@chefaid-nyc

🚨Someone just open sourced a computer that works when the entire internet goes down. It's called Project N.O.M.A.D. A self-contained offline survival server with AI, Wikipedia, maps, medical references, and full education courses. No internet. No cloud. No subscription. It just works. Here's wh…

I'm a cardiologist. I've held dying hearts in my hands in the cath lab at 3 AM. And I need to tell you something that changes everything about how we prevent heart attacks. For decades, the entire field was built on one target: lower LDL cholesterol. Statins save lives — that's settled science. But…

BREAKING 🚨 TESLA HAS PATENTED A "MATHEMATICAL CHEAT CODE" THAT FORCES CHEAP 8-BIT CHIPS TO RUN ELITE 32-BIT AI MODELS AND REWRITES THE RULES OF SILICON 🐳 How does a Tesla remember a stop sign it hasn’t seen for 30 seconds, or a humanoid robot maintain perfect balance while carrying a heavy, shif…

Launching our new paper on arXiv: we trained the largest multilingual food model ever built. 4.1M recipes. 7 languages. 1,790 ingredients. 300 dimensions. All of human cooking compressed into 2 megabytes. https://t.co/b4GiZ62UMt

🚨In 1990s, Stanford researcher Dr. Robert Sapolsky discovered something that should have broken the internet by now. He was studying dopamine pathways in primates and found that the brain doesn't just adapt to repeated stimulation. It actively fights back. When you flood dopamine receptors consis…

new release for text-to-cad, an open source CAD harness and skills for codex / claude: - mechanism validation (go from text prompt to functional mechanical design) - parameters + animations for step files - extended sdf, srdf, urdf support 3k stars, 10k downloads, we cooking

- local llms 101 - running a model = inference (using model weights) - inference = predicting the next token based on your input plus all tokens generated so far - together, these make up the "sequence" - tokens ≠ words - they're the chunks representing the text a model sees - they are represented…

PRO TIP For OpenCode & other agents Codex, Claude Code, etc There’s a crucial recipe: 1. Modularity 2. Domain-Driven Design 3. Painfully explicit specs 4. Excessive documentation If the docs don’t clearly answer: - Where - What - How - Why The agent will guess and make a mess

🤗 Model: https://t.co/5ZnEinxCrI 🎮 Hosted Demo: https://t.co/NgWszFa6ti Training recipe + a new dense-captioning/grounding benchmark dropping soon!!