Here we go the real story is emerging. U.S. Department of Justice: Binance is required to cooperate with law enforcement agencies - @Bloomberg Michael Selig, Chairman of the CFTC, commenting on the Binance hack: We want to provide cryptocurrency exchanges with the opportunity to register with the CFTC at the federal level – @CNBC

Here we go Taiwan! 🇹🇼 Hon Hai Tech Days is kicking off in Taipei, hosted by @HonHai_Foxconn. I just sat down with Leo Guo, their GM of Robotics, who walked me through Foxconn’s robotics program and where they’re currently putting their focus. I really liked their deployment approach. So, they build their own hardware (robot arms, humanoids) and their own AI models, but they also deploy third-party robots and layer their own software on top. They experiment a lot, look for new use cases, and are very curious. Looking forward to learn more here on-site! Stay tuned for more content! 😎 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Here we go! Purpose built AI generated LLMs! Then AI helped optimize the code that runs it. Recursive released Flint, a new coding/agent model designed specifically for fast local inference. Beats Qwen3.5/3.6/3.8 in some coding benchmarks! Stats … 🧠 39.8B total parameters ⚡ only ~2.5B active/token 📚 262K documented context 🤖 reasoning + tool use 🔓 Apache 2.0 And there are official GGUFs: 🟢 NVIDIA NVFP4 → 22.9GB 🍎 Apple INT4 → 26.0GB Recursive recommends 48GB-class memory for these local builds, so think: 🍎 48GB+ Apple Silicon 🟢 48GB+ NVIDIA ⚡ 64GB RTX Spark Recursive reports its optimized runtime at 🍎 M4 Pro → >125 TPs 🟢 64GB RTX Spark → >150 TPs And WIRED’s Will Knight actually ran Flint locally on a 48GB Mac and reported it was fast and successfully handled the coding jobs he tried. Side Note: Recursive built Flint using its Eureka Machine, an automated AI research system that helped 🧩 design the architecture 🧪 develop training recipes ⚙️ optimize infrastructure 💻 write hardware-specific inference kernels Nvidia says those specialized kernels improved Flint’s RTX Spark generation performance by 55% wo changing the model weights. Recursive-reported coding results SWE-bench Verified → 74.2 SWE-bench Pro v2 → 89.7 Terminal-Bench 2.1 → 54.9 LiveCodeBench v6 → 80.3 So this isn’t merely AI writing more AI code. Link in ALT