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Une annonce récente en génétique mitochondriale (ADNmt) indique que la lignée maternelle d’Ötzi, longtemps considérée comme éteinte, serait aujourd’hui attestée en Algérie.

We wrote a book on inference and we’re giving away free copies today!

I don't get to do as much mentoring as I once did, and obviously this is not a replacement for that, but it does help scale my effort to pay it forward for all of the help I've gotten along the way.

🚨 Holy shit... Alibaba just dropped a vector database that runs inside your app. It's called Zvec and it runs directly inside your application no server, no config, no infrastructure costs. No Docker. No cloud bills. No DevOps nightmare. Built on Proxima, Alibaba's battle-tested vector search engine powering their own production systems at scale. The numbers don't lie: → Searches billions of vectors in milliseconds → pip install zvec and you're searching in under 60 seconds → Dense + sparse vectors + hybrid search in a single call And it runs everywhere: → Notebooks → Servers → Edge devices → CLI tools 100% Opensource. Apache 2.0 license. This is the vector DB the RAG community has been waiting for production-grade performance without the production-grade headache. Link in the first comment 👇

🚨 BREAKING: Someone just made 70B parameter models run on a single 4GB GPU. It's called AirLLM. No quantization. No distillation. No pruning. Just raw 70B inference on hardware that costs less than a dinner. You can even run Llama 3.1 405B on 8GB VRAM. Here's how it works: → Decomposes the model layer-by-layer → Loads only one layer into GPU memory at a time → Runs inference, moves to the next layer → Prefetches the next layer while computing the current one → Supports 4-bit and 8-bit compression for 3x speed boost No cloud API. No $10K GPU. Just pip install airllm and go. Here's the wildest part: It supports almost every major model — Llama, Qwen, Mistral, ChatGLM, Baichuan, InternLM — and it auto-detects the model type. One line of code to load. One line to generate. Works on Linux, macOS (Apple Silicon), and even Google Colab free tier. Your old gaming laptop can now run the same models that needed an A100. 100% Open Source. Apache 2.0 License.

Google Gemini FULL COURSE 5 HOURS (Build & Automate Anything)

Graph Algorithms for Data Science: amzn.to/4s41GJ5

📘 Advanced Data Science and Analytics with Python Learn predictive modeling, ML techniques & real-world analytics using Python. Perfect for students & professionals 📊🐍 pyoflife.com/advanced-data-… #DataScience #Python #MachineLearning #Analytics #ArtificialInteligence #dataviz

get asked all the time how someone should approach learning to code with football data this offseason. there’s no better resource than “football analytics with python & R”. goes through EDA, regression and classification modeling and so much more link: a.co/d/0bLjBy4h

The Art of Computer Programming by Donald E. Knuth A comprehensive monograph presenting algorithms and their analysis. * Volume 1 – Fundamental algorithms * Volume 2 – Seminumerical algorithms * Volume 3 – Sorting and searching * Volume 4A/B – Combinatorial algorithms website: cs.stanford.edu/~knuth/taocp.h…

Top 10 Database Scaling Techniques You Should Know: 1. 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠 2. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 3. 𝐂𝐚𝐜𝐡𝐢𝐧𝐠 4. 𝐒𝐡𝐚𝐫𝐝𝐢𝐧𝐠 5. 𝐑𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 6. 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 7. 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧 𝐏𝐨𝐨𝐥𝐢𝐧𝐠 8. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐏𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧𝐢𝐧𝐠 9. 𝐃𝐞𝐧𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 10. 𝐌𝐚𝐭𝐞𝐫𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐕𝐢𝐞𝐰𝐬 ♻️ Repost to help others in your network.

🚨BREAKING: Someone compiled every math and science course from Stanford, MIT, Berkeley, IIT and Harvard in one place. You can learn: - Quantum Mechanics, General Relativity, String Theory - Complex Analysis, Algebraic Geometry, Topology - Astrophysics, Quantum Field Theory, Statistical Mechanics - 630+ courses with full video lectures 100% Opensource and available for everyone.

Si vous avez du temps, apprenez la programmation. Ça a d’abord un goût là quand ton code réussi. 🙂👌

If you want to master Object-Oriented Programming (OOP), consider learning these 12 concepts: 1. 𝐂𝐥𝐚𝐬𝐬 2. 𝐎𝐛𝐣𝐞𝐜𝐭 3. 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞 4. 𝐄𝐧𝐜𝐚𝐩𝐬𝐮𝐥𝐚𝐭𝐢𝐨𝐧 5. 𝐀𝐛𝐬𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧 6. 𝐈𝐧𝐡𝐞𝐫𝐢𝐭𝐚𝐧𝐜𝐞 7. 𝐏𝐨𝐥𝐲𝐦𝐨𝐫𝐩𝐡𝐢𝐬𝐦 8. 𝐀𝐬𝐬𝐨𝐜𝐢𝐚𝐭𝐢𝐨𝐧 9. 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧 10. 𝐂𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧 11. 𝐃𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐜𝐲 12. 𝐑𝐞𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 (𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧) Which other OOP concept would you add to this list? ♻️ Repost to help others learn this

Google has published a guide on creating AI agents services.google.com/fh/files/misc/…

Level up your claude code with this claude.md

BREAKING: AI can now build you a complete website in 2 hours (for free). Here are 9 insane Claude Opus 4.6 + Figma Make prompts that create $5,000 websites in 2 hours: (Save this before your competitors do)

Choosing your next tech book shouldn't feel like guesswork. Ismael Cabral shared 4 that resonated with him and explained why and who they’re for: • Python Workout, Second Edition by @reuvenmlerner • The Art of Code by Sandrine Banas • The Coder Cafe by @teivah • Timeless Algorithms by @sutton_gary If you're building your 2026 reading path, this is a practical starting point (links in the thread). Breakdown: hubs.la/Q043rGJv0

🚨BREAKING: Someone just solved Claude Code's biggest problem. It's called Claude-Mem and it gives Claude persistent memory across sessions. - You can use up to 95% fewer tokens each time. - Make 20 times more tool calls before reaching limits. 100% Opensource.

🚨BREAKING: Someone compiled every CS course from MIT, Stanford, Harvard, CMU and Berkeley in one place. You can learn: - Algorithms, OS, Distributed Systems, ML, AI - Deep Learning, Computer Vision, NLP, LLMs - Security, Databases, Quantum Computing - 500+ courses with full video lectures 70.3K stars. 100% Opensource.