idk why, but I find it satisfying that frontier labs are basically pipelining model development A 1-month model launch cadence doesn’t mean it takes 1 month to develop a new model. It's probably more like a few months up to a year. Teams obviously don't sit idle for that time, but already start working on the next thing once they are done. In the limit, launch cadence approaches the throughput of the bottleneck stage, which for frontier models is probably training compute. I'm not sure to what extent this is already true, but I think it will become more accurate over time given that models can speed up all cognitive labor.

Universities invest in bureaucracy as their future. The future, apparently, is more administrators. Every university now claims to be all in on AI. By all in, they mean creating new administrative offices, new vice provosts, new associate vice deans, new committees, new staff, and new budgets. Meanwhile, the best AI scientists either leave or want to leave and are irrelevant to the bureaucracy being constructed around them.

🔴 Japanese researchers have developed a lightweight robotic exoskeleton to improve walking movement in mice. The system moves the mice’s joints using cables and pulleys. The researchers note that this technology could contribute to studies aimed at restoring mobility after spinal cord injuries and stroke.

If college professors aren't directing their students to build AI and Robotics to provide all needs and reasonable wants for society and liberate humanity, they should be fired immediately.

🚨RESEARCHERS TRAINED AN AI TO WRITE GPU CODE.. IT NOW BEATS THE INDUSTRY-STANDARD COMPILER ON 100% OF TESTS.. CRUSHES CLAUDE AND GEMINI BY 40%.. AND WHEN THEY TRIED TO TEST CHATGPT-5.. IT REFUSED TO EVEN ANSWER.. A team from ByteDance and Tsinghua just published one of the most important AI papers of the year.. And almost nobody is talking about it.. Here's what they built.. CUDA is the programming language that runs every AI model on earth.. Every GPU.. Every neural network.. It all runs on CUDA kernels.. And writing fast CUDA code is one of the hardest, most specialized skills in all of computing.. It takes years of expertise.. Deep knowledge of GPU hardware.. Most engineers never master it.. For years.. The best way to optimize CUDA was torch.compile.. A compiler built by the smartest engineers at Meta.. It's the industry standard.. AI models couldn't come close to beating it.. So the researchers built CUDA Agent.. An AI trained specifically to write and optimize GPU code through reinforcement learning.. It writes the code.. Runs it.. Measures the speed.. Debugs its own errors.. And keeps improving until it beats the baseline.. The results are staggering.. On the benchmark's easiest tier.. It beat torch.compile on 100% of tasks.. On the medium tier.. 100% again.. With kernels running nearly 3x faster than the industry-standard compiler.. On the hardest tier.. Full neural network architectures.. It beat the compiler on 90% of tasks.. Then they compared it to the best frontier models on earth.. Claude Opus 4.5 and Gemini 3 Pro both passed most tasks.. But only about two-thirds of their kernels actually ran faster than the baseline.. They wrote code that worked but wasn't optimized.. CUDA Agent beat the compiler on 97% of tasks.. And outperformed both Claude and Gemini by roughly 40% on the hardest tier.. A specialized AI crushed the biggest general-purpose models in the world at their own game.. But here's the detail that made me stop.. When the researchers tried to benchmark OpenAI's ChatGPT-5 series.. The 5, 5.1, and 5.2 models.. They couldn't.. In their own words.. The models "consistently declined to respond to CUDA-related prompts".. The most advanced models from the most famous AI company on earth.. Refused to write GPU code.. While a Chinese open research model was busy beating the industry-standard compiler at the exact same task.. The implications are massive.. For decades.. Writing high-performance GPU code required elite human experts.. Now an AI does it better than the compiler those experts built.. And better than the frontier models everyone pays for.. This is the beginning of AI optimizing the very infrastructure that AI runs on.. The systems are starting to improve themselves.. And the model that proved it wasn't from OpenAI.. Or Anthropic.. Or Google.. It came out of a research lab.. Trained on 128 GPUs.. And published for free.