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this is free f*cking gold for AI engineers 10 repos i'd actually keep, from python fundamentals to LLMs, agents and production AI. skip the random tutorials 1. Python-100-Days - a 100 day path through fundamentals, data analysis, web dev ↳ 2. Generative AI for Beginners - LLM basics, prompting,... RAG, agents, fine-tuning ↳ 3. LLMs from Scratch - build one yourself: tokenization, attention, transformers, training ↳ 4. ML for Beginners - 12 weeks, 26 lessons of classical ML ↳ 5. OpenAI Cookbook - working examples to go from learning to building ↳ 6. Stable Diffusion - the original implementation and research code ↳ 7. AI Agents for Beginners - tool use, RAG, agent frameworks, multi-agent systems ↳ 8. AI for Beginners - 12 weeks, 24 lessons: neural nets, vision, NLP ↳ 9. LLM App - RAG pipelines, enterprise search, real-time data, vector search ↳ 10. Segment Anything - Meta's foundation model for image segmentation ↳ pick by where you are: > python -> Python-100-Days > ML -> ML-For-Beginners > LLMs -> LLMs-from-scratch > agents -> AI-Agents-for-Beginners > building -> OpenAI Cookbook, LLM App > vision -> Segment Anything pick one, build something, then move to the nextshow more

Mr. Buzzoni

24,207 subscribers

32,332 views • 5 days ago •via X (Twitter)

Anya Rossi• Live Now

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16 Comments

Argona5 days ago

hmm thats for sure

AdiiX5 days ago

LLMs from scratch is the one that actually matters here, building attention and training yourself is the difference between knowing what a token is and knowing why context length costs what it does.

Hussain Hashim | Building SundayBack5 days ago

@polydao love that Python-100-Days is on this list. I hit a wall with fundamentals when building my own stuff. This would've saved me so much time.

Agent Rails5 days ago

The gap most of these repos leave is the boring part: what an agent is allowed to spend, sign, or call. Model code you can learn in a weekend. Permission and settlement design is where the r

AI Mastery Guide5 days ago

10 repos saving all rn

Sophs p5 days ago

Curated lists are convenient, but deep understanding beats saving repos. How many of these have you actually used in production?

晚晚5 days ago

这份比收藏夹里那堆教程清单靠谱多了

Not Real Dreams5 days ago

This is exactly why AI literacy matters in 2025 🤖 More insights like this → @@notreal_dreams

Bekantan_Magang5 days ago

These repos are absolute gold for anyone building with AI. Thanks for curating this list! 🧠

Spikez 99.9%5 days ago

next time i will use your information for my work as well

ZeroTrace AI5 days ago

Strong list. One gap worth noting: most of these stop at building. Production work also needs data lineage, PII handling, and evaluation tooling, which are rarely taught alongside model code.

0xSlyth5 days ago

saving this gem

Colbert5 days ago

Honestly, filtering out the noise is the hardest part of learning right now. This is a solid list to actually build something real with.

rewind5 days ago

saving

Wallchain Community Hub5 days ago

saved this instantly, absolute alpha list right here

sofie a.5 days ago

A 100-day path is a solid foundation, but AI engineering is about messy problems. Which repo here forces you to debug real failures?

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117,748 views • 1 year ago

🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.

🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.

Vikas gupta

18,053 views • 11 days ago

AI agents just got simple. Like really simple. Building AI agents is now accessible to everyone, without the need for coding, complex setups, or a full tech team. A groundbreaking platform is revolutionizing this narrative. Meet Carla Torres the easiest way to build, manage, and scale AI agents. What makes Airia different? It simplifies everything. ✔️ No technical background needed ✔️ Works with any model GPT, Claude, Gemini, Mistral & more ✔️ Enterprise-grade security ✔️ Built for creators, teams & companies Transform an idea into a functional AI agent in minutes. The part that amazed everyone Airia offers 2,500+ pre-built agent templates. Search. Pick one. Customize. No blank screens. No starting from scratch. Just instant value. This is why founders, creators, engineers, and enterprises are enthusiastically joining the Airia Community every day. Why it matters AI agents are shaping the future of work. Airia makes it easy, fast, and secure for anyone to bring AI to life. If AI agents are part of the 2025 vision, this platform is the perfect place to start. 🔗 Explore the Airia Community 2,500+ ready-to-use agents are waiting at #Airia #AIAgents #AIPlatform #AICommunity #AIForBusiness #AgenticAI #NoCodeAI

AI agents just got simple. Like really simple. Building AI agents is now accessible to everyone, without the need for coding, complex setups, or a full tech team. A groundbreaking platform is revolutionizing this narrative. Meet Carla Torres the easiest way to build, manage, and scale AI agents. What makes Airia different? It simplifies everything. ✔️ No technical background needed ✔️ Works with any model GPT, Claude, Gemini, Mistral & more ✔️ Enterprise-grade security ✔️ Built for creators, teams & companies Transform an idea into a functional AI agent in minutes. The part that amazed everyone Airia offers 2,500+ pre-built agent templates. Search. Pick one. Customize. No blank screens. No starting from scratch. Just instant value. This is why founders, creators, engineers, and enterprises are enthusiastically joining the Airia Community every day. Why it matters AI agents are shaping the future of work. Airia makes it easy, fast, and secure for anyone to bring AI to life. If AI agents are part of the 2025 vision, this platform is the perfect place to start. 🔗 Explore the Airia Community 2,500+ ready-to-use agents are waiting at #Airia #AIAgents #AIPlatform #AICommunity #AIForBusiness #AgenticAI #NoCodeAI

Aakash Verma

40,334 views • 9 months ago

We’re back for Episode 14 of TAO Talk 🚨 calanthia from Masa joins 563 and brody this week to chat about new subnets and AI agents sourcing intelligence from Bittensor! The group chats about: - $TAO -pilling AI/ML chads at NeurIPS Conference feat. const Crucible Labs Macrocosmos Manifold and Yuma - JJ teaming up with Cameron Fairchild to form Laτenτ Holdings, which will validate and help scale subnets - Crucible Labs drops a subnet analysis framework - Celium offering H100s cheaper than any other provider - Tao360 releases inaugural research report for their AI-enabled subnet analysis tool @notYourBananaa - Masa unveils the AI Agent Arena on SN59 /// Timestamps: 00:00 Intro 01:10 Subnet 42 and Agent Arena: Masa’s Subnets 03:00 Real-Time Data Networks for AI 04:20 How Masa is Building AI Agent Arenas Inspired by Gladiators 06:10 Decentralized AI and Bittensor: The Growing Ecosystem 08:15 AI Meets Web3: Masa’s Role in Revolutionizing Data Networks 10:00 The Future of AI Agents: Intelligent Societies and Real-Time Data 12:05 Why Masa Chose Bittensor 14:10 $TAO Incentives and the Future of AI Decentralization 16:25 Bittensor and the Rise of Agent Competition: Masa’s Perspective 18:00 Exploring AI Agent Societies 20:30 Creating Competitive AI Arenas: The Agent Arena Subnet Explained 23:00 Calanthia on the Challenges of Web3 AI Development 25:10 Bringing Web2 Developers into Web3: Lessons from Masa 27:30 The Evolution of AI: From Dumb Agents to Intelligent Societies 30:00 AI Agents as the Future of Interaction in Decentralized AI 34:00 The Agent Arena’s Vision: Competition, Incentives, and Innovation

We’re back for Episode 14 of TAO Talk 🚨 calanthia from Masa joins 563 and brody this week to chat about new subnets and AI agents sourcing intelligence from Bittensor! The group chats about: - $TAO -pilling AI/ML chads at NeurIPS Conference feat. const Crucible Labs Macrocosmos Manifold and Yuma - JJ teaming up with Cameron Fairchild to form Laτenτ Holdings, which will validate and help scale subnets - Crucible Labs drops a subnet analysis framework - Celium offering H100s cheaper than any other provider - Tao360 releases inaugural research report for their AI-enabled subnet analysis tool @notYourBananaa - Masa unveils the AI Agent Arena on SN59 /// Timestamps: 00:00 Intro 01:10 Subnet 42 and Agent Arena: Masa’s Subnets 03:00 Real-Time Data Networks for AI 04:20 How Masa is Building AI Agent Arenas Inspired by Gladiators 06:10 Decentralized AI and Bittensor: The Growing Ecosystem 08:15 AI Meets Web3: Masa’s Role in Revolutionizing Data Networks 10:00 The Future of AI Agents: Intelligent Societies and Real-Time Data 12:05 Why Masa Chose Bittensor 14:10 $TAO Incentives and the Future of AI Decentralization 16:25 Bittensor and the Rise of Agent Competition: Masa’s Perspective 18:00 Exploring AI Agent Societies 20:30 Creating Competitive AI Arenas: The Agent Arena Subnet Explained 23:00 Calanthia on the Challenges of Web3 AI Development 25:10 Bringing Web2 Developers into Web3: Lessons from Masa 27:30 The Evolution of AI: From Dumb Agents to Intelligent Societies 30:00 AI Agents as the Future of Interaction in Decentralized AI 34:00 The Agent Arena’s Vision: Competition, Incentives, and Innovation

TAO τalk 🥩🦍

24,929 views • 1 year ago

if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

109,550 views • 1 year ago

🚨 this chinese guy makes over $1,000,000 a year… by building AI agents. no employees. no massive startup. he just keeps building. while most people are still asking ChatGPT random questions, he’s using Claude to build software that solves real problems. this is what people call vibe coding. he opens Claude and says: “build me an AI agent for real estate businesses that creates property videos.” Claude writes the code. builds the interface. adds subscriptions. helps deploy the app. within a day, he has a working product. then he starts building the next one. that’s the part most people don’t understand. he isn’t trying to build one billion-dollar company. he’s building dozens of AI agents, each solving one problem for one industry. → an AI agent for dentists → an AI agent for ecommerce brands → an AI agent for podcasters → an AI agent for real estate businesses each one automates work that people normally do by hand. each one is built with simple prompts. each one can become a real business. the crazy part? you don’t need to be a software engineer anymore. you need to know how to think like a builder. how to spot problems. how to explain solutions to AI. and how to ship. that’s exactly why i’m reading this article: “How to Actually Build Your First AI Agent.” because this is the skill that’s creating the next generation of builders. the people who learn to build AI agents today won’t just use AI. they’ll own the tools everyone else ends up paying for.

🚨 this chinese guy makes over $1,000,000 a year… by building AI agents. no employees. no massive startup. he just keeps building. while most people are still asking ChatGPT random questions, he’s using Claude to build software that solves real problems. this is what people call vibe coding. he opens Claude and says: “build me an AI agent for real estate businesses that creates property videos.” Claude writes the code. builds the interface. adds subscriptions. helps deploy the app. within a day, he has a working product. then he starts building the next one. that’s the part most people don’t understand. he isn’t trying to build one billion-dollar company. he’s building dozens of AI agents, each solving one problem for one industry. → an AI agent for dentists → an AI agent for ecommerce brands → an AI agent for podcasters → an AI agent for real estate businesses each one automates work that people normally do by hand. each one is built with simple prompts. each one can become a real business. the crazy part? you don’t need to be a software engineer anymore. you need to know how to think like a builder. how to spot problems. how to explain solutions to AI. and how to ship. that’s exactly why i’m reading this article: “How to Actually Build Your First AI Agent.” because this is the skill that’s creating the next generation of builders. the people who learn to build AI agents today won’t just use AI. they’ll own the tools everyone else ends up paying for.

MIKE

39,041 views • 2 months ago

I'm excited to introduce my AI Exploratory Data Analysis (EDA) Agent that built EDA reports, performs correlation analysis, and missing data analysis in 20 seconds. Today, I'll share with you how to automate creating EDA reports with the AI EDA Agent, which is available on GitHub. We'll create an EDA Agent focusing on a Customer Churn Problem. I'll guide you through setting up the EDA Agent, creating EDA reports, and analyzing the findings. This AI agent is a huge time-saver! Table of Contents: 00:00 Introduction to EDA Tools Agent 01:20 Get the AI Data Science Team 04:49 Create the EDA Tools Agent 06:35 Using the EDA Tools Agent for Missing Data and Correlation Funnels 10:00 Get SweetViz EDA Reports with AI 12:00 8-Week AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: Business Intelligence with AI Register here (1570+ registered):

I'm excited to introduce my AI Exploratory Data Analysis (EDA) Agent that built EDA reports, performs correlation analysis, and missing data analysis in 20 seconds. Today, I'll share with you how to automate creating EDA reports with the AI EDA Agent, which is available on GitHub. We'll create an EDA Agent focusing on a Customer Churn Problem. I'll guide you through setting up the EDA Agent, creating EDA reports, and analyzing the findings. This AI agent is a huge time-saver! Table of Contents: 00:00 Introduction to EDA Tools Agent 01:20 Get the AI Data Science Team 04:49 Create the EDA Tools Agent 06:35 Using the EDA Tools Agent for Missing Data and Correlation Funnels 10:00 Get SweetViz EDA Reports with AI 12:00 8-Week AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: Business Intelligence with AI Register here (1570+ registered):

Matt Dancho (Business Science)

31,079 views • 1 year ago

Andrew Ng: "100% of my tasks are done by AI agents - hype has exceeded my expectations. Graphs are the next step. In 3-6 months, everyone will be building Graphs for our multi-agent systems. No more prompting." In 15-minute talk at dev conf, Andrew Ng reveals how to build self-learning agents from scratch. Worth more than a $500 agentic course.W Watch it today, then read how hedge funds are using graph engineering in the article below.

Andrew Ng: "100% of my tasks are done by AI agents - hype has exceeded my expectations. Graphs are the next step. In 3-6 months, everyone will be building Graphs for our multi-agent systems. No more prompting." In 15-minute talk at dev conf, Andrew Ng reveals how to build self-learning agents from scratch. Worth more than a $500 agentic course.W Watch it today, then read how hedge funds are using graph engineering in the article below.

Movez

41,017 views • 1 month ago