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TodayNovember 13Friday1 items
  1. Hacker News · AI50

    NukeTorch: Four MacBook Pro M5 Max Macs Serve NVIDIA GR00T 2.45x Faster Than Fastest Published H100 Result

    Fenon AI's NukeTorch engine ran NVIDIA's GR00T N1.7 robot policy on four MacBook Pro M5 Max machines at 152.07 action chunks per second with 192 clients, 2.45x the fastest published H100 figure and 4.24x NVIDIA's own published number. For a single request the H100 still wins at 16.08 ms versus 44.34 ms, and the four Macs list at $19,996 against at least $25,000 for one H100 card. The one-Mac version is a free download, while the multi-machine build requires an evaluation license.

November 1Sunday
  1. Sakana AI Blog40

    Sakana AI Symposium: Jürgen Schmidhuber on AI's Past and Future, Japan's Push into Language Models, World Models and Physical AI

    Sakana AI held its "From World Models to Real-World AI" symposium in Tokyo on October 26, 2026, where Chief Scientific Advisor Jürgen Schmidhuber gave a keynote and Sakana AI announced it will develop its own pretrained language model series, Sakana Sawara (tentative name). The first small model, Sawara Small (tentative name), is planned for release in spring 2027 if development proceeds smoothly. More than 400 attendees joined the event, which also featured a conversation between Schmidhuber and CEO David Ha.

October 27Tuesday
  1. Apple Newsroom42

    Apple introduces the new 14-inch MacBook Pro with M6

    Apple announced the new 14-inch MacBook Pro with M6, featuring a 12-core CPU and 12-core GPU that deliver up to 30 percent faster graphics and up to 25 percent faster AI workloads than M5. The chip adds a Dual 16-core Neural Engine and GPU Neural Accelerators for on-device AI, with up to 24 hours of battery life, Wi-Fi 7, and Bluetooth 6. Pre-orders open October 28, with availability starting Friday, November 6.

October 23Friday
  1. Modal Blog58

    How Modal and Sakana AI served a ~1T-parameter MoE model for the Sakana Namazu API

    Modal's forward deployed engineering team worked with Sakana AI to run Sakana Namazu, a ~1T-parameter MoE model tuned for Japanese business context, on Modal for its public API launch. The team applied selective NVFP4 quantization to routed experts only, keeping attention, the router and shared experts in BF16, and calibrated on Japanese-heavy conversations so all 23,040 experts saw representative traffic. A custom DFlash draft model trained on more than 30 billion raw token IDs raised speculative token acceptance by 25% and cut per-token decode latency by about 35%, while a second drafter trained on roughly 1M real requests lifted average acceptance length from 3.51 to 5.01 and cut p99 latency from 68s to 52s.