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Share Your Expertise: AI, Automation, and Efficient Organizational Tools, Strategies and Routines!
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ferreiracarcaraThis week

Share Your Expertise: AI, Automation, and Efficient Organizational Tools, Strategies and Routines!

Hello everyone, As we navigate through the advancements in AI and automation, it's clear that these technologies are reshaping the way we approach work and business management. To stay ahead, sharing our collective knowledge on these subjects is crucial. I'm inviting this community to share insights and experiences with AI tools, automation strategies, and especially, innovative organizational approaches you've found effective. From automating mundane tasks to optimizing digital marketing strategies, every piece of wisdom is valuable. Here’s what we’re specifically interested in: Automated Workflows: What are your strategies for creating automated workflows that enhance productivity and efficiency? Visual Organization: How do you utilize mind maps and other visual tools to organize thoughts and projects efficiently? Canvas Maps: Have you implemented CANVAS Maps in customer interaction, ideation, strategy development, or action planning? How has it improved your processes? AI in Marketing: How has AI helped you optimize your digital marketing strategies and data analysis? What tools or methodologies have you found most effective? This thread aims to be a resource for all of us to learn from each other's successes and innovations. Whether it’s a simple tip or a comprehensive strategy, your input can significantly impact someone’s approach to challenges. What groundbreaking AI solutions, automation hacks, or organizational methods have you discovered that made a noticeable difference in your work or business? Share your stories and let’s empower each other to achieve greater efficiency and success. Thank you for contributing to our shared journey toward innovation and improvement!

My clients are obsessed with AI and it's driving me nuts – are there any actually useful AI tools for service businesses?
reddit
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Human Vibe Score-0.333
12131415161718190This week

My clients are obsessed with AI and it's driving me nuts – are there any actually useful AI tools for service businesses?

I have a roster of a few home service companies (plumbers, roofers, landscapers, etc.) that I do freelance marketing for. Lately, the owners I work with have developed a serious case of AI shiny object syndrome. They’re bombarding me with links to scammy IG ads for “game-changing” AI tools they think will save their businesses overnight. Even talking about replacing their CSRs with "virtual agents". This will obviously lead to some terrible customer experiences, but all they can see is dollar signs at the prospect of laying off that part of their labor force. If I keep pushing back and pointing out how short sighted some of these ideas are, they’ll eventually find someone else that will implement them. So, I’m trying to get out in front of this a little bit and find any AI tools that don’t suck—something I can pitch back to them that’s actually useful and not just a fancy new way to ruin their customer experience. Then when they brag to their other buddies in the trades about how "cutting edge" their business is, it will be in part because of me, not in spite of me. Any suggestions for AI tools that: Help small service businesses without completely alienating their customers? Automate repetitive tasks in a way that doesn’t scream “this was done by a robot”? Aren’t just some scammy overpriced subscription service with a flashy demo? If you’ve actually used something that works, I’d love to hear about it. Honestly, the bar is low. Just help me stop these guys from accidentally burning their businesses down with bad AI ideas.

𝐁𝐮𝐢𝐥𝐝 𝐋𝐋𝐌𝐬 𝐟𝐫𝐨𝐦 𝐬𝐜𝐫𝐚𝐭𝐜𝐡
reddit
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Human Vibe Score1
Ambitious-Fix-3376This week

𝐁𝐮𝐢𝐥𝐝 𝐋𝐋𝐌𝐬 𝐟𝐫𝐨𝐦 𝐬𝐜𝐫𝐚𝐭𝐜𝐡

“ChatGPT” is everywhere—it’s a tool we use daily to boost productivity, streamline tasks, and spark creativity. But have you ever wondered how it knows so much and performs across such diverse fields? Like many, I've been curious about how it really works and if I could create a similar tool to fit specific needs. 🤔 To dive deeper, I found a fantastic resource: “Build a Large Language Model (From Scratch)” by Sebastian Raschka, which is explained with an insightful YouTube series “Building LLM from Scratch” by Dr. Raj Dandekar (MIT PhD). This combination offers a structured, approachable way to understand the mechanics behind LLMs—and even to try building one ourselves! https://preview.redd.it/35sdlxdb2m0e1.jpg?width=1037&format=pjpg&auto=webp&s=dd228136fbf7cbdeeae253118ee7a46b04948c24 While AI and generative language models architecture shown in the figure can seem difficult to understand, I believe that by taking it step-by-step, it’s achievable—even for those without a tech background. 🚀 Learning one concept at a time can open the doors to this transformative field, and we at Vizuara.ai are excited to take you through the journey where each step is explained in detail for creating an LLM. For anyone interested, I highly recommend going through the following videos:  Lecture 1: Building LLMs from scratch: Series introduction https://youtu.be/Xpr8D6LeAtw?si=vPCmTzfUY4oMCuVl  Lecture 2: Large Language Models (LLM) Basics https://youtu.be/3dWzNZXA8DY?si=FdsoxgSRn9PmXTTz  Lecture 3: Pretraining LLMs vs Finetuning LLMs https://youtu.be/-bsa3fCNGg4?si=j49O1OX2MT2k68pl  Lecture 4: What are transformers? https://youtu.be/NLn4eetGmf8?si=GVBrKVjGa5Y7ivVY  Lecture 5: How does GPT-3 really work? https://youtu.be/xbaYCf2FHSY?si=owbZqQTJQYm5VzDx  Lecture 6: Stages of building an LLM from Scratch https://youtu.be/z9fgKz1Drlc?si=dzAqz-iLKaxUH-lZ  Lecture 7: Code an LLM Tokenizer from Scratch in Python https://youtu.be/rsy5Ragmso8?si=MJr-miJKm7AHwhu9  Lecture 8: The GPT Tokenizer: Byte Pair Encoding https://youtu.be/fKd8s29e-l4?si=aZzzV4qT\nbQ1lzk  Lecture 9: Creating Input-Target data pairs using Python DataLoader https://youtu.be/iQZFH8dr2yI?si=lH6sdboTXzOzZXP9  Lecture 10: What are token embeddings? https://youtu.be/ghCSGRgVB\o?si=PM2FLDl91ENNPJbd  Lecture 11: The importance of Positional Embeddings https://youtu.be/ufrPLpKnapU?si=cstZgif13kyYo0Rc  Lecture 12: The entire Data Preprocessing Pipeline of Large Language Models (LLMs) https://youtu.be/mk-6cFebjis?si=G4Wqn64OszI9ID0b  Lecture 13: Introduction to the Attention Mechanism in Large Language Models (LLMs) https://youtu.be/XN7sevVxyUM?si=aJy7Nplz69jAzDnC  Lecture 14: Simplified Attention Mechanism - Coded from scratch in Python | No trainable weights https://youtu.be/eSRhpYLerw4?si=1eiOOXa3V5LY-H8c  Lecture 15: Coding the self attention mechanism with key, query and value matrices https://youtu.be/UjdRN80c6p8?si=LlJkFvrC4i3J0ERj  Lecture 16: Causal Self Attention Mechanism | Coded from scratch in Python https://youtu.be/h94TQOK7NRA?si=14DzdgSx9XkAJ9Pp  Lecture 17: Multi Head Attention Part 1 - Basics and Python code https://youtu.be/cPaBCoNdCtE?si=eF3GW7lTqGPdsS6y  Lecture 18: Multi Head Attention Part 2 - Entire mathematics explained https://youtu.be/K5u9eEaoxFg?si=JkUATWM9Ah4IBRy2  Lecture 19: Birds Eye View of the LLM Architecture https://youtu.be/4i23dYoXp-A?si=GjoIoJWlMloLDedg  Lecture 20: Layer Normalization in the LLM Architecture https://youtu.be/G3W-LT79LSI?si=ezsIvNcW4dTVa29i  Lecture 21: GELU Activation Function in the LLM Architecture https://youtu.be/d\PiwZe8UF4?si=IOMD06wo1MzElY9J  Lecture 22: Shortcut connections in the LLM Architecture https://youtu.be/2r0QahNdwMw?si=i4KX0nmBTDiPmNcJ  Lecture 23: Coding the entire LLM Transformer Block https://youtu.be/dvH6lFGhFrs?si=e90uX0TfyVRasvel  Lecture 24: Coding the 124 million parameter GPT-2 model https://youtu.be/G3-JgHckzjw?si=peLE6thVj6bds4M0  Lecture 25: Coding GPT-2 to predict the next token https://youtu.be/F1Sm7z2R96w?si=TAN33aOXAeXJm5Ro  Lecture 26: Measuring the LLM loss function https://youtu.be/7TKCrt--bWI?si=rvjeapyoD6c-SQm3  Lecture 27: Evaluating LLM performance on real dataset | Hands on project | Book data https://youtu.be/zuj\NJNouAA?si=Y\vuf-KzY3Dt1d1r  Lecture 28: Coding the entire LLM Pre-training Loop https://youtu.be/Zxf-34voZss?si=AxYVGwQwBubZ3-Y9  Lecture 29: Temperature Scaling in Large Language Models (LLMs) https://youtu.be/oG1FPVnY0pI?si=S4N0wSoy4KYV5hbv  Lecture 30: Top-k sampling in Large Language Models https://youtu.be/EhU32O7DkA4?si=GKHqUCPqG-XvCMFG

My Manager Thinks ML Projects Takes 5 Minutes 🤦‍♀️
reddit
LLM Vibe Score0
Human Vibe Score1
SaraSavvy24This week

My Manager Thinks ML Projects Takes 5 Minutes 🤦‍♀️

Hey, everyone! I’ve got to vent a bit because work has been something else lately. I’m a BI analyst at a bank, and I’m pretty much the only one dealing with machine learning and AI stuff. The rest of my team handles SQL and reporting—no Python, no R, no ML knowledge AT ALL. You could say I’m the only one handling data science stuff So, after I did a Python project for retail, my boss suddenly decided I’m the go-to for all things ML. Since then, I’ve been getting all the ML projects dumped on me (yay?), but here’s the kicker: my manager, who knows nothing about ML, acts like he’s some kind of expert. He keeps making suggestions that make zero sense and setting unrealistic deadlines. I swear, it’s like he read one article and thinks he’s cracked the code. And the best part? Whenever I finish a project, he’s all “we completed this” and “we came up with these insights.” Ummm, excuse me? We? I must’ve missed all those late-night coding sessions you didn’t show up for. The higher-ups know it’s my work and give me credit, but my manager just can’t help himself. Last week, he set a ridiculous deadline of 10 days for a super complex ML project. TEN DAYS! Like, does he even know that data preprocessing alone can take weeks? I’m talking about cleaning up messy datasets, handling missing values, feature engineering, and then model tuning. And that’s before even thinking about building the model! The actual model development is like the tip of the iceberg. But I just nodded and smiled because I was too exhausted to argue. 🤷‍♀️ And then, this one time, they didn’t even invite me to a meeting where they were presenting my work! The assistant manager came to me last minute, like, “Hey, can you explain these evaluation metrics to me so I can present them to the heads?” I was like, excuse me, what? Why not just invite me to the meeting to present my own work? But nooo, they wanted to play charades on me So, I gave the most complicated explanation ever, threw in all the jargon just to mess with him. He came back 10 minutes later, all flustered, and was like, “Yeah, you should probably do the presentation.” I just smiled and said, “I know… data science isn’t for everyone.” Anyway, they called me in at the last minute, and of course, I nailed it because I know my stuff. But seriously, the nerve of not including me in the first place and expecting me to swoop in like some kind of superhero. I mean, at least give me a cape if I’m going to keep saving the day! 🤦‍♀️ Honestly, I don’t know how much longer I can keep this up. I love the work, but dealing with someone who thinks they’re an ML guru when they can barely spell Python is just draining. I have built like some sort of defense mechanism to hit them with all the jargon and watch their eyes glaze over How do you deal with a manager who takes credit for your work and sets impossible deadlines? Should I keep pushing back or just let it go and keep my head down? Any advice! TL;DR: My manager thinks ML projects are plug-and-play, takes credit for my work, and expects me to clean and process data, build models, and deliver results in 10 days. How do I deal with this without snapping? #WorkDrama

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