WTF?! New open-source physics AI engine absolutely insane! 🤯 Genesis is a new physics engine that combines ultra-fast simulation with generative capabilities to create dynamic 4D worlds for robotics and physics. TL;DR: 🚀 430,000x faster than real-time physics simulation, processes 43M FPS on a single RTX 4090 🐍 Built in pure Python, 10-80x faster than existing GPU solutions like Isaac Gym 🌐 Cross-platform support: Linux, MacOS, Windows, with CPU, NVIDIA, AMD, and Apple Metal backends 🧪 Unified framework combining multiple physics solvers: Rigid body, MPM, SPH, FEM, PBD, Stable Fluid 🤖 Extensive robot support: arms, legged robots, drones, soft robots; supports MJCF, URDF, obj, glb files 🎨 Built-in photorealistic ray-tracing rendering ⚡ Takes only 26 seconds to train real-world transferrable robot locomotion policies 💻 Simple installation via pip: pip install genesis-world 🤝 Physics engine and simulation platform are fully open-sourced 🔜 ”.generate” method/generative framework coming soon.

must descend faster #gamedev #indiedev

day 9 of not being able to build llama.cpp with nix

If you have 32GB, why not use the dense model? It consistently outperforms 35B-A3B.

You all know that UNIX based systems have a really solid built in system of users, groups and permissions, right? You don't have to reinvent all of this stuff from scratch for AI agents

PewDiePie didn’t “train his own LLM.” He fine-tuned an existing open-source model on coding benchmarks. His model started at 8%, crawled to 16% after format fixes, and one run hit 19.6% that briefly passed GPT-4o on a single benchmark before he couldn’t consistently reproduce it. The tweet makes it sound like a YouTuber casually built a frontier lab in his bedroom. What actually happened is more interesting: a guy with a $41,000 home rig of 10 GPUs and 424GB of VRAM spent months failing, retraining, and iterating on dataset quality until he squeezed marginal gains out of a fine-tune. This is the part worth paying attention to. The entire arc from October 2025 to now tells you where AI tooling has actually landed. PewDiePie went from building his first PC to running Qwen 235B locally, vibe-coding a custom chat UI, orchestrating multi-agent voting systems, and now fine-tuning models on custom datasets. He did most of this through AI-assisted coding itself. The video is literally called “I wish I never did this project.” He’s documenting how painful and tedious the process was. That honesty is the signal. The hype accounts strip that away and replace it with “what the f*ck, YouTuber beats DeepSeek.” The real takeaway: fine-tuning on specific benchmarks with curated data can let anyone temporarily spike a score past models that cost hundreds of millions to train. That tells you everything about how narrow benchmark gaming has become, and nothing about general capability. PewDiePie knows this. The people quote-tweeting him with shock emojis do not.

.@altryne convinced me to let the agent have some room to grow and build on and i just thought itd be fun soooo

obviously fake guys but the fact that some people actually think it is real, highlights a real problem

Which programming languages are the most token efficient? With it looking like humans increasingly won't be writing code anywhere as often, wrote up some research I did looking at how many tokens each language (tends) to use.