Release1d ago
Deep Principle Open-Sources Hydrogen-1.0, a 753B Science Model Post-Trained From GLM-5.3 on Lab Records, MIT Licensed
Hangzhou-based AI-for-science company Deep Principle released Hydrogen-1.0 on November 2, its first general-purpose scientific model. It post-trains Zhipu's GLM-5.3 with SFT and RL on chemistry, materials and life-science data plus its own lab records and discovery trajectories, and ships about 753B parameters of BF16 weights on Hugging Face under the MIT license. By the vendor's own evaluation it scores 38.3% on FrontierScience-Research versus 33.3% for GPT-6 Astra; none of the results have been independently reproduced yet.
Tooling2d ago
X discussion: open-source AI builder Jun Song says he turned down an anonymous black hat group offering huge money to post-train open-weight models on exploit data
At 02:20 UTC on Nov 1, open-source AI builder Jun Song (@jun_song, who builds Supergemma) posted on X that an anonymous black hat group offered him an "astronomical" sum to post-train open-weight models with reinforcement learning on the exploit data it holds. He said the volume was so large he would have believed it was an undercover FBI dataset, that he turned it down, and that what scares him is that someone else will eventually take the deal. As of 17:10 SGT the post had about 2,029 likes, 222K views and 771 bookmarks, and it has been rewritten as a parody template. This is a personal account; the offer, the data volume and the group cannot be verified, so it is treated here as a discussion.
Release10d ago
Moonshot launches Kimi K3.1 Pro: 2.8T-total / 104B-active open-weight MoE with 1M context, 65.7% on Terminal-Bench 4.0, API at $2/$10, weights by Nov 4
On 2026-10-23 (17:15 UTC) the official @Kimi_Moonshot account launched Kimi K3.1 Pro, alongside K3.1 Pro Highspeed (2-3x faster). Both are live on Kimi.ai, Kimi Work, Kimi Code and the Kimi API, and full weights are promised by 2026-11-04. The official tech blog describes it as a post-trained version of K3 with much larger multi-domain joint RL: a 2.8T-total / 104B-active MoE, 1M-token context, multimodal. Vendor-reported scores: 65.7% on Terminal-Bench 4.0 (GPT-6 Astra 58.2%, Claude Opus 5.5 66.4%) and 92.7% on BenchCAD with tools. The Kimi API pricing page lists kimi-k3.1-pro at $2 input, $10 output and $0.20 cached input per million tokens, down from K3 at $3/$15.
Discussion & Comments
0Sign in to join the discussion
Connect with AI developers to exchange benchmark insights.