Agentic AI is the future.

If you want to become god-level with AI agents, save these 10 GitHub repos:

1 Awesome LLM Apps

🔗 https://github.com/Shubhamsaboo/awesome-llm-apps

2 Generative AI for Beginners

🔗 https://github.com/microsoft/generative-ai-for-beginners

3 LLM Course

🔗 https://github.com/mlabonne/llm-course

4 Prompt Engineering Guide

🔗 https://github.com/dair-ai/Prompt-Engineering-Guide

5 AI Agents for Beginners

🔗 https://github.com/microsoft/ai-agents-for-beginners

6 AI Agents Projects

🔗 https://github.com/ashishpatel26/500-AI-Agents-Projects

7 Hugging Face Agents Course

🔗 https://github.com/huggingface/agents-course

8 Awesome AI Agents

🔗 https://github.com/e2b-dev/awesome-ai-agents

9 GenAI Agents

🔗 https://github.com/NirDiamant/GenAI_Agents

10 Agents Towards Production

🔗 https://github.com/NirDiamant/agents-towards-production

Anything else you'd add?

🔥 Free IT & Dev Study Kits – 2026 Prep, No Signup

If you’re prepping for cloud, dev, or AI roles this year, these free kits cover the gaps most people skip — cert basics, hands-on labs, and mock exams. No signup traps, no hidden fees.

📘 Cert E-Book — covers Cisco & AWS fundamentals, good for first-time cert takers
→ https://bit.ly/4yrk1Ti

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☁️ Free AI Materials — LLM basics to fine-tuning practice notebooks
→ https://bit.ly/4yt1iXy

📊 Cloud Study Guide — cheat-sheet style, handy for interview prep
→ https://bit.ly/4el4Eod

🧠 Free Mock Exam — test yourself before booking the real thing
→ https://bit.ly/4sbWlkl

📌 Save this or forward it to your dev/study group — good resources are worth sharing. 💪

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🔖 637 interactive modules on ML and AI

On VizLearn, you can visually explore:
🫡 Gradient descent;
🫡 SVM and PCA;
🫡 Bayes' theorem;
🫡 BPE tokenization;
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🫡 KV cache;
🫡 Quantization;
🫡 Neural networks and LLMs.

Change the input data and immediately see the results of the calculations.

⛓ Link to the tutorial
https://vizlearn.in/

The 25 Best Free Resources for Learning AI in 2026

Learning artificial intelligence in 2026 is both easier and harder than it was a couple of years ago. Easier, because the best courses from Stanford and MIT, books by leading researchers, and hands-on lessons from Hugging Face are all openly available. Harder, because there is now far too much material, and the models and tools change every week. We have collected 25 proven free resources and arranged them by stage: from mathematics to LLMs, agents, and reading the latest papers.

Read: https://enghusseinsheikho.substack.com/p/the-25-best-free-resources-for-learning?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

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🗂 500+ Cheat Sheets on Machine Learning

This repository contains materials on virtually everything: from basic algorithms and mathematics to neural networks, transformers, and advanced Deep Learning.

Each cheat sheet includes theory, formulas, Python examples, and tips on how to apply them.

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Write NumPy code in the familiar way, and then run it on GPUs and TPUs. Automatic differentiation and data batching will be handled automatically, without requiring any extra effort from you.

- Automatically compute derivatives of standard Python and NumPy functions.
- Compile pure functions using jit to run them on hardware accelerators.
- Combine grad, jit, and vmap in any order and scale your computations across multiple devices.

https://github.com/jax-ml/jax

https://t.me/MachineLearning9

"Advanced Calculus" is a free textbook, over 500 pages long, from the mathematics department of Harvard University, dedicated to an in-depth study of mathematical analysis.

It covers topics such as normed vector spaces, compactness and completeness, multi-dimensional mathematical analysis, integration, differential equations, multilinear algebra, differentiable manifolds, exterior calculus, potential theory, classical mechanics, and much more.

This is one of the most interesting resources I've shared in recent years. The book is definitely worth adding to your personal library of high-quality, freely available mathematics textbooks.

https://people.math.harvard.edu/~shlomo/docs/Advanced_Calculus.pdf

https://t.me/MachineLearning9

pandas — The Mental Model. A practical, example-driven guide in 18 chapters: how tables and indexes actually work, selection and filtering, groupby, reshaping, joins, time series, text, chaining, performance, and what changed in pandas 3.0. Every example is executed, with its real output shown, and the book closes with a one-page cheat sheet.

If the field is short, cut it to: A 31-page practical guide to pandas — 18 chapters, every example executed with real output, from indexes and selection to groupby, performance and pandas 3.0.

https://t.me/MachineLearning9