Andrej Karpathy coined "Claws" as an upper layer for AI agents, and June Kim answered the same question from a different angle with the Cord framework implemented with MCP and SQLite. This piece organizes the shift from single-shot agents to autonomous coordination systems from both conceptual and implementation perspectives.
Two February 2026 papers on reducing inference cost: Together AI’s Consistency DLM (up to 14.5× faster) and MIT/Harvard’s Attention Matching KV compaction (50× compaction in seconds).
NVIDIA has released Nemotron-Nano-9B-v2-Japanese. It takes first place in the sub-10B category on Nejumi Leaderboard 4, delivering strong performance in Japanese knowledge, QA, and tool calling.
Anthropic has released the mid-sized model Claude Sonnet 4.6. In Claude Code evaluations, 70% of users preferred it over Sonnet 4.5, and 59% preferred it over Opus 4.5, while pricing remains unchanged.
How to configure VRAM/main memory split on the GMKtec EVO-X2 (Strix Halo) for local LLM inference. A 29.6GB model ran fine with just 8GB of dedicated VRAM.
Building an NSFW-capable local LLM on the GMKtec EVO-X2 (Strix Halo). Getting GPU inference at ~11 tokens/s with LM Studio and MS3.2-24B-Magnum-Diamond.
MioTTS from Aratako is a family of 0.1B to 2.6B Japanese-English TTS models built from scratch around the custom MioCodec. Its key feature is that it runs directly in llama.cpp and Ollama.
Liquid AI's LFM2.5 uses a hybrid of short-range convolutions and attention, achieving edge optimization without SSMs. This article covers the architecture, benchmarks, and community use cases.
A technical overview of Qwen3‑TTS from Alibaba’s Qwen team: one‑line pip install, 3‑second voice cloning, natural‑language voice design, and support for 10 languages including Japanese. Apache 2.0 licensed.
A technical walkthrough of Alibaba's Qwen3-Omni-30B-A3B. An omni-modal model that activates only 3B out of 30B and responds with speech from text/image/audio/video inputs. The article organizes the Thinker–Talker architecture, benchmarks, and the overall Qwen3 MoE family.
A technical look at ByteDance's UI-TARS-1.5-7B, which beats OpenAI CUA and Claude 3.7 by a wide margin at identifying GUI elements from screenshots, and can run locally with a desktop app.
