HoibiNet

Hài hước luôn là thần dược

[Goldstein Records @GoldsteinRecs]

Argentina's 2026 World Cup - THE FULL MOVIE

[HOMBRES @hombres]

Celebrando la victoria española con un baño nocturno en la playa 🌊 🌊 Qué bonito es el fútbol!

[Rohit @heyrohitai]

Instead of watching an hour of Netflix, watch this 2 hour hour Stanford lecture will teach you more about how LLMs like ChatGPT and Claude are built than most people working at top AI companies learn in their entire careers.

[Abhik Sarkar e/acc @abhiksark]

Will You Read this Anon

[Rohit @heyrohitai]

INSTEAD OF WATCHING AN HOUR OF NETFLIX TONIGHT.

This 60-minute Cambridge lecture by Demis Hassabis will teach you more about the future of AI than most people will learn in the next 5 years.

Bookmark it and give it an hour, no matter what.

[Rossst.03 @Rossst_03]

Raphael Townshend, Stanford AI PhD and founder of Atomic AI (Forbes 30 Under 30):

""Wall Street will pay you $500K a year to build these models. I'd rather teach them to you for free."

this free stanford lecture holds the entire "77% win rate, pure math" random forest the 2026 quant threads sell you. and the guy teaching it didn't take the wall street money either, townshend went on to found an ai drug-discovery company and land forbes 30 under 30.

at the board he builds it from scratch: one decision tree overfits, so you grow hundreds on random subsets of the data and features and average them. the errors cancel, the signal survives. that's the whole "100 ai agents auditing the market" idea, minus the marketing.
the √N feature rule, the out-of-bag error, the probability output, all of it is standard ensemble learning, taught free by stanford for years. random forests came out of leo breiman's public paper in 2001. the thread didn't discover it. it renamed it.

and here's the honest part the win rate hides. a model that scored 77% on past data is describing the past, not promising the future. ensembles cut variance, they don't turn a weak edge into a real one, and markets shift under the model in ways the training set never warned about. the lecture is free. knowing whether your 77% survives out of sample and on live capital is exactly the part the post skips."

[Amit @HeyAmit_]

This 50-minute lecture by Jeff Bezos will teach you more about business than a 2-year MBA program.

Bookmark it and give it 50 minutes today, no matter what.

[Your Neighbor🧸❤️ @IamOffcial]

@Cristiano Cristiano Ronaldo 2008❤️🔥🔥 Now I see why people love This Man

[Google for Developers @googledevs]

Autonomous AI in action. 🤖

Check out how the new Gemma 4 31B model operates as an ADK Agent, exploring, planning, and running experiments on an unfamiliar database to optimize services and maximize revenue. 📈

✨ Dive into the full, inspiring session here to explore the complete workflow: https://t.co/CKiPA28HUz

[Ilir Aliu @IlirAliu_]

The University of Michigan put their entire robotics degree on GitHub.
Not one course. The whole curriculum.

ROB 101 — Computational Linear Algebra for Robotics
ROB 311 — How to Build Robots and Make Them Move
ROB 501 — Mathematics for Robotics
ROB 530 — Mobile Robotics

Every lecture video on YouTube. Every textbook on GitHub. Every problem set, every exam, every line of code.

Professor Jessy Grizzle said it best when they launched it:
"Linear algebra has become the language of computer vision, machine learning, robotics, and autonomy."

So instead of making students wait four semesters of calculus before touching a robot... they built a curriculum that starts with the math that actually matters, applied to real robotics problems from day one.

This is what open education looks like when a top-10 engineering school decides to mean it.

Free. GitHub. YouTube.
📌 [https://t.co/3STu1hzAz2]

Follow for more robotics resources!

——

Weekly robotics and AI insights.
Subscribe free: https://t.co/9Nm01QUcw3

[Vive con Propósito. @PropositoyVida]

Cristiano es el jugador más amable que he visto nunca

[Kirk Borne @KirkDBorne]

"Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems"

Read the Online eBook from Google at https://t.co/QTSKlAHVNP

Buy hardcopy version at https://t.co/UfpYnkHRVx

[Jian Wang @jianw851]

Most AI engineers I know quietly use one repo.

It’s basically a complete AI learning hub, all the best resources in one place.

No fluff. Just what actually matters.

Repo: https://t.co/nTQWH91Gk4

What’s inside 👇

Math → Models

• Linear algebra made simple
• Statistics from Khan Academy
• Andrew Ng’s ML math foundations

Python + ML Basics

• Google’s Machine Learning Crash Course
• Microsoft’s AI for Beginners
• Josh Starmer’s ML explanations

Deep Learning

• https://t.co/sWccrJMDmN practical deep learning
• PyTorch + TensorFlow resources
• Stanford computer vision materials

LLMs (the real stuff)

• Build GPT from scratch
• Hugging Face LLM course
• Andrej Karpathy’s deep-dive videos

Prompt Engineering

• core prompting techniques
• structured output frameworks
• practical prompt patterns

RAG & AI Agents

• 30+ RAG architectures
• Hugging Face agents course
• MCP resources from Anthropic

Deployment

• MLOps best practices
• Full Stack Deep Learning
• Stanford ML systems design

This is the kind of repo that normally takes weeks to find.

Here it’s all in one place.

Save this if you're learning AI. 🔖

#AI #MachineLearning #GenerativeAI #LLM #AIAgents #LearnAI

[Abhishek @HeyAbhishek]

Best GitHub Repos to Build Real AI Skills in 2026:

1. OpenAI Cookbook
https://t.co/Kzczt4Ovzm

2. OpenAI Agents SDK
https://t.co/OLSaUPyNsM

3. OpenAI Evals
https://t.co/yC2ICTvkVC

4. PydanticAI
https://t.co/Dt8m4vjZMi

5. Hugging Face Agents Course
https://t.co/ZIL1W2dUoL

6. AI for Beginners
https://t.co/9Uu9IyX0my

7. Hugging Face 101 Course
https://t.co/EwxC6EFWld

8. Hugging Face Smol Course
https://t.co/C2h7aH7MlG

9. AI Engineer Handbook
https://t.co/aeujyhE0Yk

10. AI Engineering Field Guide
https://t.co/QkybsQMLpN

Bookmark this.

[Kanika @KanikaBK]

🚨 BREAKING: Someone just built an App Store for Claude Code with 200,000+ agent skills.

100% Open Source. MIT License. Install in 30 seconds.

Here's what just dropped 👇

This is SkillsMP.

Think npm registry for AI agent capabilities. Except you're installing knowledge directly into Claude's brain.

Not code libraries. Pure instructions.

Install a skill. Claude becomes an expert at that thing instantly.

What's included (200,000+ skills):
→ PPTX file generation (professional decks) → GitHub PR reviews (senior engineer standards) → AWS/Azure/GCP deployments (one-click configs)

→ Data analysis pipelines (SQL + visualization) → Content automation (SEO + social media) → Business workflows (CRM + email templates)

All searchable. All free. Zero API keys.

How to install (takes 30 seconds):
1. Go to https://t.co/aESy5vc7ja
2. Search for skill you need
3. One-click install
4. Claude now has that expertise

No dependencies. No rate limits. No breakage.

The difference from ChatGPT plugins:
ChatGPT plugins need external APIs. Break constantly. Have usage limits.

Claude Skills are pure instructions.

Teach Claude HOW to think about tasks.

Work offline. Never break. No vendor lock-in.

Top skills already live:
→ "aws-lambda-deployer" (3.2K installs) - Full Lambda workflows
→ "pptx-professional" (8.7K installs) - Investor pitch decks with charts
→ "github-pr-analyst" (5.1K installs) - Security + quality reviews
→ "data-storyteller" (4.3K installs) - CSV to narrative reports

Production-ready. Not toys.

Why this matters:

Before: Hire specialist or learn skill yourself (weeks to months)
Now: Install skill. Productive in 5 minutes.

The leverage is insane.
The skill-creator skill exists:
Describe what you want Claude to do.

The skill writes itself.
Install it permanently.

Recursive capability expansion.

100% Open Source. Community-driven.

Anyone contributes. Anyone forks. Anyone improves.

No black box APIs.

This is GitHub for AI capabilities.

[Vaishnavi @_vmlops]

Just found an excellent repo to learn Agentic RAG from scratch

It shows how to build intelligent RAG agents with:
• Query rewriting
• Memory
• Self-correction
• Multi-agent workflows

Perfect for anyone building real-world AI agents using LangGraph & LLMs

Link - https://t.co/f5jsWKC75O

[Srishti @srishticodes]

The 10 Most Valuable AI Learning Repositories on GitHub

I analyzed the top GitHub repositories where Jupyter Notebooks (.ipynb) are the primary format and filtered out pure hype, keeping only the most practical, structured learning resources.

Here are the 10 repositories that will actually make you better at AI 👇

1. microsoft/generative-ai-for-beginners ⭐ ~105 k
Full repo for Microsoft’s Generative AI course with Jupyter notebooks and lessons on building GenAI apps.
🔗 https://t.co/kYbNYapApg

2. rasbt/LLMs-from-scratch ⭐ ~83 k
Educational implementation of GPT-style LLMs from scratch (code + notebooks).
🔗 https://t.co/wfWVRxm0ab

3. microsoft/ai-agents-for-beginners ⭐ ~49 k
Course on building agentic AI systems, tools, memory, planning, and workflows.
🔗 https://t.co/Dx0SaQnm3F

4. microsoft/ML-For-Beginners ⭐ ~83 k
Classic machine learning fundamentals curriculum (26 lessons).
🔗 https://t.co/ZdQUctcYFX

5. openai/openai-cookbook ⭐ ~71 k
Official OpenAI API examples, production-ready patterns, recipes, and demos in notebooks.
🔗 https://t.co/shhPvbpmnA

6. jackfrued/Python-100-Days ⭐ ~177 k
Intensive Python learning roadmap with 100 days of exercises/notebooks.
🔗 https://t.co/xGUZNhkwlz

7. pathwaycom/llm-app ⭐ ~54 k
RAG templates and real-world deployable LLM apps (prod-ready pipelines).
🔗 https://t.co/hU0mZ45Czk

8. jakevdp/PythonDataScienceHandbook ⭐ ~46 k
Foundational data science notebook collection (NumPy, Pandas, Matplotlib, Scikit-Learn).
🔗 https://t.co/wEboVo3HKM

9. CompVis/stable-diffusion ⭐ ~72 k
Original Stable Diffusion text-to-image model code (excellent learning material).
🔗 https://t.co/Kiteg9Ar4x

10. facebookresearch/segment-anything ⭐ ~53 k
Meta’s Segment Anything Model (SAM) for interactive image segmentation.
🔗 https://t.co/i78tc4AyGj

[Shruti Codes @Shruti_0810]

MIT offers 12 Books on AI & ML (FREE TO DOWNLOAD):

1. Foundations of Machine Learning
https://t.co/GkDEFl6p3R

2. Understanding Deep Learning
https://t.co/pI475UDfVy

3. Algorithms for ML
https://t.co/FFE5MbhFvo

4. Reinforcement Learning
https://t.co/RM8KbfvvgO

5. Introduction to Machine Learning Systems
https://t.co/jjOqVfTzIg

6. Deep Learning
https://t.co/Asrfeoitmg

7. Distributional Reinforcement Learning
https://t.co/an9WBTSHK8

8. Multi Agent Reinforcement Learning
https://t.co/MitEWsVDAt

9. Agents in the Long Game of AI
https://t.co/k5AZIDq6F7

10. Fairness and Machine Learning
https://t.co/kiQDLmr9dz

11. Probabilistic Machine Learning

❯ Part 1 : https://t.co/6KFHjhiibn
❯ Part 2 : https://t.co/gptvUloY3i

[Carlos E. Perez @IntuitMachine]

The World Is Not Linear: A Field Guide to the Laws That Quietly Run Everything

[Deep Psychology @DeepPsycho_HQ]

READ BANNED BOOKS, THERE'S A REASON WHY THEY DON'T WANT YOU TO READ BANNED BOOKS, JUST ASK I’LL SHARE A LIST

8 Banned Books Every Men Should Read.

1. Animal Farm By George Orwell.