𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱 powers local text agents with a 256K-token context window. Fine-tune it on a single GPU for your domain, harness and tools, then run it on your own hardware without per-token API fees. Post-trained in agent harnesses such as Hermes Agent, Pi, and OpenClaw, it can triage your inbox, manage your calendar, research the web, send you morning briefings on WhatsApp, and find the right tool among thousands of options. In our evaluations, it outperforms Qwen3.5-9B on WebArena-Verified and AutomationBench, which test multi-step tasks in browsers and business applications. At 128K tokens, its KV cache occupies 1.6 GB, compared with 4.3 GB for Qwen3.5-4B. That leaves more memory for your application as task histories grow. It also scales to server workloads. In our serving tests, p95 request latency remains stable under heavy traffic, supporting latency-sensitive applications such as online fraud detection. To demonstrate that scale, we ran more than 300,000 LFM3-1.7B agents in parallel to investigate the Enron email archive. > 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱 model card: huggingface.co/LiquidAI/LFM3… 4/

The largest math proof in human history is now computer-verified. Today, Axiom announces a complete Lean auto-formalization of the Classification of Finite Simple Groups, the "periodic table of symmetry." About 15,000 pages, built by over 100 mathematicians across generations.

An absolutely GOATed example of domain knowledge being important I'm working on my video editor. I know enough about phonetics to know that certain consonants can fiddle around with silence detection. I tell my agent that. The word 'fricative' is enough for it to start busting out full IPA (international phonetic alphabet) and we brainstorm which consonants are the most troublesome. Turns out, knowing things is really fucking valuable.