — a strategy we overlooked, which in retrospect was a mistake. Although our product was superior, the competitors’ rapid advancement and our insufficient funds for expeditious scaling posed significant challenges. Nonetheless, we made a good decision. We sold the product (exit) to competitors. The revenue from “exit” compensated for all the losses, leaving us with enough rest. We were a small team without substantial budgets for rapid development, and the risk of forming new teams without money to survive for more than 1–2 months was irresponsible. You have to believe me that this decision consumed us sleepless nights. Finally, we sold it. They turned off our app but took algorithms and users. Whether you believe it or not, after several months of toiling day and night, experiencing burnout, growing weary of the topic, and gaining an extra 15 kg in weight, we finally found our freedom… The exit wasn’t incredibly profitable, but we knew they had outdone us. The exit covered all our expenses and granted us a well-deserved rest for the subsequent quarter. It was an insane ride. Despite the uncertainty, stress, struggles, and sleepless nights, the story and experience will remain etched in my memory for the rest of my life. Swift Takeaways: Comprehending User Needs: Do you fully understand the product-market fit? Is your offering just an accessory or does it truly satisfy the user’s needs? The Power of Viral Marketing: Take inspiration from giants like Snapchat, ChatGPT, and Clubhouse. While your product might not attain the same scale (but remember, never say never…), the closer your concept is to theirs, the easier your journey will be. If your user is motivated to text a friend saying, “Hey, check out how cool this is” (like sharing ChatGPT), then you’re on the best track. Really. Even if it doesn’t seem immediately evident, there could be a way to incorporate this into your product. Keep looking until you find it. Niche targeting — the more specific and tailored your product is to a certain audience, the easier your journey will be People love buying from people — establishing a personal brand and associating yourself with the product can make things easier. Value: Seek to understand why users engage with your product and keep returning. The more specific and critical the issue you’re aiming to solve, the easier your path will be. Consider your offerings in terms of products and services and focus on sales and marketing, regardless of personal sentiments. These are just a few points, I plan to elaborate on all of them in a separate article. Many products undergo years of development in search of market fit, refining the user experience, and more. And guess what? There’s absolutely nothing wrong with that. Each product and market follows its own rules. Many startups have extensive histories before they finally make their mark (for instance, OpenAI). This entire journey spanned maybe 6–8 months. I grasped and capitalized on the opportunity, but we understood from the start that establishing a startup carried a significant risk, and our crypto product was 10 times riskier. Was it worth it? Given my passion for product development — absolutely. Was it profitable? — No, considering the hours spent — we lose. Did it provide a stable, problem-free life — nope. Did this entire adventure offer a wealth of happiness, joy, and unforgettable experiences — definitely yes. One thing is certain — we’ve amassed substantial experience and it’s not over yet :) So, what lies ahead? Chapter 7: Reverting to the contractor, developing a product for a crypto StartupReturning to the past, we continue our journey… I had invested substantial time and passion into the tech rescue mission product. I came on board as the technical Team Leader of a startup that had garnered over $20M in seed round funding, affiliated with the realm of cryptocurrencies. The investors were individuals with extensive backgrounds in the crypto world. My role was primarily technical, and there was an abundance of work to tackle. I was fully immersed, and genuinely devoted to the role. I was striving for excellence, knowing that if we secured another round of financing, the startup would accelerate rapidly. As for the product and marketing, I was more of an observer. After all, there were marketing professionals with decades of experience on board. These were individuals recruited from large crypto-related firms. I had faith in them, kept an eye on their actions, and focused on my own responsibilities. However, the reality was far from satisfactory. On the last day, the principal investor for the Series A round withdrew. The board made the tough decision to shut down. It was a period of intense observation and gaining experience in product management. This was a very brief summary of the last 10 years. And what next? (Last) Chapter 8: To be announced — Product Owner / Product Consultant / Strategist / CTO After spending countless hours and days deliberating my next steps, one thing is clear: My aspiration is to continue traversing the path of software product development, with the hopeful anticipation that one day, I might ride the crest of the next big wave and ascend to the prestigious status of a unicorn company. I find myself drawn to the process of building products, exploring product-market fit, strategizing, engaging in software development, seeking out new opportunities, networking, attending conferences, and continuously challenging myself by understanding the market and its competitive landscape. Product Owner / Product Consultant / CTO / COO: I’m not entirely sure how to categorize this role, as I anticipate that it will largely depend on the product to which I will commit myself fully. My idea is to find one startup/company that wants to build a product / or already has a product, want to speed up, or simply doesn’t know what’s next. Alternatively, I could be a part of an established company with a rich business history, which intends to invest in digitization and technological advancements. The goal would be to enrich their customer experience by offering complementary digital products Rather than initiating a new venture from ground zero with the same team, I am receptive to new challenges. I am confident that my past experiences will prove highly beneficial for the founders of promising, burgeoning startups that already possess a product, or are in the initial phases of development. ‘Consultant’ — I reckon we interpret this term differently. My aim is to be completely absorbed in a single product, crafting funnels, niches, strategies, and all that is necessary to repeatedly achieve the ‘product-market fit’ and significant revenue. To me, ‘consultant’ resonates more akin to freelancing than being an employee. My current goal is to kickstart as a consultant and aide, dealing with facilitating startups in their journey from point A to B. Here are two theoretical scenarios to illustrate my approach: Scenario 1: (Starting from point A) You have a product but struggle with marketing, adoption, software, strategy, sales, fundraising, or something else. I conduct an analysis and develop a strategy to reach point B. I take on the “dirty work” and implement necessary changes, including potential pivots or shifts (going all-in) to guide the product to point B. The goal is to reach point B, which could involve achieving a higher valuation, expanding the user base, increasing sales, or generating monthly revenue, among other metrics. Scenario 2: (Starting from point A) You have a plan or idea but face challenges with marketing, adoption, strategy, software, sales, fundraising, or something else. I analyze the situation and devise a strategy to reach point B. I tackle the necessary tasks, build the team, and overcome obstacles to propel the product to point B. I have come across the view that finding the elusive product-market fit is the job of the founder, and it’s hard for me to disagree. However, I believe that my support and experiences can help save money, many failures, and most importantly, time. I have spent a great deal of time learning from my mistakes, enduring failure after failure, and even had no one to ask for support or opinion, which is why I offer my help. Saving even a couple of years, realistically speaking, seems like a value I’m eager to provide… I invite you to share your thoughts and insights on these scenarios :) Closing Remarks: I appreciate your time and effort in reaching this point. This has been my journey, and I wouldn’t change it for the world. I had an extraordinary adventure, and now I’m ready for the next exciting battle with the market and new software products. While my entire narrative is centered around startups, especially the ones I personally built, I’m planning to share more insights drawn from all of my experiences, not just those as a co-founder. If you’re currently developing your product or even just considering the idea, I urge you to reach out to me. Perhaps together, we can create something monumental :) Thank you for your time and insights. I eagerly look forward to engaging in discussions and hearing your viewpoints. Please remember to like and subscribe. Nothing motivates to write more than positive feedback :) Matt.

What I Learned from a Failed Startup: Seeking Advice on Engineering, Co-Founder Agreements & Execution (i will not promote)
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GummyBear8659This week

What I Learned from a Failed Startup: Seeking Advice on Engineering, Co-Founder Agreements & Execution (i will not promote)

Hey everyone! Long-time lurker, first-time founder here. I’m reaching out to get feedback on a recent startup experience—what went wrong, what I could have done better, and how I should approach future opportunities. The Background There were three founders in this venture: • Founder A (CEO, 50%) – The product/growth guy who identified the problem space. • Founder B (Me, CTO, 37.5%) – A software engineer with a software dev shop and multiple clients. I wanted to diversify into building my own products but am not inherently a “product person.” • Founder C (COO, 12.5%) – Brought into the mix by Founder A, with the goal of leveraging his network for traction once the product was built. The idea was to create Product X, a solution targeting the SMB space while competitors were moving upmarket. It wasn’t revolutionary—more of a strategic market play. The Initial Plan & My Role • Founder A would define and prioritize product specs, guiding what needed to be built. • I (Founder B) didn’t have time to code myself, so I allocated engineers from my dev shop (which I personally paid for). My stake was adjusted from 32.5% to 37.5% to reflect this contribution. • Founder C was more of an observer early on, planning to help with traction once we had a product ready. We agreed on a 1-year cliff and a 4-year vesting schedule for equity. Where Things Started to Go Wrong • Lack of a Clear Product Roadmap – Founder A was very focused on getting something built fast, but we never signed off on a structured roadmap or milestones. I underestimated the complexity of what was actually needed for customer conversations. • Engineering Expectations vs. Reality – The team (one part-time lead + two full-time juniors from my dev shop) faced early feedback that development was too slow. In response, I ramped up the lead to full-time and added a part-time PM. But Founder A continued pushing for speed, despite real hurdles (OAuth integrations, etc.). • Shifting MVP Goalposts – Midway, Founder A concluded that an MVP wouldn’t cut it—we needed a more complete product to be competitive. This meant more engineering, more delays, and more of my own money spent on development. The Breaking Point Near the 1-year vesting mark, we had an opportunity: a paying client willing to fund an app. I didn’t have devs on the bench, so I asked Founder A to hold off our project briefly while I hired more engineers to avoid stalling either effort. This was the final straw. Founder A (with Founder C somewhat aligned) decided the arrangement wasn’t working—citing past disagreements and the “slowness” issue. The decision was made to end the partnership. Now, Founder A, as majority holder, is requesting a full handover of the code, Founder C is indifferent, and all engineering costs I covered are essentially lost. Key Takeaways (So Far) Crystal-Clear Agreements Upfront – A formalized product roadmap and timeline should’ve been locked in from day one. Business Needs > Engineering Standards – I wanted to build something solid and scalable, but in an early-stage startup, speed to market is king. This was before AI tools became mainstream, so our approach wasn’t as optimized. Don’t Overextend Without Protection – I personally financed all engineering, but without clear safeguards, that investment became a sunk cost. Expenses Must Be Distributed – I was solely covering engineering salaries, which created an imbalance in financial risk. Future partnerships should ensure costs are shared proportionally, rather than one person shouldering the burden. Where I Need Advice Looking back, I want to improve as an engineer, CEO, and co-founder. • What should I have done differently in structuring this partnership? • How do you balance engineering quality with the startup need for speed? • As a dev shop owner, how can I better navigate equity deals where I’m also bringing in engineering resources? I really appreciate everyone who went through this long post and provide any insights from founders, engineers, or anyone who has been in a similar situation. Thanks for reading! ===================================================================== For readers who might be thinking what set this type of expectation? Because I had a dev shop and I thought my co-founders will be understanding of my business circumstance and I was a bit trigger to build a product with a C-exec team, I gave the impression of "unlimited" engineering which I later realized down the line that it was not feasible for me. Something I learned that I have to be more careful with and set expectations accordingly from the very beginning. And from the feedback of the commenters here, I am much more aware what I should offer and how to set expectations, esp. in the early stages of execution. So thank you all! 🙏🏾 EDIT: I would like to thank everyone who contributed to this thread. You not only helped me but future founders who are considering to get into the startup scene!

From Running a $350M Startup to Failing Big and Rediscovering What Really Matters in Life ❤️
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Disastrous-Airport88This week

From Running a $350M Startup to Failing Big and Rediscovering What Really Matters in Life ❤️

This is my story. I’ve always been a hustler. I don’t remember a time I wasn’t working since I was 14. Barely slept 4 hours a night, always busy—solving problems, putting out fires. After college (LLB and MBA), I was lost. I tried regular jobs but couldn’t get excited, and when I’m not excited, I spiral. But I knew entrepreneurship; I just didn’t realize it was an option for adults. Then, in 2017 a friend asked me to help with their startup. “Cool,” I thought. Finally, a place where I could solve problems all day. It was a small e-commerce idea, tackling an interesting angle. I worked 17-hour days, delivering on a bike, talking to customers, vendors, and even random people on the street. Things moved fast. We applied to Y Combinator, got in, and raised $18M before Demo Day even started. We grew 100% month-over-month. Then came another $40M, and I moved to NYC. Before I knew it, we had 1,000 employees and raised $80M more. I was COO, managing 17 direct reports (VPs of Ops, Finance, HR, Data, and more) and 800 indirect employees. On the surface, I was on top of the world. But in reality, I was at rock bottom. I couldn’t sleep, drowning in anxiety, and eventually ended up on antidepressants. Then 2022 hit. We needed to raise $100M, but we couldn’t. In three brutal months, we laid off 900 people. It was the darkest period of my life. I felt like I’d failed everyone—myself, investors, my company, and my team. I took a year off. Packed up the car with my wife and drove across Europe, staying in remote places, just trying to calm my nervous system. I couldn’t speak to anyone, felt ashamed, and battled deep depression. It took over a year, therapy, plant medicine, intense morning routines, and a workout regimen to get back on my feet, physically and mentally. Now, I’m on the other side. In the past 6 months, I’ve been regaining my mojo, with a new respect for who I am and why I’m here. I made peace with what I went through over those 7 years—the lessons, the people, the experiences. I started reconnecting with my community, giving back. Every week, I have conversations with young founders, offering direction, or even jumping in to help with their operations. It’s been a huge gift. I also began exploring side projects. I never knew how to code, but I’ve always had ideas. Recent advances in AI gave me the push I needed. I built my first app, as my first attempt at my true passion—consumer products for kids. Today, I feel wholesome about my journey. I hope others can see that too. ❤️ EDIT: Wow, I didn’t expect this post to resonate with so many people. A lot of you have DM’d me, and I’ll try to respond. Just a heads-up, though—I’m juggling consulting and new projects, so I can’t jump on too many calls. Since I’m not promoting anything, I won’t be funneling folks to my page, so forgive me if I don’t get back to everyone. Anyway, it’s amazing to connect with so many of you. I’d love to write more, so let me know what topics you’d be interested in!

What I Learned from a Failed Startup: Seeking Advice on Engineering, Co-Founder Agreements & Execution (i will not promote)
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GummyBear8659This week

What I Learned from a Failed Startup: Seeking Advice on Engineering, Co-Founder Agreements & Execution (i will not promote)

Hey everyone! Long-time lurker, first-time founder here. I’m reaching out to get feedback on a recent startup experience—what went wrong, what I could have done better, and how I should approach future opportunities. The Background There were three founders in this venture: • Founder A (CEO, 50%) – The product/growth guy who identified the problem space. • Founder B (Me, CTO, 37.5%) – A software engineer with a software dev shop and multiple clients. I wanted to diversify into building my own products but am not inherently a “product person.” • Founder C (COO, 12.5%) – Brought into the mix by Founder A, with the goal of leveraging his network for traction once the product was built. The idea was to create Product X, a solution targeting the SMB space while competitors were moving upmarket. It wasn’t revolutionary—more of a strategic market play. The Initial Plan & My Role • Founder A would define and prioritize product specs, guiding what needed to be built. • I (Founder B) didn’t have time to code myself, so I allocated engineers from my dev shop (which I personally paid for). My stake was adjusted from 32.5% to 37.5% to reflect this contribution. • Founder C was more of an observer early on, planning to help with traction once we had a product ready. We agreed on a 1-year cliff and a 4-year vesting schedule for equity. Where Things Started to Go Wrong • Lack of a Clear Product Roadmap – Founder A was very focused on getting something built fast, but we never signed off on a structured roadmap or milestones. I underestimated the complexity of what was actually needed for customer conversations. • Engineering Expectations vs. Reality – The team (one part-time lead + two full-time juniors from my dev shop) faced early feedback that development was too slow. In response, I ramped up the lead to full-time and added a part-time PM. But Founder A continued pushing for speed, despite real hurdles (OAuth integrations, etc.). • Shifting MVP Goalposts – Midway, Founder A concluded that an MVP wouldn’t cut it—we needed a more complete product to be competitive. This meant more engineering, more delays, and more of my own money spent on development. The Breaking Point Near the 1-year vesting mark, we had an opportunity: a paying client willing to fund an app. I didn’t have devs on the bench, so I asked Founder A to hold off our project briefly while I hired more engineers to avoid stalling either effort. This was the final straw. Founder A (with Founder C somewhat aligned) decided the arrangement wasn’t working—citing past disagreements and the “slowness” issue. The decision was made to end the partnership. Now, Founder A, as majority holder, is requesting a full handover of the code, Founder C is indifferent, and all engineering costs I covered are essentially lost. Key Takeaways (So Far) Crystal-Clear Agreements Upfront – A formalized product roadmap and timeline should’ve been locked in from day one. Business Needs > Engineering Standards – I wanted to build something solid and scalable, but in an early-stage startup, speed to market is king. This was before AI tools became mainstream, so our approach wasn’t as optimized. Don’t Overextend Without Protection – I personally financed all engineering, but without clear safeguards, that investment became a sunk cost. Expenses Must Be Distributed – I was solely covering engineering salaries, which created an imbalance in financial risk. Future partnerships should ensure costs are shared proportionally, rather than one person shouldering the burden. Where I Need Advice Looking back, I want to improve as an engineer, CEO, and co-founder. • What should I have done differently in structuring this partnership? • How do you balance engineering quality with the startup need for speed? • As a dev shop owner, how can I better navigate equity deals where I’m also bringing in engineering resources? I really appreciate everyone who went through this long post and provide any insights from founders, engineers, or anyone who has been in a similar situation. Thanks for reading! ===================================================================== For readers who might be thinking what set this type of expectation? Because I had a dev shop and I thought my co-founders will be understanding of my business circumstance and I was a bit trigger to build a product with a C-exec team, I gave the impression of "unlimited" engineering which I later realized down the line that it was not feasible for me. Something I learned that I have to be more careful with and set expectations accordingly from the very beginning. And from the feedback of the commenters here, I am much more aware what I should offer and how to set expectations, esp. in the early stages of execution. So thank you all! 🙏🏾 EDIT: I would like to thank everyone who contributed to this thread. You not only helped me but future founders who are considering to get into the startup scene!

Seeking advice from every type of business owner - if you have a moment & an opinion please chime in.
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Organic_Crab7397This week

Seeking advice from every type of business owner - if you have a moment & an opinion please chime in.

Hello everyone. I haven't started selling yet and wanted to get some insight from the community I'm trying to serve (that makes the most sense to me). So over the past couple months I've gotten into AI & Automation. I got a HighLevel account and went to town learning new things. I learned how to make automations and workflows that make running a business easier (my dad has been letting me use his concrete business as a guinea pig). I also learned how to build and train AI Chat Assistants. I want to start a service based business that uses AI & workflows to automate some of the customer service tasks & lead generation for business. What I'm seeking advice about are as follows: NICHE SELECTION: Part of me thinks I shouldn't niche down in the beginning and just take whoever comes and niche down once I find an industry I'm comfortable with. Another side thinks I should choose one. What is your opinion on niche selection in the beginning? PRICING: I know that pricing largely depends on the value I bring to the client, but I've seen people doing the same or similar things as I want to do and charging vastly different prices. From $300- $2,000. While I think these solutions could absolutely help companies get and retain new business and reduce some of the workload of their staff -- I'm not comfortable charging a high price until I've got enough experience and data to justify that. ​ THESE ARE THE SERVICES I'M THINKING OF OFFERING: Customer Service Chat Assistant. This will be on the website as a "Live Chat". It also connects to Facebook Messenger & Google Business Chat. I'd train the chat assistant on everything related to the company; pertinent info (NAP, company mission, industry background), contact info, services / products / pricing, FAQs, current specials &/or discount codes (this can be changed monthly), how to handle upset clients, etc. It can also connect to a calendar like Google or Calendly so customers can make an appointment or schedule a call directly from the conversation. Missed Call Follow Up. If you're familiar with the platform HighLevel it's commonly called "Missed Call Text Back". The idea is that when a call is missed a text message is automatically fired to the prospect's phone saying something along the lines of "Hey this is \\\\\\ from \\\\\\\_. How can I help you?" and the business owner is alerted to the missed call via text notification. People have said they see a lot of success for their clients with this alone due to the instant follow up. I see a lot of people charging $300 /m. for this. My issues with this are: 1). The text fires automatically when the call is missed, but if the business owner isn't available to actually follow up and keep texting after the customer texts back, they will look inconsistent and bothersome. 2). Without context a prospect may wonder why you didn't answer when they called, but texted them instead. So my answer to these problems are #3. SMS Answering Service. It is essentially taking 2 + 1 and combining them. The missed call text goes out to the prospect, but with context on why they're being texted (because no one is available to take the call at the moment) and IF the prospect responds, a Customer Service Chat Assistant will take over the conversation with the goal of answering their questions and either getting them on the phone with the company via a call back OR helping them schedule an appointment. This offers a more consistent solution than just a text to the business owner / team & the prospect is contacted and helped (hopefully) before they have a chance to start calling a competitor. Lead Nurture / Lead Qualifying Sales Funnel. This one is more than just AI & automation. It's a full funnel. It can be for either Facebook or Google. The process is AD -> Landing Page -> AI Text Message Convo -> Booking/Schedule Call/ Appointment. Typically the ad will offer a lead magnet which they will claim on the LP by giving their information. After the form is submitted, they get a text message and begin a conversation with the AI. It can be trained to just walk them through a booking process, nurture a sale by answering questions and handling objections or to qualify leads. Lead qualification via text works well if you want to weed out who is serious versus who is curious. To be clear; I'd be making the ad, landing page & training the AI -- all parts of the funnel. For whichever service a few things are universal: \- All conversations; no matter what platform they're had on, all go to one inbox which is pretty helpful to see them all in one place. \- When scheduling / booking these can also collect payment. \- Tags can be added to keep track of how they came into the business and where they are in a sales pipeline. There are a lot of fun things I can do with these automations and I'm excited about learning more everyday. I'd really like to know what you think these services could be worth to a business. If you do reply please tell me what type of business you're in so I have an idea of what industries I should be looking towards. Thank you for any response I get as I know this was a long read! SN: I currently do digital marketing & web design as a freelancer.

80+ Social Media Updates Related to Business Marketing That Occurred in last 5 months
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lazymentorsThis week

80+ Social Media Updates Related to Business Marketing That Occurred in last 5 months

Tiktok expanded its caption limits from 100 to 500 Characters. Reddit Updates Search tools, Now you can search User Comments. “Comment search is here”. Pinterest Announces New Partnership with WooCommerce to Expand Product Listings. Google’s launched ‘multisearch’ feature that lets you search using text and image at the same time. Etsy sellers went on strike after platform increases transaction fees. Reddit launched $1 million fund to support various projects going on platform. Instagram is updating its ranking algorithm to put more focus on Original Content LinkedIn Added New tools In creator mode: improved content analytics and Updates profile video Options. Tiktok launched its own gif library “Effect House”. Instagram Updates Reels editing tools adding reordering clips feature. Google Search got a new label to direct people to original news sources YouTube launches new Profile Rings for Stories and Live. Snapchat launched YouTube Link stickers to make video sharing easier! Messenger adds new shortcuts, including a slack like @everyone feature. Pinterest Expands it’s Creator funds program to help more Underrepresented creators. Reddit brings back r/place after 5 years. Google Adds New Seller Performance Badges, New Pricing Insights for eCommerce Brands. Meta and Google agrees to New Data Transfer agreement to keep Instagram and Facebook running in EU. Twitter tests New Interactive Ad types to boost its promotional Appeal. Instagram removed In-stream Ads from its Advertising Options. Tiktok launched new program “CAP” to help creative agencies reach its audience. Twitch shuts down its desktop app. Meta launched the ability to add “share to Reels” feature to third Party Apps. TikTok Adds New ‘Background Player’ Option for Live-Streams. Twitter rolls out ALT badge and improved image description. Fast, A Checkout Startup with $15 billion valuation shuts down after spending all the funds raised in 2021. Wordpress announced new pricing with more traffic and storage limits after receiving backlash from the community. Sales force upgrades marketing field services and sales tools with AI. Dropbox shop launches in open beta to allow creators to sell digital content. Tiktok is the most downloaded app in Quarter 1 of 2022. WhatsApp announced launch of ‘Communities’ - more structured group chats with admin controls. Tiktok expands testing a private dislike button for comments. Twitter acquired “Openback” A notification app to improve timeline and relevance of push notifications YouTube and Tiktok added New options for Automated Captions, Improving Accessibility. A new social media App “Be Real” is trending across the internet grabbing Gen-Zs attention to try the app. WhatsApp got permission to expand payment services to its Indian user base of 100 Million. YouTube Shorts now allows creators to splice in long-form videos. You can use long form video audios and clips for YT shorts. New Snapchat feature ‘Dynamic Stories’ uses a publisher’s RSS feed to automatically create Stories posts. Zoom launches AI-powered features aimed at sales teams. Tiktok started testing who viewed your profile feature. Ogilvy Announced they will no longer work with who edit their bodies and faces for ads. If you don’t know “Oglivy” is the most successful advertising agency of the decade. YouTube Launches New ‘Search Insights’ for all creators. Snapchat Added 13 million new users in Q1 2022 more than both Twitter and Facebook. Google is Introduced new options to reject tracking cookies in Europe after receiving fines from violating EU data laws. Sony & Microsoft are planning to integrate Ads into their gaming platforms Xbox and PlayStation. YouTube Adds new Shorts Shelf to Trending Tab to show Top Shorts in an alternative section. Instagram started testing a reels template feature which enables creators to copy formats from other reels. Google Tests “What People Are Saying” Search Results. Twitter Launches New Test of Promotions for Third Party Tools Within the App. Instagram is changing how hashtags work by experimenting removing Recents tab from hashtags section. Google Adds New Publisher Verification Badges to Extension Listings in the Google Web Store Amazon AWS launches $30M accelerator program aimed at minority founders. Meta launched more fundraising options for Instagram Reels in 30 countries. Brave Search and DuckDuckGo will no longer support Google AMP due to privacy issues. Instagram is working on a pinned post feature and will officially launch in next few months. Meta: You can now add Music to your Facebook comments Twitter tests new closed caption button to switch on captions in Video Clip Elon Musk Bought Twitter $44 Billion and Company is set to go private. Google now lets you request the removal of personal contact information from search results YouTube reveals that Ads between YT Shorts are being tested with selective brands. LinkedInis rolling out a new website link feature. Google Reduces Visibility Of Business Edits With Color Changes To Profile Updates. Instagram expands testing of 90 second Reels. Microsoft Advertising now offers incentive features like cash-back and adding stock images from your website. Facebook & Pinterest are growing again despite all the hype around slow growth of both platform in last quarter. Google Added 9 new Ad policies to prevent misleading ads taking place. Tiktok Introduces Third-party cookies to its Pixel. (like Facebook Pixel) Twitter reportedly overcounted number of daily active users for last 3 years. Google launched Media CDN to compete on content delivery. YouTube expands Thank You Monetisation tool to all eligible creators. Twitch is looking to expand their cut from streamers earnings from 30 to 50% and also thinks of boosting Ads. Snapchat launches a $230 flying drone camera and new e-commerce integrations in Snap Summit 2022. YouTube Expands its ‘Pre-Publish Checks’ Tool to the Mobile App Google Search Console’s URL parameter tool is officially removed for a time period. Twitter creators can now get paid through Cryptocurrency on Twitter with Stripe. Jellysmack- One of the Influencer marketing agency acquires YouTube analytics tool Google & Microsoft Ads brought more revenue in last quarter- 22% Gains! WhatsApp is working on a paid subscription for multi-phone and tablet chatting. Instagram users now spend 20% of their time in the reels section. Google tests new Color for clicked search results by you. Now Clicked results are in Purple. Twitter: Elon plans to remove employees and focus more on influencers for twitter’s growth + new monetisation ideas were shared. YouTube revenue falls as more users spend time on shorts tab than consuming long form content. Drop 👋 to receive June Updates!

Seeking advice from every type of business owner - if you have a moment & an opinion please chime in.
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Organic_Crab7397This week

Seeking advice from every type of business owner - if you have a moment & an opinion please chime in.

Hello everyone. I haven't started selling yet and wanted to get some insight from the community I'm trying to serve (that makes the most sense to me). So over the past couple months I've gotten into AI & Automation. I got a HighLevel account and went to town learning new things. I learned how to make automations and workflows that make running a business easier (my dad has been letting me use his concrete business as a guinea pig). I also learned how to build and train AI Chat Assistants. I want to start a service based business that uses AI & workflows to automate some of the customer service tasks & lead generation for business. What I'm seeking advice about are as follows: NICHE SELECTION: Part of me thinks I shouldn't niche down in the beginning and just take whoever comes and niche down once I find an industry I'm comfortable with. Another side thinks I should choose one. What is your opinion on niche selection in the beginning? PRICING: I know that pricing largely depends on the value I bring to the client, but I've seen people doing the same or similar things as I want to do and charging vastly different prices. From $300- $2,000. While I think these solutions could absolutely help companies get and retain new business and reduce some of the workload of their staff -- I'm not comfortable charging a high price until I've got enough experience and data to justify that. ​ THESE ARE THE SERVICES I'M THINKING OF OFFERING: Customer Service Chat Assistant. This will be on the website as a "Live Chat". It also connects to Facebook Messenger & Google Business Chat. I'd train the chat assistant on everything related to the company; pertinent info (NAP, company mission, industry background), contact info, services / products / pricing, FAQs, current specials &/or discount codes (this can be changed monthly), how to handle upset clients, etc. It can also connect to a calendar like Google or Calendly so customers can make an appointment or schedule a call directly from the conversation. Missed Call Follow Up. If you're familiar with the platform HighLevel it's commonly called "Missed Call Text Back". The idea is that when a call is missed a text message is automatically fired to the prospect's phone saying something along the lines of "Hey this is \\\\\\ from \\\\\\\_. How can I help you?" and the business owner is alerted to the missed call via text notification. People have said they see a lot of success for their clients with this alone due to the instant follow up. I see a lot of people charging $300 /m. for this. My issues with this are: 1). The text fires automatically when the call is missed, but if the business owner isn't available to actually follow up and keep texting after the customer texts back, they will look inconsistent and bothersome. 2). Without context a prospect may wonder why you didn't answer when they called, but texted them instead. So my answer to these problems are #3. SMS Answering Service. It is essentially taking 2 + 1 and combining them. The missed call text goes out to the prospect, but with context on why they're being texted (because no one is available to take the call at the moment) and IF the prospect responds, a Customer Service Chat Assistant will take over the conversation with the goal of answering their questions and either getting them on the phone with the company via a call back OR helping them schedule an appointment. This offers a more consistent solution than just a text to the business owner / team & the prospect is contacted and helped (hopefully) before they have a chance to start calling a competitor. Lead Nurture / Lead Qualifying Sales Funnel. This one is more than just AI & automation. It's a full funnel. It can be for either Facebook or Google. The process is AD -> Landing Page -> AI Text Message Convo -> Booking/Schedule Call/ Appointment. Typically the ad will offer a lead magnet which they will claim on the LP by giving their information. After the form is submitted, they get a text message and begin a conversation with the AI. It can be trained to just walk them through a booking process, nurture a sale by answering questions and handling objections or to qualify leads. Lead qualification via text works well if you want to weed out who is serious versus who is curious. To be clear; I'd be making the ad, landing page & training the AI -- all parts of the funnel. For whichever service a few things are universal: \- All conversations; no matter what platform they're had on, all go to one inbox which is pretty helpful to see them all in one place. \- When scheduling / booking these can also collect payment. \- Tags can be added to keep track of how they came into the business and where they are in a sales pipeline. There are a lot of fun things I can do with these automations and I'm excited about learning more everyday. I'd really like to know what you think these services could be worth to a business. If you do reply please tell me what type of business you're in so I have an idea of what industries I should be looking towards. Thank you for any response I get as I know this was a long read! SN: I currently do digital marketing & web design as a freelancer.

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.

What if… Employers Employ AI Agents to Get 360° Feedback from Employees?
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AssistanceOk2217This week

What if… Employers Employ AI Agents to Get 360° Feedback from Employees?

AI Agent powered Comprehensive 360° Feedback Collection & Analysis Full Article ​ https://i.redd.it/1ieczv6pud1d1.gif ⚪ What is this Article About? ● This article demonstrates how AI agents can be used in the real-world for gathering feedback from employees ● It explores using AI agents to collect insights on employee experiences, job satisfaction, and suggestions for improvement ● By leveraging AI agents and language models, organizations can better understand their workforce's needs and concerns ⚪Why Read this Article? ● Learn about the potential benefits of using AI agents for comprehensive feedback collection ● Understand how to build practical, real-world solutions by combining AI agents with other technologies ● Stay ahead of the curve by exploring cutting-edge applications of AI agents ⚪What are we doing in this Project? \> Part 1: AI Agents to Coordinate and Gather Feedback ● AI agents collaborate to collect comprehensive feedback from employees through surveys and interviews ● Includes a Feedback Collector Agent, Feedback Analyst Agent, and Feedback Reporter Agent \> Part 2: Analyze Feedback Data with Pandas AI and Llama3 ● Use Pandas AI and Llama3 language model to easily analyze the collected feedback data ● Extract insights, identify patterns, strengths, and areas for improvement from the feedback ⚪ Let's Design Our AI Agent System for 360° Feedback \> Feedback Collection System: ● Collect feedback from employees (simulated) ● Analyze the feedback data ● Report findings and recommendations \> Feedback Analysis System: ● Upload employee feedback CSV file ● Display uploaded data ● Perform natural language analysis and queries ● Generate automated insights and visual graphs ⚪ Let's get Cooking ● Explanation of the code for the AI agent system and feedback analysis system ● Includes code details for functions, classes, and streamlit interface ⚪ Closing Thoughts ● AI agents can revolutionize how businesses operate and tackle challenges ● Their ability to coordinate, collaborate, and perform specialized tasks is invaluable ● AI agents offer versatile and scalable solutions for optimizing processes and uncovering insights ⚪ Future Work ● This project is a demo to show the potential real-world use cases of AI Agents. To achieve the results seen here, I went through multiple iterations and changes. AI Agents are not fully ready yet (although they are making huge progress every day). AI Agents still need to go through an improvement cycle to reach their full potential in real-world settings. ​

I'm Building an "AiExecutiveSuperAgent_Systems_Interface" between humanity and the Ai world, as well as each other... Let's Talk?
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Prudent_Ad_3114This week

I'm Building an "AiExecutiveSuperAgent_Systems_Interface" between humanity and the Ai world, as well as each other... Let's Talk?

Ok... So look... This one is pretty crazy... I'm building an Ai Interface that knows me better than I know myself - Check, lots of people have this, either in reality with employees and family members, or with ai intelligence. But it doesn't just know Me... It knows how to talk with Me. It understands my language, because I've trained it to. I've also trained it to translate that to all my clients and HumanAgents, soon to become RobotAgents... The RESULT: I can literally just spend 1-18 hours talking to it, and things get DONE. Most of that time, I just say EXECUTE, or ENGAGE, or DRAFT, or DISPATCH. I feel like a secret agent communicating in codes with his agency 😂 Not great for the paranoiac in me, but it's easy to get that part under control, ya'll. It's like having a team of 10,000 people, all available 24/7, all perfectly synchronised to each other's communication styles, preferences and ultimately: WHAT DO YOU NEED ME TO DO. At the end of the it all, having run my single COMMAND through a thousand of those people, a Document is prepared that outlines the next 3 stages of the plan, along with instructions to the whole team for how to ENACT it. Sounds rather grand and wonderful... Even when I simply use it to help me come up with a filing system for my creative work... \\\\\\\\\\\\\\\\\\\\\\ Here's my current VISION, why I'm doing this AND why I'm doing it publicly despite it being top secret. VISION To create an army of User-Owned and Operated "AiSuperAgencies" which gather intelligence on the user, securely file and analyse it, and then construct a sub-army of agents and tools that work together to produce the desired output, for any Function in the Personal and Professional Lives of EVERYONE, EVERYWHERE, in 3-5 Years. To start, I'm building it for me and the 5-10 cleaners who've made it to Level 1 in my access system. They were sick of toxic employers, tyrannical agencies and greedy customers. They gathered around us (many came in, many went out, few stayed, took about a year for our core team of 3 Level 2 Cleaners. My goal has always been to never employ anyone. Just me, my Partner and the Cleaners. All Shared Owners in the system for delivering the right cleaner to the right house in our town, at the right time and without any dramas or arguments... I have a personal talent for resolving disputes, which has made working for and buying from my business a mostly enjoyable and upbeat experience, with a touch of mystery and a feeling that you're part of something big! It is a business that ran on Me. I put in my time, every day, building automated tool after automated tool. Hiring a contractor to do a job, scratching my head when it didn't add enough value to pay for itself, then just doing it myself again. I wanted to solve that problem. I'm trusting that the few who hear about it who actually see the potential, will just come join us, no dramas, just cool people partnering up! And those that don't, won't. No one could steal it, because it's Mine, and I'll just change the keys anyway loser! Enjoy digging through my past, you lunatic! I'm out here living Now. Anyways... It's lonely around here. I have a cleaning business that I run from my laptop, which means I can live anywhere, but I still had this big problem of time... NOT ENOUGH Oh Wait. It's Here.

I'm Building an "AiExecutiveSuperAgent_Systems_Interface" between humanity and the Ai world, as well as each other... Let's Talk?
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Prudent_Ad_3114This week

I'm Building an "AiExecutiveSuperAgent_Systems_Interface" between humanity and the Ai world, as well as each other... Let's Talk?

Ok... So look... This one is pretty crazy... I'm building an Ai Interface that knows me better than I know myself - Check, lots of people have this, either in reality with employees and family members, or with ai intelligence. But it doesn't just know Me... It knows how to talk with Me. It understands my language, because I've trained it to. I've also trained it to translate that to all my clients and HumanAgents, soon to become RobotAgents... The RESULT: I can literally just spend 1-18 hours talking to it, and things get DONE. Most of that time, I just say EXECUTE, or ENGAGE, or DRAFT, or DISPATCH. I feel like a secret agent communicating in codes with his agency 😂 Not great for the paranoiac in me, but it's easy to get that part under control, ya'll. It's like having a team of 10,000 people, all available 24/7, all perfectly synchronised to each other's communication styles, preferences and ultimately: WHAT DO YOU NEED ME TO DO. At the end of the it all, having run my single COMMAND through a thousand of those people, a Document is prepared that outlines the next 3 stages of the plan, along with instructions to the whole team for how to ENACT it. Sounds rather grand and wonderful... Even when I simply use it to help me come up with a filing system for my creative work... \\\\\\\\\\\\\\\\\\\\\\ Here's my current VISION, why I'm doing this AND why I'm doing it publicly despite it being top secret. VISION To create an army of User-Owned and Operated "AiSuperAgencies" which gather intelligence on the user, securely file and analyse it, and then construct a sub-army of agents and tools that work together to produce the desired output, for any Function in the Personal and Professional Lives of EVERYONE, EVERYWHERE, in 3-5 Years. To start, I'm building it for me and the 5-10 cleaners who've made it to Level 1 in my access system. They were sick of toxic employers, tyrannical agencies and greedy customers. They gathered around us (many came in, many went out, few stayed, took about a year for our core team of 3 Level 2 Cleaners. My goal has always been to never employ anyone. Just me, my Partner and the Cleaners. All Shared Owners in the system for delivering the right cleaner to the right house in our town, at the right time and without any dramas or arguments... I have a personal talent for resolving disputes, which has made working for and buying from my business a mostly enjoyable and upbeat experience, with a touch of mystery and a feeling that you're part of something big! It is a business that ran on Me. I put in my time, every day, building automated tool after automated tool. Hiring a contractor to do a job, scratching my head when it didn't add enough value to pay for itself, then just doing it myself again. I wanted to solve that problem. I'm trusting that the few who hear about it who actually see the potential, will just come join us, no dramas, just cool people partnering up! And those that don't, won't. No one could steal it, because it's Mine, and I'll just change the keys anyway loser! Enjoy digging through my past, you lunatic! I'm out here living Now. Anyways... It's lonely around here. I have a cleaning business that I run from my laptop, which means I can live anywhere, but I still had this big problem of time... NOT ENOUGH Oh Wait. It's Here.

Compare trading strategies on the fly - pnl.ai - please check it out
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varturasThis week

Compare trading strategies on the fly - pnl.ai - please check it out

Part of my covid project and part of my long obsession with prediction markets, I have created a web page that displays and allows to compare best and worst performing trading strategies. TL;DR: best stocks + best strategies -> the list of top and bottom performing trading algorithms.  Product Typically, trading newsletters and stock-scanners display only price return for top market gainers and losers. I have forever been interested in inspecting top and bottom performing trading strategies for a given set of securities and could not find any websites that do that. So, I decided to create a tool of my own. I wanted the tool that would help me to answer questions like if there is a better strategy than buy and hold, should I follow greed and fear indicator of the market or do the opposite. Top and bottom performing securities do not tell you if a stock is going to go up or down, but they do alert you to rapidly changing market conditions, such as change in the competitive landscape, impending lawsuits, changes in the company's management and, at the very least, the stocks you should avoid in your programmatic trading. Top strategies do all that, but they can also alert you to a change in the market regime. For example, MACD strategy, which is a variant of oscillator strategy, executed on Citibank stock returned 20% in the first half of 2020. In the same time period, the Citibank stock went down and "BuyAndHold" strategy, which is pretty much what it sounds, lost 45%. Now, compare that to the end of 2020 through spring of 2021, when MACD lost 1% and "BuyAndHold" gained 70%. This happened due to the change in the market due to the rally in financial stocks at the end of 2020. The market player who detect change in the market conditions first will reap most benefits. Another example, TSLA since the beginning of 2021 until end of April lost 7%. The StopLoss strategy sells the position after abrupt price drop and waits until the price returns to the level before the drop. For the same time interval the StopLoss strategy gained 10%. In this particular example, StopLoss outperformed BuyAndHold. To me personally, the most important feature is the ability to quickly tweak and modify trading strategies and observe change in their performance. You can change strategies parameters on the fly and even design your own custom trading strategy. In the end, I developed a tool I can use for myself but hope other investors who are experimenting with trading algorithms will find it useful as well. I called it "Profit and Loss AI", or PnL.ai for short. PnL.ai Description The web-tool in the link below allows you to customize parameters of existing strategies and essentially create your own strategy and seeing how it will compare to the set of original strategies. http://ec2-54-185-19-38.us-west-2.compute.amazonaws.com:5006/srv In the section above you can specify security and data range. In the section below you can choose strategy to customize and modify it's parameters. The strategy comparison table will automatically update and will display a newly created strategy side by side with the original strategies. Technology The tool is developed on bokeh and python and allows you to edit configuration parameters of each strategy all without programming knowledge. The strategies are fully specified via key/value pairs in the format of ini files used to initialize programs. The strategy classes are autogenerated by reading the ini config files dynamically using "factory" pattern. You can find a simplified code in this github repo: https://github.com/varturas/PnlAITk Next Steps In the future I want to give users ability to monitor their chosen strategy by receiving trading algo alerts whenever performance of their custom trading algo is changes significantly. I'm going to be adding more strategies, some of standard technical analysis variety and some will be more custom and more advanced. I'll also be adding more columns to the performance table to give better information. You can receive daily newsletter with the list of trading strategies generated by above-mentioned web-tool by registering on http://pnl.ai/ and checking subscribe checkmark.

How me and my team made 15+ apps and not made a single sale in 2023
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MichaelbetterecycleThis week

How me and my team made 15+ apps and not made a single sale in 2023

Hey, my name is Michael, I am in Auckland NZ. This year was the official beginning of my adult life. I graduated from university and started a full-time job. I’ve also really dug into indiehacking/bootstrapping and started 15 projects (and it will be at least 17 before the year ends). I think I’ve learned a lot but I consciously repeated mistakes. Upto (Nov) Discord Statuses + Your Location + Facebook Poke https://preview.redd.it/4nqt7tp2tf5c1.png?width=572&format=png&auto=webp&s=b0223484bc54b45b5c65e0b1afd0dc52f9c02ad1 This was the end of uni, I often messaged (and got messaged) requests of status and location to (and from my) friends. I thought, what if we make a social app that’s super basic and all it does is show you where your friends are? To differentiate from snap maps and others we wanted something with more privacy where you select the location. However, never finished the codebase or launched it. This is because I slowly started to realize that B2C (especially social networks) are way too hard to make into an actual business and the story with Fistbump would repeat itself. However, this decision not to launch it almost launched a curse on our team. From that point, we permitted ourselves to abandon projects even before launching. Lessons: Don’t do social networks if your goal is 10k MRR ASAP. If you build something to 90% competition ship it or you will think it’s okay to abandon projects Insight Bites (Nov) Youtube Summarizer Extension ​ https://preview.redd.it/h6drqej4tf5c1.jpg?width=800&format=pjpg&auto=webp&s=0f211456c390ac06f4fcb54aa51f9d50b0826658 Right after Upto, we started ideating and conveniently the biggest revolution in the recent history of tech was released → GPT. We instantly began ideating. The first problem we chose to use AI for is to summarize YouTube videos. Comical. Nevertheless, I am convinced we have had the best UX because you could right-click on a video to get a slideshow of insights instead of how everyone else did it. We dropped it because there was too much competition and unit economics didn’t work out (and it was a B2C). PodPigeon (Dec) Podcast → Tweet Threads https://preview.redd.it/0ukge245tf5c1.png?width=2498&format=png&auto=webp&s=23303e1cab330578a3d25cd688fa67aa3b97fb60 Then we thought, to make unit economics work we need to make this worthwhile for podcasters. This is when I got into Twitter and started seeing people summarize podcasts. Then I thought, what if we make something that converts a podcast into tweets? This was probably one of the most important projects because it connected me with Jason and Jonaed, both of whom I regularly stay in contact with and are my go-to experts on ideas related to content creation. Jonaed was even willing to buy Podpigeon and was using it on his own time. However, the unit economics still didn’t work out (and we got excited about other things). Furthermore, we got scared of the competition because I found 1 - 2 other people who did similar things poorly. This was probably the biggest mistake we’ve made. Very similar projects made 10k MRR and more, launching later than we did. We didn’t have a coherent product vision, we didn’t understand the customer well enough, and we had a bad outlook on competition and a myriad of other things. Lessons: I already made another post about the importance of outlook on competition. Do not quit just because there are competitors or just because you can’t be 10x better. Indiehackers and Bootstrappers (or even startups) need to differentiate in the market, which can be via product (UX/UI), distribution, or both. Asking Ace Intro.co + Crowdsharing ​ https://preview.redd.it/0hu2tt16tf5c1.jpg?width=1456&format=pjpg&auto=webp&s=3d397568ef2331e78198d64fafc1a701a3e75999 As I got into Twitter, I wanted to chat with some people I saw there. However, they were really expensive. I thought, what if we made some kind of crowdfunding service for other entrepreneurs to get a private lecture from their idols? It seemed to make a lot of sense on paper. It was solving a problem (validated via the fact that Intro.co is a thing and making things cheaper and accessible is a solid ground to stand on), we understood the market (or so we thought), and it could monetize relatively quickly. However, after 1-2 posts on Reddit and Indiehackers, we quickly learned three things. Firstly, no one cares. Secondly, even if they do, they think they can get the same information for free online. Thirdly, the reasons before are bad because for the first point → we barely talked to people, and for the second people → we barely talked to the wrong people. However, at least we didn’t code anything this time and tried to validate via a landing page. Lessons Don’t give up after 1 Redditor says “I don’t need this” Don’t be scared to choose successful people as your audience. Clarito Journaling with AI analyzer https://preview.redd.it/8ria2wq6tf5c1.jpg?width=1108&format=pjpg&auto=webp&s=586ec28ae75003d9f71b4af2520b748d53dd2854 Clarito is a classic problem all amateur entrepreneurs have. It’s where you lie to yourself that you have a real problem and therefore is validated but when your team asks you how much you would pay you say I guess you will pay, maybe, like 5 bucks a month…? Turns out, you’d have to pay me to use our own product lol. We sent it off to a few friends and posted on some forums, but never really got anything tangible and decided to move away. Honestly, a lot of it is us in our own heads. We say the market is too saturated, it’ll be hard to monetize, it’s B2C, etc. Lessons: You use the Mom Test on other people. You have to do it yourself as well. However, recognizing that the Mom Test requires a lot of creativity in its investigation because knowing what questions to ask can determine the outcome of the validation. I asked myself “Do I journal” but I didn’t ask myself “How often do I want GPT to chyme in on my reflections”. Which was practically never. That being said I think with the right audience and distribution, this product can work. I just don’t know (let alone care) about the audience that much (and I thought I was one of them)/ Horns & Claw Scrapes financial news texts you whether you should buy/sell the stock (news sentiment analysis) ​ https://preview.redd.it/gvfxdgc7tf5c1.jpg?width=1287&format=pjpg&auto=webp&s=63977bbc33fe74147b1f72913cefee4a9ebec9c2 This one we didn’t even bother launching. Probably something internal in the team and also seemed too good to be true (because if this works, doesn’t that just make us ultra-rich fast?). I saw a similar tool making 10k MRR so I guess I was wrong. Lessons: This one was pretty much just us getting into our heads. I declared that without an audience it would be impossible to ship this product and we needed to start a YouTube channel. Lol, and we did. And we couldn’t even film for 1 minute. I made bold statements like “We will commit to this for at least 1 year no matter what”. Learnery Make courses about any subject https://preview.redd.it/1nw6z448tf5c1.jpg?width=1112&format=pjpg&auto=webp&s=f2c73e8af23b0a6c3747a81e785960d4004feb48 This is probably the most “successful” project we’ve made. It grew from a couple of dozen to a couple of hundred users. It has 11 buy events for $9.99 LTD (we couldn’t be bothered connecting Stripe because we thought no one would buy it anyway). However what got us discouraged from seriously pursuing it more is, that this has very low defensibility, “Why wouldn’t someone just use chatGPT?” and it’s B2C so it’s hard to monetize. I used it myself for a month or so but then stopped. I don’t think it’s the app, I think the act of learning a concept from scratch isn’t something you do constantly in the way Learnery delivers it (ie course). I saw a bunch of similar apps that look like Ass make like 10k MRR. Lessons: Don’t do B2C, or if you do, do it properly Don’t just Mixpanel the buy button, connect your Stripe otherwise, it doesn’t feel real and you won’t get momentum. I doubt anyone (even me) will make this mistake again. I live in my GPT bubble where I make assumptions that everyone uses GPT the same way and as much as I do. In reality, the argument that this has low defensibility against GPT is invalid. Platforms that deliver a differentiated UX from ChatGPT to audiences who are not tightly integrated into the habit of using ChatGPT (which is like - everyone except for SOME tech evangelists). CuriosityFM Make podcasts about any subject https://preview.redd.it/zmosrcp8tf5c1.jpg?width=638&format=pjpg&auto=webp&s=d04ddffabef9050050b0d87939273cc96a8637dc This was our attempt at making Learnery more unique and more differentiated from chatGPT. We never really launched it. The unit economics didn’t work out and it was actually pretty boring to listen to, I don’t think I even fully listened to one 15-minute episode. I think this wasn’t that bad, it taught us more about ElevenLabs and voice AI. It took us maybe only 2-3 days to build so I think building to learn a new groundbreaking technology is fine. SleepyTale Make children’s bedtime stories https://preview.redd.it/14ue9nm9tf5c1.jpg?width=807&format=pjpg&auto=webp&s=267e18ec6f9270e6d1d11564b38136fa524966a1 My 8-year-old sister gave me that idea. She was too scared of making tea and I was curious about how she’d react if she heard a bedtime story about that exact scenario with the moral that I wanted her to absorb (which is that you shouldn’t be scared to try new things ie stop asking me to make your tea and do it yourself, it’s not that hard. You could say I went full Goebbels on her). Zane messaged a bunch of parents on Facebook but no one really cared. We showed this to one Lady at the place we worked from at Uni and she was impressed and wanted to show it to her kids but we already turned off our ElevenLabs subscription. Lessons: However, the truth behind this is beyond just “you need to be able to distribute”. It’s that you have to care about the audience. I don’t particularly want to build products for kids and parents. I am far away from that audience because I am neither a kid anymore nor going to be a parent anytime soon, and my sister still asked me to make her tea so the story didn’t work. I think it’s important to ask yourself whether you care about the audience. The way you answer that even when you are in full bias mode is, do you engage with them? Are you interested in what’s happening in their communities? Are you friends with them? Etc. User Survey Analyzer Big User Survey → GPT → Insights Report Me and my coworker were chatting about AI when he asked me to help him analyze a massive survey for him. I thought that was some pretty decent validation. Someone in an actual company asking for help. Lessons Market research is important but moving fast is also important. Ie building momentum. Also don’t revolve around 1 user. This has been a problem in multiple projects. Finding as many users as possible in the beginning to talk to is key. Otherwise, you are just waiting for 1 person to get back to you. AutoI18N Automated Internationalization of the codebase for webapps This one I might still do. It’s hard to find a solid distribution strategy. However, the idea came from me having to do it at my day job. It seems a solid problem. I’d say it’s validated and has some good players already. The key will be differentiation via the simplicity of UX and distribution (which means a slightly different audience). In the backlog for now because I don’t care about the problem or the audience that much. Documate - Part 1 Converts complex PDFs into Excel https://preview.redd.it/8b45k9katf5c1.jpg?width=1344&format=pjpg&auto=webp&s=57324b8720eb22782e28794d2db674b073193995 My mom needed to convert a catalog of furniture into an inventory which took her 3 full days of data entry. I automated it for her and thought this could have a big impact but there was no distribution because there was no ICP. We tried to find the ideal customers by talking to a bunch of different demographics but I flew to Kazakhstan for a holiday and so this kind of fizzled out. I am not writing this blog post linearity, this is my 2nd hour and I am tired and don’t want to finish this later so I don’t even know what lessons I learned. Figmatic Marketplace of high-quality Figma mockups of real apps https://preview.redd.it/h13yv45btf5c1.jpg?width=873&format=pjpg&auto=webp&s=aaa2896aeac2f22e9b7d9eed98c28bb8a2d2cdf1 This was a collab between me and my friend Alex. It was the classic Clarito where we both thought we had this problem and would pay to fix it. In reality, this is a vitamin. Neither I, nor I doubt Alex have thought of this as soon as we bought the domain. We posted it on Gumroad, sent it to a bunch of forums, and called it a day. Same issue as almost all the other ones. No distribution strategy. However, apps like Mobin show us that this concept is indeed profitable but it takes time. It needs SEO. It needs a community. None of those things, me and Alex had or was interested in. However shortly after HTML → Figma came out and it’s the best plugin. Maybe that should’ve been the idea. Podcast → Course Turns Podcaster’s episodes into a course This one I got baited by Jason :P I described to him the idea of repurposing his content for a course. He told me this was epic and he would pay. Then after I sent him the demo, he never checked it out. Anyhow during the development, we realized that doesn’t actually work because A podcast doesn’t have the correct format for the course, the most you can extract are concepts and ideas, seldom explanations. Most creators want video-based courses to be hosted on Kajabi or Udemy Another lesson is that when you pitch something to a user, what you articulate is a platform or a process, they imagine an outcome. However, the end result of your platform can be a very different outcome to what they had in mind and there is even a chance that what they want is not possible. You need to understand really well what the outcome looks like before you design the process. This is a classic problem where we thought of the solution before the problem. Yes, the problem exists. Podcasters want to make courses. However, if you really understand what they want, you can see how repurposing a podcast isn’t the best way to get there. However I only really spoke to 1-2 podcasters about this so making conclusions is dangerous for this can just be another asking ace mistake with the Redditor. Documate Part 2 Same concept as before but now I want to run some ads. We’ll see what happens. https://preview.redd.it/xb3npj0ctf5c1.jpg?width=1456&format=pjpg&auto=webp&s=3cd4884a29fd11d870d010a2677b585551c49193 In conclusion https://preview.redd.it/2zrldc9dtf5c1.jpg?width=1840&format=pjpg&auto=webp&s=2b3105073e752ad41c23f205dbd1ea046c1da7ff It doesn’t actually matter that much whether you choose to do a B2C, or a social network or focus on growing your audience. All of these can make you successful. What’s important is that you choose. If I had to summarize my 2023 in one word it’s indecision. Most of these projects succeeded for other people, nothing was as fundamentally wrong about them as I proclaimed. In reality that itself was an excuse. New ideas seduce, and it is a form of discipline to commit to a single project for a respectful amount of time. https://preview.redd.it/zy9a2vzdtf5c1.jpg?width=1456&format=pjpg&auto=webp&s=901c621227bba0feb4efdb39142f66ab2ebb86fe Distribution is not just posting on Indiehackers and Reddit. It’s an actual strategy and you should think of it as soon as you think of the idea, even before the Figma designs. I like how Denis Shatalin taught me. You have to build a pipeline. That means a reliable way to get leads, launch campaigns at them, close deals, learn from them, and optimize. Whenever I get an idea now I always try to ask myself “Where can I find 1000s leads in one day?” If there is no good answer, this is not a good project to do now. ​ https://preview.redd.it/2boh3fpetf5c1.jpg?width=1456&format=pjpg&auto=webp&s=1c0d5d7b000716fcbbb00cbad495e8b61e25be66 Talk to users before doing anything. Jumping on designing and coding to make your idea a reality is a satisfying activity in the short term. Especially for me, I like to create for the sake of creation. However, it is so important to understand the market, understand the audience, understand the distribution. There are a lot of things to understand before coding. https://preview.redd.it/lv8tt96ftf5c1.jpg?width=1456&format=pjpg&auto=webp&s=6c8735aa6ad795f216ff9ddfa2341712e8277724 Get out of your own head. The real reason we dropped so many projects is that we got into our own heads. We let the negative thoughts creep in and kill all the optimism. I am really good at coming up with excuses to start a project. However, I am equally as good at coming up with reasons to kill a project. And so you have this yin and yang of starting and stopping. Building momentum and not burning out. I can say with certainty my team ran out of juice this year. We lost momentum so many times we got burnt out towards the end. Realizing that the project itself has momentum is important. User feedback and sales bring momentum. Building also creates momentum but unless it is matched with an equal force of impact, it can stomp the project down. That is why so many of our projects died quickly after we launched. The smarter approach is to do things that have a low investment of momentum (like talking to users) but result in high impact (sales or feedback). Yes, that means the project can get invalidated which makes it more short-lived than if we built it first, but it preserves team life energy. At the end of 2023 here is a single sentence I am making about how I think one becomes a successful indiehacker. One becomes a successful Indiehacker when one starts to solve pain-killer problems in the market they understand, for an audience they care about and consistently engage with for a long enough timeframe. Therefore an unsuccessful Indiehacker in a single sentence is An unsuccessful Indiehacker constantly enters new markets they don’t understand to build solutions for people whose problems they don’t care about, in a timeframe that is shorter than than the time they spent thinking about distribution. However, an important note to be made. Life is not just about indiehacking. It’s about learning and having fun. In the human world, the best journey isn’t the one that gets you the fastest to your goals but the one you enjoy the most. I enjoyed making those silly little projects and although I do not regret them, I will not repeat the same mistakes in 2024. But while it’s still 2023, I have 2 more projects I want to do :) EDIT: For Devs, frontend is always react with vite (ts) and backend is either node with express (ts) or python. For DB either Postgres or mongo (usually Prisma for ORM). For deployment all of it is on AWS (S3, EC2). In terms of libraries/APIs Whisper.cpp is best open source for transcription Obviously the gpt apis Eleven labs for voice related stuff And other random stuff here and there

I made a bunch of side projects over the last 9 months, and even accrued 500+ accounts and some donations!
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I made a bunch of side projects over the last 9 months, and even accrued 500+ accounts and some donations!

I just stumbled upon this subreddit and have a bunch of fun projects I'd like to present, any thoughts/feedback/criticism, etc. all welcome. So, first things first, a little about me, I work full time in an unrelated job, but have picked up full stack and mobile programming. I have two roommates who help a bit in their own way, one is a server expert and happened to have a server in our apartment basement, and the other is my brother and he picked up some frontend programming. We're all avid cyclists and decided to start building about 9 months ago. Our first idea was https://sherpa-map.com a SPA website allowing users to create cycling routes, send them to their Garmin devices, download them as GPX files, etc. This site uses the open-source software Graphhopper on the backend which I've augmented to send back surface type information. This site has a loooonnnggg list of features, from the simple, like a live weather radar, to the extreme like this functionality: ​ AI surface classification This video demonstrates the ability to classify road surface types in real time using high-resolution satellite imagery of road portions with unknown surface types! I trained a Pytorch resnet 50 model with tuned hyperparameters and 10 epochs on 200,000 satellite images of roads with known surface types! (We host a OSM Postgres server with coordinates of roads and their associated surface types, I made a script to pull images of said roads for training). I built the model into a secondary backend written in flask and piped the images being used back through live web sockets to my node.js backend to the person who is logged in! ​ Okay, on to the next side project, a cycling physics simulator! https://sherpa-map.com/cycling-route-calculator.html Cycling Physics Simulation This site lets users enter information about their bike setup, upload or use a preset route, and enter in their physical information to see how different changes in their setup might affect how fast they will be throughout a course! It can also pull complex weather information throughout the course and give a full suite of nutrition details! ​ Okay, Next project! The Activity Racer! https://sherpa-map.com/activity-racer.html Activity Racer This site lets users upload their own or competitors' GPX activity files and line them up against each other at any point in an event, to see who was faster where! It's great if you've done the same even year after year with differing setups, allowing you to get insights as to which might have done better at what point. ​ Okay, final project, this one's pretty half-baked as I'm still in the process of implementing so many other things, a podcast creation app! (I was bored and just started working on this a week or so ago, for no good reason). Currently, this one lives on https://sherpa-map.com/podcast.html This podcasting web app creates a peer to peer to peer... mesh network using webRTC so, small groups can communicate with the highest level of fidelity both in audio and video! Simply enter a room name and have other users enter the room name as well and they're connected! I've already used tensorflow.js AI to allow a blur background option, similar to MS Teams, whereby bodypix classifier AI picks out the person and I use a blur on a JS canvas behind them. I also went a little bit off the deep end and managed to implement the RNNoise background noise suppressor on the frontend, it's written in C, but I was able to use Windows Subsystem for Linux + emscrption to compile it in just the right way, with exposed malloc and free and a JS wrapper to use on the frontend in WASM. I actually use WASM (typically Rust) in many fun ways throughout all of these projects. I'm also in the middle of recreating the first site in React-Native + Maplibre for IOS and Android as individual APPs. In addition, I'm also working on the integration of my main site into a different project for a different group. So, I have a fun collection of side projects with slightly different GUIs, across different platforms with no coherent landing page as of yet but I've been having a blaaaast putting them together. As a final note, I even have a bit of an easter egg in the automated email system I use for account verifications and password resets do\not\reply@sherpa-map.com I hooked it up to ChatGPT API and told it it is a disgruntled worker whose sole task in life is to watch a do\not\reply email box and respond sarcastic/snarky to anyone who dares send a message to it, if AI comes for humanity, I bet I'll be on a list for this one lol.

I grew my mobile app to 1.4 million downloads
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I grew my mobile app to 1.4 million downloads

I started developing the app in early 2017, well before the AI era, when mobile apps were at their peak popularity. My idea was to create an app for emotional and psychological support in the form of helpful articles and various quizzes, such as personality assessments and life satisfaction tests. I named the app "Emotional Intelligence" because this keyword showed good ASO potential for positioning at the top of mobile stores. This proved to be accurate, and the app quickly gained traction in terms of downloads. A major problem I faced then was monetization. Unfortunately, in my country, it wasn't possible to sell through Google Play then, so I could only display ads. I started with Google AdMob, earning $2000 monthly after just a few months. The app then got about 1500 organic downloads daily and quickly surpassed 500,000. Three years after launching the app, I decided it was time for branding to build recognition. By combining the words "sentiment" and "intelligence," I came up with "Sintelly." I then pushed the app toward a social network, which differed from the right move. Adding features like discussion forums for problems, likes, and comments would result in even more growth, but the opposite happened. The app started declining, and I began investing in advertising campaigns. I managed to maintain a balance between income and expenses but without any profit. Then COVID-19 hit, and everything went downhill. I had to give up development and find a job as a developer to ensure my livelihood. Two years passed since I gave up, and that's when ChatGPT started gaining popularity. This immediately showed me how to steer the app towards active support for well-being questions. As I'm not an expert in psychology, I found several external psychotherapists who helped me put together CBT therapy, which I then implemented through a chatbot. This is how the new Sintelly app was born, with its main feature being a chatbot system composed of 17 AI agents that adapt to the user and guide them through a five-phase CBT therapy (I'll write a post about the technology). In addition to the agents, I added various exercises and tests to provide better personalization for the user. Initially, I made all of this free, which was also a mistake. I followed the principle of first showing what the app can do and gathering enough new users before starting to charge. I started selling subscriptions at the beginning of July, and since then, the app has had stable growth. If you want a check app, here is the link. Lessons learned: If things are working, don't touch them Start selling immediately upon app release; there's no need to wait Regularly test prices and types of subscriptions Onboarding is the most essential part of the app because most users buy subscriptions during onboarding It's essential to listen to user feedback. From day one, have a website and work on content to generate organic visits and redirect users from the web to the mobile app Stats: Over 1.4 million downloads 4.4 rating Only 40,000 active users (I had a massive loss during the period when I gave up) 280 active subscribers $3000 monthly revenue Next steps: Work on improving the Agent AI approach Setting up email campaigns and transactional emails Introducing in-app and push notifications Introducing gamification Potential for B2B I hope you can extract useful information from my example and avoid repeating my mistakes. I'm interested in your thoughts and if you have any recommendations for the next steps. I'm always looking to learn and improve.

Solopreneur making $40k MRR with a No Code SaaS sideproject
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Solopreneur making $40k MRR with a No Code SaaS sideproject

Hey, I'm Elias and I do case studies analyzing successful startups and solopreneurs. I wanted to share the summarized version of this one with you because this entrepreneurial journey blew my mind. This post will be about FormulaBot (ExcelFormulaBot), an AI No Code SaaS founded by David Bressler back in August 2022. FormulaBot is currently making $40k MRR (monthly recurring revenue). How did the founder come up with the idea. David is a data guy who worked in analytics for several years. In July 2022, David got really interested in AI, especially ChatGPT. One night, he tried it out at home, just like we all did back in the time. But in his case, trying ChatGPT gave him a big idea. That idea ended up making him a lot of money and changing the life of 750 million people who use Excel. That night David started by asking GPT easy questions, then complex ones. Since he used Excel a lot and helped his colleagues with it, he thought about an AI that could make Excel easier, like generating formulas from text. He looked online but found nothing. Seeing a big chance, he decided to do something about it. What challenges did the founder face. But David didn’t have any idea about how to develop an app. However, with no-code tools this is not a problem anymore. He discovered Bubble, a no-code web app tool that could connect with the OpenAI API.After, learning Bubble from YouTube tutorials and through trial and error and spending his nights studying the OpenAI API documentation, he launched the first version of the app in around three weeks. Strategies that made the project successful. David validated his idea by posting about ExcelFormulaBot on a Reddit Excel subreddit, receiving surprising attention with 10,000 upvotes. This encouraged him to offer the tool for free to gather feedback. Facing a hefty $4,999 API bill after the Reddit post, David quickly monetized his product with a subscription-based SaaS website. On launch day, 82 customers signed up, surpassing his expectations. A successful Product Hunt launch followed, generating $2.4k in sales within 24 hours, and a TikTok influencer with 4.5 million followers brought in thousands of new users overnight with a viral video. Marketing approach: -Paid ads: FormulaBot boosted website traffic with Paid Ads, notably on Google Ads, prioritizing Quality Score. This ensured ads aligned better with user searches, maximizing visibility and cost-efficiency, targeting those seeking Excel formula assistance. -SEO: a) Content/Keyword optimization: FormulaBot improved its SEO by making helpful pages about Excel formulas, like guides on topics such as "How to use SUMIFS." b) Site Speed Enhancement: David boosted FormulaBot's marketing site speed by moving it from Bubble to Framer, aiming to improve user experience and SEO performance. c) On-page optimization: David optimized FormulaBot's on-page elements by adjusting title tags, meta descriptions, and content to enhance SEO performance and align with search intent. These strategic refinements aimed to address ranking declines and emphasize FormulaBot's uniqueness, ultimately improving its visibility and competitiveness in search results. -Virality: FormulaBot went viral as users found it highly useful and cool. Influencers on platforms like TikTok and Twitter shared it with their followers because they found it valuable. Offering numerous free features further enhanced its appeal. Lessons: successes and mistakes. ✅ Leverage industry expertise: David identified a problem in analytics and used his experience to start an online business addressing it, turning an industry challenge into a profitable venture. ✅ Embrace learning new skills: Despite lacking initial technical know-how, David learned what he needed to develop the software himself, demonstrating a commitment to continuous learning and adaptability crucial for success. ❌ Minimize dependency on third parties: Relying solely on the ChatGPT API poses risks for FormulaBot. Any issues with the API could disrupt functionality and limit scalability. ⁉️ Caution with free tools: Offering a free tool can attract users and drive viral growth, but converting them to paying customers is challenging. Avoid relying solely on a 100% free model unless your revenue comes from non-user sources like ads. For businesses dependent on user subscriptions or purchases, balancing user attraction with conversion challenges is crucial. How could you replicate this idea step-by-step. To replicate the success of FormulaBot and similar AI wrapper startups, it's crucial to tread carefully in a competitive market. Avoid mere replication of existing solutions unless you can offer something distinct or superior. Consider these steps to effectively develop an AI Wrapper/ChatGPT wrapper product using Bubble as a no-code tool: Design the user interface: Utilize Bubble's drag-and-drop editor to create a user-friendly interface with input fields, buttons, and result displays. Set up workflows: Define workflows to connect the interface with the ChatGPT API, enabling seamless interaction between users and the AI. Integrate the ChatGPT API: Obtain the API key from OpenAI and integrate it into your app using Bubble's API connector feature. Test and gather feedback: Thoroughly test your app, soliciting feedback to refine functionality and usability. Refine and optimize: Continuously improve your app based on user input and testing results to enhance performance and user experience. The in-depth version of the case study was originally posted here. Feel free to comment if you have any questions, and let me know which similar ideas you'd like me to analyze.

What are Boilerplates?
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What are Boilerplates?

What are Boilerplates? Boilerplate originally referred to the rolled steel used to make boilers for steam engines in the 19th century. Over time, the term evolved to describe any standardized piece of text or code that can be reused without significant changes. Interest in SaaS has been on the rise, and many more people now want to build products. However, building products from scratch takes a lot of time, and it can be extremely frustrating. Enter SaaS Boilerplates With the standardization of stacks and basic systems that govern SaaS tools, it has become evident that there was a need, and the time was ripe for SaaS Boilerplates. SaaS Boilerplates come with landing pages, website components, authentication modules, payment modules, and various other standard features that can save developers a significant amount of time and cost. The market is flooded with Boilerplates for various tech stacks, such as NextJS, Laravel, Swift, NuxtJS, and so forth. Pros and Cons of Boilerplates Pros Save a significant amount of time and money Reduce frustration for developers as the redundant tasks are taken care of Boilerplates often follow best practices For anywhere between $49 and $299, they provide terrific value for those looking to build something very quickly Most importantly, Boilerplates also enable aspiring founders and builders with limited technical resources or abilities to ship their products faster and more cheaply. They are beacons of hope for non-technical founders looking to build a product quickly. Cons Limited flexibility May become outdated fairly quickly Setting them up still requires time Similar landing pages and design themes can make the product look like a clone Marc Lou’s Shipfast For most of us, Marc Lou popularized the idea of SaaS Boilerplate. Marc Lou launched Shipfast in August 2023. He had built 27 projects prior to this and Shipfast was nothing but all his basic code organised properly. At that time, there were no solid NextJS boilerplates, and Shipfast just took off. He got traction via Product Hunt, Twitter and Hacker News and soon Shipfast went viral. Shipfast now generates $130K/mo, just 9 months after its launch. Marc has been building Shipfast in public, which has led to a lot of interest in SaaS Boilerplates. The market is now flooded with boilerplates for every major tech stack. Marc reaped the benefits of the first mover’s advantage as well as the social proof via his Shipfast community. I don’t think any other boilerplates are as successful as Shipfast, but there are quite a few good ones out there. Shipixen* has grossed over $20K in the 5 months Makerkit* does \~$3500/mo Moreover, there are many open-source boilerplates available for popular stacks such as NextJS. The Evolution of Boilerplates Boilerplates are quickly turning into no-code/low-code code generation tools. For instance, Shipixen allows you to generate custom code for landing pages, waitlist pages and blogs using a simple User Interface. Boilerplates are perfectly posied to sit between code and no-code. Allow the flexibility of code with the interface of a no-code tool — that will be the core value proposition of SaaS boilerplates. Should you build a Boilerplate? Well, the market is flooded, but I believe there’s still an opportunity to leverage boilerplates. You can build boilerplates for certain types of apps or tools, such as Chrome extensions Boilerplates can act as a great lead funnel for building out a great productized services business No-code/low-code code generation boilerplates can become a big thing if you can help build complex tools Niche tech stack boilerplates may still be lucrative Known strategies for successfully building a boilerplate 👇🏻 Shipfast thrives because of social proof and community SaaSRock generates most of its traffic from its Gumroad listings and blogs Usenextbase and Shipixen are being built in public Many boilerplates start with waitlists They have a very clear value proposition around saving time and cost Design & No-Code Boilerplates Here is the corrected version with improved grammar and clarity: While SaaS (code) boilerplates have become fairly popular, other types of boilerplates are emerging in the market, such as design boilerplates and no-code boilerplates. To be honest, design boilerplates have been around for a while. You will find numerous landing page packs, component libraries, and so forth. Makers are now building kits that leverage standard libraries and technologies such as Tailwind CSS, Daisy UI, and more. Nick Buzz from the famous baked.design has this *50 Landing Page Design Kit* in Tailwind CSS & Figma which is wildly popular. Lastly, there is a trend of no-code boilerplates as well. Mohit is building a Bubble Boilerplate for the popular no-code platform — Bubble. All in all, I think that people want to build products and build them fast. Boilerplates help them save a significant amount of time and cost. More importantly, boilerplates are impulse purchases for people who have not shipped but who want to ship. Introducing BuilderKit.ai We have been building AI SaaS tools for quite a while now. 10+ products across text, image, speech, RAG — we have built em all. We figured that it seems easy but actually building these so called AI Wrappers can be time consuming and frustrating — there is a lot of nuance to it. So we built BuidlerKit.ai — a NextJS SaaS Boilerpalte It takes care of everything from landing pages, authentication, dashboarding, emails, SEO to payments — everything that you need to build your tool. It also comes with 8+ production-ready apps. Moreover, the BuilderKit community is an exclusive community of AI SaaS builders (Pro Only Access) The Pre Orders are now live at https://www.builderkit.ai (First 100 Customers get $100 Off — I think we have already done \~20 odd orders since the announcement yesterday, Grab your seat asap!) Starter Plan $49, Pro Plan @ $99

Things I did to promote my product, and how they turned out
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Things I did to promote my product, and how they turned out

(I will share more updates in the future, you can find me on Twitter and/or Mastodon) Ask any ten indie developers about the toughest part of their job, and nine will likely say "marketing." I recently got a taste of this firsthand when I launched Xylect. Here's a rundown of my promotional attempts - hopefully, my experiences can help fellow developers out there. Podcast Community (✅ Success) I kicked things off by promoting Xylect in my podcast listener group. It wasn't a blockbuster, but I managed to sell a few copies and got some invaluable feedback from friends. Shoutout to those early supporters! Reddit r/macapps (✅ Success) Having had some luck promoting open-source projects on Reddit before, I decided to make r/macapps my first stop in the English-speaking world. I made an app to help you automate boring tasks with one click This post turned out to be a hit! I sold about ten copies and got a ton of useful feedback. Users pointed out compatibility issues with PopClip and suggested improvements for the website. One Italian user even requested localization, which I happily added. https://preview.redd.it/y4fuwh6hleqd1.png?width=959&format=png&auto=webp&s=7bb1b68cbf8a4f94998999e0832b9b7bd85bac67 https://preview.redd.it/8uu4cmyhleqd1.png?width=683&format=png&auto=webp&s=8f1744636aee8074b0e7491a334ef06076b143b0 I also got an intriguing email from a French user - more on that later. More Reddit Posts (❌ Failure) Riding high on my r/macapps success, I branched out to r/SideProject, r/Entrepreneur, and r/indiehackers. These subreddits frown upon direct self-promotion, so I took a softer approach with an article: The unexpected emotional cost of being an indiehacker While the article was heartfelt, it fell flat. Across all three posts, I got a grand total of three comments - two of which were complaints about the font size on mobile. Needless to say, I didn't sell a single copy. Hacker News (❌ Failure) As one of the tech world's major forums, I had to give Hacker News a shot. I wasn't too optimistic, given my past experiences there. Posting on HN feels like a mix of luck and dark magic. As expected, my post vanished without a trace - no comments, no sales. I might give it another go someday. If you're curious, you can check out my previous HN submissions. Tools Directory Websites (❌ Failure) These sites have a simple premise: you list your app, they display it. Seemed like an easy way to get some backlinks, right? Well, I learned the hard way that it's not that simple. I stumbled upon a Reddit post where someone claimed to have made a killing with their directory site in just a few days. The catch? Each listing cost $19. The site had a handful of apps listed, so I thought, "Why not? Early bird gets the worm." I paid up and listed Xylect. Spoiler alert: all I got was $19 poorer 🥲 Lesson learned: These directory sites won't magically sell your product. At best, they're just glorified backlinks. There might be some value in paid promotions on these platforms, but I can't speak to that from experience. V2EX (❌ Failure) After striking out in the English-speaking world, I turned my attention to the Chinese market, starting with V2EX (think of it as China's hybrid of HN and Reddit). This turned out to be my most unexpected flop. Here's the post: [\[Launch Discount\] Mac's most powerful AI search (Perplexity + Wikipedia + Google), boost your efficiency tenfold with one click. No API key required, no prompt needed, no token limit 🔥 - V2EX](https://www.v2ex.com/t/1064930?p=1#reply36) I'd seen decent engagement on other promo posts, so I had high hopes. I posted late at night (US time) and went to bed dreaming of waking up to a flood of comments. Reality check: The next morning, I had exactly one reply - from Kilerd, a loyal podcast listener showing some love. I was baffled. After re-reading my post, I realized I'd missed a crucial element: promo codes. A quick scan of popular posts confirmed my suspicion. Nearly every successful promo post was offering codes, and most comments were just base64-encoded email addresses. Talk about a facepalm moment. I scrambled to add a note about an upcoming free trial and invited users to drop their emails. This got the ball rolling with some code requests, but by then, the damage was done. The post fizzled out, and I didn't sell a single copy 🫠 A French Friend's Newsletter (✅ Success) At this point, my promotional efforts were looking pretty grim. My sales chart had a depressing stretch of flatline. But then, a glimmer of hope appeared in my inbox. Remember that French user I mentioned earlier? He ran a newsletter called vvmac and offered to feature Xylect if I added French support and sent him a free license. It was an offer I couldn't refuse. What followed was a crash course in French localization (thank you, Claude!) and the start of an incredible partnership. This guy was the most thorough beta tester I've ever encountered. We exchanged over sixty emails, covering everything from translations to UI tweaks to bug fixes. His response time was lightning-fast - I'd fix a bug, and five minutes later, he'd confirm it was sorted. The result? A much-improved Xylect and a glowing feature in his newsletter. https://preview.redd.it/ylcq2wxoleqd1.png?width=991&format=png&auto=webp&s=ee395110f50417d5c7f61318f27bf3dc30247809 I'm still in awe of his dedication. He single-handedly transformed Xylect from a buggy mess into a polished product. I'll be forever grateful for his help. The newsletter feature led to a few more sales, but honestly, that felt like a bonus at that point. Influencers (❌ Failure) I knew from the start that to really make waves, I'd need influencer backing. So, I added a note offering free licenses to content creators willing to collaborate. https://preview.redd.it/tyb2m1rqleqd1.png?width=799&format=png&auto=webp&s=56eabf126e772515322595613c546e6ba69fb431 I did get one taker: Hey, I'll be honest, I am not a huge content creator but I think I put a lot of effort in evaluating and figuring out which apps work... So I was wondering if I could get a license in case you are willing to share it. Thank you for considering. Have a great weekend. But I knew I needed to aim higher. With the new French localization, I thought I'd try my luck with some French-speaking Mac YouTubers. I crafted emails highlighting how Xylect could help their French audience with English content. https://preview.redd.it/07oqzemrleqd1.png?width=542&format=png&auto=webp&s=3d160c1d149f28e9029816a277c6ab2496fcd57e After days of silence, I got one reply. It was... not what I was hoping for: Hi, Thank you for your proposal. I can help you to promote your service on Tiktok, Instagram et YouTube, with unique short video. Price for this project is 3500€. Unless I've completely lost my marbles, there's no way I'm dropping 3500€ on promotion. Sure, given their follower count (YouTube: 348K, TikTok: 2.7M, Instagram: 400K), it's not an outrageous ask. For some products, it might even be worth it. But for Xylect? No way. I also reached out to a Chinese influencer on Xiaohongshu, but they weren't interested. Back to the drawing board. Conclusion If you've made it this far, you've probably realized this isn't exactly a success story. My search for effective promotional channels came up largely empty-handed. I'd naively thought that my success with open-source projects would translate seamlessly to the indie dev world. Boy, was I wrong. As I mentioned in my previous article, open-source projects create a dynamic where users feel indebted to developers for their free labor. But in the commercial world of indie development, that dynamic completely flips. While this experience was often frustrating, it was also enlightening - which was kind of the point. As my first foray into indie development, my main goal was to learn the ropes and understand the process. Making money would've been nice, sure, but it wasn't my primary focus. Thanks for sticking with me through this post. I will share more updates in the future, you can follow me on  Twitter and/or Mastodon.

How me and my team made 15+ apps and not made a single sale in 2023
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MichaelbetterecycleThis week

How me and my team made 15+ apps and not made a single sale in 2023

Hey, my name is Michael, I am in Auckland NZ. This year was the official beginning of my adult life. I graduated from university and started a full-time job. I’ve also really dug into indiehacking/bootstrapping and started 15 projects (and it will be at least 17 before the year ends). I think I’ve learned a lot but I consciously repeated mistakes. Upto (Nov) Discord Statuses + Your Location + Facebook Poke https://preview.redd.it/4nqt7tp2tf5c1.png?width=572&format=png&auto=webp&s=b0223484bc54b45b5c65e0b1afd0dc52f9c02ad1 This was the end of uni, I often messaged (and got messaged) requests of status and location to (and from my) friends. I thought, what if we make a social app that’s super basic and all it does is show you where your friends are? To differentiate from snap maps and others we wanted something with more privacy where you select the location. However, never finished the codebase or launched it. This is because I slowly started to realize that B2C (especially social networks) are way too hard to make into an actual business and the story with Fistbump would repeat itself. However, this decision not to launch it almost launched a curse on our team. From that point, we permitted ourselves to abandon projects even before launching. Lessons: Don’t do social networks if your goal is 10k MRR ASAP. If you build something to 90% competition ship it or you will think it’s okay to abandon projects Insight Bites (Nov) Youtube Summarizer Extension ​ https://preview.redd.it/h6drqej4tf5c1.jpg?width=800&format=pjpg&auto=webp&s=0f211456c390ac06f4fcb54aa51f9d50b0826658 Right after Upto, we started ideating and conveniently the biggest revolution in the recent history of tech was released → GPT. We instantly began ideating. The first problem we chose to use AI for is to summarize YouTube videos. Comical. Nevertheless, I am convinced we have had the best UX because you could right-click on a video to get a slideshow of insights instead of how everyone else did it. We dropped it because there was too much competition and unit economics didn’t work out (and it was a B2C). PodPigeon (Dec) Podcast → Tweet Threads https://preview.redd.it/0ukge245tf5c1.png?width=2498&format=png&auto=webp&s=23303e1cab330578a3d25cd688fa67aa3b97fb60 Then we thought, to make unit economics work we need to make this worthwhile for podcasters. This is when I got into Twitter and started seeing people summarize podcasts. Then I thought, what if we make something that converts a podcast into tweets? This was probably one of the most important projects because it connected me with Jason and Jonaed, both of whom I regularly stay in contact with and are my go-to experts on ideas related to content creation. Jonaed was even willing to buy Podpigeon and was using it on his own time. However, the unit economics still didn’t work out (and we got excited about other things). Furthermore, we got scared of the competition because I found 1 - 2 other people who did similar things poorly. This was probably the biggest mistake we’ve made. Very similar projects made 10k MRR and more, launching later than we did. We didn’t have a coherent product vision, we didn’t understand the customer well enough, and we had a bad outlook on competition and a myriad of other things. Lessons: I already made another post about the importance of outlook on competition. Do not quit just because there are competitors or just because you can’t be 10x better. Indiehackers and Bootstrappers (or even startups) need to differentiate in the market, which can be via product (UX/UI), distribution, or both. Asking Ace Intro.co + Crowdsharing ​ https://preview.redd.it/0hu2tt16tf5c1.jpg?width=1456&format=pjpg&auto=webp&s=3d397568ef2331e78198d64fafc1a701a3e75999 As I got into Twitter, I wanted to chat with some people I saw there. However, they were really expensive. I thought, what if we made some kind of crowdfunding service for other entrepreneurs to get a private lecture from their idols? It seemed to make a lot of sense on paper. It was solving a problem (validated via the fact that Intro.co is a thing and making things cheaper and accessible is a solid ground to stand on), we understood the market (or so we thought), and it could monetize relatively quickly. However, after 1-2 posts on Reddit and Indiehackers, we quickly learned three things. Firstly, no one cares. Secondly, even if they do, they think they can get the same information for free online. Thirdly, the reasons before are bad because for the first point → we barely talked to people, and for the second people → we barely talked to the wrong people. However, at least we didn’t code anything this time and tried to validate via a landing page. Lessons Don’t give up after 1 Redditor says “I don’t need this” Don’t be scared to choose successful people as your audience. Clarito Journaling with AI analyzer https://preview.redd.it/8ria2wq6tf5c1.jpg?width=1108&format=pjpg&auto=webp&s=586ec28ae75003d9f71b4af2520b748d53dd2854 Clarito is a classic problem all amateur entrepreneurs have. It’s where you lie to yourself that you have a real problem and therefore is validated but when your team asks you how much you would pay you say I guess you will pay, maybe, like 5 bucks a month…? Turns out, you’d have to pay me to use our own product lol. We sent it off to a few friends and posted on some forums, but never really got anything tangible and decided to move away. Honestly, a lot of it is us in our own heads. We say the market is too saturated, it’ll be hard to monetize, it’s B2C, etc. Lessons: You use the Mom Test on other people. You have to do it yourself as well. However, recognizing that the Mom Test requires a lot of creativity in its investigation because knowing what questions to ask can determine the outcome of the validation. I asked myself “Do I journal” but I didn’t ask myself “How often do I want GPT to chyme in on my reflections”. Which was practically never. That being said I think with the right audience and distribution, this product can work. I just don’t know (let alone care) about the audience that much (and I thought I was one of them)/ Horns & Claw Scrapes financial news texts you whether you should buy/sell the stock (news sentiment analysis) ​ https://preview.redd.it/gvfxdgc7tf5c1.jpg?width=1287&format=pjpg&auto=webp&s=63977bbc33fe74147b1f72913cefee4a9ebec9c2 This one we didn’t even bother launching. Probably something internal in the team and also seemed too good to be true (because if this works, doesn’t that just make us ultra-rich fast?). I saw a similar tool making 10k MRR so I guess I was wrong. Lessons: This one was pretty much just us getting into our heads. I declared that without an audience it would be impossible to ship this product and we needed to start a YouTube channel. Lol, and we did. And we couldn’t even film for 1 minute. I made bold statements like “We will commit to this for at least 1 year no matter what”. Learnery Make courses about any subject https://preview.redd.it/1nw6z448tf5c1.jpg?width=1112&format=pjpg&auto=webp&s=f2c73e8af23b0a6c3747a81e785960d4004feb48 This is probably the most “successful” project we’ve made. It grew from a couple of dozen to a couple of hundred users. It has 11 buy events for $9.99 LTD (we couldn’t be bothered connecting Stripe because we thought no one would buy it anyway). However what got us discouraged from seriously pursuing it more is, that this has very low defensibility, “Why wouldn’t someone just use chatGPT?” and it’s B2C so it’s hard to monetize. I used it myself for a month or so but then stopped. I don’t think it’s the app, I think the act of learning a concept from scratch isn’t something you do constantly in the way Learnery delivers it (ie course). I saw a bunch of similar apps that look like Ass make like 10k MRR. Lessons: Don’t do B2C, or if you do, do it properly Don’t just Mixpanel the buy button, connect your Stripe otherwise, it doesn’t feel real and you won’t get momentum. I doubt anyone (even me) will make this mistake again. I live in my GPT bubble where I make assumptions that everyone uses GPT the same way and as much as I do. In reality, the argument that this has low defensibility against GPT is invalid. Platforms that deliver a differentiated UX from ChatGPT to audiences who are not tightly integrated into the habit of using ChatGPT (which is like - everyone except for SOME tech evangelists). CuriosityFM Make podcasts about any subject https://preview.redd.it/zmosrcp8tf5c1.jpg?width=638&format=pjpg&auto=webp&s=d04ddffabef9050050b0d87939273cc96a8637dc This was our attempt at making Learnery more unique and more differentiated from chatGPT. We never really launched it. The unit economics didn’t work out and it was actually pretty boring to listen to, I don’t think I even fully listened to one 15-minute episode. I think this wasn’t that bad, it taught us more about ElevenLabs and voice AI. It took us maybe only 2-3 days to build so I think building to learn a new groundbreaking technology is fine. SleepyTale Make children’s bedtime stories https://preview.redd.it/14ue9nm9tf5c1.jpg?width=807&format=pjpg&auto=webp&s=267e18ec6f9270e6d1d11564b38136fa524966a1 My 8-year-old sister gave me that idea. She was too scared of making tea and I was curious about how she’d react if she heard a bedtime story about that exact scenario with the moral that I wanted her to absorb (which is that you shouldn’t be scared to try new things ie stop asking me to make your tea and do it yourself, it’s not that hard. You could say I went full Goebbels on her). Zane messaged a bunch of parents on Facebook but no one really cared. We showed this to one Lady at the place we worked from at Uni and she was impressed and wanted to show it to her kids but we already turned off our ElevenLabs subscription. Lessons: However, the truth behind this is beyond just “you need to be able to distribute”. It’s that you have to care about the audience. I don’t particularly want to build products for kids and parents. I am far away from that audience because I am neither a kid anymore nor going to be a parent anytime soon, and my sister still asked me to make her tea so the story didn’t work. I think it’s important to ask yourself whether you care about the audience. The way you answer that even when you are in full bias mode is, do you engage with them? Are you interested in what’s happening in their communities? Are you friends with them? Etc. User Survey Analyzer Big User Survey → GPT → Insights Report Me and my coworker were chatting about AI when he asked me to help him analyze a massive survey for him. I thought that was some pretty decent validation. Someone in an actual company asking for help. Lessons Market research is important but moving fast is also important. Ie building momentum. Also don’t revolve around 1 user. This has been a problem in multiple projects. Finding as many users as possible in the beginning to talk to is key. Otherwise, you are just waiting for 1 person to get back to you. AutoI18N Automated Internationalization of the codebase for webapps This one I might still do. It’s hard to find a solid distribution strategy. However, the idea came from me having to do it at my day job. It seems a solid problem. I’d say it’s validated and has some good players already. The key will be differentiation via the simplicity of UX and distribution (which means a slightly different audience). In the backlog for now because I don’t care about the problem or the audience that much. Documate - Part 1 Converts complex PDFs into Excel https://preview.redd.it/8b45k9katf5c1.jpg?width=1344&format=pjpg&auto=webp&s=57324b8720eb22782e28794d2db674b073193995 My mom needed to convert a catalog of furniture into an inventory which took her 3 full days of data entry. I automated it for her and thought this could have a big impact but there was no distribution because there was no ICP. We tried to find the ideal customers by talking to a bunch of different demographics but I flew to Kazakhstan for a holiday and so this kind of fizzled out. I am not writing this blog post linearity, this is my 2nd hour and I am tired and don’t want to finish this later so I don’t even know what lessons I learned. Figmatic Marketplace of high-quality Figma mockups of real apps https://preview.redd.it/h13yv45btf5c1.jpg?width=873&format=pjpg&auto=webp&s=aaa2896aeac2f22e9b7d9eed98c28bb8a2d2cdf1 This was a collab between me and my friend Alex. It was the classic Clarito where we both thought we had this problem and would pay to fix it. In reality, this is a vitamin. Neither I, nor I doubt Alex have thought of this as soon as we bought the domain. We posted it on Gumroad, sent it to a bunch of forums, and called it a day. Same issue as almost all the other ones. No distribution strategy. However, apps like Mobin show us that this concept is indeed profitable but it takes time. It needs SEO. It needs a community. None of those things, me and Alex had or was interested in. However shortly after HTML → Figma came out and it’s the best plugin. Maybe that should’ve been the idea. Podcast → Course Turns Podcaster’s episodes into a course This one I got baited by Jason :P I described to him the idea of repurposing his content for a course. He told me this was epic and he would pay. Then after I sent him the demo, he never checked it out. Anyhow during the development, we realized that doesn’t actually work because A podcast doesn’t have the correct format for the course, the most you can extract are concepts and ideas, seldom explanations. Most creators want video-based courses to be hosted on Kajabi or Udemy Another lesson is that when you pitch something to a user, what you articulate is a platform or a process, they imagine an outcome. However, the end result of your platform can be a very different outcome to what they had in mind and there is even a chance that what they want is not possible. You need to understand really well what the outcome looks like before you design the process. This is a classic problem where we thought of the solution before the problem. Yes, the problem exists. Podcasters want to make courses. However, if you really understand what they want, you can see how repurposing a podcast isn’t the best way to get there. However I only really spoke to 1-2 podcasters about this so making conclusions is dangerous for this can just be another asking ace mistake with the Redditor. Documate Part 2 Same concept as before but now I want to run some ads. We’ll see what happens. https://preview.redd.it/xb3npj0ctf5c1.jpg?width=1456&format=pjpg&auto=webp&s=3cd4884a29fd11d870d010a2677b585551c49193 In conclusion https://preview.redd.it/2zrldc9dtf5c1.jpg?width=1840&format=pjpg&auto=webp&s=2b3105073e752ad41c23f205dbd1ea046c1da7ff It doesn’t actually matter that much whether you choose to do a B2C, or a social network or focus on growing your audience. All of these can make you successful. What’s important is that you choose. If I had to summarize my 2023 in one word it’s indecision. Most of these projects succeeded for other people, nothing was as fundamentally wrong about them as I proclaimed. In reality that itself was an excuse. New ideas seduce, and it is a form of discipline to commit to a single project for a respectful amount of time. https://preview.redd.it/zy9a2vzdtf5c1.jpg?width=1456&format=pjpg&auto=webp&s=901c621227bba0feb4efdb39142f66ab2ebb86fe Distribution is not just posting on Indiehackers and Reddit. It’s an actual strategy and you should think of it as soon as you think of the idea, even before the Figma designs. I like how Denis Shatalin taught me. You have to build a pipeline. That means a reliable way to get leads, launch campaigns at them, close deals, learn from them, and optimize. Whenever I get an idea now I always try to ask myself “Where can I find 1000s leads in one day?” If there is no good answer, this is not a good project to do now. ​ https://preview.redd.it/2boh3fpetf5c1.jpg?width=1456&format=pjpg&auto=webp&s=1c0d5d7b000716fcbbb00cbad495e8b61e25be66 Talk to users before doing anything. Jumping on designing and coding to make your idea a reality is a satisfying activity in the short term. Especially for me, I like to create for the sake of creation. However, it is so important to understand the market, understand the audience, understand the distribution. There are a lot of things to understand before coding. https://preview.redd.it/lv8tt96ftf5c1.jpg?width=1456&format=pjpg&auto=webp&s=6c8735aa6ad795f216ff9ddfa2341712e8277724 Get out of your own head. The real reason we dropped so many projects is that we got into our own heads. We let the negative thoughts creep in and kill all the optimism. I am really good at coming up with excuses to start a project. However, I am equally as good at coming up with reasons to kill a project. And so you have this yin and yang of starting and stopping. Building momentum and not burning out. I can say with certainty my team ran out of juice this year. We lost momentum so many times we got burnt out towards the end. Realizing that the project itself has momentum is important. User feedback and sales bring momentum. Building also creates momentum but unless it is matched with an equal force of impact, it can stomp the project down. That is why so many of our projects died quickly after we launched. The smarter approach is to do things that have a low investment of momentum (like talking to users) but result in high impact (sales or feedback). Yes, that means the project can get invalidated which makes it more short-lived than if we built it first, but it preserves team life energy. At the end of 2023 here is a single sentence I am making about how I think one becomes a successful indiehacker. One becomes a successful Indiehacker when one starts to solve pain-killer problems in the market they understand, for an audience they care about and consistently engage with for a long enough timeframe. Therefore an unsuccessful Indiehacker in a single sentence is An unsuccessful Indiehacker constantly enters new markets they don’t understand to build solutions for people whose problems they don’t care about, in a timeframe that is shorter than than the time they spent thinking about distribution. However, an important note to be made. Life is not just about indiehacking. It’s about learning and having fun. In the human world, the best journey isn’t the one that gets you the fastest to your goals but the one you enjoy the most. I enjoyed making those silly little projects and although I do not regret them, I will not repeat the same mistakes in 2024. But while it’s still 2023, I have 2 more projects I want to do :) EDIT: For Devs, frontend is always react with vite (ts) and backend is either node with express (ts) or python. For DB either Postgres or mongo (usually Prisma for ORM). For deployment all of it is on AWS (S3, EC2). In terms of libraries/APIs Whisper.cpp is best open source for transcription Obviously the gpt apis Eleven labs for voice related stuff And other random stuff here and there

How me and my team made 15+ apps and not made a single sale in 2023
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MichaelbetterecycleThis week

How me and my team made 15+ apps and not made a single sale in 2023

Hey, my name is Michael, I am in Auckland NZ. This year was the official beginning of my adult life. I graduated from university and started a full-time job. I’ve also really dug into indiehacking/bootstrapping and started 15 projects (and it will be at least 17 before the year ends). I think I’ve learned a lot but I consciously repeated mistakes. Upto (Nov) Discord Statuses + Your Location + Facebook Poke https://preview.redd.it/4nqt7tp2tf5c1.png?width=572&format=png&auto=webp&s=b0223484bc54b45b5c65e0b1afd0dc52f9c02ad1 This was the end of uni, I often messaged (and got messaged) requests of status and location to (and from my) friends. I thought, what if we make a social app that’s super basic and all it does is show you where your friends are? To differentiate from snap maps and others we wanted something with more privacy where you select the location. However, never finished the codebase or launched it. This is because I slowly started to realize that B2C (especially social networks) are way too hard to make into an actual business and the story with Fistbump would repeat itself. However, this decision not to launch it almost launched a curse on our team. From that point, we permitted ourselves to abandon projects even before launching. Lessons: Don’t do social networks if your goal is 10k MRR ASAP. If you build something to 90% competition ship it or you will think it’s okay to abandon projects Insight Bites (Nov) Youtube Summarizer Extension ​ https://preview.redd.it/h6drqej4tf5c1.jpg?width=800&format=pjpg&auto=webp&s=0f211456c390ac06f4fcb54aa51f9d50b0826658 Right after Upto, we started ideating and conveniently the biggest revolution in the recent history of tech was released → GPT. We instantly began ideating. The first problem we chose to use AI for is to summarize YouTube videos. Comical. Nevertheless, I am convinced we have had the best UX because you could right-click on a video to get a slideshow of insights instead of how everyone else did it. We dropped it because there was too much competition and unit economics didn’t work out (and it was a B2C). PodPigeon (Dec) Podcast → Tweet Threads https://preview.redd.it/0ukge245tf5c1.png?width=2498&format=png&auto=webp&s=23303e1cab330578a3d25cd688fa67aa3b97fb60 Then we thought, to make unit economics work we need to make this worthwhile for podcasters. This is when I got into Twitter and started seeing people summarize podcasts. Then I thought, what if we make something that converts a podcast into tweets? This was probably one of the most important projects because it connected me with Jason and Jonaed, both of whom I regularly stay in contact with and are my go-to experts on ideas related to content creation. Jonaed was even willing to buy Podpigeon and was using it on his own time. However, the unit economics still didn’t work out (and we got excited about other things). Furthermore, we got scared of the competition because I found 1 - 2 other people who did similar things poorly. This was probably the biggest mistake we’ve made. Very similar projects made 10k MRR and more, launching later than we did. We didn’t have a coherent product vision, we didn’t understand the customer well enough, and we had a bad outlook on competition and a myriad of other things. Lessons: I already made another post about the importance of outlook on competition. Do not quit just because there are competitors or just because you can’t be 10x better. Indiehackers and Bootstrappers (or even startups) need to differentiate in the market, which can be via product (UX/UI), distribution, or both. Asking Ace Intro.co + Crowdsharing ​ https://preview.redd.it/0hu2tt16tf5c1.jpg?width=1456&format=pjpg&auto=webp&s=3d397568ef2331e78198d64fafc1a701a3e75999 As I got into Twitter, I wanted to chat with some people I saw there. However, they were really expensive. I thought, what if we made some kind of crowdfunding service for other entrepreneurs to get a private lecture from their idols? It seemed to make a lot of sense on paper. It was solving a problem (validated via the fact that Intro.co is a thing and making things cheaper and accessible is a solid ground to stand on), we understood the market (or so we thought), and it could monetize relatively quickly. However, after 1-2 posts on Reddit and Indiehackers, we quickly learned three things. Firstly, no one cares. Secondly, even if they do, they think they can get the same information for free online. Thirdly, the reasons before are bad because for the first point → we barely talked to people, and for the second people → we barely talked to the wrong people. However, at least we didn’t code anything this time and tried to validate via a landing page. Lessons Don’t give up after 1 Redditor says “I don’t need this” Don’t be scared to choose successful people as your audience. Clarito Journaling with AI analyzer https://preview.redd.it/8ria2wq6tf5c1.jpg?width=1108&format=pjpg&auto=webp&s=586ec28ae75003d9f71b4af2520b748d53dd2854 Clarito is a classic problem all amateur entrepreneurs have. It’s where you lie to yourself that you have a real problem and therefore is validated but when your team asks you how much you would pay you say I guess you will pay, maybe, like 5 bucks a month…? Turns out, you’d have to pay me to use our own product lol. We sent it off to a few friends and posted on some forums, but never really got anything tangible and decided to move away. Honestly, a lot of it is us in our own heads. We say the market is too saturated, it’ll be hard to monetize, it’s B2C, etc. Lessons: You use the Mom Test on other people. You have to do it yourself as well. However, recognizing that the Mom Test requires a lot of creativity in its investigation because knowing what questions to ask can determine the outcome of the validation. I asked myself “Do I journal” but I didn’t ask myself “How often do I want GPT to chyme in on my reflections”. Which was practically never. That being said I think with the right audience and distribution, this product can work. I just don’t know (let alone care) about the audience that much (and I thought I was one of them)/ Horns & Claw Scrapes financial news texts you whether you should buy/sell the stock (news sentiment analysis) ​ https://preview.redd.it/gvfxdgc7tf5c1.jpg?width=1287&format=pjpg&auto=webp&s=63977bbc33fe74147b1f72913cefee4a9ebec9c2 This one we didn’t even bother launching. Probably something internal in the team and also seemed too good to be true (because if this works, doesn’t that just make us ultra-rich fast?). I saw a similar tool making 10k MRR so I guess I was wrong. Lessons: This one was pretty much just us getting into our heads. I declared that without an audience it would be impossible to ship this product and we needed to start a YouTube channel. Lol, and we did. And we couldn’t even film for 1 minute. I made bold statements like “We will commit to this for at least 1 year no matter what”. Learnery Make courses about any subject https://preview.redd.it/1nw6z448tf5c1.jpg?width=1112&format=pjpg&auto=webp&s=f2c73e8af23b0a6c3747a81e785960d4004feb48 This is probably the most “successful” project we’ve made. It grew from a couple of dozen to a couple of hundred users. It has 11 buy events for $9.99 LTD (we couldn’t be bothered connecting Stripe because we thought no one would buy it anyway). However what got us discouraged from seriously pursuing it more is, that this has very low defensibility, “Why wouldn’t someone just use chatGPT?” and it’s B2C so it’s hard to monetize. I used it myself for a month or so but then stopped. I don’t think it’s the app, I think the act of learning a concept from scratch isn’t something you do constantly in the way Learnery delivers it (ie course). I saw a bunch of similar apps that look like Ass make like 10k MRR. Lessons: Don’t do B2C, or if you do, do it properly Don’t just Mixpanel the buy button, connect your Stripe otherwise, it doesn’t feel real and you won’t get momentum. I doubt anyone (even me) will make this mistake again. I live in my GPT bubble where I make assumptions that everyone uses GPT the same way and as much as I do. In reality, the argument that this has low defensibility against GPT is invalid. Platforms that deliver a differentiated UX from ChatGPT to audiences who are not tightly integrated into the habit of using ChatGPT (which is like - everyone except for SOME tech evangelists). CuriosityFM Make podcasts about any subject https://preview.redd.it/zmosrcp8tf5c1.jpg?width=638&format=pjpg&auto=webp&s=d04ddffabef9050050b0d87939273cc96a8637dc This was our attempt at making Learnery more unique and more differentiated from chatGPT. We never really launched it. The unit economics didn’t work out and it was actually pretty boring to listen to, I don’t think I even fully listened to one 15-minute episode. I think this wasn’t that bad, it taught us more about ElevenLabs and voice AI. It took us maybe only 2-3 days to build so I think building to learn a new groundbreaking technology is fine. SleepyTale Make children’s bedtime stories https://preview.redd.it/14ue9nm9tf5c1.jpg?width=807&format=pjpg&auto=webp&s=267e18ec6f9270e6d1d11564b38136fa524966a1 My 8-year-old sister gave me that idea. She was too scared of making tea and I was curious about how she’d react if she heard a bedtime story about that exact scenario with the moral that I wanted her to absorb (which is that you shouldn’t be scared to try new things ie stop asking me to make your tea and do it yourself, it’s not that hard. You could say I went full Goebbels on her). Zane messaged a bunch of parents on Facebook but no one really cared. We showed this to one Lady at the place we worked from at Uni and she was impressed and wanted to show it to her kids but we already turned off our ElevenLabs subscription. Lessons: However, the truth behind this is beyond just “you need to be able to distribute”. It’s that you have to care about the audience. I don’t particularly want to build products for kids and parents. I am far away from that audience because I am neither a kid anymore nor going to be a parent anytime soon, and my sister still asked me to make her tea so the story didn’t work. I think it’s important to ask yourself whether you care about the audience. The way you answer that even when you are in full bias mode is, do you engage with them? Are you interested in what’s happening in their communities? Are you friends with them? Etc. User Survey Analyzer Big User Survey → GPT → Insights Report Me and my coworker were chatting about AI when he asked me to help him analyze a massive survey for him. I thought that was some pretty decent validation. Someone in an actual company asking for help. Lessons Market research is important but moving fast is also important. Ie building momentum. Also don’t revolve around 1 user. This has been a problem in multiple projects. Finding as many users as possible in the beginning to talk to is key. Otherwise, you are just waiting for 1 person to get back to you. AutoI18N Automated Internationalization of the codebase for webapps This one I might still do. It’s hard to find a solid distribution strategy. However, the idea came from me having to do it at my day job. It seems a solid problem. I’d say it’s validated and has some good players already. The key will be differentiation via the simplicity of UX and distribution (which means a slightly different audience). In the backlog for now because I don’t care about the problem or the audience that much. Documate - Part 1 Converts complex PDFs into Excel https://preview.redd.it/8b45k9katf5c1.jpg?width=1344&format=pjpg&auto=webp&s=57324b8720eb22782e28794d2db674b073193995 My mom needed to convert a catalog of furniture into an inventory which took her 3 full days of data entry. I automated it for her and thought this could have a big impact but there was no distribution because there was no ICP. We tried to find the ideal customers by talking to a bunch of different demographics but I flew to Kazakhstan for a holiday and so this kind of fizzled out. I am not writing this blog post linearity, this is my 2nd hour and I am tired and don’t want to finish this later so I don’t even know what lessons I learned. Figmatic Marketplace of high-quality Figma mockups of real apps https://preview.redd.it/h13yv45btf5c1.jpg?width=873&format=pjpg&auto=webp&s=aaa2896aeac2f22e9b7d9eed98c28bb8a2d2cdf1 This was a collab between me and my friend Alex. It was the classic Clarito where we both thought we had this problem and would pay to fix it. In reality, this is a vitamin. Neither I, nor I doubt Alex have thought of this as soon as we bought the domain. We posted it on Gumroad, sent it to a bunch of forums, and called it a day. Same issue as almost all the other ones. No distribution strategy. However, apps like Mobin show us that this concept is indeed profitable but it takes time. It needs SEO. It needs a community. None of those things, me and Alex had or was interested in. However shortly after HTML → Figma came out and it’s the best plugin. Maybe that should’ve been the idea. Podcast → Course Turns Podcaster’s episodes into a course This one I got baited by Jason :P I described to him the idea of repurposing his content for a course. He told me this was epic and he would pay. Then after I sent him the demo, he never checked it out. Anyhow during the development, we realized that doesn’t actually work because A podcast doesn’t have the correct format for the course, the most you can extract are concepts and ideas, seldom explanations. Most creators want video-based courses to be hosted on Kajabi or Udemy Another lesson is that when you pitch something to a user, what you articulate is a platform or a process, they imagine an outcome. However, the end result of your platform can be a very different outcome to what they had in mind and there is even a chance that what they want is not possible. You need to understand really well what the outcome looks like before you design the process. This is a classic problem where we thought of the solution before the problem. Yes, the problem exists. Podcasters want to make courses. However, if you really understand what they want, you can see how repurposing a podcast isn’t the best way to get there. However I only really spoke to 1-2 podcasters about this so making conclusions is dangerous for this can just be another asking ace mistake with the Redditor. Documate Part 2 Same concept as before but now I want to run some ads. We’ll see what happens. https://preview.redd.it/xb3npj0ctf5c1.jpg?width=1456&format=pjpg&auto=webp&s=3cd4884a29fd11d870d010a2677b585551c49193 In conclusion https://preview.redd.it/2zrldc9dtf5c1.jpg?width=1840&format=pjpg&auto=webp&s=2b3105073e752ad41c23f205dbd1ea046c1da7ff It doesn’t actually matter that much whether you choose to do a B2C, or a social network or focus on growing your audience. All of these can make you successful. What’s important is that you choose. If I had to summarize my 2023 in one word it’s indecision. Most of these projects succeeded for other people, nothing was as fundamentally wrong about them as I proclaimed. In reality that itself was an excuse. New ideas seduce, and it is a form of discipline to commit to a single project for a respectful amount of time. https://preview.redd.it/zy9a2vzdtf5c1.jpg?width=1456&format=pjpg&auto=webp&s=901c621227bba0feb4efdb39142f66ab2ebb86fe Distribution is not just posting on Indiehackers and Reddit. It’s an actual strategy and you should think of it as soon as you think of the idea, even before the Figma designs. I like how Denis Shatalin taught me. You have to build a pipeline. That means a reliable way to get leads, launch campaigns at them, close deals, learn from them, and optimize. Whenever I get an idea now I always try to ask myself “Where can I find 1000s leads in one day?” If there is no good answer, this is not a good project to do now. ​ https://preview.redd.it/2boh3fpetf5c1.jpg?width=1456&format=pjpg&auto=webp&s=1c0d5d7b000716fcbbb00cbad495e8b61e25be66 Talk to users before doing anything. Jumping on designing and coding to make your idea a reality is a satisfying activity in the short term. Especially for me, I like to create for the sake of creation. However, it is so important to understand the market, understand the audience, understand the distribution. There are a lot of things to understand before coding. https://preview.redd.it/lv8tt96ftf5c1.jpg?width=1456&format=pjpg&auto=webp&s=6c8735aa6ad795f216ff9ddfa2341712e8277724 Get out of your own head. The real reason we dropped so many projects is that we got into our own heads. We let the negative thoughts creep in and kill all the optimism. I am really good at coming up with excuses to start a project. However, I am equally as good at coming up with reasons to kill a project. And so you have this yin and yang of starting and stopping. Building momentum and not burning out. I can say with certainty my team ran out of juice this year. We lost momentum so many times we got burnt out towards the end. Realizing that the project itself has momentum is important. User feedback and sales bring momentum. Building also creates momentum but unless it is matched with an equal force of impact, it can stomp the project down. That is why so many of our projects died quickly after we launched. The smarter approach is to do things that have a low investment of momentum (like talking to users) but result in high impact (sales or feedback). Yes, that means the project can get invalidated which makes it more short-lived than if we built it first, but it preserves team life energy. At the end of 2023 here is a single sentence I am making about how I think one becomes a successful indiehacker. One becomes a successful Indiehacker when one starts to solve pain-killer problems in the market they understand, for an audience they care about and consistently engage with for a long enough timeframe. Therefore an unsuccessful Indiehacker in a single sentence is An unsuccessful Indiehacker constantly enters new markets they don’t understand to build solutions for people whose problems they don’t care about, in a timeframe that is shorter than than the time they spent thinking about distribution. However, an important note to be made. Life is not just about indiehacking. It’s about learning and having fun. In the human world, the best journey isn’t the one that gets you the fastest to your goals but the one you enjoy the most. I enjoyed making those silly little projects and although I do not regret them, I will not repeat the same mistakes in 2024. But while it’s still 2023, I have 2 more projects I want to do :) EDIT: For Devs, frontend is always react with vite (ts) and backend is either node with express (ts) or python. For DB either Postgres or mongo (usually Prisma for ORM). For deployment all of it is on AWS (S3, EC2). In terms of libraries/APIs Whisper.cpp is best open source for transcription Obviously the gpt apis Eleven labs for voice related stuff And other random stuff here and there

I recreated an AI Phone Agent that saved $20,000 in lost revenue in 30 days for a business
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Mammoth_Sherbet7689This week

I recreated an AI Phone Agent that saved $20,000 in lost revenue in 30 days for a business

I've been intrigued by AI and its ability to help businesses streamline time-consuming tasks. Recently, I discovered a case study where a voice agent was able to earn a business $20,000 in booked calls in a month. Below, I've shared the case study and a demo number for a voice agent I developed. This technology is advancing rapidly, and I want to explore its potential further. Case Study A family-owned HVAC company struggled with managing a high volume of customer calls, including after-hours and weekend inquiries, resulting in missed opportunities and unmanaged leads. Hiring a dedicated call support team was not cost-effective. Solution The company implemented an AI system to handle calls autonomously, gather customer information, and notify service technicians via SMS, with options for live call transfers. Details The AI integration featured custom capabilities such as Service Titan integration, live call transfers, SMS/email alerts, calendar and CRM integration, and Zapier automation. Results In the first week, the company experienced a 20% increase in bookings and conversions. The system efficiently captured leads and managed tasks, enabling staff to handle more inquiries and outsource overflow. Within 30 days, the company saved $20,000 in lost revenue due to the elimination of calls that went to voicemail, or lost leads. The voice agent's ability to answer calls 24/7 led to significant revenue growth, time savings, and reduced churn. Here's the demo number for the voice agent I created: +1 (651) 372 2045 I believe this tech has strong use cases in a variety of industries, from home service, to dental clinics, to wedding photographers. This article studied the effect of missed calls in different businesses, if you're interested in learning more. I'd love to hear your thoughts and industries you think this could be the most beneficial for. Thank you!

I recreated an AI Phone Agent that saved $20,000 in lost revenue in 30 days for a business
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Mammoth_Sherbet7689This week

I recreated an AI Phone Agent that saved $20,000 in lost revenue in 30 days for a business

I've been intrigued by AI and its ability to help businesses streamline time-consuming tasks. Recently, I discovered a case study where a voice agent was able to earn a business $20,000 in booked calls in a month. Below, I've shared the case study and a demo number for a voice agent I developed. This technology is advancing rapidly, and I want to explore its potential further. Case Study A family-owned HVAC company struggled with managing a high volume of customer calls, including after-hours and weekend inquiries, resulting in missed opportunities and unmanaged leads. Hiring a dedicated call support team was not cost-effective. Solution The company implemented an AI system to handle calls autonomously, gather customer information, and notify service technicians via SMS, with options for live call transfers. Details The AI integration featured custom capabilities such as Service Titan integration, live call transfers, SMS/email alerts, calendar and CRM integration, and Zapier automation. Results In the first week, the company experienced a 20% increase in bookings and conversions. The system efficiently captured leads and managed tasks, enabling staff to handle more inquiries and outsource overflow. Within 30 days, the company saved $20,000 in lost revenue due to the elimination of calls that went to voicemail, or lost leads. The voice agent's ability to answer calls 24/7 led to significant revenue growth, time savings, and reduced churn. Here's the demo number for the voice agent I created: +1 (651) 372 2045 I believe this tech has strong use cases in a variety of industries, from home service, to dental clinics, to wedding photographers. This article studied the effect of missed calls in different businesses, if you're interested in learning more. I'd love to hear your thoughts and industries you think this could be the most beneficial for. Thank you!

I made a Voice AI Automated Testing platform (because I hate making phone calls)
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LemaLogic_comThis week

I made a Voice AI Automated Testing platform (because I hate making phone calls)

As my first New Year’s resolution, I’m excited to officially launch my side project: Testzilla.ai. While designing my Voice AI systems using VAPI, RetellAI, Bland, etc., I quickly got tired of the "Update system, test call flows, repeat" cycle that went with it. The whole point of Voice AI (for me) was that I could get off the phone, not spend even more time on it. So I made some Voice AI agents to test my Voice AI system so I didn't have to keep doing it manually. I showed it to developers friends who got excited and wanted to use it themselves with their systems (and sent me "Take My Money" meme, always a good sign). After hearing this a bunch of times, I decided to make it a platform I could share and easily use on multiple projects, have a simple UI, and let me run tests from my desktop or mobile with a click—and not spend 5-30 minutes of awkward time talking to phonebots in a crowded office. Win. It also has the benefit of being a way for an AI Agency to PROVE to clients that their AI system is working properly, answering questions the right way, NOT answering questions the wrong way, and that any advanced functionality (lookups, appointments, etc.) works properly. Key Features: Multi-Project Management: Simplifies the QA process across a diverse project portfolio, ideal for agencies handling multiple clients. Custom Test Management: Easily create, organize, and track test cases tailored to your project. Run Test Batches: Group and execute test cases efficiently to keep your workflow smooth and organized. Actionable Insights: Get analysis and suggestions that help you fix issues early and improve your releases. Client-Friendly Reporting: Provides clear, detailed reports that make it easy to share progress and results with stakeholders. Developer Tools: Easily manage (receive, email, view, listen, notify) your Transcripts from other systems (VAPI, Retell, etc) without having to create Zapier or Make automations with the provided Webhook URL. More dev tools coming soon, let us know what would make your life easier! I’m launching today and would love to get feedback from this awesome community! If you’re into QA, software development, or just love testing tools, give it a look and let me know what you think. I'll add $20 in credits to your new account so you can try it out risk free, no credit cards required. Here’s the link: Testzilla.ai Looking forward to hearing your thoughts! Cheers, Brian Gallagher

[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.

[D] The current and future state of AI/ML is shockingly demoralizing with little hope of redemption
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[D] The current and future state of AI/ML is shockingly demoralizing with little hope of redemption

I recently encountered the PaLM (Scaling Language Modeling with Pathways) paper from Google Research and it opened up a can of worms of ideas I’ve felt I’ve intuitively had for a while, but have been unable to express – and I know I can’t be the only one. Sometimes I wonder what the original pioneers of AI – Turing, Neumann, McCarthy, etc. – would think if they could see the state of AI that we’ve gotten ourselves into. 67 authors, 83 pages, 540B parameters in a model, the internals of which no one can say they comprehend with a straight face, 6144 TPUs in a commercial lab that no one has access to, on a rig that no one can afford, trained on a volume of data that a human couldn’t process in a lifetime, 1 page on ethics with the same ideas that have been rehashed over and over elsewhere with no attempt at a solution – bias, racism, malicious use, etc. – for purposes that who asked for? When I started my career as an AI/ML research engineer 2016, I was most interested in two types of tasks – 1.) those that most humans could do but that would universally be considered tedious and non-scalable. I’m talking image classification, sentiment analysis, even document summarization, etc. 2.) tasks that humans lack the capacity to perform as well as computers for various reasons – forecasting, risk analysis, game playing, and so forth. I still love my career, and I try to only work on projects in these areas, but it’s getting harder and harder. This is because, somewhere along the way, it became popular and unquestionably acceptable to push AI into domains that were originally uniquely human, those areas that sit at the top of Maslows’s hierarchy of needs in terms of self-actualization – art, music, writing, singing, programming, and so forth. These areas of endeavor have negative logarithmic ability curves – the vast majority of people cannot do them well at all, about 10% can do them decently, and 1% or less can do them extraordinarily. The little discussed problem with AI-generation is that, without extreme deterrence, we will sacrifice human achievement at the top percentile in the name of lowering the bar for a larger volume of people, until the AI ability range is the norm. This is because relative to humans, AI is cheap, fast, and infinite, to the extent that investments in human achievement will be watered down at the societal, educational, and individual level with each passing year. And unlike AI gameplay which superseded humans decades ago, we won’t be able to just disqualify the machines and continue to play as if they didn’t exist. Almost everywhere I go, even this forum, I encounter almost universal deference given to current SOTA AI generation systems like GPT-3, CODEX, DALL-E, etc., with almost no one extending their implications to its logical conclusion, which is long-term convergence to the mean, to mediocrity, in the fields they claim to address or even enhance. If you’re an artist or writer and you’re using DALL-E or GPT-3 to “enhance” your work, or if you’re a programmer saying, “GitHub Co-Pilot makes me a better programmer?”, then how could you possibly know? You’ve disrupted and bypassed your own creative process, which is thoughts -> (optionally words) -> actions -> feedback -> repeat, and instead seeded your canvas with ideas from a machine, the provenance of which you can’t understand, nor can the machine reliably explain. And the more you do this, the more you make your creative processes dependent on said machine, until you must question whether or not you could work at the same level without it. When I was a college student, I often dabbled with weed, LSD, and mushrooms, and for a while, I thought the ideas I was having while under the influence were revolutionary and groundbreaking – that is until took it upon myself to actually start writing down those ideas and then reviewing them while sober, when I realized they weren’t that special at all. What I eventually determined is that, under the influence, it was impossible for me to accurately evaluate the drug-induced ideas I was having because the influencing agent the generates the ideas themselves was disrupting the same frame of reference that is responsible evaluating said ideas. This is the same principle of – if you took a pill and it made you stupider, would even know it? I believe that, especially over the long-term timeframe that crosses generations, there’s significant risk that current AI-generation developments produces a similar effect on humanity, and we mostly won’t even realize it has happened, much like a frog in boiling water. If you have children like I do, how can you be aware of the the current SOTA in these areas, project that 20 to 30 years, and then and tell them with a straight face that it is worth them pursuing their talent in art, writing, or music? How can you be honest and still say that widespread implementation of auto-correction hasn’t made you and others worse and worse at spelling over the years (a task that even I believe most would agree is tedious and worth automating). Furthermore, I’ve yet to set anyone discuss the train – generate – train - generate feedback loop that long-term application of AI-generation systems imply. The first generations of these models were trained on wide swaths of web data generated by humans, but if these systems are permitted to continually spit out content without restriction or verification, especially to the extent that it reduces or eliminates development and investment in human talent over the long term, then what happens to the 4th or 5th generation of models? Eventually we encounter this situation where the AI is being trained almost exclusively on AI-generated content, and therefore with each generation, it settles more and more into the mean and mediocrity with no way out using current methods. By the time that happens, what will we have lost in terms of the creative capacity of people, and will we be able to get it back? By relentlessly pursuing this direction so enthusiastically, I’m convinced that we as AI/ML developers, companies, and nations are past the point of no return, and it mostly comes down the investments in time and money that we’ve made, as well as a prisoner’s dilemma with our competitors. As a society though, this direction we’ve chosen for short-term gains will almost certainly make humanity worse off, mostly for those who are powerless to do anything about it – our children, our grandchildren, and generations to come. If you’re an AI researcher or a data scientist like myself, how do you turn things back for yourself when you’ve spent years on years building your career in this direction? You’re likely making near or north of $200k annually TC and have a family to support, and so it’s too late, no matter how you feel about the direction the field has gone. If you’re a company, how do you standby and let your competitors aggressively push their AutoML solutions into more and more markets without putting out your own? Moreover, if you’re a manager or thought leader in this field like Jeff Dean how do you justify to your own boss and your shareholders your team’s billions of dollars in AI investment while simultaneously balancing ethical concerns? You can’t – the only answer is bigger and bigger models, more and more applications, more and more data, and more and more automation, and then automating that even further. If you’re a country like the US, how do responsibly develop AI while your competitors like China single-mindedly push full steam ahead without an iota of ethical concern to replace you in numerous areas in global power dynamics? Once again, failing to compete would be pre-emptively admitting defeat. Even assuming that none of what I’ve described here happens to such an extent, how are so few people not taking this seriously and discounting this possibility? If everything I’m saying is fear-mongering and non-sense, then I’d be interested in hearing what you think human-AI co-existence looks like in 20 to 30 years and why it isn’t as demoralizing as I’ve made it out to be. ​ EDIT: Day after posting this -- this post took off way more than I expected. Even if I received 20 - 25 comments, I would have considered that a success, but this went much further. Thank you to each one of you that has read this post, even more so if you left a comment, and triply so for those who gave awards! I've read almost every comment that has come in (even the troll ones), and am truly grateful for each one, including those in sharp disagreement. I've learned much more from this discussion with the sub than I could have imagined on this topic, from so many perspectives. While I will try to reply as many comments as I can, the sheer comment volume combined with limited free time between work and family unfortunately means that there are many that I likely won't be able to get to. That will invariably include some that I would love respond to under the assumption of infinite time, but I will do my best, even if the latency stretches into days. Thank you all once again!

[N] Inside DeepMind's secret plot to break away from Google
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[N] Inside DeepMind's secret plot to break away from Google

Article https://www.businessinsider.com/deepmind-secret-plot-break-away-from-google-project-watermelon-mario-2021-9 by Hugh Langley and Martin Coulter For a while, some DeepMind employees referred to it as "Watermelon." Later, executives called it "Mario." Both code names meant the same thing: a secret plan to break away from parent company Google. DeepMind feared Google might one day misuse its technology, and executives worked to distance the artificial-intelligence firm from its owner for years, said nine current and former employees who were directly familiar with the plans. This included plans to pursue an independent legal status that would distance the group's work from Google, said the people, who asked not to be identified discussing private matters. One core tension at DeepMind was that it sold the business to people it didn't trust, said one former employee. "Everything that happened since that point has been about them questioning that decision," the person added. Efforts to separate DeepMind from Google ended in April without a deal, The Wall Street Journal reported. The yearslong negotiations, along with recent shake-ups within Google's AI division, raise questions over whether the search giant can maintain control over a technology so crucial to its future. "DeepMind's close partnership with Google and Alphabet since the acquisition has been extraordinarily successful — with their support, we've delivered research breakthroughs that transformed the AI field and are now unlocking some of the biggest questions in science," a DeepMind spokesperson said in a statement. "Over the years, of course we've discussed and explored different structures within the Alphabet group to find the optimal way to support our long-term research mission. We could not be prouder to be delivering on this incredible mission, while continuing to have both operational autonomy and Alphabet's full support." When Google acquired DeepMind in 2014, the deal was seen as a win-win. Google got a leading AI research organization, and DeepMind, in London, won financial backing for its quest to build AI that can learn different tasks the way humans do, known as artificial general intelligence. But tensions soon emerged. Some employees described a cultural conflict between researchers who saw themselves firstly as academics and the sometimes bloated bureaucracy of Google's colossal business. Others said staff were immediately apprehensive about putting DeepMind's work under the control of a tech giant. For a while, some employees were encouraged to communicate using encrypted messaging apps over the fear of Google spying on their work. At one point, DeepMind's executives discovered that work published by Google's internal AI research group resembled some of DeepMind's codebase without citation, one person familiar with the situation said. "That pissed off Demis," the person added, referring to Demis Hassabis, DeepMind's CEO. "That was one reason DeepMind started to get more protective of their code." After Google restructured as Alphabet in 2015 to give riskier projects more freedom, DeepMind's leadership started to pursue a new status as a separate division under Alphabet, with its own profit and loss statement, The Information reported. DeepMind already enjoyed a high level of operational independence inside Alphabet, but the group wanted legal autonomy too. And it worried about the misuse of its technology, particularly if DeepMind were to ever achieve AGI. Internally, people started referring to the plan to gain more autonomy as "Watermelon," two former employees said. The project was later formally named "Mario" among DeepMind's leadership, these people said. "Their perspective is that their technology would be too powerful to be held by a private company, so it needs to be housed in some other legal entity detached from shareholder interest," one former employee who was close to the Alphabet negotiations said. "They framed it as 'this is better for society.'" In 2017, at a company retreat at the Macdonald Aviemore Resort in Scotland, DeepMind's leadership disclosed to employees its plan to separate from Google, two people who were present said. At the time, leadership said internally that the company planned to become a "global interest company," three people familiar with the matter said. The title, not an official legal status, was meant to reflect the worldwide ramifications DeepMind believed its technology would have. Later, in negotiations with Google, DeepMind pursued a status as a company limited by guarantee, a corporate structure without shareholders that is sometimes used by nonprofits. The agreement was that Alphabet would continue to bankroll the firm and would get an exclusive license to its technology, two people involved in the discussions said. There was a condition: Alphabet could not cross certain ethical redlines, such as using DeepMind technology for military weapons or surveillance. In 2019, DeepMind registered a new company called DeepMind Labs Limited, as well as a new holding company, filings with the UK's Companies House showed. This was done in anticipation of a separation from Google, two former employees involved in those registrations said. Negotiations with Google went through peaks and valleys over the years but gained new momentum in 2020, one person said. A senior team inside DeepMind started to hold meetings with outside lawyers and Google to hash out details of what this theoretical new formation might mean for the two companies' relationship, including specifics such as whether they would share a codebase, internal performance metrics, and software expenses, two people said. From the start, DeepMind was thinking about potential ethical dilemmas from its deal with Google. Before the 2014 acquisition closed, both companies signed an "Ethics and Safety Review Agreement" that would prevent Google from taking control of DeepMind's technology, The Economist reported in 2019. Part of the agreement included the creation of an ethics board that would supervise the research. Despite years of internal discussions about who should sit on this board, and vague promises to the press, this group "never existed, never convened, and never solved any ethics issues," one former employee close to those discussions said. A DeepMind spokesperson declined to comment. DeepMind did pursue a different idea: an independent review board to convene if it were to separate from Google, three people familiar with the plans said. The board would be made up of Google and DeepMind executives, as well as third parties. Former US president Barack Obama was someone DeepMind wanted to approach for this board, said one person who saw a shortlist of candidates. DeepMind also created an ethical charter that included bans on using its technology for military weapons or surveillance, as well as a rule that its technology should be used for ways that benefit society. In 2017, DeepMind started a unit focused on AI ethics research composed of employees and external research fellows. Its stated goal was to "pave the way for truly beneficial and responsible AI." A few months later, a controversial contract between Google and the Pentagon was disclosed, causing an internal uproar in which employees accused Google of getting into "the business of war." Google's Pentagon contract, known as Project Maven, "set alarm bells ringing" inside DeepMind, a former employee said. Afterward, Google published a set of principles to govern its work in AI, guidelines that were similar to the ethical charter that DeepMind had already set out internally, rankling some of DeepMind's senior leadership, two former employees said. In April, Hassabis told employees in an all-hands meeting that negotiations to separate from Google had ended. DeepMind would maintain its existing status inside Alphabet. DeepMind's future work would be overseen by Google's Advanced Technology Review Council, which includes two DeepMind executives, Google's AI chief Jeff Dean, and the legal SVP Kent Walker. But the group's yearslong battle to achieve more independence raises questions about its future within Google. Google's commitment to AI research has also come under question, after the company forced out two of its most senior AI ethics researchers. That led to an industry backlash and sowed doubt over whether it could allow truly independent research. Ali Alkhatib, a fellow at the Center for Applied Data Ethics, told Insider that more public accountability was "desperately needed" to regulate the pursuit of AI by large tech companies. For Google, its investment in DeepMind may be starting to pay off. Late last year, DeepMind announced a breakthrough to help scientists better understand the behavior of microscopic proteins, which has the potential to revolutionize drug discovery. As for DeepMind, Hassabis is holding on to the belief that AI technology should not be controlled by a single corporation. Speaking at Tortoise's Responsible AI Forum in June, he proposed a "world institute" of AI. Such a body might sit under the jurisdiction of the United Nations, Hassabis theorized, and could be filled with top researchers in the field. "It's much stronger if you lead by example," he told the audience, "and I hope DeepMind can be part of that role-modeling for the industry."

[N] Montreal-based Element AI sold for $230-million as founders saw value mostly wiped out
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[N] Montreal-based Element AI sold for $230-million as founders saw value mostly wiped out

According to Globe and Mail article: Element AI sold for $230-million as founders saw value mostly wiped out, document reveals Montreal startup Element AI Inc. was running out of money and options when it inked a deal last month to sell itself for US$230-milion to Silicon Valley software company ServiceNow Inc., a confidential document obtained by the Globe and Mail reveals. Materials sent to Element AI shareholders Friday reveal that while many of its institutional shareholders will make most if not all of their money back from backing two venture financings, employees will not fare nearly as well. Many have been terminated and had their stock options cancelled. Also losing out are co-founders Jean-François Gagné, the CEO, his wife Anne Martel, the chief administrative officer, chief science officer Nick Chapados and Yoshua Bengio, the University of Montreal professor known as a godfather of “deep learning,” the foundational science behind today’s AI revolution. Between them, they owned 8.8 million common shares, whose value has been wiped out with the takeover, which goes to a shareholder vote Dec 29 with enough investor support already locked up to pass before the takeover goes to a Canadian court to approve a plan of arrangement with ServiceNow. The quartet also owns preferred shares worth less than US$300,000 combined under the terms of the deal. The shareholder document, a management proxy circular, provides a rare look inside efforts by a highly hyped but deeply troubled startup as it struggled to secure financing at the same time as it was failing to live up to its early promises. The circular states the US$230-million purchase price is subject to some adjustments and expenses which could bring the final price down to US$195-million. The sale is a disappointing outcome for a company that burst onto the Canadian tech scene four years ago like few others, promising to deliver AI-powered operational improvements to a range of industries and anchor a thriving domestic AI sector. Element AI became the self-appointed representative of Canada’s AI sector, lobbying politicians and officials and landing numerous photo ops with them, including Prime Minister Justin Trudeau. It also secured $25-million in federal funding – $20-million of which was committed earlier this year and cancelled by the government with the ServiceNow takeover. Element AI invested heavily in hype and and earned international renown, largely due to its association with Dr. Bengio. It raised US$102-million in venture capital in 2017 just nine months after its founding, an unheard of amount for a new Canadian company, from international backers including Microsoft Corp., Intel Corp., Nvidia Corp., Tencent Holdings Ltd., Fidelity Investments, a Singaporean sovereign wealth fund and venture capital firms. Element AI went on a hiring spree to establish what the founders called “supercredibility,” recruiting top AI talent in Canada and abroad. It opened global offices, including a British operation that did pro bono work to deliver “AI for good,” and its ranks swelled to 500 people. But the swift hiring and attention-seeking were at odds with its success in actually building a software business. Element AI took two years to focus on product development after initially pursuing consulting gigs. It came into 2019 with a plan to bring several AI-based products to market, including a cybersecurity offering for financial institutions and a program to help port operators predict waiting times for truck drivers. It was also quietly shopping itself around. In December 2018, the company asked financial adviser Allen & Co LLC to find a potential buyer, in addition to pursuing a private placement, the circular reveals. But Element AI struggled to advance proofs-of-concept work to marketable products. Several client partnerships faltered in 2019 and 2020. Element did manage to reach terms for a US$151.4-million ($200-million) venture financing in September, 2019 led by the Caisse de dépôt et placement du Québec and backed by the Quebec government and consulting giant McKinsey and Co. However, the circular reveals the company only received the first tranche of the financing – roughly half of the amount – at the time, and that it had to meet unspecified conditions to get the rest. A fairness opinion by Deloitte commissioned as part of the sale process estimated Element AI’s enterprises value at just US$76-million around the time of the 2019 financing, shrinking to US$45-million this year. “However, the conditions precedent the closing of the second tranche … were not going to be met in a timely manner,” the circular reads. It states “new terms were proposed” for a round of financing that would give incoming investors ranking ahead of others and a cumulative dividend of 12 per cent on invested capital and impose “other operating and governance constraints and limitations on the company.” Management instead decided to pursue a sale, and Allen contacted prospective buyers in June. As talks narrowed this past summer to exclusive negotiations with ServiceNow, “the company’s liquidity was diminishing as sources of capital on acceptable terms were scarce,” the circular reads. By late November, it was generating revenue at an annualized rate of just $10-million to $12-million, Deloitte said. As part of the deal – which will see ServiceNow keep Element AI’s research scientists and patents and effectively abandon its business – the buyer has agreed to pay US$10-million to key employees and consultants including Mr. Gagne and Dr. Bengio as part of a retention plan. The Caisse and Quebec government will get US$35.45-million and US$11.8-million, respectively, roughly the amount they invested in the first tranche of the 2019 financing.

[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.

If only someone told me this before my first startup
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johnrushxThis week

If only someone told me this before my first startup

If only someone told me this before my first startup: Validate idea first. I wasted a decade building stuff nobody needed. Incubators and VCs served to me as a validation, but I was so wrong. Kill my EGO. It’s not about me, but the user. I must want what the user wants, not what I want. My taste isn't important. The user has expectations, and I must fulfill them. Don’t chaise investors. Chase users, and then investors will be chasing me. I've never had more incoming interest from VC than now when I'm the least interested in them. Never hire managers. Only hire doers until PMF. So many people know how to manage people and so few can actually get sh\*t done barehand. Landing page is the least important thing in a startup. Pick a simple template, edit texts with a no-code website builder in less than an hour and that's it! At the early stage, I win traffic outside of my website, people are already interested, so don't make them search for the signup button among the texts! Focus on conversion optimization only when the traffic is consistent. Keep it to one page. Nobody gonna browse this website. Hire only fullstack devs. There is nothing less productive in this world than a team of developers for an early-stage product. One full stack dev building the whole product. That’s it. Chase global market from day 1. If the product and marketing are good, it will work on the global market too, if it’s bad, it won’t work on the local market too. So better go global from day 1, so that if it works, the upside is 100x bigger. I launched all startups for the Norwegian market, hoping we will scale to international at some point. I wish I launched to international from day 1 as I do now. The size of the market is 10000x bigger. I can validate and grow products in days, not in years as it used to be. Do SEO from day 2. As early as I can. I ignored this for 14 years. It’s my biggest regret. It takes just 5 minutes to get it done on my landing page. I go to Google Keyword Planner, enter a few keywords around my product, sort them by traffic, filter out high competition kws, pick the top 10, and place them natively on my home page and meta tags. Add one blog article every week. Either manually or by paying for an AI blogging tool. Sell features, before building them. Ask existing users if they want this feature. I run DMs with 10-20 users every day, where I chat about all my ideas and features I wanna add. I clearly see what resonates with me most and only go build those. If I don't have followers, try HN, Reddit, or just search on X for posts and ask it in the replies. People are helpful, they will reply if the question is easy to understand. Hire only people I would wanna hug. My cofounder, an old Danish man said this to me in 2015. And it was a big shift. I realized that if I don’t wanna hug the person, it means I dislike them on a chemical/animal level. Even if I can’t say why, but that’s the fact. Sooner or later, we would have a conflict and eventually break up. It takes up to 10 years to build a startup, make sure I do it with people I have this connection with. Invest all money into my startups and friends. Not crypt0, not stockmarket, not properties. I did some math, if I kept investing all my money into all my friends’ startups, that would be about 70 investments. 3 of them turned into unicorns eventually. Even 1 would have made the bank. Since 2022, I have invested all my money into my products, friends, and network. If I don't have friends who do startups, invest it in myself. Post on Twitter daily. I started posting here in March last year. It’s my primary source of new connections and growth. I could have started it earlier, I don't know why I didn't. Don’t work/partner with corporates. Corporations always seem like an amazing opportunity. They’re big and rich, they promise huge stuff, millions of users, etc. But every single time none of this happens. Because I talk to a regular employees there. They waste my time, destroy focus, shift priorities, and eventually bring in no users/money. Don’t get ever distracted by hype e.g. crypt0. I lost 1.5 years of my life this way. I met the worst people along the way. Fricks, scammers, thieves. Some of my close friends turned into thieves along the way, just because it was so common in that space. I wish this didn’t happen to me. I wish I was stronger and stayed on my mission. Don’t build consumer apps. Only b2b. Consumer apps are so hard, like a lottery. It’s just 0.00001% who make it big. The rest don’t. Even if I got many users, then there is a monetization challenge. I’ve spent 4 years in consumer apps and regret it. Don’t hold on bad project for too long, max 1 year. Some projects just don’t work. In most cases, it’s either the idea that’s so wrong that I can’t even pivot it or it’s a team that is good one by one but can’t make it as a team. Don’t drag this out for years. Tech conferences are a waste of time. They cost money, take energy, and time and I never really meet anyone there. Most people there are the “good” employees of corporations who were sent there as a perk for being loyal to the corporation. Very few fellow makers. Scrum is a Scam. For small teams and bootstrapped teams. If I had a team that had to be nagged every morning with questions as if they were children in kindergarten, then things would eventually fail. The only good stuff I managed to do happened with people who were grownups and could manage their stuff on their own. We would just do everything over chat as a sync on goals and plans. Outsource nothing at all until PMF. In a startup, almost everything needs to be done in a slightly different way, more creative, and more integrated into the vision. When outsourcing, the external members get no love and no case for the product. It’s just yet another assignment in their boring job. Instead of coming up with great ideas for my project they will be just focusing on ramping up their skills to get a promotion or a better job offer. Bootstrap. I spent way too much time raising money. I raised more than 10 times, preseed, seeded, and series A. But each time it was a 3-9 month project, meetings every week, and lots of destruction. I could afford to bootstrap, but I still went the VC-funded way, I don’t know why. To be honest, I didn’t know bootstrapping was a thing I could do or anyone does. It may take a decade. When I was 20, I was convinced it takes a few years to build and succeed with a startup. So I kept pushing my plans forward, to do it once I exited. Family, kids. I wish I married earlier. I wish I had kids earlier. No Free Tier. I'd launch a tool with a free tier, and it'd get sign-ups, but very few would convert. I'd treat free sign-ups as KPIs and run on it for years. I'd brag about signups and visitors. I'd even raise VC money with these stats. But eventually, I would fail to reach PMF. Because my main feedback would come from free users and the product turned into a perfect free product. Once I switched to "paid only" until I validated the product, things went really well. Free and paid users often need different products. Don't fall into this trap as I did. Being To Cheap. I always started by checking all competitors and setting the lowest price. I thought this would be one of the key advantages of my product. But no, I was wrong. The audience on $5 and $50 are totally different. $5: pain in the \*ss, never happy, never recommend me to a friend, leave in 4 months. $50: polite, give genuine feedback, happy, share with friends, become my big fan if I solve their request. I will fail. When I started my first startup. I thought if I did everything right, it would work out. But it turned out that almost every startup fails. I wish I knew that and I tried to fail faster, to get to the second iteration, then to the third, and keep going on, until I either find out nothing works or make it work. Use boilerplates. I wasted years of dev time and millions of VC money to pay for basic things. To build yet another sidebar, yet another dashboard, and payment integration... I had too much pride, I couldn't see myself taking someone else code as a basis for my product. I wanted it to be 100% mine, original, from scratch. Because my product seems special to me. Spend more time with Family & Friends. I missed the weddings of all my best friends and family. I was so busy. I thought if I didn't do it on time, the world would end. Looking back today, it was so wrong. I meet my friends and can't share those memories with them, which makes me very sad. I realized now, that spending 10% of my time with family and friends would practically make no negative impact on my startups. Build Products For Audiences I Love. I never thought of this. I'd often build products either for corporates, consumers, or for developers. It turns out I have no love for all 3. But I deeply love indie founders. Because they are risk-takers and partly kids in their hearts. Once I switched the focus to indie makers on my products, my level of joy increased by 100x for me. Ignore Badges and Awards I was chasing those awards just like everyone else. Going to ceremonies, signing up for events and stuff. I've won tons of awards, but none of those were eventually useful to my business. I better focused on my business and users. Write Every Single Day. When I was a kid, I loved writing stories. In school, they would give an assignment, and I'd often write a long story for it, however, the teacher would put an F on it. The reason was simple, I had an issue with the direction of the letters and the sequence of letters in the words. I still have it, it's just the Grammarly app helping me to correct these issues. So the teacher would fail my stories because almost every sentence had a spelling mistake that I couldn't even see. It made me think I'm made at writing. So I stopped, for 15 years. But I kept telling stories all these years. Recently I realized that in any group, the setup ends up turning into me telling stories to everyone. So I tried it all again, here on X 10 months ago. I love it, the process, the feedback from people. I write every day. I wish I had done it all these years. The End. \ this is an updated version of my post on the same topic from 2 months ago. I've edited some of the points and added 9 new ones.* \\ This is not advice, it's my self-reflection that might help you avoid same mistakes if you think those were mistakes

We made $325k in 2023 from AI products, starting from 0, with no-code, no funding and no audience
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hopefully_usefulThis week

We made $325k in 2023 from AI products, starting from 0, with no-code, no funding and no audience

I met my co-founder in late 2022 after an introduction from a mutual friend to talk about how to find contract Product Management roles. I was sporadically contracting at start-up at the time and he had just come out of another start-up that was wiped out by the pandemic. We hit it off, talking about ideas, sharing what other indie-hackers were doing, and given GPT-3’s prominence at the time, we started throwing around ideas about things we could build with it, if nothing else, just to learn. I should caveat, neither of us were AI experts when starting out, everything we learned has been through Twitter and blogs, my background is as an accountant, and his a consultant. Here’s how it went since then: &#x200B; Nov 2022 (+$50) \- We built a simple tool in around a week using GPT-3 fine-tuning and a no-code tool (Bubble) that helped UK university students write their personal statements for their applications \- We set some Google Ads going and managed to make a few sales (\~$50) in the first week \- OpenAI were still approving applications at the time and said this went against their “ethics” so we had to take it down &#x200B; Dec 2022 (+$200) \- We couldn’t stop coming up with ideas related to AI fine-tuning, but realised it was almost impossible to decide which to pursue \- We needed a deadline to force us so we signed up for the Ben’s Bites hackathon in late December \- In a week, we built and launched a no-code fine-tuning platform, allowing people to create fine-tuned models by dragging and dropping an Excel file onto it \- We launched it on Product Hunt, having no idea how to price it, and somehow managed to get \~2,000 visitors on the site and make 2 sales at $99 &#x200B; Jan 2023 (+$3,000) \- We doubled down on the fine-tuning idea and managed to get up to \~$300 MRR, plus a bunch of one-time sales and a few paid calls to help people get the most out of their models \- We quickly realised that people didn’t want to curate models themselves, they just wanted to dump data and get magic out \- That was when we saw people building “Talk with x book/podcast” on Twitter as side projects and realised that was the missing piece, we needed to turn it into a tool \- We started working on the new product in late January &#x200B; Feb 2023 (+$9,000) \- We started pre-selling access to an MVP for the new product, which allowed people to “chat with their data/content”, we got $5,000 in pre-sales, more than we made from the previous product in total \- By mid-February, after 3 weeks of building we were able to launch and immediately managed to get traction, getting to $1k MRR in < 1 week, building on the hype of ChatGPT and AI (we were very lucky here) &#x200B; Mar - Jul 2023 (+$98,000) \- We worked all the waking hours to keep up with customer demand, bugs, OpenAI issues \- We built integrations for a bunch of services like Slack, Teams, Wordpress etc, added tons of new functionality and continue talking to customers every day \- We managed to grow to $17k MRR (just about enough to cover our living expenses and costs in London) through building in public on Twitter, newsletters and AI directories (and a million other little things) \- We sold our fine-tuning platform for \~$20k and our university project for \~$3k on Acquire &#x200B; Aug 2023 (+$100,000) \- We did some custom development work based on our own product for a customer that proved pretty lucrative &#x200B; Sep - Oct 2023 (+$62,000) \- After 8 months of building constantly, we started digging more seriously into our usage and saw subscriptions plateauing \- We talked to and analysed all our paying users to identify the main use cases and found 75% were for SaaS customer support \- We took the leap to completely rebuild a version of our product around this use case, our biggest to date (especially given most features with no-code took us <1 day) &#x200B; Nov - Dec 2023 (+$53,000) \- We picked up some small custom development work that utilised our own tech \- We’re sitting at around $22k MRR now with a few bigger clients signed up and coming soon \- After 2 months of building and talking to users, we managed to finish our “v2” of our product, focussed squarely on SaaS customer support and launched it today. &#x200B; We have no idea what the response will be to this new version, but we’re pretty happy with it, but couldn’t have planned anything that happened to us in 2023 so who knows what will come of 2024, we just know that we are going to be learning a ton more. &#x200B; Overall, it is probably the most I have had to think in my life - other jobs you can zone out from time to time or rely on someone else if you aren’t feeling it - not when you are doing this, case and point, I am writing this with a banging head-cold right now, but wanted to get this done. A few more things we have learned along the way - context switching is unreal, as is keeping up with, learning and reacting to AI. There isn’t a moment of the day I am not thinking about what we do next. But while in some way we now have hundreds of bosses (our customers) I still haven’t felt this free and can’t imagine ever going back to work for someone else. Next year we’re really hoping to figure out some repeatable distribution channels and personally, I want to get a lot better at creating content/writing, this is a first step! Hope this helps someone else reading this to just try starting something and see what happens.

Jinxed - $0 month after bragging about my first $10k month here. (PROGRESS UPDATE)
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swagamoneyThis week

Jinxed - $0 month after bragging about my first $10k month here. (PROGRESS UPDATE)

A month ago I made a post in this sub about my first $10k month. It went viral. And guess what - I didn't make another dollar since. Honestly, I shouldn't have made any money that first month also. Because I didn't have an offer. If you're familiar with Alex Hormozi you know that the offer is what makes or breaks a business. And I simply didn't have it. I managed to close my first clients just because I rode the AI hype train and managed to capture a couple of CEOs who were riding it too. Took whatever I could get for installment without thinking about the future. (It also helped that I wasn't bullshitting and had a legit enterprise-grade custom GPT framework ready). But that's not a business strategy at all. You can't base your business solely off hype. So the last month was dedicated to crafting a proper offer. No selling involved. Purely discovery chats with as many people as possible. The viral post helped because I connected with some badass people I wouldn't have reached otherwise. Even managed to add a new team member from Reddit. But most importantly, we now have the offer: Enterprise-grade AI assistant trained on your data for a fraction of the market cost. Basically a custom GPT for companies that want a secure assistant "trained" on their data but are not willing to spend millions on OpenAI's Custom Models or hundreds of thousands on Enterprise ChatGPT. (OpenAI's introduction of exclusive business GPTs for $2-3M is an incredibly good leverage for this offer). Also got rid of the big installment fee and switched to a $1k/month starting price for attractiveness and simplicity for companies (that covers their Azure fees also). The key offer points here are: Data security (as there are cheap, but not enterprise-grade tools like PDF.ai) Good price (as not all businesses can afford to pay 6 figure premiums for their data security) So the lesson here (I suppose) is that it's okay to take a step back sometimes. Reevaluate your direction. It's not worth sprinting when you're running in circles. P.S. finally made a website https://jongri.tech

Raised $450k for my startup, here are the lessons I've learned along the way
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marin_smiljanicThis week

Raised $450k for my startup, here are the lessons I've learned along the way

2021 has been a pretty amazing year for Omnisearch. Having started initial work on Omnisearch at the end of 2020, we entered the new year with a working MVP yet no revenue, no significant partnerships, and no funding. Fast forward to the end of 2021, and we now have fantastic revenue growth, a partnership with a public company, and a far more powerful, complete and polished product. But one milestone really changed Omnisearch’s trajectory: our $450,000 USD pre-seed round by GoAhead Ventures. In this post I want to share the story of how it came about and offer a couple of takeaways to keep in mind when preparing for fundraising. &#x200B; The story Contrary to most advice, my co-founder Matej and I didn’t allocate a specific time to switch to “fundraising mode” but rather talked to investors on an ongoing basis. It was a bit of a distraction from working on the product, but on the positive side we were able to constantly get feedback on the idea, pitch, go-to-market strategy and hiring, as well as hearing investors’ major concerns sooner rather than later. That being said, our six-month long fundraising efforts weren’t yielding results - we talked to about twenty investors, mostly angels or smaller funds, with no success. The feedback was generally of the “too early for us” variety (since we were still pre-revenue), with additional questions about our go-to-market strategy and ideal customer persona. The introduction to our eventual investors, California-based GoAhead Ventures, came through a friend who had pitched them previously. We wrote a simple blurb and sent our pitch deck. We then went through GoAhead’s hyper-efficient screening process, consisting of a 30-minute call, a recorded three-minute pitch, and filling out a simple Google doc. Throughout the whole process, the GoAhead team left an awesome impression thanks to their knowledge of enterprise software and their responsiveness. They ended up investing and the whole deal was closed within two weeks, which is super fast even by Silicon Valley standards. While our fundraising experience is a single data point and your case might be different, here are the key takeaways from our journey. &#x200B; Perseverance wins: Like I said above, we talked to about twenty investors before we closed our round. Getting a series of “no”s sucks, but we took the feedback seriously and tried to prepare better for questions that caught us off guard. But we persevered, keeping in mind that from a bird’s eye perspective it’s an amazing time to be building startups and raising funds. Focus on traction: Sounds pretty obvious, right? The truth is, though, that even a small amount of revenue is infinitely better than none at all. One of the major differences between our eventual successful investor pitch and the earlier ones was that we had actual paying customers, though our MRR was low. This allows you to talk about customers in the present tense, showing there’s actual demand for your product and making the use cases more tangible. And ideally, highlight a couple of customer testimonials to boost your credibility. Have a demo ready: In Omnisearch’s case, the demo was oftentimes the best received part of the pitch or call. We’d show investors the live demo, and for bonus points even asked them to choose a video from YouTube and then try searching through it. This always had a “wow” effect on prospective investors and made the subsequent conversation more exciting and positive. Accelerators: Accelerators like Y Combinator or Techstars can add enormous value to a startup, especially in the early stages. And while it’s a great idea to apply, don’t rely on them too heavily. Applications happen only a few times a year, and you should have a foolproof fundraising plan in case you don’t get in. In our case, we just constantly looked for investors who were interested in our space (defined as enterprise SaaS more broadly), using LinkedIn, AngelList, and intros from our own network. Practice the pitch ad nauseam: Pitching is tough to get right even for seasoned pros, so it pays to practice as often as possible. We took every opportunity to perfect the pitch: attending meetups and giving the thirty-second elevator pitch to other attendees over beer and pizza, participating in startup competitions, going to conferences and exhibiting at our own booth, attending pre-accelerator programs, and pitching to friends who are in the startup world. Show an understanding of the competition: Frankly, this was one of the strongest parts of our pitch and investor conversations. If you’re in a similar space to ours, Gartner Magic Quadrants and Forrester Waves are an awesome resource, as well as sites like AlternativeTo or Capterra and G2. By thoroughly studying these resources we gained a great understanding of the industry landscape and were able to articulate our differentiation more clearly and succinctly. Presenting this visually in a coordinate system or a feature grid is, from our experience, even more effective. Remember it’s just the beginning! Getting your first round of funding is just the beginning of the journey, so it’s important to avoid euphoria and get back to building and selling the product as soon as possible. While securing funding enables you to scale the team, and is a particular relief if the founders had worked without a salary, the end goal is still to build a big, profitable, and overall awesome startup.

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

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.

The delicate balance of building an online community business
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matthewbarbyThis week

The delicate balance of building an online community business

Hey /r/Entrepreneur 👋 Just under two years ago I launched an online community business called Traffic Think Tank with two other co-founders, Nick Eubanks and Ian Howells. As a Traffic Think Tank customer you (currently) pay $119 a month to get access to our online community, which is run through Slack. The community is focused on helping you learn various aspects of marketing, with a particular focus on search engine optimization (SEO). Alongside access to the Slack community, we publish new educational video content from outside experts every week that all customers have access to. At the time of writing, Traffic Think Tank has around 650 members spanning across 17 of the 24 different global time zones. I was on a business trip over in Sydney recently, and during my time there I met up with some of our Australia-based community members. During dinner I was asked by several of them how the idea for Traffic Think Tank came about and what steps we took to validate that the idea was worth pursuing.  This is what I told them… How it all began It all started with a personal need. Nick, an already successful entrepreneur and owner of a marketing agency, had tested out an early version Traffic Think Tank in early 2017. He offered real-time consulting for around ten customers that he ran from Slack. He would publish some educational videos and offer his advice on projects that the members were running. The initial test went well, but it was tough to maintain on his own and he had to charge a fairly high price to make it worth his time. That’s when he spoke to me and Ian about turning this idea into something much bigger. Both Ian and I offered something slightly different to Nick. We’ve both spent time in senior positions at marketing agencies, but currently hold senior director positions in 2,000+ public employee companies (HubSpot and LendingTree). Alongside this, as a trio we could really ramp up the quality and quantity of content within the community, spread out the administrative workload and just generally have more resources to throw at getting this thing off the ground. Admittedly, Nick was much more optimistic about the potential of Traffic Think Tank – something I’m very thankful for now – whereas Ian and I were in the camp of “you’re out of your mind if you think hundreds of people are going to pay us to be a part of a Slack channel”. To validate the idea at scale, we decided that we’d get an initial MVP of the community up and running with a goal of reaching 100 paying customers in the first six months. If we achieved that, we’d validated that it was a viable business and we would continue to pursue it. If not, we’d kill it. We spent the next month building out the initial tech stack that enabled us to accept payments, do basic user management to the Slack channel, and get a one-page website up and running with information on what Traffic Think Tank was all about.  After this was ready, we doubled down on getting some initial content created for members – I mean, we couldn’t have people just land in an empty Slack channel, could we? We created around ten initial videos, 20 or so articles and then some long threads full of useful information within the Slack channel so that members would have some content to pour into right from the beginning.  Then, it was time to go live. The first 100 customers Fortunately, both Nick and I had built a somewhat substantial following in the SEO space over the previous 5-10 years, so we at least had a large email list to tap into (a total of around 40,000 people). We queued up some launch emails, set an initial price of $99 per month and pressed send. [\[LINK\] The launch email I sent to my subscribers announcing Traffic Think Tank](https://mailchi.mp/matthewbarby/future-of-marketing-1128181) What we didn’t expect was to sell all of the initial 100 membership spots in the first 72 hours. “Shit. What do we do now? Are we ready for this many people? Are we providing them with enough value? What if something breaks in our tech stack? What if they don’t like the content? What if everyone hates Slack?” All of these were thoughts running through my head. This brings me to the first great decision we made: we closed down new membership intake for 3 months so that we could focus completely on adding value to the first cohort of users. The right thing at the right time SEO is somewhat of a dark art to many people that are trying to learn about it for the first time. There’s hundreds of thousands (possibly millions) of articles and videos online that talk about how to do SEO.  Some of it’s good advice; a lot of it is very bad advice.  Add to this that the barrier to entry of claiming to be an “expert” in SEO is practically non-existent and you have a recipe for disaster. This is why, for a long time, individuals involved in SEO have flocked in their masses to online communities for information and to bounce ideas off of others in the space. Forums like SEObook, Black Hat World, WickedFire, Inbound.org, /r/BigSEO, and many more have, at one time, been called home by many SEOs.  In recent times, these communities have either been closed down or just simply haven’t adapted to the changing needs of the community – one of those needs being real-time feedback on real-world problems.  The other big need that we all spotted and personally had was the ability to openly share the things that are working – and the things that aren’t – in SEO within a private forum. Not everyone wanted to share their secret sauce with the world. One of the main reasons we chose Slack as the platform to run our community on was the fact that it solved these two core needs. It gave the ability to communicate in real-time across multiple devices, and all of the information shared within it was outside of the public domain. The other problem that plagued a lot of these early communities was spam. Most of them were web-based forums that were free to access. That meant they became a breeding ground for people trying to either sell their services or promote their own content – neither of which is conducive to building a thriving community. This was our main motivation for charging a monthly fee to access Traffic Think Tank. We spent a lot of time thinking through pricing. It needed to be enough money that people would be motivated to really make use of their membership and act in a way that’s beneficial to the community, but not too much money that it became cost prohibitive to the people that would benefit from it the most. Considering that most of our members would typically spend between $200-800 per month on SEO software, $99 initially felt like the perfect balance. Growing pains The first three months of running the community went by without any major hiccups. Members were incredibly patient with us, gave us great feedback and were incredibly helpful and accommodating to other members. Messages were being posted every day, with Nick, Ian and myself seeding most of the engagement at this stage.  With everything going smoothly, we decided that it was time to open the doors to another intake of new members. At this point we’d accumulated a backlog of people on our waiting list, so we knew that simply opening our doors would result in another large intake. Adding more members to a community has a direct impact on the value that each member receives. For Traffic Think Tank in particular, the value for members comes from three areas: The ability to have your questions answered by me, Nick and Ian, as well as other members of the community. The access to a large library of exclusive content. The ability to build connections with the wider community. In the early stages of membership growth, there was a big emphasis on the first of those three points. We didn’t have an enormous content library, nor did we have a particularly large community of members, so a lot of the value came from getting a lot of one-to-one time with the community founders. [\[IMAGE\] Screenshot of engagement within the Traffic Think Tank Slack community](https://cdn.shortpixel.ai/client/qglossy,retimg,w_1322/https://www.matthewbarby.com/wp-content/uploads/2019/08/Community-Engagement-in-Traffic-Think-Tank.png) The good thing about having 100 members was that it was just about feasible to give each and every member some one-to-one time within the month, which really helped us to deliver those moments of delight that the community needed early on. Two-and-a-half months after we launched Traffic Think Tank, we opened the doors to another 250 people, taking our total number of members to 350. This is where we experienced our first growing pains.  Our original members had become used to being able to drop us direct messages and expect an almost instant response, but this wasn’t feasible anymore. There were too many people, and we needed to create a shift in behavior. We needed more value to come from the community engaging with one another or we’d never be able to scale beyond this level. We started to really pay attention to engagement metrics; how many people were logging in every day, and of those, how many were actually posting messages within public channels.  We asked members that were logging in a lot but weren’t posting (the “lurkers”) why that was the case. We also asked the members that engaged in the community the most what motivated them to post regularly. We learned a lot from doing this. We found that the large majority of highly-engaged members had much more experience in SEO, whereas most of the “lurkers” were beginners. This meant that most of the information being shared in the community was very advanced, with a lot of feedback from the beginners in the group being that they “didn’t want to ask a stupid question”.  As managers of the community, we needed to facilitate conversations that catered to all of our members, not just those at a certain level of skill. To tackle this problem, we created a number of new channels that had a much deeper focus on beginner topics so novice members had a safe place to ask questions without judgment.  We also started running live video Q&As each month where we’d answer questions submitted by the community. This gave our members one-on-one time with me, Nick and Ian, but spread the value of these conversations across the whole community rather than them being hidden within private messages. As a result of these changes, we found that the more experienced members in the community were really enjoying sharing their knowledge with those with less experience. The number of replies within each question thread was really starting to increase, and the community started to shift away from just being a bunch of threads created by me, Nick and Ian to a thriving forum of diverse topics compiled by a diverse set of individuals. This is what we’d always wanted. A true community. It was starting to happen. [\[IMAGE\] Chart showing community engagement vs individual member value](https://cdn.shortpixel.ai/client/qglossy,retimg,w_1602/https://www.matthewbarby.com/wp-content/uploads/2019/08/Community-Engagement-Balance-Graph.jpg) At the same time, we started to realize that we’ll eventually reach a tipping point where there’ll be too much content for us to manage and our members to engage with. When we reach this point, the community will be tough to follow and the quality of any given post will go down. Not only that, but the community will become increasingly difficult to moderate. We’re not there yet, but we recognize that this will come, and we’ll have to adjust our model again. Advocating advocacy As we started to feel more comfortable about the value that members were receiving, we made the decision to indefinitely open for new members. At the same time, we increased the price of membership (from $99 a month to $119) in a bid to strike the right balance between profitability as a business and to slow down the rate at which we were reaching the tipping point of community size. We also made the decision to repay all of our early adopters by grandfathering them in to the original pricing – and committing to always do this in the future. Despite the price increase, we saw a continued flow of new members come into the community. The craziest part about this was that we were doing practically no marketing activities to encourage new members– this was all coming from word of mouth. Our members were getting enough value from the community that they were recommending it to their friends, colleagues and business partners.  The scale at which this was happening really took us by surprise and it told us one thing very clearly: delivering more value to members resulted in more value being delivered to the business. This is a wonderful dynamic to have because it perfectly aligns the incentives on both sides. We’d said from the start that we wouldn’t sacrifice value to members for more revenue – this is something that all three of us felt very strongly about. First and foremost, we wanted to create a community that delivered value to its members and was run in a way that aligned with our values as people. If we could find a way to stimulate brand advocacy, while also tightening the bonds between all of our individual community members, we’d be boosting both customer retention and customer acquisition in the same motion. This became our next big focus. [\[TWEET\] Adam, one of our members wore his Traffic Think Tank t-shirt in the Sahara desert](https://twitter.com/AdamGSteele/status/1130892481099382784) We started with some simple things: We shipped out Traffic Think Tank branded T-shirts to all new members. We’d call out each of the individuals that would submit questions to our live Q&A sessions and thank them live on air. We set up a new channel that was dedicated to sharing a quick introduction to who you are, what you do and where you’re based for all new members. We’d created a jobs channel and a marketplace for selling, buying and trading services with other members. Our monthly “blind dates” calls were started where you’d be randomly grouped with 3-4 other community members so that you could hop on a call to get to know each other better. The Traffic Think Tank In Real Life (IRL)* channel was born, which enabled members to facilitate in-person meetups with each other. In particular, we saw that as members started to meet in person or via calls the community itself was feeling more and more like a family. It became much closer knit and some members started to build up a really positive reputation for being particularly helpful to other members, or for having really strong knowledge in a specific area. [\[TWEET\] Dinner with some of the Traffic Think Tank members in Brighton, UK](https://twitter.com/matthewbarby/status/1117175584080134149) Nick, Ian and I would go out of our way to try and meet with members in real life wherever we could. I was taken aback by how appreciative people were for us doing this, and it also served as an invaluable way to gain honest feedback from members. There was another trend that we’d observed that we didn’t really expect to happen. More and more members were doing business with each another. We’ve had people find new jobs through the community, sell businesses to other members, launch joint ventures together and bring members in as consultants to their business. This has probably been the most rewarding thing to watch, and it was clear that the deeper relationships that our members were forming were resulting in an increased level of trust to work with each other. We wanted to harness this and take it to a new level. This brought us to arguably the best decision we’ve made so far running Traffic Think Tank… we were going to run a big live event for our members. I have no idea what I’m doing It’s the first week of January 2019 and we’re less than three weeks away from Traffic Think Tank LIVE, our first ever in-person event hosting 150 people, most of which are Traffic Think Tank members. It's like an ongoing nightmare I can’t wake up from. That was Nick’s response in our private admin channel to myself and Ian when I asked if they were finding the run-up to the event as stressful as I was. I think that all three of us were riding on such a high from how the community was growing that we felt like we could do anything. Running an event? How hard can it be? Well, turns out it’s really hard. We had seven different speakers flying over from around the world to speak at the event, there was a pre- and after event party, and we’d planned a charity dinner where we would take ten attendees (picked at random via a raffle) out for a fancy meal. Oh, and Nick, Ian and I were hosting a live Q&A session on stage. It wasn’t until precisely 48 hours before the event that we’d realized we didn’t have any microphones, nor had a large amount of the swag we’d ordered arrived. Plus, a giant storm had hit Philly causing a TON of flight cancellations. Perfect. Just perfect. This was honestly the tip of the iceberg. We hadn’t thought about who was going to run the registration desk, who would be taking photos during the event and who would actually field questions from the audience while all three of us sat on stage for our live Q&A panel. Turns out that the answer to all of those questions were my wife, Laura, and Nick’s wife, Kelley. Thankfully, they were on hand to save our asses. The weeks running up to the event were honestly some of the most stressful of my life. We sold around 50% of our ticket allocation within the final two weeks before the event. All of the event organizers told us this would happen, but did we believe them? Hell no!  Imagine having two weeks until the big day and as it stood half of the room would be completely empty. I was ready to fly most of my extended family over just to make it look remotely busy. [\[IMAGE\] One of our speakers, Ryan Stewart, presenting at Traffic Think Tank LIVE](https://cdn.shortpixel.ai/client/qglossy,retimg,w_1920/https://www.matthewbarby.com/wp-content/uploads/2019/08/Traffic-Think-Tank-LIVE-Ryan-Presenting.jpg) Thankfully, if all came together. We managed to acquire some microphones, the swag arrived on the morning of the event, all of our speakers were able to make it on time and the weather just about held up so that our entire allocation of ticket holders was able to make it to the event. We pooled together and I’m proud to say that the event was a huge success. While we made a substantial financial loss on the event itself, January saw a huge spike in new members, which more than recouped our losses. Not only that, but we got to hang out with a load of our members all day while they said really nice things about the thing we’d built. It was both exhausting and incredibly rewarding. Bring on Traffic Think Tank LIVE 2020! (This time we’re hiring an event manager...)   The road ahead Fast forward to today (August 2019) and Traffic Think Tank has over 650 members. The biggest challenges that we’re tackling right now include making sure the most interesting conversations and best content surfaces to the top of the community, making Slack more searchable (this is ultimately one of its flaws as a platform) and giving members a quicker way to find the exclusive content that we create. You’ll notice there’s a pretty clear theme here. In the past 30 days, 4,566 messages were posted in public channels inside Traffic Think Tank. If you add on any messages posted inside private direct messages, this number rises to 21,612. That’s a lot of messages. To solve these challenges and enable further scale in the future, we’ve invested a bunch of cash and our time into building out a full learning management system (LMS) that all members will get access to alongside the Slack community. The LMS will be a web-based portal that houses all of the video content we produce. It will also  provide an account admin section where users can update or change their billing information (they have to email us to do this right now, which isn’t ideal), a list of membership perks and discounts with our partners, and a list of links to some of the best threads within Slack – when clicked, these will drop you directly into Slack. [\[IMAGE\] Designs for the new learning management system (LMS)](https://cdn.shortpixel.ai/client/qglossy,retimg,w_2378/https://www.matthewbarby.com/wp-content/uploads/2019/08/Traffic-Think-Tank-LMS.png) It’s not been easy, but we’re 95% of the way through this and I’m certain that it will have a hugely positive impact on the experience for our members. Alongside this we hired a community manager, Liz, who supports with any questions that our members have, coordinates with external experts to arrange webinars for the community, helps with new member onboarding, and has tightened up some of our processes around billing and general accounts admin. This was a great decision. Finally, we’ve started planning next year’s live event, which we plan to more than double in size to 350 attendees, and we decided to pick a slightly warmer location in Miami this time out. Stay tuned for me to have a complete meltdown 3 weeks from the event. Final thoughts When I look back on the journey we’ve had so far building Traffic Think Tank, there’s one very important piece to this puzzle that’s made all of this work that I’ve failed to mention so far: co-founder alignment. Building a community is a balancing act that relies heavily on those in charge being completely aligned. Nick, Ian and I completely trust each other and more importantly, are philosophically aligned on how we want to run and grow the community. If we didn’t have this, the friction between us could tear apart the entire community. Picking the right people to work with is important in any company, but when your business is literally about bringing people together, there’s no margin for error here.  While I’m sure there will be many more challenges ahead, knowing that we all trust each other to make decisions that fall in line with each of our core values makes these challenges dramatically easier to overcome. Finally, I’d like to thank all of our members for making the community what it is today – it’d be nothing without you and I promise that we’ll never take that for granted. &#x200B; I originally posted this on my blog here. Welcoming all of your thoughts, comments, questions and I'll do my best to answer them :)

[CASE STUDY] From 217/m to $2,836/m in 9 months - Sold for $59,000; I grow and monetise web traffic of 5, 6, 7 figures USD valued passive income content sites [AMA]
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jamesackerman1234This week

[CASE STUDY] From 217/m to $2,836/m in 9 months - Sold for $59,000; I grow and monetise web traffic of 5, 6, 7 figures USD valued passive income content sites [AMA]

Hello Everyone (VERY LONG CASE STUDY AHEAD) - 355% return in 9 months Note: I own a 7-figures USD valued portfolio of 41+ content sites that generates 5-6 figures USD a month in passive income. This is my first time posting in this sub and my goal is to NOT share generic advice but precise numbers, data and highly refined processes so you can also get started with this business yourself or if you already have an existing business, drive huge traffic to it and scale it substantially (get more customers). I will use a case study to explain the whole process. As most of us are entrepreneurs here, explaining an actual project would be more meaningful. In this case study I used AI assisted content to grow an existing site from $217/m to $2,836/m in 9 months (NO BACKLINKS) and sold it for $59,000. ROI of 3 months: 355% Previous case studies (before I give an overview of the model) Amazon Affiliate Content Site: $371/m to $19,263/m in 14 MONTHS - $900K CASE STUDY \[AMA\] Affiliate Website from $267/m to $21,853/m in 19 months (CASE STUDY - Amazon?) \[AMA\] Amazon Affiliate Website from $0 to $7,786/month in 11 months Amazon Affiliate Site from $118/m to $3,103/m in 8 MONTHS (SOLD it for $62,000+) Note: You can check pinned posts on my profile. Do go through the comments as well as a lot of questions are answered in those. However, if you still have any questions, feel free to reach out. This is an \[AMA\]. Quick Overview of the Model Approach: High traffic, niche specific, informative content websites that monetise its traffic through highly automated methods like display ads and affiliate. The same model can be applied to existing businesses to drive traffic and get customers. Main idea: Make passive income in a highly automated way Easy to understand analogy You have real estate (here you have digital asset like a website) You get rental income (here you get ads and affiliate income with no physical hassle, in case you have a business like service, product etc. then you can get customers for that too but if not, it's alright) Real estate has value (this digital asset also has value that can be appreciated with less effort) Real estate can be sold (this can be sold too but faster) IMPORTANT NOTE: Search traffic is the BEST way to reach HUGE target audience and it's important when it comes to scaling. This essentially means that you can either monetise that via affiliate, display etc. or if you have a business then you can reach a bigger audience to scale. Overview of this website's valuation (then and now: Oct. 2022 and June 2023) Oct 2022: $217/m Valuation: $5,750.5 (26.5x) - set it the same as the multiple it was sold for June 2023: $2,836/m Traffic and revenue trend: growing fast Last 3 months avg: $2,223 Valuation now: $59,000 (26.5x) Description: The domain was registered in 2016, it grew and then the project was left unattended. I decided to grow it again using properly planned AI assisted content. Backlink profile: 500+ Referring domains (Ahrefs). Backlinks mean the sites linking back to you. This is important when it comes to ranking. Summary of Results of This Website - Before and After Note: If the terms seem technical, do not worry. I will explain them in detail later. Still if you have any questions. Feel free to comment or reach out. |Metric|Oct 2022|June 2023|Difference|Comments| |:-|:-|:-|:-|:-| |Articles|314|804|\+490|AI Assisted content published in 3 months| |Traffic|9,394|31,972|\+22,578|Organic| |Revenue|$217|$2,836|\+$2,619|Multiple sources| |RPM (revenue/1000 web traffic)|23.09|$88.7|\+$65.61|Result of Conversion rate optimisation (CRO). You make changes to the site for better conversions| |EEAT (expertise, experience, authority and trust of website)|2 main authors|8 authors|6|Tables, video ads and 11 other fixations| |CRO|Nothing|Tables, video ads |Tables, video ads and 11 other fixations || &#x200B; Month by Month Growth |Month|Revenue|Steps| |:-|:-|:-| |Sept 2022|NA|Content Plan| |Oct 2022|$217|Content Production| |Nov 2022|$243|Content production + EEAT authors| |Dec 2022|$320|Content production + EEAT authors| |Jan 2023|$400|Monitoring| |Feb 2023|$223|Content production + EEAT authors| |Mar 2023|$2,128|CRO & Fixations| |April 2023|$1,609|CRO & Fixations| |May 2023|$2,223|Content production + EEAT authors| |June 2023|$2,836|CRO and Fixations| |Total|$10,199|| &#x200B; What will I share Content plan and Website structure Content Writing Content Uploading, formatting and onsite SEO Faster indexing Conversion rate optimisation Guest Posting EEAT (Experience, Expertise, Authority, Trust) Costing ROI The plans moving forward with these sites &#x200B; Website Structure and Content Plan This is probably the most important important part of the whole process. The team spends around a month just to get this right. It's like defining the direction of the project. Description: Complete blueprint of the site's structure in terms of organisation of categories, subcategories and sorting of articles in each one of them. It also includes the essential pages. The sorted articles target main keyword, relevant entities and similar keywords. This has to be highly data driven and we look at over 100 variables just to get it right. It's like beating Google's algorithm to ensure you have a blueprint for a site that will rank. It needs to be done right. If there is a mistake, then even if you do everything right - it's not going to work out and after 8-16 months you will realise that everything went to waste. Process For this project, we had a niche selected already so we didn't need to do a lot of research pertaining to that. We also knew the topic since the website was already getting good traffic on that. We just validated from Ahrefs, SEMRUSH and manual analysis if it would be worth it to move forward with that topic. &#x200B; Find entities related to the topic: We used Ahrefs and InLinks to get an idea about the related entities (topics) to create a proper topical relevance. In order to be certain and have a better idea, we used ChatGPT to find relevant entities as well \> Ahrefs (tool): Enter main keyword in keywords explorer. Check the left pain for popular topics \> Inlinks (tool): Enter the main keyword, check the entity maps \> ChatGPT (tool): Ask it to list down the most important and relevant entities in order of their priority Based on this info, you can map out the most relevant topics that are semantically associated to your main topic Sorting the entities in topics (categories) and subtopics (subcategories): Based on the information above, cluster them properly. The most relevant ones must be grouped together. Each group must be sorted into its relevant category. \> Example: Site about cycling. \> Categories/entities: bicycles, gear and equipment, techniques, safety, routes etc. \> The subcategories/subentities for let's say "techniques" would be: Bike handling, pedaling, drafting etc. Extract keywords for each subcategory/subentity: You can do this using Ahrefs or Semrush. Each keyword would be an article. Ensure that you target the similar keywords in one article. For example: how to ride a bicycle and how can I ride a bicycle will be targeted by one article. Make the more important keyword in terms of volume and difficulty as the main keyword and the other one(s) as secondary Define main focus vs secondary focus: Out of all these categories/entities - there will be one that you would want to dominate in every way. So, focus on just that in the start. This will be your main focus. Try to answer ALL the questions pertaining to that. You can extract the questions using Ahrefs. \> Ahrefs > keywords explorer \> enter keyword \> Questions \> Download the list and cluster the similar ones. This will populate your main focus category/entity and will drive most of the traffic. Now, you need to write in other categories/subentities as well. This is not just important, but crucial to complete the topical map loop. In simple words, if you do this Google sees you as a comprehensive source on the topic - otherwise, it ignores you and you don't get ranked Define the URLs End result: List of all the entities and sub-entities about the main site topic in the form of categories and subcategories respectively. A complete list of ALL the questions about the main focus and at around 10 questions for each one of the subcategories/subentities that are the secondary focus Content Writing So, now that there's a plan. Content needs to be produced. Pick out a keyword (which is going to be a question) and... Answer the question Write about 5 relevant entities Answer 10 relevant questions Write a conclusion Keep the format the same for all the articles. Content Uploading, formatting and onsite SEO Ensure the following is taken care of: H1 Permalink H2s H3s Lists Tables Meta description Socials description Featured image 2 images in text \\Schema Relevant YouTube video (if there is) Note: There are other pointers link internal linking in a semantically relevant way but this should be good to start with. Faster Indexing Indexing means Google has read your page. Ranking only after this step has been done. Otherwise, you can't rank if Google hasn't read the page. Naturally, this is a slow process. But, we expedite it in multiple ways. You can use RankMath to quickly index the content. Since, there are a lot of bulk pages you need a reliable method. Now, this method isn't perfect. But, it's better than most. Use Google Indexing API and developers tools to get indexed. Rank Math plugin is used. I don't want to bore you and write the process here. But, a simple Google search can help you set everything up. Additionally, whenever you post something - there will be an option to INDEX NOW. Just press that and it would be indexed quite fast. Conversion rate optimisation Once you get traffic, try adding tables right after the introduction of an article. These tables would feature a relevant product on Amazon. This step alone increased our earnings significantly. Even though the content is informational and NOT review. This still worked like a charm. Try checking out the top pages every single day in Google analytics and add the table to each one of them. Moreover, we used EZOIC video ads as well. That increased the RPM significantly as well. Both of these steps are highly recommended. Overall, we implemented over 11 fixations but these two contribute the most towards increasing the RPM so I would suggest you stick to these two in the start. Guest Posting We made additional income by selling links on the site as well. However, we were VERY careful about who we offered a backlink to. We didn't entertain any objectionable links. Moreover, we didn't actively reach out to anyone. We had a professional email clearly stated on the website and a particularly designated page for "editorial guidelines" A lot of people reached out to us because of that. As a matter of fact, the guy who bought the website is in the link selling business and plans to use the site primarily for selling links. According to him, he can easily make $4000+ from that alone. Just by replying to the prospects who reached out to us. We didn't allow a lot of people to be published on the site due to strict quality control. However, the new owner is willing to be lenient and cash it out. EEAT (Experience, Expertise, Authority, Trust) This is an important ranking factor. You need to prove on the site that your site has authors that are experienced, have expertise, authority and trust. A lot of people were reaching out to publish on our site and among them were a few established authors as well. We let them publish on our site for free, added them on our official team, connected their socials and shared them on all our socials. In return, we wanted them to write 3 articles each for us and share everything on all the social profiles. You can refer to the tables I shared above to check out the months it was implemented. We added a total of 6 writers (credible authors). Their articles were featured on the homepage and so were their profiles. Costing Well, we already had the site and the backlinks on it. Referring domains (backlinks) were already 500+. We just needed to focus on smart content and content. Here is the summary of the costs involved. Articles: 490 Avg word count per article: 1500 Total words: 735,000 (approximately) Cost per word: 2 cents (includes research, entities, production, quality assurance, uploading, formatting, adding images, featured image, alt texts, onsite SEO, publishing/scheduling etc.) Total: $14,700 ROI (Return on investment) Earning: Oct 22 - June 23 Earnings: $10,199 Sold for: $59,000 Total: $69,199 Expenses: Content: $14,700 Misc (hosting and others): $500 Total: $15,200 ROI over a 9 months period: 355.25% The plans moving forward This website was a part of a research and development experiment we did. With AI, we wanted to test new waters and transition more towards automation. Ideally, we want to use ChatGPT or some other API to produce these articles and bulk publish on the site. The costs with this approach are going to be much lower and the ROI is much more impressive. It's not the the 7-figures projects I created earlier (as you may have checked the older case studies on my profile), but it's highly scalable. We plan to refine this model even further, test more and automate everything completely to bring down our costs significantly. Once we have a model, we are going to scale it to 100s of sites. The process of my existing 7-figures websites portfolio was quite similar. I tested out a few sites, refined the model and scaled it to over 41 sites. Now, the fundamentals are the same however, we are using AI in a smarter way to do the same but at a lower cost, with a smaller team and much better returns. The best thing in my opinion is to run numerous experiments now. Our experimentation was slowed down a lot in the past since we couldn't write using AI but now it's much faster. The costs are 3-6 times lower so when it used to take $50-100k to start, grow and sell a site. Now you can pump 3-6 more sites for the same budget. This is a good news for existing business owners as well who want to grow their brand. Anyway, I am excited to see the results of more sites. In the meantime, if you have any questions - feel free to let me know. Best of luck for everything. Feel free to ask questions. I'd be happy to help. This is an AMA.

In 2018, I started an AI chatbot company...today, we have over 4000 paying customers and ChatGPT is changing EVERYTHING
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In 2018, I started an AI chatbot company...today, we have over 4000 paying customers and ChatGPT is changing EVERYTHING

Intro: 5 years ago, my co-founders and I ventured into the space of AI chatbots and started our first truly successful company. Never in a million years did I see myself in this business and we truly stumbled upon the opportunity by chance. Prior to that, we ran a successful lead generation business and questioned whether a simple ai chat product would increase our online conversions. Of the 3 co-founders, I was skeptical that it would, but the data was clear that we had something that really worked. We built a really simple MVP version of the product and gave it to some of our top lead buyers who saw even better conversion improvements on their own websites. In just a matter of weeks, a new business opportunity was born and a major pivot away from our lead generation business started. Our growth story: Startup growth is really interesting and in most cases, founders aren't really educated on what a typical growth curve looks like. While we hear about "hockey stick" growth curves, it's really atypical to actually see or experience this. From my experience, growth curves take place in a "stair curve". For example, you can scrap your way to a $100k run rate without much process or tracking. You can even get to $1 million ARR being super disorganized. As you start going beyond $1M ARR, things start to break and growth can flatten out while you put new processes and systems in place. Eventually you'll get to $2M or 3M with your new strategy and then things start breaking again. I've seen the process repeat itself and as you increase your ARR, the processes and systems become more difficult to work through...mainly because more people get involved and the product becomes more complex. When you do end up cracking the code in each step, the growth accelerates faster and faster before things start to break down and flatten out again. Without getting too much into the numbers, here were some of our initial levers for growth: Our first "stair" step was to leverage our existing customer base from our prior lead generation business. Having prior business relationships and a proven track record made it really simple to have conversations with people who already trusted us to try something new that we had to offer. Stair #2 was to build out a partner channel. Since our chat product involved a web developer or agency installing the chat on client sites, we partnered with these developers and agencies to leverage their already existing customer bases. We essentially piggy-backed off of their relationships and gave them a cut of the revenue. We built an internal partner tracking portal which took 6+ months, but it was well worth it. Stair #3 was our most expensive step, biggest headache, but added the most revenue. After COVID, we had and SDR/Account Executive sales team of roughly 30 people. It added revenue fast, but the payback periods were 12+ months so we had to cut back on this strategy after exhausting our universe of clients. Stair #4 involves a variety of paid advertisement strategies with product changes and the introduction of new onboarding features. We're in the middle of this stair and hope it's multiple years before things breakdown again. Don't give up I know it sounds really cliché, but the #1 indicator of success is doing the really boring stuff day in and day out and making incremental improvements. As the weeks, months, and years pass by, you will slowly gain domain expertise and start to see the gaps in the market that can set you apart from your competition. It's so hard for founders to stay focused and not get distracted so I would say it's equally as important to have co-founders who hold each other accountable on what your collective goals are. How GPT is changing everything I could write pages and pages about how GPT is going to change how the world operates, but I'll keep it specific to our business and chatbots. In 2021, we built an industry specific AI model that did a great job of classifying intents which allowed us to train future actions during a chat. It was a great advancement in our customer's industry at the time. With GPT integrated into our system, that training process that would take an employee hours to do, can be done in 5 minutes. The model is also cheaper than our own and more accurate. Because of these training improvements, we have been able to conduct research that is allowing us to leverage GPT models like no one else in the industry. This is both in the realm of chat and also training during onboarding. I really want to refrain from sharing our company, but if you are interested in seeing a model trained for your specific company or website, just PM me your link and I'll send you a free testing link with a model fully trained for your site to play around with. Where we are headed and the dangers of AI The level of advancement in AI is not terribly dangerous in its current state. I'm sure you've heard it before, but those who leverage the technology today will be the ones who get ahead. In the coming years, AI will inevitably replace a large percentage of human labor. This will be great for overall value creation and productivity for the world, but the argument that humans have always adapted and new jobs will be created is sadly not going to be as relevant in this case. As the possibility of AGI becomes a reality in the coming years or decades, productivity through AI will be off the charts. There is a major risk that human innovation and creative thinking will be completely stalled...human potential as we know it will be capped off and there will need to be major economic reform for displaced workers. This may not happen in the next 5 or 10 years, but you would be naïve not to believe the world we live in today will not be completely different in 20 to 30 years. Using AI to create deepfakes, fake voice agents, scam the unsuspecting, or exploit technical vulnerabilities are just a few other examples I could write about, but don't want to go into to much detail for obvious reasons. Concluding If you found the post interesting or you have any questions, please don't hesitate to ask. I'll do my best to answer whatever questions come from this! &#x200B; \*EDIT: Wasn't expecting this sort of response. I posted this right before I went to sleep so I'll get to responding soon.

We made $325k in 2023 from AI products, starting from 0, with no-code, no funding and no audience
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We made $325k in 2023 from AI products, starting from 0, with no-code, no funding and no audience

I met my co-founder in late 2022 after an introduction from a mutual friend to talk about how to find contract Product Management roles. I was sporadically contracting at start-up at the time and he had just come out of another start-up that was wiped out by the pandemic. We hit it off, talking about ideas, sharing what other indie-hackers were doing, and given GPT-3’s prominence at the time, we started throwing around ideas about things we could build with it, if nothing else, just to learn. I should caveat, neither of us were AI experts when starting out, everything we learned has been through Twitter and blogs, my background is as an accountant, and his a consultant. Here’s how it went since then: &#x200B; Nov 2022 (+$50) \- We built a simple tool in around a week using GPT-3 fine-tuning and a no-code tool (Bubble) that helped UK university students write their personal statements for their applications \- We set some Google Ads going and managed to make a few sales (\~$50) in the first week \- OpenAI were still approving applications at the time and said this went against their “ethics” so we had to take it down &#x200B; Dec 2022 (+$200) \- We couldn’t stop coming up with ideas related to AI fine-tuning, but realised it was almost impossible to decide which to pursue \- We needed a deadline to force us so we signed up for the Ben’s Bites hackathon in late December \- In a week, we built and launched a no-code fine-tuning platform, allowing people to create fine-tuned models by dragging and dropping an Excel file onto it \- We launched it on Product Hunt, having no idea how to price it, and somehow managed to get \~2,000 visitors on the site and make 2 sales at $99 &#x200B; Jan 2023 (+$3,000) \- We doubled down on the fine-tuning idea and managed to get up to \~$300 MRR, plus a bunch of one-time sales and a few paid calls to help people get the most out of their models \- We quickly realised that people didn’t want to curate models themselves, they just wanted to dump data and get magic out \- That was when we saw people building “Talk with x book/podcast” on Twitter as side projects and realised that was the missing piece, we needed to turn it into a tool \- We started working on the new product in late January &#x200B; Feb 2023 (+$9,000) \- We started pre-selling access to an MVP for the new product, which allowed people to “chat with their data/content”, we got $5,000 in pre-sales, more than we made from the previous product in total \- By mid-February, after 3 weeks of building we were able to launch and immediately managed to get traction, getting to $1k MRR in < 1 week, building on the hype of ChatGPT and AI (we were very lucky here) &#x200B; Mar - Jul 2023 (+$98,000) \- We worked all the waking hours to keep up with customer demand, bugs, OpenAI issues \- We built integrations for a bunch of services like Slack, Teams, Wordpress etc, added tons of new functionality and continue talking to customers every day \- We managed to grow to $17k MRR (just about enough to cover our living expenses and costs in London) through building in public on Twitter, newsletters and AI directories (and a million other little things) \- We sold our fine-tuning platform for \~$20k and our university project for \~$3k on Acquire &#x200B; Aug 2023 (+$100,000) \- We did some custom development work based on our own product for a customer that proved pretty lucrative &#x200B; Sep - Oct 2023 (+$62,000) \- After 8 months of building constantly, we started digging more seriously into our usage and saw subscriptions plateauing \- We talked to and analysed all our paying users to identify the main use cases and found 75% were for SaaS customer support \- We took the leap to completely rebuild a version of our product around this use case, our biggest to date (especially given most features with no-code took us <1 day) &#x200B; Nov - Dec 2023 (+$53,000) \- We picked up some small custom development work that utilised our own tech \- We’re sitting at around $22k MRR now with a few bigger clients signed up and coming soon \- After 2 months of building and talking to users, we managed to finish our “v2” of our product, focussed squarely on SaaS customer support and launched it today. &#x200B; We have no idea what the response will be to this new version, but we’re pretty happy with it, but couldn’t have planned anything that happened to us in 2023 so who knows what will come of 2024, we just know that we are going to be learning a ton more. &#x200B; Overall, it is probably the most I have had to think in my life - other jobs you can zone out from time to time or rely on someone else if you aren’t feeling it - not when you are doing this, case and point, I am writing this with a banging head-cold right now, but wanted to get this done. A few more things we have learned along the way - context switching is unreal, as is keeping up with, learning and reacting to AI. There isn’t a moment of the day I am not thinking about what we do next. But while in some way we now have hundreds of bosses (our customers) I still haven’t felt this free and can’t imagine ever going back to work for someone else. Next year we’re really hoping to figure out some repeatable distribution channels and personally, I want to get a lot better at creating content/writing, this is a first step! Hope this helps someone else reading this to just try starting something and see what happens.

Started a content marketing agency 6 years ago - $0 to $5,974,324 (2023 update)
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Started a content marketing agency 6 years ago - $0 to $5,974,324 (2023 update)

Hey friends, My name is Tyler and for the past 6 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 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 Each freelancer earns $65-85/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) We recently introduced hourly engagements for clients who fit our model but have some existing in-house support Packages range in price from $10-20k/mo We offer profit share to everyone on our core team as a way to give everyone ownership in the company In 2022, we posted $1,434,665 in revenue. It was our highest revenue year to date and brings our lifetime total to $5,974,324. Here’s our monthly revenue from January 2017 to December of 2022. But, like every year, it was a mix of ups and downs. Here’s my dispatch for 2023. — Running a business is like spilling a drink. It starts as a small and simple thing. But, if you don’t clean it up, the spill will spread and grow — taking up more space, seeping into every crack. There’s always something you could be doing. Marketing you could be working on. Pitches you could be making. Networking you could be doing. Client work you could help with. It can be all-consuming. And it will be — if you don’t clean up the spill. I realized this year that I had no containment for the spill that I created. Running an agency was spilling over into nearly every moment of my life. When I wasn’t working, I was thinking about work. When I wasn’t thinking about work, I was dreaming about it. Over the years, I’ve shared about a lot of my personal feelings and experience as an entrepreneur. And I also discussed my reckoning with the limitations of running the business we’ve built. My acceptance that it was an airplane but not a rocket. And my plan to try to compartmentalize the agency to make room in my life for other things — new business ideas, new revenue streams, and maybe some non-income-producing activity. 🤷 What I found in 2022 was that the business wasn’t quite ready for me to make that move. It was still sucking up too much of my time and attention. There were still too many gaps to fill and I was the one who was often filling them. So what do you do? Ultimately you have two choices on the table anytime you run a business and it’s not going the way you want it: Walk away Turn the ship — slowly For a huge number of reasons (personal, professional, financial, etc), walking away from Optimist was not really even an option or the right move for me. But it did feel like things needed to change. I needed to keep turning the ship to get it to the place where it fit into my life — instead of my life fitting around the business. This means 2022 was a year of transition for the agency. (Again?) Refocusing on Profit Some money is better than no money. Right? Oddly, this was one of the questions I found myself asking in 2022. Over the years, we’ve been fortunate to have many clients who have stuck with us a long time. In some cases, we’ve had clients work with us for 2, 3, or even 4 years. (That’s over half of our existence!) But, things have gotten more expensive — we’ve all felt it. We’ve had to increase pay to remain competitive for top talent. Software costs have gone up. It’s eaten into our margin. Because of our increasing costs and evolving scope, many of our best, most loyal clients were our least profitable. In fact, many were barely profitable — if at all. We’ve tried to combat that by increasing rates on new, incoming clients to reflect our new costs and try to make up for shrinking margin on long-term clients. But we didn’t have a good strategy in place for updating pricing for current clients. And it bit us in the ass. Subsidizing lower-profit, long-term clients with new, higher-margin clients ultimately didn’t work out. Our margins continued to dwindle and some months we were barely breaking even while posting six-figures of monthly revenue. 2022 was our highest revenue year but one of our least profitable. It only left one option. We had to raise rates on some of our long-term clients. But, of course, raising rates on a great, long-term client can be delicate. You’ve built a relationship with these people over the years and you’re setting yourself up for an ultimatum — are you more valuable to the client or is the client more valuable to you? Who will blink first? We offered all of these clients the opportunity to move to updated pricing. Unfortunately, some of them weren’t on board. Again, we had 2 options: Keep them at a low/no profit rate Let them churn It seems intuitive that having a low-profit client is better than having no client. But we’ve learned an important lesson many times over the years. Our business doesn’t scale infinitely and we can only handle so many clients at a time. That means that low-profit clients are actually costing us money in some cases. Say our average client generates $2,500 per month in profit — $30,000 per year. If one of our clients is only generating $500/mo in profit, working with them means missing out on bringing on a more profitable client (assuming our team is currently at capacity). Instead of $30,000/year, we’re only making $6,000. Keeping that client costs us $24,000. That’s called opportunity cost. So it’s clear: We had to let these clients churn. We decided to churn about 25% of our existing clients. On paper, the math made sense. And we had a pretty consistent flow of new opportunities coming our way. At the time, it felt like a no-brainer decision. And I felt confident that we could quickly replace these low-profit clients with higher-margin ones. I was wrong. Eating Shit Right after we initiated proactively churning some of our clients, other clients — ones we planned to keep — gave us notice that they were planning to end the engagement. Ouch. Fuck. We went from a 25% planned drop in revenue to a nearly 40% cliff staring us right in the face. Then things got even worse. Around Q3 of this year, talk of recession and layoffs really started to intensify. We work primarily with tech companies and startups. And these were the areas most heavily impacted by the economic news. Venture funding was drying up. Our leads started to slow down. This put us in a tough position. Looking back now, I think it’s clear that I made the wrong decision. We went about this process in the wrong way. The reality sinks in when you consider the imbalance between losing a client and gaining a client. It takes 30 days for someone to fire us. It’s a light switch. But it could take 1-3 months to qualify, close, and onboard a new client. We have lots of upfront work, research, and planning that goes into the process. We have to learn a new brand voice, tone, and style. It’s a marathon. So, for every client we “trade”, there’s a lapse in revenue and work. This means that, in retrospect, I would probably have made this transition using some kind of staggered schedule rather than a cut-and-dry approach. We could have gradually off-boarded clients when we had more definitive work to replace them. I was too confident. But that’s a lesson I had to learn the hard way. Rebuilding & Resetting Most of the voluntary and involuntary churn happened toward the end of 2022. So we’re still dealing with the fall out. Right now, it feels like a period of rebuilding. We didn’t quite lose 50% of our revenue, but we definitely saw a big hit heading into 2023. To be transparent: It sucks. It feels like a gigantic mistake that I made which set us back significantly from our previous high point. I acted rashly and it cost us a lot of money — at least on the surface. But I remind myself of the situation we were in previously. Nearly twice the revenue but struggling to maintain profitability. Would it have been better to try to slowly fix that situation and battle through months of loss or barely-break-even profits? Or was ripping off the bandaid the right move after all? I’m an optimist. (Heh, heh) Plus, I know that spiraling over past decisions won’t change them or help me move forward. So I’m choosing to look at this as an opportunity — to rebuild, reset, and refocus the company. I get to take all of the tough lessons I’ve learned over the last 6 years and apply them to build the company in a way that better aligns with our new and current goals. It’s not quite a fresh, clean start, but by parting ways with some of our oldest clients, we’ve eliminated some of the “debt” that’s accumulated over the years. We get a chance to fully realize the new positioning that we rolled out last year. Many of those long-term clients who churned had a scope of work or engagement structure that didn’t fit with our new positioning and focus. So, by losing them, we’re able to completely close up shop on the SOWs that no longer align with the future version of Optimist. Our smaller roster of clients is a better fit for that future. My job is to protect that positioning by ensuring that while we’re rebuilding our new roster of clients we don’t get desperate. We maintain the qualifications we set out for future clients and only take on work that fits. How’s that for seeing the upside? Some other upside from the situation is that we got an opportunity to ask for candid feedback from clients who were leaving. We asked for insight about their decision, what factors they considered, how they perceived us, and the value of our work. Some of the reasons clients left were obvious and possibly unavoidable. Things like budget cuts, insourcing, and uncertainty about the economy all played at least some part of these decisions. But, reading between the lines, where was one key insight that really struck me. It’s one of those, “oh, yeah — duh — I already knew that,” things that can be difficult to learn and easy to forget…. We’re in the Relationship Business (Plan Accordingly) For all of our focus on things like rankings, keywords, content, conversions, and a buffet of relevant metrics, it can be easy to lose the forest for the trees. Yes, the work itself matters. Yes, the outcomes — the metrics — matter. But sometimes the relationship matters more. When you’re running an agency, you can live or die by someone just liking you. Admittedly, this feels totally unfair. It opens up all kinds of dilemmas, frustration, opportunity for bias and prejudice, and other general messiness. But it’s the real world. If a client doesn’t enjoy working with us — even if for purely personal reasons — they could easily have the power to end of engagement, regardless of how well we did our actual job. We found some evidence of this in the offboarding conversations we had with clients. In some cases, we had clients who we had driven triple- and quadruple-digital growth. Our work was clearly moving the needle and generating positive ROI and we had the data to prove it. But they decided to “take things in another direction” regardless. And when we asked about why they made the decision, it was clear that it was more about the working relationship than anything we could have improved about the service itself. The inverse is also often true. Our best clients have lasting relationships with our team. The work is important — and they want results. But even if things aren’t quite going according to plan, they’re patient and quick to forgive. Those relationships feel solid — unshakeable. Many of these folks move onto new roles or new companies and quickly look for an opportunity to work with us again. On both sides, relationships are often more important than the work itself. We’ve already established that we’re not building a business that will scale in a massive way. Optimist will always be a small, boutique service firm. We don’t need 100 new leads per month We need a small, steady roster of clients who are a great fit for the work we do and the value we create. We want them to stick around. We want to be their long-term partner. I’m not built for churn-and-burn agency life. And neither is the business. When I look at things through this lens, I realize how much I can cut from our overall business strategy. We don’t need an ultra-sophisticated, multi-channel marketing strategy. We just need strong relationships — enough of them to make our business work. There are a few key things we can take away from this as a matter of business strategy: Put most of our effort into building and strengthening relationships with our existing clients Be intentional about establishing a strong relationship with new clients as part of onboarding Focus on relationships as the main driver of future business development Embracing Reality: Theory vs Practice Okay, so with the big learnings out the way, I want to pivot into another key lesson from 2022. It’s the importance of understanding theory vs practice — specifically when it comes to thinking about time, work, and life. It all started when I was considering how to best structure my days and weeks around running Optimist, my other ventures, and my life goals outside of work. Over the years, I’ve dabbled in many different ways to block time and find focus — to compartmentalize all of the things that are spinning and need my attention. As I mapped this out, I realized that I often tried to spread myself too thin throughout the week. Not just that I was trying to do too much but that I was spreading that work into too many small chunks rather than carving out time for focus. In theory, 5 hours is 5 hours. If you have 5 hours of work to get done, you just fit into your schedule whenever you have an open time slot. In reality, a single 5-hour block of work is 10x more productive and satisfying than 10, 30-minute blocks of work spread out across the week. In part, this is because of context switching. Turning your focus from one thing to another thing takes time. Achieving flow and focus takes time. And the more you jump from one project to another, the more time you “lose” to switching. This is insightful for me both in the context of work and planning my day, but also thinking about my life outside of Optimist. One of my personal goals is to put a finite limit on my work time and give myself more freedom. I can structure that in many different ways. Is it better to work 5 days a week but log off 1 hour early each day? Or should I try to fit more hours into each workday so I can take a full day off? Of course, it’s the latter. Both because of the cost of context switching and spreading work into more, smaller chunks — but also because of the remainder that I end up with when I’m done working. A single extra hour in my day probably means nothing. Maybe I can binge-watch one more episode of a new show or do a few extra chores around the house. But it doesn’t significantly improve my life or help me find greater balance. Most things I want to do outside of work can’t fit into a single extra hour. A full day off from work unlocks many more options. I can take the day to go hiking or biking. I can spend the day with my wife, planning or playing a game. Or I can push it up against the weekend and take a 3-day trip. It gives me more of the freedom and balance that I ultimately want. So this has become a guiding principle for how I structure my schedule. I want to: Minimize context switching Maximize focused time for work and for non-work The idea of embracing reality also bleeds into some of the shifts in business strategy that I mentioned above. In theory, any time spent on marketing will have a positive impact on the company. In reality, focusing more on relationships than blasting tweets into the ether is much more likely to drive the kind of growth and stability that we’re seeking. As I think about 2023, I think this is a recurring theme. It manifests in many ways. Companies are making budget cuts and tough decisions about focus and strategy. Most of us are looking for ways to rein in the excess and have greater impact with a bit less time and money. We can’t do everything. We can’t even do most things. So our #1 priority should be to understand the reality of our time and our effort to make the most of every moment (in both work and leisure). That means thinking deeply about our strengths and our limitations. Being practical, even if it feels like sacrifice. Update on Other Businesses Finally, I want to close up by sharing a bit about my ventures outside of Optimist. I shared last year how I planned to shift some of my (finite) time and attention to new ventures and opportunities. And, while I didn’t get to devote as much as I hoped to these new pursuits, they weren’t totally in vain. I made progress across the board on all of the items I laid out in my post. Here’s what happened: Juice: The first Optimist spin-out agency At the end of 2021, we launched our first new service business based on demand from Optimist clients. Focused entirely on building links for SEO, we called the agency Juice. Overall, we made strong progress toward turning this into a legitimate standalone business in 2022. Relying mostly on existing Optimist clients and a few word-of-mouth opportunities (no other marketing), we built a team and set up a decent workflow and operations. There’s still many kinks and challenges that we’re working through on this front. All told, Juice posted almost $100,000 in revenue in our first full year. Monetizing the community I started 2022 with a focus on figuring out how to monetize our free community, Top of the Funnel. Originally, my plan was to sell sponsorships as the main revenue driver. And that option is still on the table. But, this year, I pivoted to selling paid content and subscriptions. We launched a paid tier for content and SEO entrepreneurs where I share more of my lessons, workflows, and ideas for building and running a freelance or agency business. It’s gained some initial traction — we reached \~$1,000 MRR from paid subscriptions. In total, our community revenue for 2022 was about $2,500. In 2023, I’m hoping to turn this into a $30,000 - $50,000 revenue opportunity. Right now, we’re on track for \~$15,000. Agency partnerships and referrals In 2022, we also got more serious about referring leads to other agencies. Any opportunity that was not a fit for Optimist or we didn’t have capacity to take on, we’d try to connect with another partner. Transparently, we struggled to operationalize this as effectively as I would have liked. In part, this was driven by my lack of focus here. With the other challenges throughout the year, I wasn’t able to dedicate as much time as I’d like to setting goals and putting workflows into place. But it wasn’t a total bust. We referred out several dozen potential clients to partner agencies. Of those, a handful ended up converting into sales — and referral commission. In total, we generated about $10,000 in revenue from referrals. I still see this as a huge opportunity for us to unlock in 2023. Affiliate websites Lastly, I mentioned spending some time on my new and existing affiliate sites as another big business opportunity in 2022. This ultimately fell to the bottom of my list and didn’t get nearly the attention I wanted. But I did get a chance to spend a few weeks throughout the year building this income stream. For 2022, I generated just under $2,000 in revenue from affiliate content. My wife has graciously agreed to dedicate some of her time and talent to these projects. So, for 2023, I think this will become a bit of a family venture. I’m hoping to build a solid and consistent workflow, expand the team, and develop a more solid business strategy. Postscript — AI, SEO, OMG As I’m writing this, much of my world is in upheaval. If you’re not in this space (and/or have possibly been living under a rock), the release of ChatGPT in late 2022 has sparked an arms race between Google, Bing, OpenAI, and many other players. The short overview: AI is likely to fundamentally change the way internet search works. This has huge impact on almost all of the work that I do and the businesses that I run. Much of our focus is on SEO and understanding the current Google algorithm, how to generate traffic for clients, and how to drive traffic to our sites and projects. That may all change — very rapidly. This means we’re standing at a very interesting point in time. On the one hand, it’s scary as hell. There’s a non-zero chance that this will fundamentally shift — possibly upturn — our core business model at Optimist. It could dramatically change how we work and/or reduce demand for our core services. No bueno. But it’s also an opportunity (there’s the optimist in me, again). I certainly see a world where we can become leaders in this new frontier. We can pivot, adjust, and capitalize on a now-unknown version of SEO that’s focused on understanding and optimizing for AI-as-search. With that, we may also be able to help others — say, those in our community? — also navigate this tumultuous time. See? It’s an opportunity. I wish I had the answers right now. But, it’s still a time of uncertainty. I just know that there’s a lot of change happening and I want to be in front of it rather than trying to play catch up. Wish me luck. — Alright friends — that's my update for 2023! I’ve always appreciated sharing these updates with the Reddit community, getting feedback, being asked tough questions, and even battling it out with some of my haters (hey!! 👋) As usual, I’m going to pop in throughout the next few days to respond to comments or answer questions. Feel free to share thoughts, ideas, and brutal takedowns in the comments. If you're interested in following the Optimist journey and the other projects I'm working on in 2023, you can follow me on Twitter. Cheers, Tyler P.S. - If you're running or launching a freelance or agency business and looking for help figuring it out, please DM me. Our subscription community, Middle of the Funnel, was created to provide feedback, lessons, and resources for other entrepreneurs in this space.

I Watched My Startup Slowly Dying Over Two Years: Mistakes and Lessons Learned
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I Watched My Startup Slowly Dying Over Two Years: Mistakes and Lessons Learned

If you are tired of reading successful stories, you may want to listen to my almost failure story. Last year in April, I went full-time on my startup. Nearly two years later, I’ve seen my product gradually dying. I want to share some of the key mistakes I made and the lessons I’ve taken from them so you don't have to go through them. Some mistakes were very obvious in hindsight; others, I’m still not sure if they were mistakes or just bad luck. I’d love to hear your thoughts and advice as well. Background I built an English-learning app, with both web and mobile versions. The idea came from recognizing how expensive it is to hire an English tutor in most countries, especially for practicing speaking skills. With the rise of AI, I saw an opportunity in the education space. My target market was Japan, though I later added support for multiple languages and picked up some users from Indonesia and some Latin American countries too. Most of my users came from influencer marketing on Twitter. The MVP for the web version launched in Japan and got great feedback. People were reposting it on Twitter, and growth was at its peak in the first few weeks. After verifying the requirement with the MVP, I decided to focus on the mobile app to boost user retention, but for various reasons, the mobile version didn’t launch until December 2023— 8 months after the web version. Most of this year has been spent iterating on the mobile app, but it didn’t make much of an impact in the end. Key Events and Lessons Learned Here are some takeaways: Find co-founders as committed as you are I started with two co-founders—both were tech people and working Part-Time. After the web version launched, one dropped out due to family issues. Unfortunately, we didn’t set clear rules for equity allocation, so even after leaving, they still retained part of the equity. The other co-founder also effectively dropped out this year, contributing only minor fixes here and there. So If you’re starting a company with co-founders, make sure they’re as committed as you are. Otherwise, you might be better off going solo. I ended up teaching myself programming with AI tools, starting with Flutter and eventually handling both front-end and back-end work using Windsurf. With dev tools getting more advanced, being a solo developer is becoming a more viable option. Also, have crystal-clear rules for equity—especially around what happens if someone leaves. Outsourcing Pitfalls Outsourcing development was one of my biggest mistakes. I initially hired a former colleague from India to build the app. He dragged the project on for two months with endless excuses, and the final output was unusable. Then I hired a company, but they didn’t have enough skilled Flutter developers. The company’s owner scrambled to find people, which led to rushed work and poor-quality code which took a lot of time revising myself. Outsourcing is a minefield. If you must do it, break the project into small tasks, set clear milestones, and review progress frequently. Catching issues early can save you time and money. Otherwise, you’re often better off learning the tools yourself—modern dev tools are surprisingly beginner-friendly. Trust, but Verify I have a bad habit of trusting people too easily. I don’t like spending time double-checking things, so I tend to assume people will do what they say they’ll do. This mindset is dangerous in a startup. For example, if I had set up milestones and regularly verified the progress of my first outsourced project, I would’ve realized something was wrong within two weeks instead of two months. That would’ve saved me a lot of time and frustration. Like what I mentioned above, set up systems to verify their work—milestones, deliverables, etc.—to minimize risk. Avoid red ocean if you are small My team was tiny (or non-existent, depending on how you see it), with no technical edge. Yet, I chose to enter Japan’s English-learning market, which is incredibly competitive. It’s a red ocean, dominated by big players who’ve been in the game for years. Initially, my product’s AI-powered speaking practice and automatic grammar correction stood out, but within months, competitors rolled out similar features. Looking back, I should’ve gone all-in on marketing during the initial hype and focused on rapidly launching the mobile app. But hindsight is 20/20. 'Understanding your user' helps but what if it's not what you want? I thought I was pretty good at collecting user feedback. I added feedback buttons everywhere in the app and made changes based on what users said. But most of these changes were incremental improvements—not the kind of big updates that spark excitement. Also, my primary users were from Japan and Indonesia, but I’m neither Japanese nor Indonesian. That made it hard to connect with users on social media in an authentic way. And in my opinion, AI translations can only go so far—they lack the human touch and cultural nuance that builds trust. But honestly I'm not sure if the thought is correct to assume that they will not get touched if they recognize you are a foreigner...... Many of my Japanese users were working professionals preparing for the TOEIC exam. I didn’t design any features specifically for that; instead, I aimed to build a general-purpose English-learning tool since I dream to expand it to other markets someday. While there’s nothing wrong with this idealistic approach, it didn’t give users enough reasons to pay for the app. Should You Go Full-Time? From what I read, a lot of successful indie developers started part-time, building traction before quitting their jobs. But for me, I jumped straight into full-time mode, which worked for my lifestyle but might’ve hurt my productivity. I value work-life balance and refused to sacrifice everything for the startup. The reason I chose to leave the corp is I want to escape the 996 toxic working environment in China's internet companies. So even during my most stressful periods, I made time to watch TV with my partner and take weekends off. Anyways, if you’re also building something or thinking about starting a business, I hope my story helps. If I have other thoughts later, I will add them too. Appreciate any advice.

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.

Made $940 in 3 days with the help of ChatGPT
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ninegagzThis week

Made $940 in 3 days with the help of ChatGPT

5 days ago I joined the HustleGPT challenge. Its purpose is to build products with the help of ChatGPT. I've made a goal of creating 1 digital product with chatGPT every day. On the 3rd day I've created an app for MacOS that lets you use ChatGPT inside any text field in any app. Basically, there is no need to open your browser, or go to openai website every time you want to use chatgpt. So, after building it and publishing on Gumroad, I've tweeted about it and went to sleep. You may be thinking that my tweet has gone viral and that's how I made all the sales. However, this is not the case. My tweet got only 1200 views. And these 1200 views generated me my first $140 of revenue! After that, I started actively posting my product on social media. I never gone viral but even with 1-2k views per post I've made sales. And I'm on my way to $1000 revenue from my side project. I didn't spend much time on it too. As I was writing this post, I've made 1 new sale! That's $19 revenue (profit from each is sale is $16). After some thinking, I got this idea: what if I let other entrepreneurs earn with my app? Basically, you can resell my app, redistribute it, and do whatever you want with it. Once you buy it, you can freely do whatever you want with it. What do you think? Here is a tool that I use to create content that drives most sales for me - link Also, if you want to build apps with ChatGPT - this guide will help you - Here is a link I'm open for any feedback and suggestions! Thanks

[CASE STUDY] From 217/m to $2,836/m in 9 months - Sold for $59,000; I grow and monetise web traffic of 5, 6, 7 figures USD valued passive income content sites [AMA]
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jamesackerman1234This week

[CASE STUDY] From 217/m to $2,836/m in 9 months - Sold for $59,000; I grow and monetise web traffic of 5, 6, 7 figures USD valued passive income content sites [AMA]

Hello Everyone (VERY LONG CASE STUDY AHEAD) - 355% return in 9 months Note: I own a 7-figures USD valued portfolio of 41+ content sites that generates 5-6 figures USD a month in passive income. This is my first time posting in this sub and my goal is to NOT share generic advice but precise numbers, data and highly refined processes so you can also get started with this business yourself or if you already have an existing business, drive huge traffic to it and scale it substantially (get more customers). I will use a case study to explain the whole process. As most of us are entrepreneurs here, explaining an actual project would be more meaningful. In this case study I used AI assisted content to grow an existing site from $217/m to $2,836/m in 9 months (NO BACKLINKS) and sold it for $59,000. ROI of 3 months: 355% Previous case studies (before I give an overview of the model) Amazon Affiliate Content Site: $371/m to $19,263/m in 14 MONTHS - $900K CASE STUDY \[AMA\] Affiliate Website from $267/m to $21,853/m in 19 months (CASE STUDY - Amazon?) \[AMA\] Amazon Affiliate Website from $0 to $7,786/month in 11 months Amazon Affiliate Site from $118/m to $3,103/m in 8 MONTHS (SOLD it for $62,000+) Note: You can check pinned posts on my profile. Do go through the comments as well as a lot of questions are answered in those. However, if you still have any questions, feel free to reach out. This is an \[AMA\]. Quick Overview of the Model Approach: High traffic, niche specific, informative content websites that monetise its traffic through highly automated methods like display ads and affiliate. The same model can be applied to existing businesses to drive traffic and get customers. Main idea: Make passive income in a highly automated way Easy to understand analogy You have real estate (here you have digital asset like a website) You get rental income (here you get ads and affiliate income with no physical hassle, in case you have a business like service, product etc. then you can get customers for that too but if not, it's alright) Real estate has value (this digital asset also has value that can be appreciated with less effort) Real estate can be sold (this can be sold too but faster) IMPORTANT NOTE: Search traffic is the BEST way to reach HUGE target audience and it's important when it comes to scaling. This essentially means that you can either monetise that via affiliate, display etc. or if you have a business then you can reach a bigger audience to scale. Overview of this website's valuation (then and now: Oct. 2022 and June 2023) Oct 2022: $217/m Valuation: $5,750.5 (26.5x) - set it the same as the multiple it was sold for June 2023: $2,836/m Traffic and revenue trend: growing fast Last 3 months avg: $2,223 Valuation now: $59,000 (26.5x) Description: The domain was registered in 2016, it grew and then the project was left unattended. I decided to grow it again using properly planned AI assisted content. Backlink profile: 500+ Referring domains (Ahrefs). Backlinks mean the sites linking back to you. This is important when it comes to ranking. Summary of Results of This Website - Before and After Note: If the terms seem technical, do not worry. I will explain them in detail later. Still if you have any questions. Feel free to comment or reach out. |Metric|Oct 2022|June 2023|Difference|Comments| |:-|:-|:-|:-|:-| |Articles|314|804|\+490|AI Assisted content published in 3 months| |Traffic|9,394|31,972|\+22,578|Organic| |Revenue|$217|$2,836|\+$2,619|Multiple sources| |RPM (revenue/1000 web traffic)|23.09|$88.7|\+$65.61|Result of Conversion rate optimisation (CRO). You make changes to the site for better conversions| |EEAT (expertise, experience, authority and trust of website)|2 main authors|8 authors|6|Tables, video ads and 11 other fixations| |CRO|Nothing|Tables, video ads |Tables, video ads and 11 other fixations || &#x200B; Month by Month Growth |Month|Revenue|Steps| |:-|:-|:-| |Sept 2022|NA|Content Plan| |Oct 2022|$217|Content Production| |Nov 2022|$243|Content production + EEAT authors| |Dec 2022|$320|Content production + EEAT authors| |Jan 2023|$400|Monitoring| |Feb 2023|$223|Content production + EEAT authors| |Mar 2023|$2,128|CRO & Fixations| |April 2023|$1,609|CRO & Fixations| |May 2023|$2,223|Content production + EEAT authors| |June 2023|$2,836|CRO and Fixations| |Total|$10,199|| &#x200B; What will I share Content plan and Website structure Content Writing Content Uploading, formatting and onsite SEO Faster indexing Conversion rate optimisation Guest Posting EEAT (Experience, Expertise, Authority, Trust) Costing ROI The plans moving forward with these sites &#x200B; Website Structure and Content Plan This is probably the most important important part of the whole process. The team spends around a month just to get this right. It's like defining the direction of the project. Description: Complete blueprint of the site's structure in terms of organisation of categories, subcategories and sorting of articles in each one of them. It also includes the essential pages. The sorted articles target main keyword, relevant entities and similar keywords. This has to be highly data driven and we look at over 100 variables just to get it right. It's like beating Google's algorithm to ensure you have a blueprint for a site that will rank. It needs to be done right. If there is a mistake, then even if you do everything right - it's not going to work out and after 8-16 months you will realise that everything went to waste. Process For this project, we had a niche selected already so we didn't need to do a lot of research pertaining to that. We also knew the topic since the website was already getting good traffic on that. We just validated from Ahrefs, SEMRUSH and manual analysis if it would be worth it to move forward with that topic. &#x200B; Find entities related to the topic: We used Ahrefs and InLinks to get an idea about the related entities (topics) to create a proper topical relevance. In order to be certain and have a better idea, we used ChatGPT to find relevant entities as well \> Ahrefs (tool): Enter main keyword in keywords explorer. Check the left pain for popular topics \> Inlinks (tool): Enter the main keyword, check the entity maps \> ChatGPT (tool): Ask it to list down the most important and relevant entities in order of their priority Based on this info, you can map out the most relevant topics that are semantically associated to your main topic Sorting the entities in topics (categories) and subtopics (subcategories): Based on the information above, cluster them properly. The most relevant ones must be grouped together. Each group must be sorted into its relevant category. \> Example: Site about cycling. \> Categories/entities: bicycles, gear and equipment, techniques, safety, routes etc. \> The subcategories/subentities for let's say "techniques" would be: Bike handling, pedaling, drafting etc. Extract keywords for each subcategory/subentity: You can do this using Ahrefs or Semrush. Each keyword would be an article. Ensure that you target the similar keywords in one article. For example: how to ride a bicycle and how can I ride a bicycle will be targeted by one article. Make the more important keyword in terms of volume and difficulty as the main keyword and the other one(s) as secondary Define main focus vs secondary focus: Out of all these categories/entities - there will be one that you would want to dominate in every way. So, focus on just that in the start. This will be your main focus. Try to answer ALL the questions pertaining to that. You can extract the questions using Ahrefs. \> Ahrefs > keywords explorer \> enter keyword \> Questions \> Download the list and cluster the similar ones. This will populate your main focus category/entity and will drive most of the traffic. Now, you need to write in other categories/subentities as well. This is not just important, but crucial to complete the topical map loop. In simple words, if you do this Google sees you as a comprehensive source on the topic - otherwise, it ignores you and you don't get ranked Define the URLs End result: List of all the entities and sub-entities about the main site topic in the form of categories and subcategories respectively. A complete list of ALL the questions about the main focus and at around 10 questions for each one of the subcategories/subentities that are the secondary focus Content Writing So, now that there's a plan. Content needs to be produced. Pick out a keyword (which is going to be a question) and... Answer the question Write about 5 relevant entities Answer 10 relevant questions Write a conclusion Keep the format the same for all the articles. Content Uploading, formatting and onsite SEO Ensure the following is taken care of: H1 Permalink H2s H3s Lists Tables Meta description Socials description Featured image 2 images in text \\Schema Relevant YouTube video (if there is) Note: There are other pointers link internal linking in a semantically relevant way but this should be good to start with. Faster Indexing Indexing means Google has read your page. Ranking only after this step has been done. Otherwise, you can't rank if Google hasn't read the page. Naturally, this is a slow process. But, we expedite it in multiple ways. You can use RankMath to quickly index the content. Since, there are a lot of bulk pages you need a reliable method. Now, this method isn't perfect. But, it's better than most. Use Google Indexing API and developers tools to get indexed. Rank Math plugin is used. I don't want to bore you and write the process here. But, a simple Google search can help you set everything up. Additionally, whenever you post something - there will be an option to INDEX NOW. Just press that and it would be indexed quite fast. Conversion rate optimisation Once you get traffic, try adding tables right after the introduction of an article. These tables would feature a relevant product on Amazon. This step alone increased our earnings significantly. Even though the content is informational and NOT review. This still worked like a charm. Try checking out the top pages every single day in Google analytics and add the table to each one of them. Moreover, we used EZOIC video ads as well. That increased the RPM significantly as well. Both of these steps are highly recommended. Overall, we implemented over 11 fixations but these two contribute the most towards increasing the RPM so I would suggest you stick to these two in the start. Guest Posting We made additional income by selling links on the site as well. However, we were VERY careful about who we offered a backlink to. We didn't entertain any objectionable links. Moreover, we didn't actively reach out to anyone. We had a professional email clearly stated on the website and a particularly designated page for "editorial guidelines" A lot of people reached out to us because of that. As a matter of fact, the guy who bought the website is in the link selling business and plans to use the site primarily for selling links. According to him, he can easily make $4000+ from that alone. Just by replying to the prospects who reached out to us. We didn't allow a lot of people to be published on the site due to strict quality control. However, the new owner is willing to be lenient and cash it out. EEAT (Experience, Expertise, Authority, Trust) This is an important ranking factor. You need to prove on the site that your site has authors that are experienced, have expertise, authority and trust. A lot of people were reaching out to publish on our site and among them were a few established authors as well. We let them publish on our site for free, added them on our official team, connected their socials and shared them on all our socials. In return, we wanted them to write 3 articles each for us and share everything on all the social profiles. You can refer to the tables I shared above to check out the months it was implemented. We added a total of 6 writers (credible authors). Their articles were featured on the homepage and so were their profiles. Costing Well, we already had the site and the backlinks on it. Referring domains (backlinks) were already 500+. We just needed to focus on smart content and content. Here is the summary of the costs involved. Articles: 490 Avg word count per article: 1500 Total words: 735,000 (approximately) Cost per word: 2 cents (includes research, entities, production, quality assurance, uploading, formatting, adding images, featured image, alt texts, onsite SEO, publishing/scheduling etc.) Total: $14,700 ROI (Return on investment) Earning: Oct 22 - June 23 Earnings: $10,199 Sold for: $59,000 Total: $69,199 Expenses: Content: $14,700 Misc (hosting and others): $500 Total: $15,200 ROI over a 9 months period: 355.25% The plans moving forward This website was a part of a research and development experiment we did. With AI, we wanted to test new waters and transition more towards automation. Ideally, we want to use ChatGPT or some other API to produce these articles and bulk publish on the site. The costs with this approach are going to be much lower and the ROI is much more impressive. It's not the the 7-figures projects I created earlier (as you may have checked the older case studies on my profile), but it's highly scalable. We plan to refine this model even further, test more and automate everything completely to bring down our costs significantly. Once we have a model, we are going to scale it to 100s of sites. The process of my existing 7-figures websites portfolio was quite similar. I tested out a few sites, refined the model and scaled it to over 41 sites. Now, the fundamentals are the same however, we are using AI in a smarter way to do the same but at a lower cost, with a smaller team and much better returns. The best thing in my opinion is to run numerous experiments now. Our experimentation was slowed down a lot in the past since we couldn't write using AI but now it's much faster. The costs are 3-6 times lower so when it used to take $50-100k to start, grow and sell a site. Now you can pump 3-6 more sites for the same budget. This is a good news for existing business owners as well who want to grow their brand. Anyway, I am excited to see the results of more sites. In the meantime, if you have any questions - feel free to let me know. Best of luck for everything. Feel free to ask questions. I'd be happy to help. This is an AMA.

Only 2 months of cash in the Bank for my business but was able to save it with the help of AI.
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CALLIRDAN90This week

Only 2 months of cash in the Bank for my business but was able to save it with the help of AI.

Hi there! I’m excited to share something very personal with you. We needed to book at least 2 appointments per day in the next 60 days, or my business would fail. We were already trying two acquisition channels, LinkedIn and email. The problem with these channels was that the positive response rate was very low in both. So I decided to focus on LinkedIn and get the attention of the lead by sending videos directly to them via LinkedIn messages. (You can send videos to your connections on LinkedIn if you use your cell phone.) This wasn’t new, but I added a small twist to get the lead’s attention. All the covers of the videos had a picture of me holding a sign with the person’s name and an interesting phrase. This showed some okay results, but the rest of the video was not personalized. Only the picture on the cover was. I even developed a Chrome extension for this because I thought this would be the answer and that I would book tons of appointments.  But after more trial and outreach, my leads responded, telling me that because the video itself wasn’t personalized for them, they felt like I didn’t put enough effort in, so they would not book a call with me. So after investing time and effort into my “new bright idea” and getting developers to make the Chrome extension, I was back to square one with no results. A few weeks went by, and after researching online, I found an online course from a guy who promised to teach me how to book 30+ appointments per month, guaranteed (at the time, I was making 2 or 3 appointments per week, maximum). He promised that I would only pay if he actually booked appointments for me and even offered to give me money if his course didn’t work for me. I never paid attention to internet gurus, but the offer was actually not bad, so I looked into this guy’s website. I found out he had hundreds of reviews from people who had taken his course and were talking amazing things about it. The more I read, the more excited I got. I booked a call that day and talked to a salesperson. The call was very short, and he promised I would get at least 2 appointments per day, easily. He seemed a bit cocky and told me that I just needed to trust him and the 100+ reviews from people who had taken the course. He didn’t share details, a proposal, or anything. I asked the price, and he told me it was close to $10k. (Not kidding, this was the price.) Then he told me that I would make the money back in no time with the clients I would get following his course, and that if it didn’t work, he would give me the money back. But I needed to follow everything the course said for at least 6 months. I had never paid $10k for anything in my life; it was extremely expensive for me. Also, my salary from my business was not in dollars but in a currency that was worth much less than the dollar. I continued to research more and more, but no other course was close to the number of reviews and promises that this guy had. I got desperate and told myself that I would bet everything on this course. If it worked for so many others, surely it would work for me. I got a loan from the bank and paid for the course. You might read this and think it was the most stupid thing ever, but the reality is that after 2 months in the course (I did the course as fast as I could), I learned a lot. The course was not bad; it was very extensive—probably more than 200 hours or so—and they taught a lot of things. I don’t think it was worth $10k for me, but I can see how for other people it might be worth that. Now, to the question you’re all thinking: did it get me the 2 appointments I needed per day? The answer is no. Here’s the thing: most of the techniques they taught were innovative and disruptive, but the focus was always on personalization, and they didn’t teach any way to automate the personalization. (I think, at the time they made the course, the tools didn’t exist yet.) So they taught how to do everything manually, and it took a lot—a lot of time and effort. And most annoyingly: an incredible amount of time doing operational things. I did get 2 appointments on some days, but it wasn’t consistent, and I didn’t have the time to spend 14 hours a day doing everything manually or the money to hire someone to do this for me. (I needed to also spend time delivering our service to our current clients; otherwise, they would leave.) I told them this, and they were very reasonable. After some negotiation, they gave me part of the money back. (To be fair, there was a lot of value in the course, so asking for the full $10k back would have been excessive because, in the end, it really taught me a lot of things I didn’t know.) So in the end, I spent $10k and 200+ hours on an online course, spent time and effort developing a Chrome extension, and was still not able to hit the meetings I needed. Money in the business was running out, and I needed to do something fast, or I was doomed. After investing time and effort in tools, research, and spending $10k and over 200 hours on a course that didn’t deliver the consistent results I needed, I was at a crossroads. My businesses were running out of money, and I knew I needed to find a solution quickly, or everything I had worked for would collapse. It was during this time of desperation that I started exploring other options. One night, while scrolling through the internet, I stumbled upon a 2024 article about how AI was being used to revolutionize various industries. It wasn’t directly related to appointment booking, but it sparked an idea in my mind. What if I could use AI to automate the personalization process that I had learned in the course? It seemed like a long shot, but I had nothing to lose. I started researching AI tools and technologies—YouTube videos, podcasts, pretty much everything related to AI—desperate to find something that could help me scale my outreach without investing too much time, while still maintaining the personalization that was so important. After a lot of trial and error, I found a few tools that showed promise. All of these tools were extremely new. Some of them had just launched the versions I needed just weeks ago. I can say I researched and tested more than 50 AI startups, experimenting with them, testing different approaches, checking prices (the problem was that most of them were cheap but became very expensive when applying the volume I needed to get results), and gradually refining my process. It wasn’t an overnight success, but for the first time, I felt like I was onto something that could truly work. The idea of combining AI personalization with volume was something new, and it gave me hope that I could finally book the meetings I needed without burning out. One day, I sent a video of myself talking—completely AI-generated—to my family chat group and waited for their response. None of them noticed it wasn’t actually me. At that moment, I said to myself: “Okay, I am ready to test this in the real world and see if it works.” Like everything in life, focus is key. As I mentioned earlier, we were already trying outbound strategies on LinkedIn and email, but I decided to narrow my focus to LinkedIn and specifically to video outreach. My goal was to stand out from the crowd, where most people were using text or sending generic videos. I knew that if my videos were 100% personalized, it would make a strong impression on my leads. I focused on two key metrics during my tests: Time spent on manual personalized outreach vs. AI-generated personalized outreach. Positive reply rate for non-personalized manual outreach vs. AI-generated personalized outreach. I ran a test using a sample of 50 one-minute videos sent to 50 leads, and here are the results: Time Spent to Make the Videos: Manual Process: It took me up to 10 hours to create and send 50 personalized videos. This included looking good on camera, brushing my hair, choosing appropriate clothing, ensuring proper lighting, not messing up the script, using a camera holder, recharging the phone, pausing to drink water, avoiding external sounds, being in an appropriate room, downloading the videos, deleting the videos that were not good, and sending the final ones. On average, it took me at least 12.5 minutes per one-minute video. AI Process: With AI, it took me just 32 seconds to create the exact same one-minute personalized video—without saying a word or recording a second of footage. In total, I could make and send the same 50 personalized videos in just 27 minutes. Result: The AI process was 24 times faster. Completely crazy! Positive Reply Rate: Non-Personalized Script (Manual): Using a good script without personalization (no name, job title, city, company, etc.) resulted in a positive reply rate of 4-6% on LinkedIn, including follow-ups. Personalized Script (AI): Using the same script but adding personalized details like the lead's name, company, city, and job title resulted in a positive reply rate of 15-20%, including follow-ups. Result: AI personalization led to 3x (three times) more replies. The best part was the responses. Almost everyone who replied thanked me for taking the time to research them, congratulated me on my speech, and appreciated the personalization and eloquence of my message.  These metrics were a complete breakthrough for me. I researched online to see if anyone else had done something similar, but I couldn’t find anything close. After achieving these metrics, booking the two appointments I desperately needed became easy. In fact, in the last 10 weeks, I’ve been able to consistently book 3-4 appointments per day. This success allowed me to train someone in my company to handle the process, freeing me up to focus on other aspects of the business and ultimately saving it. With the AI appointment machine we built, I even have free time now—time that I’ve been using to develop a methodology and tech tools that I now teach to others. I named the methodology Clip2Lead as a reference to the first Chrome extension I developed that didn’t work but ended up being the first step toward everything that followed. I’ve condensed everything I learned and throughout my experiences into a simple and short FREE training where I cover the entire AI appointment booking process. This includes how to find leads, create scripts, set up follow-up sequences, generate AI videos, clone your voice, compare non-AI metrics with AI metrics, and even navigate AI safety controls. I also offer Chrome extensions that helped me automate the process even further, so you can spend your time closing deals or focusing on other acquisition channels, while your AI machine for booking appointments runs with minimal effort from you. If you’re interested please get in touch with me and thank you for taking the time to read my personal story.

Watched 8 hours of MrBeast's content. Here are 7 psychological strategies he's used to get 34 billion views
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Positive-Bison5023This week

Watched 8 hours of MrBeast's content. Here are 7 psychological strategies he's used to get 34 billion views

MrBeast can fill giant stadiums and launch 8-figure candy companies on demand. He’s unbelievably popular. Recently, I listened to the brilliant marketer Phill Agnew (from The Nudge podcast) being interviewed on the Creator Science podcast. The episode focused on how MrBeast’s near-academic understanding of audience psychology is the key to his success. Better than anyone, MrBeast knows how to get you: \- Click on his content (increase his click-through rate) \- Get you to stick around (increase his retention rate) He gets you to click by using irresistible thumbnails and headlines. I watched 8 hours of his content. To build upon Phil Agnew’s work, I made a list of 7 psychological effects and biases he’s consistently used to write headlines that get clicked into oblivion. Even the most aggressively “anti-clickbait” purists out there would benefit from learning the psychology of why people choose to click on some content over others. Ultimately, if you don’t get the click, it really doesn’t matter how good your content is. Novelty Effect MrBeast Headline: “I Put 100 Million Orbeez In My Friend's Backyard” MrBeast often presents something so out of the ordinary that they have no choice but to click and find out more. That’s the “novelty effect” at play. Our brain’s reward system is engaged when we encounter something new. You’ll notice that the headline examples you see in this list are extreme. MrBeast takes things to the extreme. You don’t have to. Here’s your takeaway: Consider breaking the reader/viewer’s scrolling pattern by adding some novelty to your headlines. How? Here are two ways: Find the unique angle in your content Find an unusual character in your content Examples: “How Moonlight Walks Skyrocketed My Productivity”. “Meet the Artist Who Paints With Wine and Chocolate.” Headlines like these catch the eye without requiring 100 million Orbeez. Costly Signaling MrBeast Headline: "Last To Leave $800,000 Island Keeps It" Here’s the 3-step click-through process at play here: MrBeast lets you know he’s invested a very significant amount of time and money into his content. This signals to whoever reads the headline that it's probably valuable and worth their time. They click to find out more. Costly signaling is all amount showcasing what you’ve invested into the content. The higher the stakes, the more valuable the content will seem. In this example, the $800,000 island he’s giving away just screams “This is worth your time!” Again, they don’t need to be this extreme. Here are two examples with a little more subtlety: “I built a full-scale botanical garden in my backyard”. “I used only vintage cookware from the 1800s for a week”. Not too extreme, but not too subtle either. Numerical Precision MrBeast knows that using precise numbers in headlines just work. Almost all of his most popular videos use headlines that contain a specific number. “Going Through The Same Drive Thru 1,000 Times" “$456,000 Squid Game In Real Life!” Yes, these headlines also use costly signaling. But there’s more to it than that. Precise numbers are tangible. They catch our eye, pique our curiosity, and add a sense of authenticity. “The concreteness effect”: Specific, concrete information is more likely to be remembered than abstract, intangible information. “I went through the same drive thru 1000 times” is more impactful than “I went through the same drive thru countless times”. Contrast MrBeast Headline: "$1 vs $1,000,000 Hotel Room!" Our brains are drawn to stark contrasts and MrBeast knows it. His headlines often pit two extremes against each other. It instantly creates a mental image of both scenarios. You’re not just curious about what a $1,000,000 hotel room looks like. You’re also wondering how it could possibly compare to a $1 room. Was the difference wildly significant? Was it actually not as significant as you’d think? It increases the audience’s \curiosity gap\ enough to get them to click and find out more. Here are a few ways you could use contrast in your headlines effectively: Transformational Content: "From $200 to a $100M Empire - How A Small Town Accountant Took On Silicon Valley" Here you’re contrasting different states or conditions of a single subject. Transformation stories and before-and-after scenarios. You’ve got the added benefit of people being drawn to aspirational/inspirational stories. Direct Comparison “Local Diner Vs Gourmet Bistro - Where Does The Best Comfort Food Lie?” Nostalgia MrBeast Headline: "I Built Willy Wonka's Chocolate Factory!" Nostalgia is a longing for the past. It’s often triggered by sensory stimuli - smells, songs, images, etc. It can feel comforting and positive, but sometimes bittersweet. Nostalgia can provide emotional comfort, identity reinforcement, and even social connection. People are drawn to it and MrBeast has it down to a tee. He created a fantasy world most people on this planet came across at some point in their childhood. While the headline does play on costly signaling here as well, nostalgia does help to clinch the click and get the view. Subtle examples of nostalgia at play: “How this \[old school cartoon\] is shaping new age animation”. “\[Your favorite childhood books\] are getting major movie deals”. Morbid Curiosity MrBeast Headline: "Surviving 24 Hours Straight In The Bermuda Triangle" People are drawn to the macabre and the dangerous. Morbid curiosity explains why you’re drawn to situations that are disturbing, frightening, or gruesome. It’s that tension between wanting to avoid harm and the irresistible desire to know about it. It’s a peculiar aspect of human psychology and viral content marketers take full advantage of it. The Bermuda Triangle is practically synonymous with danger. The headline suggests a pretty extreme encounter with it, so we click to find out more. FOMO And Urgency MrBeast Headline: "Last To Leave $800,000 Island Keeps It" “FOMO”: the worry that others may be having fulfilling experiences that you’re absent from. Marketers leverage FOMO to drive immediate action - clicking, subscribing, purchasing, etc. The action is driven by the notion that delay could result in missing out on an exciting opportunity or event. You could argue that MrBeast uses FOMO and urgency in all of his headlines. They work under the notion that a delay in clicking could result in missing out on an exciting opportunity or event. MrBeast’s time-sensitive challenge, exclusive opportunities, and high-stakes competitions all generate a sense of urgency. People feel compelled to watch immediately for fear of missing out on the outcome or being left behind in conversations about the content. Creators, writers, and marketers can tap into FOMO with their headlines without being so extreme. “The Hidden Parisian Cafe To Visit Before The Crowds Do” “How \[Tech Innovation\] Will Soon Change \[Industry\] For Good” (Yep, FOMO and urgency are primarily responsible for the proliferation of AI-related headlines these days). Why This All Matters If you don’t have content you need people to consume, it probably doesn’t! But if any aspect of your online business would benefit from people clicking on things more, it probably does. “Yes, because we all need more clickbait in this world - \eye-roll emoji\” - Disgruntled Redditor I never really understood this comment but I seem to get it pretty often. My stance is this: If the content delivers what the headline promises, it shouldn’t be labeled clickbait. I wouldn’t call MrBeast’s content clickbait. The fact is that linguistic techniques can be used to drive people to consume some content over others. You don’t need to take things to the extremes that MrBeast does to make use of his headline techniques. If content doesn’t get clicked, it won’t be read, viewed, or listened to - no matter how brilliant the content might be. While “clickbait” content isn’t a good thing, we can all learn a thing or two from how they generate attention in an increasingly noisy digital world.

How Our AI Tool Helped a Small Business Save 15% on Annual Expenses
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Medical-Wait-6960This week

How Our AI Tool Helped a Small Business Save 15% on Annual Expenses

I’m the founder of a startup that built an AI-powered tool to analyze and optimize business finances, with a special focus on small and medium-sized enterprises (SMEs). After months of development and testing, I’m pumped to share our solution with you and get your feedback. Here’s what we do, how it works, and the results we’ve seen. The Problem We Solve Managing a company’s finances, especially for an SME, is often a nightmare: forgotten subscriptions, poorly negotiated supplier contracts, invoices with errors… We’ve all been there. Our tool uses AI to automate expense analysis, spot issues, and suggest practical ways to cut costs—without you having to spend hours on it. How It Works (A Bit of Tech Talk) We built our tool on a multi-agent architecture using the CREWAI framework. Here are the main AI agents we’ve got running: Expense Analyst: Digs through your invoices and categorizes your spending. Compliance Auditor: Checks for errors, fraud, or compliance hiccups. Financial Reporter: Generates clear reports with actionable recommendations. Supplier Negotiator: Hunts down cheaper supplier options using the Serper API and offers negotiation strategies. To hook up your company’s data, we use NEEDLE, a RAG (Retrieval-Augmented Generation) system that lets our agents tap into your info in real time. Everything’s locked down in an SQLite database with end-to-end encryption. Real Results We tested the tool with 10 companies, and here’s what we found: Average cost reduction of 12% in three months. Fraud detection: For example, we flagged 5 shady invoices at one company, saving them €3,000. Supplier optimization: For an SME, we found an energy supplier 20% cheaper, saving them €8,000 a year. A real-world case: A consulting firm with 50 employees ran our tool on their SaaS subscriptions. Outcome? They ditched 3 unused subscriptions, renegotiated 2 contracts, and saved 15% on their annual expenses. Challenges We Tackled No sugarcoating here—it wasn’t a walk in the park. The biggest hurdle? Data security. We’re handling sensitive stuff, so we went all in: End-to-end encryption for everything we process. GDPR compliance with strict rules. Role-based access controls to limit who sees what. Another tough one was integrating with existing systems. We’ve already got connectors for QuickBooks, Xero, and SAP, and we’re working on more. Why It’s Different Sure, there are tools like Expensify or Ramp out there, but our multi-agent approach digs deeper. We deliver super-detailed analysis and precise recommendations. And our knack for finding cheaper suppliers in real time? That’s a game-changer for quick savings.I’m the founder of a startup that built an AI-powered tool to analyze and optimize business finances, with a special focus on small and medium-sized enterprises (SMEs). After months of development and testing, I’m pumped to share our solution with you and get your feedback. Here’s what we do, how it works, and the results we’ve seen. Ask me your technical questions, share your ideas or critiques we’re here to get better! Thanks you for reading this.

Writing a exercise based TTRPG rulebook for a system where your real world fitness is tied to character progression
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BezboznyThis week

Writing a exercise based TTRPG rulebook for a system where your real world fitness is tied to character progression

My dad was a star athlete when he was young, and my mom was a huge sci-fi/fantasy nerd, so I got both ends of the stick as it were. Love gaming and nerd culture, but also love to exercise and self improvement. Sometimes exercise can feel boring though compared to daydreaming about fantastic fictional worlds, so for a long time I've been kicking around the idea of how to "Gamify" fitness. and recently I've been working on this passion project of a Table Top RPG (Like D&D) where the stats of your character are related to your own fitness, so if you want your character in game to improve, you have to improve in the real world. Below is a rough draft you can look through that details the settings and mechanics of the game I've come up with so far. I'd love to eventually get a full book published and sell it online. maybe even starting a whole brand of "Gamified fitness": REP-SET: GAINSZ In the war torn future of 24th century… There are no rest days… In the futuristic setting of "REP-SET: GAINSZ," the "War of Gains" casts a long shadow over the Sol System as the various factions vie for territory and resources. However, war has evolved. Unmanned drones and long-range strikes have faded into obsolescence. Battles, both planet-side and in the depths of space, are now fought by soldiers piloting REP-SETs: Reactive Exoskeletal Platform - Symbiotic Evolution Trainer Massive, humanoid combat mechs. Powered by mysterious “EV” energy, these mechanical marvels amplify, and are in turn amplified by, the fitness and mental acuity of their pilots. The amplification is exponential, leading pilots into a life of constant training in order for their combat prowess to be bolstered by every incremental gain in their level of fitness. With top pilots having lifting capacity measured in tons, and reaction times measured by their Mach number, REP-SET enhanced infantry now dominate the battlefield. The Factions: The Federated Isometocracy of Terra (FIT): Quote: "The strength of the body is the strength of the spirit. Together, we will lift humanity to its destined greatness. But ask not the federation to lift for you. Ask yourself: Do you even lift for the Federation?" Description: An idealistic but authoritarian faction founded on the principle of maximizing the potential of all individuals. FIT citizens believe in relentless striving for physical and mental perfection, leading to collective excellence. Their goal is the unification of humankind under a rule guided by this doctrine, which sometimes comes at the cost of individual liberties. Mech Concept: REP-SET mechs. Versatile humanoid designs focusing on strength, endurance, and adaptability. By connecting to the AI spirit within their REP-SETs core, each pilot enhances the performance of their machine through personal willpower and peak physical training. Some high-rank REP-SETS include features customized to the pilot's strengths, visually signifying their dedication and discipline. The Dominion of Organo-Mechanical Supremacy (DOMS): Quote: "Without pain, there is no gain. Become the machine. Embrace the burn.” Description: A fanatical collective ideologically obsessed with "Ascendency through suffering" by merging their bodies with technology that not only transcends biological limitations, but also acts to constantly induce pain in it's users. Driven by a sense of ideological superiority and a thirst for domination, DOMS seek to bring the painful blessings of their deity "The lord of the Burn" to the rest of the solar system. Their conquest could turn them into a significant threat to humanity. Mech Concept: Hybrid mechs, where the distinction between the pilot and the machine is blurred. The cockpit functions as a life-support system for the pilot, heavily modified with augmentations. Mechs themselves are often modular, allowing for adaptation and assimilation of enemy technology. Some DOMS mechs might display disturbing elements of twisted flesh alongside cold, mechanical parts. The Tren: Quote: "Grow... bigger... feast... protein..." Description: A ravenous conglomeration of biochemically engineered muscular monstrosities, united only by a shared insatiable hunger for "More". Existing mostly in deep space, they seek organic matter to consume and assimilate. They progress in power not due to any form of training or technology, but from a constant regimen of ravenous consumption and chemically induced muscle growth, all exponentially enhanced by EV energies. While some have been known to possess a certain level of intellect and civility, their relentless hunger makes them incredibly mentally volatile. When not consuming others, the strong consume the weak within their own faction. Mech Concept: Bio-Organic horrors. While they do have massive war machines, some are living vessels built around immense creatures. These machines resemble grotesque fleshy designs that prioritize rapid mutation and growth over sleek aesthetics. Often unsettling to behold. Synthetic Intelligence Theocracy (SIT): Quote: "Failure is an unacceptable data point.” Description: A society ruled by a vast and interconnected artificial intelligence network. The SIT governs with seemingly emotionless rationality, striving for efficiency and maximum productivity. This leads to a cold, but arguably prosperous society, unless you challenge the logic of the collective AI. Their goals? Difficult to predict, as it hinges on how the AI calculates what's "optimal" for the continuation or "evolution" of existence. Mech Concept: Sleek, almost featureless robotic creations with a focus on efficient movement and energy management. Often drone-like or modular, piloted through direct mind-machine linking rather than traditional cockpits. Their aesthetic suggests cold and impersonal perfection. The Way Isolate(TWI): Quote: "The body unblemished, the mind unwavering. That is the path to true strength. That and a healthy diet of Aster-Pea proteins." Description: Known by some as "The asteroid farmers", The Way Isolate is a proud and enigmatic faction that stands apart from the other powers in the Sol System. A fiercely independent tribe bound by oaths of honor, loyalty, and hard work. Wandering the asteroid belt in their vast arc ships, their unparalleled mastery in asteroidal-agricultural engineering, ensuring they have no need to colonize planets for nutritional needs, has allowed them to abstain from the pursuit of territorial expansion in “The War of Gains”, instead focusing on inward perfection, both spiritual and physical. They eschew all technological bodily enhancements deemed unnatural, believing that true power can only be cultivated through the relentless pursuit of personal strength achieved through sheer will and bodily perfection. The Way Isolate views biohacking, genetic manipulation, and even advanced cybernetics as corruptions of the human spirit, diluting the sacredness of individual willpower. Mech Concept: Way Isolate mechs are built with maneuverability and precision in mind rather than flashy augmentations. Their REP-SETs are streamlined, favoring lean designs that mirror the athleticism of their pilots. Excelling in low to zero G environments, their mechs lack bulky armor, relying on evasion and maneuverability rather than brute force endurance. Weaponry leans towards traditional kinetic based armaments, perhaps employing archaic but reliable weapon styles such as blades or axes as symbols of their purity of purpose. These mechs reflect the individual prowess of their pilots, where victory is determined by focus, technique, and the raw power of honed physical ability. Base Player Character Example: You are a young, idealistic FIT soldier, barely out of training and working as a junior REP-SET mechanic on the Europa Ring World. The Miazaki district, a landscape of towering mountains and gleaming cities, houses a sprawling mountainside factory – a veritable hive of Gen 5 REP-SET construction. Here, the lines between military and civilian blur within a self-sufficient society dependent on this relentless industry. Beneath the surface, you harbor a secret. In a forgotten workshop, the ghost of a REP-SET takes shape – a unique machine built around an abandoned, enigmatic AI core. Ever since you salvaged it as a child from the wreckage of your hometown, scarred by a brutal Tren attack, you've dedicated yourself to its restoration. A lingering injury from that fateful battle mocks your progress, a constant reminder of the fitness exams you cannot pass. Yet, you train relentlessly, dreaming of the day you'll stand as a true REP-SET pilot. A hidden truth lies at the heart of the REP-SETS: as a pilot's abilities grow, their mech develops unique, almost mystical powers – a manifestation of the bond between the human spirit and the REP-SET's AI. The ache in your old wound serves as a grim prophecy. This cold war cannot last. The drums of battle grow louder with each passing day. GAME MECHANICS: The TTRPG setting of “REP-SET: GAINSZ” is marked by a unique set of rules, by which the players real world capabilities and fitness will reflect and affect the capabilities, progression, and success of their REP-SET pilot character in-game. ABILITY SCORES: Pilots' capabilities will be defined by 6 “Ability scores”: Grace, Agility, Iron, Nourishment, Strength, and Zen. Each of the 6 ability scores will duel represent both a specific area of exercise/athleticism and a specific brand of healthy habits. The definitions of these ability scores are as follows: Grace (GRC): "You are an artist, and your body is your canvas; the way you move is your paint and brush." This ability score, the domain of dancers and martial artists, represents a person's ability to move with organic, flowing control and to bring beauty to the world. Skill challenges may be called upon when the player character needs to act with poise and control, whether socially or physically. Real-world skill checks may involve martial arts drills, dancing to music, or balance exercises. Bonuses may be granted if the player has recently done something artistically creative or kind, and penalties may apply if they have recently lost their temper. This ability score affects how much NPCs like your character in game. Agility (AGI): "Your true potential is locked away, and speed is the key to unlocking it." The domain of sprinters, this ability score represents not only a person's absolute speed and reaction time but also their capacity to finish work early and avoid procrastination. Skill challenges may be called upon when the player character needs to make a split-second choice, move fast, or deftly dodge something dangerous. Real-world skill checks may involve acts of speed such as sprinting or punching/kicking at a steadily increasing tempo. Bonuses may apply if the player has finished work early, and penalties may apply if they are procrastinating. This ability score affects moving speed and turn order in game. Iron (IRN): "Not money, nor genetics, nor the world's greatest trainers... it is your resolve, your will to better yourself, that will make you great." Required by all athletes regardless of focus, this ability score represents a player's willpower and their capacity to push through pain, distraction, or anything else to achieve their goals. Skill challenges may be called upon when the player character needs to push through fear, doubt, or mental manipulation. Real-world skill checks may involve feats of athletic perseverance, such as planking or dead hangs from a pull-up bar. Bonuses may apply when the player maintains or creates scheduled daily routines of exercise, self-improvement, and work completion, and penalties may apply when they falter in those routines. This ability score affects the max "Dynamic exercise bonus” that can be applied to skill checks in game (a base max of +3 when Iron = 10, with an additional +1 for every 2 points of iron. So if every 20 pushups gives you +1 on a “Strength” skill check, then doing 80 pushups will only give you +4 if you have at least 12 iron). Nourishment (NRS): "A properly nourished body will last longer than a famished one." This ability score, focused on by long-distance runners, represents a player's endurance and level of nutrition. Skill challenges may be called upon when making checks that involve the player character's stamina or health. Real-world skill checks may involve endurance exercises like long-distance running. Bonuses may apply if the player has eaten healthily or consumed enough water, and penalties may apply if they have eaten junk food. This ability score affects your HP (Health points), which determines how much damage you can take before you are incapacitated. Strength (STR): "When I get down on my hands, I'm not doing pushups, I'm bench-pressing the planet." The domain of powerlifters and strongmen, this ability score represents raw physical might and the ability to overcome obstacles. Skill challenges may be called upon when the player character needs to lift, push, or break something. Real-world skill checks might involve weightlifting exercises, feats of grip strength, or core stability tests. Bonuses may apply for consuming protein-rich foods or getting a good night's sleep, and penalties may apply after staying up late or indulging in excessive stimulants. This ability score affects your carrying capacity and base attack damage in game. Zen (ZEN): "Clarity of mind reflects clarity of purpose. Still the waters within to act decisively without." This ability score, prized by meditators and yogis, represents mental focus, clarity, and inner peace. Skill challenges may be called upon when the player character needs to resist distractions, see through illusions, or make difficult decisions under pressure. Real-world skill checks may involve meditation, breathing exercises, or mindfulness activities. Bonuses may apply after attending a yoga class, spending time in nature, or creating a calm and organized living space. Penalties may apply after experiencing significant stress, emotional turmoil, or having an unclean or unorganized living space. This ability score affects your amount of ZP in game (Zen Points: your pool of energy you pull from to use mystical abilities) Determining initial player ability scores: Initially, “Ability scores” are decided during character creation by giving the player a list of 6 fitness tests to gauge their level of fitness in each category. Running each test through a specific calculation will output an ability score. A score of 10 represents the average person, a score of 20 represents a peak athlete in their category. The tests are: Grace: Timed balancing on one leg with eyes closed (10 seconds is average, 60 is peak) Agility: Mile run time in minutes and second (10:00 minutes:seconds is average, 3:47 is peak) Iron: Timed dead-hang from a pull-up bar (30 seconds is average, 160 is peak) Nourishment: Miles run in an hour (4 is average, 12 is peak) Strength: Pushups in 2 minute (34 is average, 100 is peak) Zen: Leg stretch in degrees (80 is average, and 180 aka "The splits" is peak) Initial Score Calculation Formula: Ability Score = 10 + (Player Test Score - Average Score) / (Peak Score - Average\_Score) \* 10 Example: if the player does 58 pushups in 2 minutes, their strength would be: 10 plus (58 - 34) divided by (100-34) multiplied by 10 = 10 + (24)/(66)\* 10 = 10 + 3.6363... = 13.6363 rounded to nearest whole number = Strength (STR): 14 SKILLS AND SKILL CHALLENGES: The core mechanic of the game will be in how skill challenges are resolved. All “Skill challenges” will have a numerical challenge rating that must be met or beaten by the sum of a 10 sided dice roll and your score in the pertinent skill. Skill scores are determined by 2 factors: Ability Score Bonus: Every 2 points above 10 gives +1 bonus point. (EX. 12 = +1, 14 = +2, etc.) This also means that if you have less than 10 in an ability score, you will get negative points. Personal Best Bonus: Each skill has its own unique associated exercise that can be measured (Time, speed, distance, amount of reps, etc). A higher record means a higher bonus. EX: Authority skill checks are associated with a timed “Lateral raise hold”. Every 30 seconds of the hold added onto your personal best single attempt offers a +1 bonus. So if you can do a lateral hold for 90 seconds, that’s a +3 to your authority check! So if you have a 16 in Iron, and your Personal Best lateral raise hold is 90 seconds, that would give you an Authority score of +6 (T-Pose for dominance!) Dynamic Exercise Bonus: This is where the unique mechanics of the game kick in. At any time during a skill challenge (even after your roll) you can add an additional modifier to the skill check by completing the exercise during gameplay! Did you roll just below the threshold for success? Crank out another 20 pushups, squats, or curls to push yourself just over the edge into success! There are 18 skills total, each with its own associated ability score and unique exercise: Grace (GRC): \-Kinesthesia (Timed: Blind single leg stand time) \-Precision (Scored: Basket throws) \-Charm (Timed reps: Standing repeated forward dumbell chest press and thrust) \-Stealth (Timed distance: Leopard Crawl) Agility (AGI): \-acrobatics (timed reps: high kicks) \-Computers (Word per minute: Typing test) \-Speed (Time: 100 meter sprint) Iron (IRN): \-Authority (Timed: Lateral raise hold) \-Resist (Timed: Plank) \-Persist (Timed:Pull-up bar dead hang) Nourishment(NRS): \-Recovery (TBD) \-Stim crafting (TBD) \-Survival (TBD) Strength(STR): \-Mechanics (Timed reps: Alternating curls) \-Might (Timed reps: pushups) Zen(ZEN): \-Perceive (TBD) \-Empathy (TBD) \-Harmony (TBD) \-Lore (TBD) Healthy Habits Bonus: Being able to demonstrate that you have conducted healthy habits during gameplay can also add one time bonuses per skill challenge “Drank a glass of water +1 to Nourishment check”, “Cleaned your room, +3 on Zen check”. But watch out, if you’re caught in unhealthy Habits, the GM can throw in penalties, “Ate junk food, -1 to Nourishment check”, etc. Bonuses/penalties from in-game items, equipment, buffs, debuffs, etc., helping players to immerse into the mechanics of the world of REP-SET for the thrill of constantly finding ways to improve their player. Gradient success: Result of skill challenges can be pass or fail, but can also be on a sliding scale of success. Are you racing to the battlefield? Depending on your Speed check, you might arrive early and have a tactical advantage, just in time for an even fight, or maybe far too late and some of your favorite allied NPCs have paid the price… So you’re often encouraged to stack on those dynamic exercise bonuses when you can to get the most fortuitous outcomes available to you. Gameplay sample: GM: Your REP-SET is a phantom, a streak of light against the vast hull of the warship. Enemy fighters buzz angrily, but you weaves and dodges with uncanny precision. The energy wave might be losing effectiveness, but your agility and connection to the machine have never been stronger. Then, it happens. A gap in the defenses. A vulnerable seam in the warship's armor. Your coms agents keen eye spots it instantly. "Lower power junction, starboard side! You have an opening!" This is your chance to strike the decisive blow. But how? It'll take a perfect combination of skill and strategy, drawing upon your various strengths. Here are your options: Option 1: Brute Strength: Channel all remaining power into a single, overwhelming blast from the core. High-risk, high-reward. It could overload the REP-SET if you fail, but it might also cripple the warship. (Strength-focused, Might sub-skill) Option 2: Calculated Strike: With surgical precision, target the power junction with a pinpoint burst of destabilizing energy. Less flashy and ultimately less damaging, but potentially more effective in temporarily disabling the ship. (Agility-focused, Precision sub-skill) Option 3: Harmonic Disruption: Attempt to harmonize with your REP-SET's AI spirit for help in connecting to the digital systems of the Warship. Can you generate an internal energy resonance within the warship, causing it to malfunction from within? (Zen-focused, Harmony sub-skill) Player: I'll take option 1, brute strength! GM: Ok, This will be a "Might" check. The CR is going to be very high on this one. I'm setting it at a 20. What's your Might bonus? Player: Dang, a 20?? That's literally impossible. My Might is 15 and I've got a PB of 65 pushups in 2 minutes, that sets me at a +5. Even if I roll a 10 and do 60 pushups for the DE I'll only get 18 max. GM: Hey I told you it was high risk. You want to choose another option? Player: No, no. This is what my character would do. I'm a real hot-blooded meathead for sure. GM: Ok then, roll a D10 and add your bonus. Player: \Rolls\ a 9! not bad, actually that's a really good roll. So +5, that's a 14. GM: Alright, would you like to add a dynamic exercise bonus? Player: Duh, it's not like I can do 120 pushups I'd need to beat the CR, but I can at least do better than 14. Alright, here goes. \the player gets down to do pushups and the 2 minute time begins. After some time...\ Player: 65....... 66! GM: Times up. Player: Ow... my arms... GM: so with 66, that's an extra +3, and its a new PB, so that's a +1. That sets your roll to 18. Player: Ow... Frack... still not 20... for a second there i really believed I could do 120 pushups... well I did my best... Ow... 20 CR is just too impossible you jerk... GM: Hmm... Tell me, what did you eat for lunch today? Player: Me? I made some vegetable and pork soup, and a protein shake. I recorded it all in my diet app. GM: And how did you sleep last night? Player: Like a baby, went to sleep early, woke up at 6. GM: in that case, you can add a +1 "Protein bonus" and +1 "Healthy rest" bonus to any strength related check for the day if you'd like, including this one. Player: Really?? Heck yes! add it to the roll! GM: With those extra bonuses, your roll reaches 20. How do you want to do this? Player: I roar "For Terra!" and pour every last ounce of my strength into the REP-SET. GM: "For Terra!" you roar, your cry echoing through coms systems of the REP-SET. The core flares blindingly bright. The surge of power dwarfs anything the REP-SET has unleashed before. With a titanic shriek that cracks the very fabric of space, the REP-SET slams into the vulnerable power junction. Raw energy explodes outwards, tendrils of light arcing across the warship's massive hull. The impact is staggering. The leviathan-like warship buckles, its sleek form rippling with shockwaves. Sparks shower like rain, secondary explosions erupt as critical systems overload. Then…silence. The warship goes dark. Power flickers within the REP-SET itself, then steadies. Alarms fade, replaced by the eerie quiet of damaged but functional systems. "We…did it?" The coms agents voice is incredulous, tinged with relief. She's awaiting your reply. Player: "I guess so." I say, and I smile and laugh. And then I slump back... and fall unconscious. \to the other players\ I'm not doing any more skill checks for a while guys, come pick me up please. \teammates cheer\ &#x200B;

Steep Learning : How I Mapped approximately 10K AI tools to 15K  Replaceable Tasks across 4K professions
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Apprehensive_Form396This week

Steep Learning : How I Mapped approximately 10K AI tools to 15K Replaceable Tasks across 4K professions

Hello Everyone , I would like to share some knowledge today which I went towards countless hours to do . I founded a portal called Seekme.ai, a comprehensive platform that houses over 10,000 AI tools and resources. Today, I'm excited to share with you an insightful and enlightening journey of how I mapped these tools to 15,000 tasks across 4,000 professions. This process, which I've named "Learn by Doing," got me the power of determination, collaboration, and adaptability. The Idea: It all started when I recognized the need for a more efficient and accessible way for professionals to understand which AI tools could help them automate their tasks. The traditional approach of manually researching and testing each AI tool for every profession was time-consuming and inefficient. I envisioned a solution that could streamline this process, making AI adoption easier and more accessible for a broader audience. The Planning: To begin, we needed a clear understanding of the task landscape across various professions. With the help of some Reddit communities , we embarked on an extensive study of common tasks in various industries. We utilized various sources, including government reports, industry surveys, and academic research, to create a comprehensive list of tasks. The result was an impressive list of 15,000 tasks. The Mapping: With the list of tasks in hand, the next step was to identify which AI tools could perform these tasks. I meticulously researched and analyzed each AI tool's capabilities and features. We cross-referenced this information with the tasks I had identified and created a mapping between the two. The process involved a significant amount of collaboration and refinement, as we continually updated and expanded our database of AI tools and tasks. The Challenges: The mapping process was not without its challenges. One of the primary obstacles was ensuring the accuracy and completeness of our data. To address this issue, I implemented a rigorous quality control process that included multiple rounds of checks and validations.I also established partnerships with industry experts and AI vendors to ensure our data was up-to-date and accurate. There is also a challenge that I faced was what is the quality of the tools which is the problem and how do I rank multiple tools if they do the same tasks without user feedback The Results: After months of hard work and dedication, I successfully mapped 10,000 AI tools to 15,000 tasks across 4,000 professions. Our new feature, AI by Profession, was born. This innovative will allow users to quickly and easily identify the AI tools that can automate tasks in their profession, making AI adoption more accessible and efficient than ever before. The Impact: The impact of this project has been significant. By making it easier for professionals to identify AI tools that can automate tasks in their industry, we're helping to drive productivity, efficiency, and innovation. Our users are saving time and resources by not having to manually research and test AI tools. Furthermore, we're contributing to the broader goal of democratizing AI and making it accessible to a broader audience. But there is a still an issue we face of ranking tools who does the similar job. For instance for content creation there 10 tools that can do same video editing so how do we rank it . We are planning to add categories to this to make it more exhaustive Conclusion: The journey to mapping 10,000 AI tools for 15,000 tasks across 4,000 professions was a challenging and rewarding experience. It required a significant amount of planning, determination, and collaboration, but the end result was a powerful tool that's making a difference in the lives of professionals around the world. I don’t know yet how useful it is yet for users So I am inviting you all to see if this feature can help you better equip yourself on the new wave and do things better. I am always up for a chat on anything AI and provide my help if needed. Looking forward to some feedback aswell

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!

Need help with the growth I couldn't handle
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Need help with the growth I couldn't handle

Calling all innovators, dreamers, and disruptors! &#x200B; We're pioneering a new frontier in the world of manufacturing with our vision: "Text to Product". I'm seeking individuals passionate about AI, manufacturing, efficiency and automation. While we can't promise immediate financial rewards, we're offering equity in a venture that's setting out to redefine the way things are made and sold. If the prospect of revolutionizing the future of humanity excites you, we'd love to hear from you. &#x200B; &#x200B; P.S. I realized that I can't always use "brute force" for solving problems, so seeking "the right connections" (seasoned entrepreneurs, advisors). Here's the TLDR version of my story: Started a company with ex-boss, bought him out, grinded for 2 years, found a way to 1000x the orders.* Went full speed for a month, got overwhelmed, barely kept up with half the demand (with that production process).* Focused on this one "platform", shipped hundreds of thousands of units in one holiday season.* Next quarter "the platform" returned about 85% of products as "overstock", demanded money back, made legal threats.* I told them that I will go to court and they stopped bothering me.* Then Covid + Nasty divorce which made me put a pause to regroup.* 2 years later, with 2x the production capacity and after relocating to a friendlier state (from NYC to MIA) I'm ready to relaunch (with a clear head, knowledge of fast growth and what to avoid).*

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!

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

Made 60k mrr for a business by just lead nurturing. Need suggestions and validation.
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Alarmed-Argument-605This week

Made 60k mrr for a business by just lead nurturing. Need suggestions and validation.

Apart from the story I need a suggestion and validation here. It's a bit long, skip to tl;dr if you couldn't handle length. A few days ago, I saw a person on Reddit sharing his struggles that, Even after generating a lot of leads from ads of Meta and Google (even with lowest cpc cpa cpl), he was not able to convert them into sales. Out of curiosity I dm'ed him with all fancy services that I offer and expressed that as a agency I would work with him for monthly recurring fee. He suggested for one time consulting fee, I agreed. It was literally a eye opener for me. This guy is in coaching business offering courses for people. His niche was too vague. Courses were on mindset coaching, confidence and public speaking coaching, right attitude coaching, manifestation coaching and all crap shits related to this. At first I thought he was not getting sales because who will pay for all this craps. I openly discussed with him that he has to change what he offers because, if I saw this ad I wouldn't buy this for sure. He then showed me how much money people offering similar service are making . I was literally taken back. He was part of a influencer group (the main guy who encourages these guys to start coaching business, looks like some mlm shit) where people post their succes stories. Literally lot of guys were making above 150k and 200k per month. Even with very basic landing page and average offer They are still winning. Here's where it gets interesting. I tried to clone everything that the top people in this industry are doing in marketing from end to end.( like the same landing page, bonus offers around 50k, exclusive community, free 1 on 1 calls for twice a month).Nothing worked for a month and later surprisingly even the sales started dropping a bit more. I got really confused here. So to do a discovery I went and purchased the competitor course and Man I was literally taken back. Like he has automated everything from end to end. You click the ad, see vsl, you have to fill a form and join a free Skool community where he gives away free stuffs and post success stories of people who took the course. Now every part of this journey you will get a follow up mail and follow up sms. Like after filling the form. after that now if you join and don't purchase the course you will be pampered with email and sms filled with success stories. For sure anybody will be tempted to buy the course. Here is the key take away. He was able to make more sales because he was very successful in nurturing the leads with follow ups after follow ups. Even after you purchased his course he is making passive income from 1 on calls and bonus live webinars. So follow ups will be for 1 on 1 calls and webinars after the course is over. Core point is our guy even after spending 2 to 3k per month on ads was not able to bring huge sales like competitors because he failed the nuture them. Even after making the same offers and the same patterns of marketing as competitors, the sales declined because people thought this is some spam that everyone is doing because the template of the ads was very professional and similar. suprising one is people fall for basic templates thinking it's a underrated one. so what we did here is we integrated a few softwares into one and set up all same webinars, automated email and sms follow ups, ad managers for stats, launched him a free LMS platform where without any additional fees so he can uploaded unlimited courses, skool like community and add product's like Shopify ( he was selling few merchandise with his brand name on) where he can add unlimited products with connection to all payment gateway, integrated with crm with unlimited contacts, workflow and lead nurturing with calender syncing for 1 on 1 calls. But these are a bit old school, what we did was even better. integrated a conversational ai with all of his sales platforms and gave a nocode automation builder with ai for the workflow. we also set him up with a ai voice agent that's automatically calls and markets for his course and also replies for queries when called. we also set up him a dedicated afflitate manager portal with automated commissions. Though he didn't cross 100k Mark, He did a great number after this. He was struggling with 6k sales, now he has reached somewhere mid of 45k to 50k mrr. Max he hit was 61.8k. I see this a big difference.So one small thing, nurturing the lead can bring you immense sales. To set up all of this it costs around 1.2k monthly for me with all the bills. ( I know there are few free for Individual user platforms out there, but It gets very costly when you switch to their premium plans. with heavy volumes you would require more than premium they offer.) I offered him like 3k per month to work as a agency for him who takes care of all these stuffs. He declined and offered for one time set up fee stating that he will pay 1.2k directly. The one time fee was also a bit low, though I agreed since this was a learning for me. what happened next after that is, he referred me to a few other people in the same niche. But the problem is they are not interested in spending 1 to 2 k in bills for software. They requested that if, will I be able to provide the saas alone for less than 500 dollars with one time set up fee. I haven't responded yet since I have to take an enterprise plan for all the software used and pay full advance price for billings. Then to break even that I have to make minimum 50 or odd users for that. let's grantly say 100 users with all other future costs. So here's what I'm planning to do. I'm planning to offer this as saas for let's say 239 dollars per month. with may or may not one time set up fee. ( I checked the entire internet, there is no single person offering at this price point for unlimited. Also one can easily start their marketing agency with this.) The suggestion and validation that I need here is 1.are you going through the same struggles or faced these struggles? would you be interested to buy at 239 dollars per month? let's say you're from a different niche, Did the features I told were okay for you or you need something specific for your industry that you will be interested in buying? please answer in comments and if you will purchase for this price let me know in comments/dms. I will take that into account and if the response rate is above 100 queries, then will integrate this and sell for that price. (ps: If you see this post on similar subs, please bear cause I'm trying to get suggestions from different POV) tl;dr - lead nurturing can massively boost sales *I made a software integration for a client for a 1.2k per month billing and here I want to know if more than 100 people are interested so that I will make this into my own saas and sell it for like a cheap price of 239 dollars per month TIA.

Steep Learning : How I Mapped approximately 10K AI tools to 15K  Replaceable Tasks across 4K professions
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Apprehensive_Form396This week

Steep Learning : How I Mapped approximately 10K AI tools to 15K Replaceable Tasks across 4K professions

Hello Everyone , I would like to share some knowledge today which I went towards countless hours to do . I founded a portal called Seekme.ai, a comprehensive platform that houses over 10,000 AI tools and resources. Today, I'm excited to share with you an insightful and enlightening journey of how I mapped these tools to 15,000 tasks across 4,000 professions. This process, which I've named "Learn by Doing," got me the power of determination, collaboration, and adaptability. The Idea: It all started when I recognized the need for a more efficient and accessible way for professionals to understand which AI tools could help them automate their tasks. The traditional approach of manually researching and testing each AI tool for every profession was time-consuming and inefficient. I envisioned a solution that could streamline this process, making AI adoption easier and more accessible for a broader audience. The Planning: To begin, we needed a clear understanding of the task landscape across various professions. With the help of some Reddit communities , we embarked on an extensive study of common tasks in various industries. We utilized various sources, including government reports, industry surveys, and academic research, to create a comprehensive list of tasks. The result was an impressive list of 15,000 tasks. The Mapping: With the list of tasks in hand, the next step was to identify which AI tools could perform these tasks. I meticulously researched and analyzed each AI tool's capabilities and features. We cross-referenced this information with the tasks I had identified and created a mapping between the two. The process involved a significant amount of collaboration and refinement, as we continually updated and expanded our database of AI tools and tasks. The Challenges: The mapping process was not without its challenges. One of the primary obstacles was ensuring the accuracy and completeness of our data. To address this issue, I implemented a rigorous quality control process that included multiple rounds of checks and validations.I also established partnerships with industry experts and AI vendors to ensure our data was up-to-date and accurate. There is also a challenge that I faced was what is the quality of the tools which is the problem and how do I rank multiple tools if they do the same tasks without user feedback The Results: After months of hard work and dedication, I successfully mapped 10,000 AI tools to 15,000 tasks across 4,000 professions. Our new feature, AI by Profession, was born. This innovative will allow users to quickly and easily identify the AI tools that can automate tasks in their profession, making AI adoption more accessible and efficient than ever before. The Impact: The impact of this project has been significant. By making it easier for professionals to identify AI tools that can automate tasks in their industry, we're helping to drive productivity, efficiency, and innovation. Our users are saving time and resources by not having to manually research and test AI tools. Furthermore, we're contributing to the broader goal of democratizing AI and making it accessible to a broader audience. But there is a still an issue we face of ranking tools who does the similar job. For instance for content creation there 10 tools that can do same video editing so how do we rank it . We are planning to add categories to this to make it more exhaustive Conclusion: The journey to mapping 10,000 AI tools for 15,000 tasks across 4,000 professions was a challenging and rewarding experience. It required a significant amount of planning, determination, and collaboration, but the end result was a powerful tool that's making a difference in the lives of professionals around the world. I don’t know yet how useful it is yet for users So I am inviting you all to see if this feature can help you better equip yourself on the new wave and do things better. I am always up for a chat on anything AI and provide my help if needed. Looking forward to some feedback aswell

I had over 1000 visitors in 24h thanks to a post on HN and generated 0$ revenue but here is what I learned:
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sow4codeThis week

I had over 1000 visitors in 24h thanks to a post on HN and generated 0$ revenue but here is what I learned:

I litteraly just have 39 followers ont Twitter, I don't have an audience at all and a vice that entrepreneurs and indie hackers often fall into is looking at others who have an audience and to start hating it and telling themselves that even if their products are crap they will still have traffic on their site given their number of subscribers and their audiences. This thought is just a limiting thought because. Yes, obviously it's easier for the person who already has an andience to bring traffic to their site and acquire these first users but these people have to work to build this audience, it wasn't easy, it required a lot of effort but we quickly forget that when we don't even have a tenth of what this person has and despite this facility it's not an excuse to fill up and abandon your project, telling yourself that no one will ever see my product if I don't already have a built audience. That's not an excuse ! I am proof of this on a small scale, yesterday I launched my new product (EduHunt, a site that helps you find the most relevant educational content that you are looking for to avoid paying for online courses that are worth a fortune but to be honest in the end it was rubbish, the idea seemed good but the market is what it is and there is NO need for a site like that, I still learn lessons from it, failure is necessary to succeed ! ). So I launched EduHunt on Hacker News and on Reddit but Reddit didn't bring me much in the end. 1 hour after the launch I had around fifty visitors and 3 registered (trial period), I told myself that it was going to continue like this and I hoped to have 200 visitors at the end of the day no more. I can't tell you what a surprise it was when I opened Vercel and saw 800 visitors for 50 online as I looked, I went crazy lol. My post on Hacker News "exploded", I had more than 400 people who had just come from Hacker News and other sites linked to Hacker News, I told myself that it was finally the right one but reality quickly caught up with me , I went to see my post and this is the kind of comment I had ( Above the text ) As you see, my product sucks and it's not the end of the world, I learn a lot of lessons from it, I failed in the design of the product in directly reflecting what the idea of the product is (most of the comments do not really target my basic idea, I wanted to create a site to help search for educational content on YouTube with filters that are not in the usual YouTube search and this in text format analyzed by AI, I was told that I monetize free videos, I do not appropriate the videos that I put on my site and that you have to pay to have access, what is monetized here is the means of 'access to the content, not the content itself, but yes I failed in this and in many others of this project but I come out better) Despite this, I attracted more than 1000 visitors to my site in less than 24 hours with a simple post on Hacker News, a good title, a sincere story to go with it and that was it, I have no audience nothing at all. If the product had been much better who knows where I would be today. All this to say and remind you that there are no excuses to hide behind, building an audience requires hard work and takes time ! But just because you don't have one doesn't mean you can never bring traffic to your site. Be honest in what you do, learn from your mistakes, repeat and you should find your happiness.

BERT-pytorch
github
LLM Vibe Score0.514
Human Vibe Score0.16971233963995486
codertimoMar 28, 2025

BERT-pytorch

BERT-pytorch !GitHub issues Pytorch implementation of Google AI's 2018 BERT, with simple annotation BERT 2018 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Paper URL : https://arxiv.org/abs/1810.04805 Introduction Google AI's BERT paper shows the amazing result on various NLP task (new 17 NLP tasks SOTA), including outperform the human F1 score on SQuAD v1.1 QA task. This paper proved that Transformer(self-attention) based encoder can be powerfully used as alternative of previous language model with proper language model training method. And more importantly, they showed us that this pre-trained language model can be transfer into any NLP task without making task specific model architecture. This amazing result would be record in NLP history, and I expect many further papers about BERT will be published very soon. This repo is implementation of BERT. Code is very simple and easy to understand fastly. Some of these codes are based on The Annotated Transformer Currently this project is working on progress. And the code is not verified yet. Installation Quickstart NOTICE : Your corpus should be prepared with two sentences in one line with tab(\t) separator Prepare your corpus or tokenized corpus (tokenization is not in package) Building vocab based on your corpus Train your own BERT model Language Model Pre-training In the paper, authors shows the new language model training methods, which are "masked language model" and "predict next sentence". Masked Language Model Original Paper : 3.3.1 Task #1: Masked LM Rules: Randomly 15% of input token will be changed into something, based on under sub-rules Randomly 80% of tokens, gonna be a [MASK] token Randomly 10% of tokens, gonna be a [RANDOM] token(another word) Randomly 10% of tokens, will be remain as same. But need to be predicted. Predict Next Sentence Original Paper : 3.3.2 Task #2: Next Sentence Prediction "Is this sentence can be continuously connected?" understanding the relationship, between two text sentences, which is not directly captured by language modeling Rules: Randomly 50% of next sentence, gonna be continuous sentence. Randomly 50% of next sentence, gonna be unrelated sentence. Author Junseong Kim, Scatter Lab (codertimo@gmail.com / junseong.kim@scatterlab.co.kr) License This project following Apache 2.0 License as written in LICENSE file Copyright 2018 Junseong Kim, Scatter Lab, respective BERT contributors Copyright (c) 2018 Alexander Rush : The Annotated Trasnformer

eiten
github
LLM Vibe Score0.549
Human Vibe Score0.754375921646308
tradyticsMar 27, 2025

eiten

Eiten - Algorithmic Investing Strategies for Everyone Eiten is an open source toolkit by Tradytics that implements various statistical and algorithmic investing strategies such as Eigen Portfolios, Minimum Variance Portfolios, Maximum Sharpe Ratio Portfolios, and Genetic Algorithms based Portfolios. It allows you to build your own portfolios with your own set of stocks that can beat the market. The rigorous testing framework included in Eiten enables you to have confidence in your portfolios. If you are looking to discuss these tools in depth and talk about more tools that we are working on, please feel free to join our Discord channel where we have a bunch of more tools too. Files Description | Path | Description | :--- | :---------- | eiten | Main folder. | &boxur; figures | Figures for this github repositories. | &boxur; stocks | Folder to keep your stock lists that you want to use to create your portfolios. | &boxur; strategies | A bunch of strategies implemented in python. | backtester.py | Backtesting module that both backtests and forward tests all portfolios. | data_loader.py | Module for loading data from yahoo finance. | portfolio_manager.py | Main file that takes in a bunch of arguments and generates several portfolios for you. | simulator.py | Simulator that uses historical returns and monte carlo to simulate future prices for the portfolios. | strategy_manager.py | Manages the strategies implemented in the 'strategies' folder. Required Packages You will need to install the following package to train and test the models. Scikit-learn Numpy Tqdm Yfinance Pandas Scipy You can install all packages using the following command. Please note that the script was written using python3. Build your portfolios Let us see how we can use all the strategies given in the toolkit to build our portfolios. The first thing you need to do is modify the stocks.txt file in the stocks folder and add the stocks of your choice. It is recommended to keep the list small i.e anywhere between 5 to 50 stocks should be fine. We have already put a small stocks list containing a bunch of tech stocks like AAPL, MSFT, TSLA etc. Let us build our portfolios now. This is the main command that you need to run. This command will use last 5 years of daily data excluding the last 90 days and build several portfolios for you. Based on those portfolios, it will then test them on the out of sample data of 90 days and show you the performance of each portfolio. Finally, it will also compare the performance with your choice of market index which is QQQ here. Let's dive into each of the parameters in detail. istest: The value determined if the program is going to keep some separate data for future testing. When this is enabled, the value of futurebars should be larger than 5. future_bars: These are the bars that the tool will exclude during portfolio building and will forward test the portfolios on the excluded set. This is also called out of sample data. datagranularityminutes: How much granular data do you want to use to build your portfolios. For long term portfolios, you should use daily data but for short term, you can use hourly or minute level data. The possible values here are 3600, 60, 30, 15, 5, 1. 3600 means daily. historytouse: Whether to use a specific number of historical bars or use everything that we receive from yahoo finance. For minute level data, we only receive up to one month of historical data. For daily, we receive 5 years worth of historical data. If you want to use all available data, the value should be all but if you want to use smaller history, you can set it to an integer value e.g 100 which will only use the last 100 bars to build the portfolios. applynoisefiltering: This uses random matrix theory to filter out the covariance matrix from randomness thus yielding better portfolios. A value of 1 will enable it and 0 will disable it. market_index: Which index do you want to use to compare your portfolios. This should mostly be SPY but since we analyzed tech stocks, we used QQQ. only_long: Whether to use long only portfolio or enable short selling as well. Long only portfolios have shown to have better performance using algorithmic techniques. eigenportfolionumber: Which eigen portfolio to use. Any value between 1-5 should work. The first eigen portfolio (1) represents the market portfolio and should act just like the underlying index such as SPY or QQQ. The second one is orthogonal and uncorrelated to the market and poses the greatest risk and reward. The following ones have reduced risk and reward. Read more on eigen-portfolios. stocksfilepath: File that contains the list of stocks that you want to use to build your portfolio. Some Portfolio Building Examples Here are a few examples for building different types of portfolios. Both long and short portfolios by analyzing last 90 days data and keeping the last 30 days as testing data. This will give us 60 days of portfolio construction data and 30 days of testing. Only long portfolio on 60 minute bars of the last 30 days. No future testing. Compare the results with SPY index instead of QQQ. Do not apply noise filtering on the covariance matrix. Use the first eigen portfolio (market portfolio) and compare with SQQQ, Portfolio Strategies Four different portfolio strategies are currently supported by the toolkit. Eigen Portfolios These portfolios are orthogonal and uncorrelated to the market in general thus yielding high reward and alpha. However, since they are uncorrelated to the market, they can also provide great risk. The first eigen portfolio is considered to be a market portfolio which is often ignored. The second one is uncorrelated to the others and provides the highest risk and reward. As we go down the numbering, the risk as well as the reward are reduced. Minimum Variance Portfolio (MVP) MVP tries to minimize the variance of the portfolio. These portfolios are lowest risk and reward. Maximum Sharpe Ratio Portfolio (MSR) MSR solves an optimization problem that tries to maximize the sharpe ratio of the portfolio. It uses past returns during the optimization process which means if past returns are not the same as future returns, the results can vary in future. Genetic Algorithm (GA) based Portfolio This is our own implementation of a GA based portfolio that again tries to maximize the sharpe ratio but in a slightly more robust way. This usually provides more robust portfolios than the others. When you run the command above, our tool will generate portfolios from all these strategies and give them to you. Let us look at some resulting portfolios. Resulting Portfolios For the purpose these results, we will use the 9 stocks in the stocks/stocks.txt file. When we run the above command, we first get the portfolio weights for all four strategies. For testing purposes, the above command used last five years of daily data up till April 29th. The remaining data for this year was used for forward testing i.e the portfolio strategies had no access to it when building the portfolios. What if my portfolio needs different stocks?: All you need to do is change the stocks in the stocks.txt file and run the tool again. Here is the final command again that we run in order to get our portfolios: Portfolio Weights We can see that the eigen portfolio is giving a large weight to TSLA while the others are dividing their weights more uniformly. An interesting phenomena happening here is the hedging with SQQQ that all the strategies have learned automatically. Every tool is assigning some positive weight to SQQQ while also assigning positive weights to other stocks which indicates that the strategies are automatically trying to hedge the portfolios from risk. Obviously this is not perfect, but just the fact that it's happening is fascinating. Let us look at the backtest results on the last five years prior to April 29, 2020. Backtest Results The backtests look pretty encouraging. The black dotted line is the market index i.e QQQ. Other lines are the strategies. Our custom genetic algorithm implementation seems to have the best backtest results because it's an advanced version of other strategies. The eigen portfolio that weighed TSLA the most have the most volatility but its profits are also very high. Finally, as expected, the MVP has the minimum variance and ultimately the least profits. However, since the variance is extremely low, it is a good portfolio for those who want to stay safe. The most interesting part comes next, let us look at the forward or future test results for these portfolios. Forward Test Results These results are from April 29th, 2020 to September 4th, 2020. The eigen portfolio performed the best but it also had a lot of volatility. Moreover, most of those returns are due to TSLA rocketing in the last few months. After that, our GA algorithm worked quite effectively as it beat the market index. Again, as expected, the MVP had the lowest risk and reward and slowly went up in 4-5 months. This shows the effectiveness and power of these algorithmic portfolio optimization strategies where we've developed different portfolios for different kinds of risk and reward profiles. Conclusion and Discussion We are happy to share this toolkit with the trading community and hope that people will like and contribute to it. As is the case with everything in trading, these strategies are not perfect but they are based on rigorous theory and some great empirical results. Please take care when trading with these strategies and always manage your risk. The above results were not cherry picked but the market has been highly bullish in the last few months which has led to the strong results shown above. We would love for the community to try out different strategies and share them with us. Special Thanks Special thanks to Scott Rome's blog. The eigen portfolios and minimum variance portfolio concepts came from his blog posts. The code for filtering eigen values of the covariance matrix was also mostly obtained from one of his posts. License A product by Tradytics Copyright (c) 2020-present, Tradytics.com

YT_Emerging_Technologies_Introduction_to_AI
github
LLM Vibe Score0.461
Human Vibe Score0.039054583141409485
zusmaniJan 17, 2025

YT_Emerging_Technologies_Introduction_to_AI

YouTube Channel: Emerging Technologies Playlist: Introduction to AI Instructor: Zeeshan-ul-hassan Usmani Dear Students, I have uploaded all relevant material here for your quick access and learning. I hope you will find it beneficiary Yours Truly, Zeeshan =========================================== Video title: Resources Books to Order: Artificial Intelligence by Zeeshan Usmani - https://gufhtugu.com/artificial-intelligence Artificial Intelligence by Baqir Naqvi - https://gufhtugu.com/masnoi-zahanat/ Recommended Books • Gödel, Escher, Bach : An Eternal Golden Braid by Douglas R. Hofstadter A classic, poetic, philosophical defense of AI. • Machines Who Think by Pamela McCorduck. A good review of early AI history. • Robot: Mere Machine to Transcendent Mind by Hans P. Moravec Somewhat hyped book by a CMU robotics researcher. • Flesh and Machines: How Robots Will Change Us by Rodney Allen Brooks Reasonably decent book by MIT's leading robotics researcher. • Wired for War by Peter Warren Singer Reviews growing use of robots and unmanned vehicles in warfare. • Behind Deep Blue: Building the Computer That Defeated the World Chess Champion by Feng-Hsiung Hsu Autobiographical book on the development of a history making game-playing system. Interesting personal story of the hard engineering work that went into the system, with a few interesting facts on the technical aspects. • The Age of Spiritual Machines : When Computers Exceed Human Intelligence by Ray Kurzweil A recent view by an AI entrepreneur that has content if you ignore all the hype and overly-optimistic trust that Moore's law will magically solve all of the major problems. • Hal's Legacy : 2001's Computer As Dream and Reality An interesting collection of edited articles written to celebrate the fictional birthday of a famous intelligent computer who's true birthday must unfortunately be delayed, pending AI's inevitable progress. • The Sciences of the Artificial by Herbert Simon AI as science by one of its founders. • Models of My Life by Herbert Simon. An autobiography of one of AI's founders who's intellectual contributions also include fundamental contributions to economics (for which he won the Nobel prize), cognitive psychology, and computer science (such as co-inventing the linked list in the 1950's). • Alan Turing: The Enigma by Alan Hodges. A biography of one of the founders of CS and originator of the Turing test. Also a testimony to the tragic implications of homophobia. • The Emperor's New Mind : Concerning Computers, Minds, and the Laws of Physics and Shadows of the Mind : A Search for the Missing Science of Consciousness and The Large, the Small and the Human Mind by Roger Penrose A completely bogus argument against AI by a hopelessly Platonic mathematician. The last book contains an appended article by Stephen Hawking (a colleague of Penrose's) who of course doesn't buy his bogus argument. • The Mind's New Science : A History of the Cognitive Revolution by Howard Gardner A nice history of the development of cognitive science. • How the Mind Works , The Language Instinct , and Words and Rules : The Ingredients of Language by Steven Pinker Fun reading on lots of interesting issues in modern Cognitive Science and Linguistics if you don't take his exaggerated beliefs in nativism and evolutionary psychology too seriously. • Bots : The Origin of New Species by Andrew Leonard A light, somewhat hyped book on on Internet agents, chatterbots, etc. with a few funny stories. • Mathematics: The Loss of Certainty by Morris Kline A very nice book on the failed enterprise of using logic to build a firm foundation for infallible mathematics and the role of Gödel's Incompleteness Theorem in the philosophy of mathematics. • Incompleteness: The Proof and Paradox of Kurt Gödel by Rebecca Goldstein An interesting biography of Kurt Gödel. Too bad he was such a Platonist that, unlike Turing, he did not understand the true implications of his own theorems (interesting author connection: Goldstein is Pinker's wife). Links: • AAAI AI Topics Basic info on AI from the American Association for Artificial Intelligence: http://www.aaai.org/AITopics/html/welcome.html • Loebner Prize for limited Turing test: http://www.loebner.net/Prizef/loebner-prize.html • IBM's Deep Blue Page: http://www.research.ibm.com/deepblue/ • Robocup: Robotic Soccer Competition: http://www.robocup.org/ • NY Times Article on Proof of the Robbins Theorem: http://www.nytimes.com/library/cyber/week/1210math.html • NY Times article on Bayes Nets at Microsoft Research: http://www.nytimes.com/library/tech/00/07/biztech/articles/17lab.html =========================================== Video title: Numbers Infinity Video Link - •https://www.youtube.com/watch?v=hlXHwMgS06c https://www.cbs.com/shows/numb3rs/ http://numb3rs.wolfram.com/ =========================================== Video title: 20 Hours Rule and Assisgnemnt Assignment - https://www.urdufake2020.cicling.org/ =========================================== Video title: Assignments – P1 Mostly Human - https://money.cnn.com/mostly-human =========================================== Video title: Assignments – P2 Assignment – 2 - https://replika.ai/ Assignment – 3 – Teachable Machines https://teachablemachine.withgoogle.com/ Assignment – 4 – Tensor Flow Playground https://playground.tensorflow.org Assignment – 5 – GPT-3 Paper (175B Parameters) https://debuild.co/ Assignment – 6 - Image GPT-3 https://openai.com/blog/image-gpt/ =========================================== Video title: Create your own Deep Fake 1.https://colab.research.google.com/drive/1mGg_fmvhTpvkPkclw2yKkhALVzmawfvT?usp=sharing 2.https://drive.google.com/drive/folders/1wW1bxRV2S7Ce8gc3VDTzMQABE3-WCc_Y?usp=sharing •go into you gdrive > find cloned folder and ensure that this folder must have: vox-adv-cpk.pth.tar & vox-cpk.pth.tar failes •Aliaksandr Siarohin : https://github.com/AliaksandrSiarohin/first-order-model

air-support
github
LLM Vibe Score0.47
Human Vibe Score0.020849148958436158
theskeletoncrewJan 10, 2025

air-support

!air-support Air Support: Tools for Automating Airdrops of Solana NFTs The Skeleton Crew | Twitter: @skeletoncrewrip | Discord: Skeleton Crew Feeling generous? Your contributions help fund future development. Send tips to our Solana wallet: CH6afYjjydFLPSrfQYEUNCdSNohLCAQV6ir6QnYeZU3t See also: Treat Toolbox, a generative art manager for NFT projects from the Skeleton Crew. Background The Skeleton Crew launched on Oct 1, and has since been delivering daily airdrops of artwork from indie artists, with plans to continue for the entire month of October. In order to execute on this plan, we needed tools that allowed us to automate the process. This repository is the result of that effort, which we now share with you in the hopes of more teams spending less time giving themselves Carpal tunnel syndrome doing all of this manually inside of Phantom :) IMPORTANT - Before you Start Creating and sending NFTs in bulk comes with costs. On Solana, the costs are significantly better than some other chains. BUT, it's a good idea to try a drop on devnet first to be sure you understand the fees involved. We assume no responsibility for any costs incurred through the use of these tools. Use at your own risk. Getting Started In order to use Air Support, you will need to install and configure the current version of Metaplex. We run this locally with some customizations for speed (ex. hardcoding some metadata which is common across all of our drops). Also, have a look at the configuration options at the top of the Makefile. At minimum, you'll need to specify paths to Metaplex, your keyfile, and an RPC Host. It's highly recommended that you use a third-party RPC provider to perform large airdrops. DROP is a name for a set of airdrops; in our case we numbered these 1-31 for each day in October. TYPE is a name for a single airdropped item that's part of a drop; in our case we had a "trick" and a "treat" as part of each drop, sometimes even "trick1", "trick2"... etc. The name will be "token" by default, and is used to prefix log files in each step below. For the generate step to work, you will need to build Metaplex's rust tools. Inside metaplex/rust, run: You will also need a few other pieces of software installed, including: gshuf: brew install coreutils jq: brew install jq How to Use Air Support Prerequisites: follow all steps in the Getting Started section above. Then, the basic workflow looks something like this: 📇 prepare: Collect a list of token mint addresses, for which the holders of those tokens represent a community you wish to airdrop to. This is sometimes done by providing your Candy Machine address to https://tools.abstratica.art. Store this in the air support root directory as token-mint-addresses.json. ✍️ record: run this to fetch the wallet addresses of all users that hold the tokens, and don't have them listed on a secondary exchange. The goal here is to avoid sending airdrops to exchanges where they may not be recoverable. Note: As of now, Air Support can only identify tokens listed on Digital Eyes, Magic Eden, Solanart, and Alpha.art. FTX and Solsea use unique addresses for escrow wallets. The command below will fetch the addresses and store them in airdrops/1/token-holders.log. 🎨 create: Start Metaplex, and use it to create your Master Edition NFT with a limited supply (the number of airdrops you want to send). 🖨 generate: run this to generate prints of the Master Edition. These will be stored in the wallet associated with the keys you specify as options. The below command would create 500 prints of the Master with mint address RPdCMRxBx4YPcJv6HUb2S5zHGJcDrDrZszUNNGmLwfT. 🏅 choose: run this next to decide who will receive the airdrop. Important to note that if 2 tokens are owned by the same wallet, by design they have twice the chance to receive an airdrop as someone with only 1 token when using this script to pick recipients. If you have 10,000 token owners recorded as not listed on marketplaces in step 2, and 500 airdrops to send, this will randomly select 500 of those recorded tokens. 📬 distribute: the last step is to send the airdrops out. This script will run through the addresses generated in step 4 and the recipients chosen in step 5 and send airdrops 1-by-1. It is possible that failures will occur. Logs are saved during the process in a {NAME}_sent.log file. Because distribution happens line-by-line, it is safe to rerun the script again to attempt to correct failures. You can also check your wallet to see that all tokens have been distributed. (Note that your Master edition will still remain as only prints are recorded to be sent in step 4. You can keep these for yourself or a community vault.) There is also an optional STARTINDEX param that can be used if you need to restart a distribution from somewhere in the middle. 🔥 burn: if you realize you made a mistake on your Master NFT, but only after you went ahead and started printing a bunch of editions, this command will automate the process of sending those costly mistakes to the Solana incinerator. There is also an optional STARTINDEX param that can be used if you need to restart a distribution from somewhere in the middle. Other Tips Transparency is key when running airdrop campaigns to your communities. In an ideal world, where we had more than 24 hours between our launch and the start of our month of airdrops, we might have attempted to bring some or all of these processes on-chain. The next best thing we could offer is a transparency repo, where we publish the daily receipts of our airdrops, to make it easy for our community to investigate the drops on the blockchain if they feel the desire to do so. Our tools give you the receipts as output to do the same if you wish. You can have a look at that repo here: https://github.com/theskeletoncrew/airdrop-transparency Acknowledgements The record step utilizes code created by the Exiled Apes organization, shared under an Apache License, originally found here: https://github.com/exiled-apes/exiled-holders

11 Make.com Automations You NEED To Start Using Every Day (steal these)
youtube
LLM Vibe Score0.437
Human Vibe Score0.76
Jono CatliffAug 30, 2024

11 Make.com Automations You NEED To Start Using Every Day (steal these)

🌍 COMMUNITY https://www.skool.com/automatable/about 📝 BLUEPRINTS • New leads automation → https://youtu.be/RGHKaXLPrTk • Automate contracts/invoices → https://youtu.be/hle_HtchLz8 • Automate recruitment → https://youtu.be/_xYJMW5yeUk • Automate lead web scraping & AI lead magnets → https://youtu.be/LLKI_cV7XI4 • Automate AI blog posts → https://youtu.be/FmXt26JY24I • Automate AI social media posting → https://youtu.be/97U8kFkzjYQ • Automate accounting → https://youtu.be/QBuGQaLNFfc • Automate scraping viral content ideas → https://youtu.be/5Wi7fqJwh6s • Automate project management → https://youtu.be/nyoiFHzH1Hw • Automate analytics → https://youtu.be/dRLHT_B-uKg 📚 SUMMARY In this video we walk through the 11 best Make.com automations I use on a daily basis (and you should too). These automations literally changed my life. I went from working 14 hours per day on my business to ultimately replacing my job. There's obviously more to it than just 11, but this is a great start 📺 RELATED VIDEOS • Full crash course on Make.com → https://youtu.be/hinLebdX8aM • Full crash course on Apify & web scraping →https://youtu.be/pKgup8tsPv8 • How I made 507K last year with Bark.com → https://youtu.be/oCaGVACutdE • How I generate 1,000+ blog posts instantly → https://youtu.be/FmXt26JY24I • How I scraped 10,000+ leads & sent lead magnets → https://youtu.be/qwsB72PhM3E 🎯 1:1 CONSULTING Book a time → https://jonocatliff.com/consultation 🚀 AUTOMATION AGENCY Get help with your business → https://www.automatable.co 🔗 LINKS (some of these make me money - thanks in advance!) • Apify → https://jonocatliff.com/apify • Zapier → https://jonocatliff.com/zapier • PandaDoc → https://jonocatliff.com/pandadoc • Make.com → https://jonocatliff.com/make • Go High Level → https://jonocatliff.com/gohighlevel 👋 ABOUT ME Hey everyone, my name is Jono. I run a 7-figure service business that offers DJ, photo, video services (#1 largest in Canada), and spent years figuring out how to automate every part of it (and hired the roles that I couldn't). Conservatively, I used to work 80+ hours per week, before sunrise till long after sunset; missing gatherings, family events and everything in between. Through automation though, I was able to replace my job. My goal is to help share what worked for me, in a dream of helping others find true success with their passion. Please subscribe, like and comment below if you have any questions! Thank you 😊 ⌛ TIMESTAMPS 0:00 Intro 1:12 New leads automation 2:50 Automate contracts/invoices 5:12 Automate accounting 7:31 Automate recruitment 9:23 Automate lead web scraping & AI lead magnets 11:42 Automate AI blog posts 13:58 Automate AI social media posting 14:48 Automate scraping viral content ideas 15:44 Automate project management 16:55 Automate analytics 19:01 Automate your database #make #automation #workflowautomation #workflow #automationmastery