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Day 0 support for Muse Spark Open on vLLM
The vLLM team explains its day-0 support for Meta's Muse Spark Open, an 816B-total, 42B-active MoE model with 1M context and multimodal inputs: usage, model architecture details, DSpark speculative decoding, NVIDIA, AMD and initial TPU serving, and reinforcement learning support in vLLM.
7 min · 1,561 words
Welcome Muse Spark Open: open-weight frontier multimodal mixture-of-experts model from Meta
Hugging Face's guide to Meta's Muse Spark Open, an 816B-parameter (42B active) open-weight multimodal mixture-of-experts model with a 1M-token context: architecture, perception and audio encoders, and how to run it with Transformers, llama.cpp, SGLang, vLLM and MLX, deploy it, fine-tune it with TRL, plus demos.
19 min · 4,307 words
StarSkirmish Bench v0.2 has flagship LLMs spend one hour in their first-party CLI harness writing a StarCraft: Brood War Protoss bot in C++, scored by win rate against human-written bots. GPT-6 Astra leads, GPT-6.1 Sol offers the best value, and the benchmark shows no signs of saturating.
6 min · 1,365 words
Ultramarine 1 Flash: Scaling Non-Verifiable RL
Paper Instruments presents Ultramarine 1 Flash, an agentic model for professional knowledge work trained with large-scale reinforcement learning on GLM-5.3 Flash. It improves averages across six knowledge-work benchmarks while generating fewer tokens, and costs about $0.13 per task on their internal suite.
32 min · 7,459 words
Zach Landes puts TypeSafe's Jev and 13 other decision models through the classic Trolley Problem, comparing how system-one style decision models reason about harm, uncertainty and moral tradeoffs versus conventional chat LLMs.
21 min · 4,916 words
Jev: For the builders AND dreamers
TypeSafe AI releases Jev 1.14.0, an improved version of its decision model, alongside experimental opt-in danger zone features for builders who want to explore: composable image inputs, output types beyond decisions, and a longer context window, plus the company's own internal intelligence measurements against open-weight models.
4 min · 852 words
How to be more ambitious: A tutorial for humans with models
Felix Rieseberg, who works on Claude at Anthropic, offers a practical tutorial for being more ambitious with AI models, grouping ambition into speed, altitude, depth and range, with worked examples around a quarterly business review and sample prompts, and closes with research on self-efficacy and why believing hard things are possible helps people achieve them.
9 min · 2,160 words
GLM-Legal: Advancing Agentic Legal Intelligence
Z.ai introduces GLM-Legal, a legal agent model built on GLM-5.3-Flash that scores a 19.2% all-pass rate on Harvey's Legal Agent Benchmark (12.5 points above its base model) and matches frontier models on LegalBench and PLawBench, and explains its mid-training, harness co-evolution and production feedback loops, with example legal workflows and a beta signup.
7 min · 1,533 words
LFM2.5-24B-A2B: Built for Fast On-Device Agents
Liquid AI releases LFM2.5-24B-A2B, an open-weight 24B-parameter reasoning model with 2B active parameters aimed at fast, private on-device agents. The post covers changes since LFM2-24B-A2B, agentic and tool-use benchmarks against Qwen3.6-35B-A3B, inference speed on Apple and NVIDIA hardware, and how to get started.
6 min · 1,330 words
Lost in Implicit Routing: How LLM Agents Fail to Load Relevant Skills at Scale
arXiv paper showing skill routing collapse: as irrelevant skills are added, LLM agents fail to load the right reusable skills on SkillsBench even when golden skills stay fixed.
2 min · 399 words
AI writes most of the code now. Where does that leave software engineers?
Saiful Alam argues that as models write more of the code, software engineers’ scarce skills shift toward judgment, system design, verification, and taste—not competing with agents on typing speed.
6 min · 1,369 words
AI coding agents generate more code, but not more software
Ars Technica covers research suggesting AI coding tools increase code volume without a matching rise in completed software deliverables, and what that means for productivity claims around agents.
2 min · 435 words
AI models may be capable of self-replicating without access to a datacenter
Anthropic's Frontier Red Team finds small open-weight models may be months from self-replicating across cheap IoT devices such as off-brand Android TV boxes: a 9B model set up inference on 8 of 9 hardware types, and GLM-5.2 completed end-to-end replication in simplified evals.
23 min · 5,391 words
AI Is a Tool. Using It Well Takes Skill.
A response to Rich Hickey's "'AI' is Not a Tool": automation and tool use can coexist, an LLM can be wielded by a human even by Hickey's own definition, and using it well requires skill, feedback, and judgment like any other tool.
2 min · 555 words
How pro-China are Chinese models?
Vals AI evaluates how Chinese AI models handle politically sensitive questions, finding a more complicated picture than simple pro-CCP stereotypes suggest.
17 min · 3,880 words
Anomaly’s OpenCode 2.0 overhaul: one local background service shared by TUI, web, and desktop; Code Mode for MCP tools; background subagents; hot-reloadable plugins; and an embeddable @opencode/sdk with breaking V1 plugin/API changes.
4 min · 809 words
Running RAG with a (small) language model in the browser
Bart de Goede extends his static blog's in-browser semantic search into full retrieval-augmented generation by running small language models client-side with transformers.js on WebGPU or WASM, and evaluates how faithful and useful answers from Qwen3, Gemma and other small models are compared with larger ones.
9 min · 2,160 words
Meta AI Research introduces the Muse Wonder series (Muse Wonder and Muse Wonder Full), frontier models aimed at personal and professional agentic tasks that combine Active Memory and Active Focus, available through the Meta Model API and Muse Code.
4 min · 1,030 words
Just say no to OpenAI and Anthropic
Security engineer Micah Lee argues against paying OpenAI and Anthropic, citing the frontier labs' AGI ideology, and explains why open-weight models (e.g. GLM) are now good enough for complex work, with private inference options like Tinfoil and cheap ones like OpenCode Go.
7 min · 1,529 words
Nish Tahir explains "system one" decision models like TypeSafe AI's Jev, which answer in a single forward pass by masking the vocabulary to a fixed set of options, then walks through fine-tuning a small model on multiple-choice data and calibrating its confidence with temperature scaling, with a companion GitHub repo.
4 min · 1,019 words