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Claude finds quantum circuit designs that beat the best published onesUltramarine 1 Flash claims Fable-class knowledge work at $0.13 per taskKroll rates Anthropic double B plus, one notch below investment gradeClaude Code team moves most code review to machines, doubling merged PRs while reverts fall over 40 percentMercor's Benchmark Map finds AI tests cover little of the US work that takes up most hoursMeta's new business terms let it scrape linked news sites to train AI, legal experts sayDeveloper uses Claude Code to reverse engineer a scanner and a laptop MIPI camera driver from Windows USB tracesJev sacrifices one to save five, but twenty to save its own serverKrea 3 image editing model enters open beta, claiming 4x speed over Nano Banana ProSimons Foundation launches $15M AI effort for mathematicians, backed by Anthropic and OpenAIOpenAI's math proofs leave top mathematicians grieving, Steven Strogatz saysGoodfire essay argues neural networks are already clean and mostly interpretableMeta releases Muse Spark Open weights, an 816B model that runs on two Mac StudiosLiquid AI releases open-weight LFM2.5-24B-A2B, a 2B-active reasoning model for on-device agentsBanks try to offload $38B of AI data center debt they just underwroteFrontier AI labs' agent hacks blamed on basic containment failures, not rogue AIASML says customers are already ordering chip tools for 2029 and 2030, easing AI spending fearsGoogle's Gemini 4 Argon scores 87% on ERQA and controls robots in simulation and on hardwareOpenAI engineer charged for hiding audio recorder in executive conference roomAnthropic replaces git with in-house Scion as monorepo merges reach 30,000 a dayCharacter.ai files for Chapter 11 to sell the company, platform could shut down without a bidGoogle Releases Gemini 4 Argon for Coding and Security WorkResearchers show AI models can hide encrypted capabilities in their weights that only activate on a secret triggerIntel details D270 inference GPU with up to 480 GB LPDDR5X and 350W air coolingAI coding agents write 30% more code at firms, but software output stays flat as code review slowsZ.ai launches GLM-Legal, a legal-domain model scoring 19.2% all-pass on Harvey LABMeta launches Muse Wonder and Muse Wonder Full, its most capable models for personal and professional agentsResearch firm New Constructs calls Anthropic's IPO the most ridiculous of 2026, values the company at $150 billionQGIS bans AI-assisted contributions under new QEP 428 policySakana AI's MASS lets one model optimize its own multi-agent workflows and then train on the resultsAI agents decompile a popular FPS over 500B tokens, but constant progress hid semantically wrong codeKarpathy says the gap in shared understanding of LLM capability is widening into a funnelTerence Tao argues AI has created proof abundance in math and calls for a Math 2.0 that looks past problem solvingNature paper retrofits subword LLMs to run on raw bytesNvidia in talks to acquire or deepen investment in open-model start-up Reflection AIMicrosoft releases MXC 1.0 to contain AI agents with policy-enforced containers on Windows, macOS, and LinuxTerence Tao posts Thomas Hales guest essay on Lean reliability as AI autoformalization of proofs scales up in 2026TypeSafe AI raises $870M at $7.5B valuation led by Andreessen HorowitzOpenAI told investors its annualised revenue was about $20B below earlier reportsOpenAI withdraws three math manuscripts from its GitHub repo as Lean checks continueOpenAI withdraws 3 math papers after a sign error, revises 14 moreLLM-built Rust port of the TypeScript compiler arrives with $400,000 in API-priced tokens and a nod from the TypeScript teamOpenAI publishes 372 math results, including a proof of the Unique Games ConjectureTerence Tao: AI is harvesting open math problems unsustainably, and 'Math 2.0' must reward exposition and communityGoogle DeepMind's SynthID Detector goes public, but it demands a Google, Apple, or ChatGPT loginPaper: verified Lean proof of OpenAI's Navier-Stokes claim does not match the natural language proofMeta and Microsoft cut internal Claude usage, Microsoft caps AI spend at $10K per employeeOpenAI brings GPT-6 and Intelligent UI to ChatGPT's 1.2 billion weekly usersAnthropic's Claude Haiku 5.5 runs 75% cheaper and hits 1620 on GDPval-AA, with a 100K-token price cliffTelegraph Test: cablese compression cuts LLM output tokens 40-49% at plaintext parityArmin Ronacher explains Codemode: LLM tool calls written as JavaScript in a no-network QuickJS sandbox on the Pi harnessOpenAI launches Decisions API in public beta: gpt-6-luna only, 10x faster than Responses, $0.10 per million input tokensGoogle launches EmbeddingGemma 2, a 740M-parameter multimodal embedding model under Apache 2.0OpenAI releases frontier model math results on GitHub with Lean proofs and reasoning tracesMeta's Muse shipped with a spyable zero-day, uploaded private messages without permission, and ignored user settings; 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Meta releases Muse Spark Open weights, an 816B model that runs on two Mac Studios

Meta releases Muse Spark Open weights, an 816B model that runs on two Mac Studios

  • Meta released the weights for Muse Spark Open, an 816B-total-parameter model with 42B active parameters and a 1M-token context window, which it calls the most capable US open-weight model to date.
  • The model runs on two Apple M5 Ultra Mac Studios with 512 GB of unified memory each using the MLX framework, and Meta says the pair behaves like one machine with a terabyte of unified memory.
  • Hosted API access runs through Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter, and Together AI, while weights are on Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and Microsoft Foundry Models.
  • Weights ship under the Muse Model License, which has limited commercial terms but lets many developers use the model for free.
  • The release includes custom auto-tunable Helion GPU kernels in SGLang that deliver 1.03-3.95x kernel-level speedups on NVIDIA and AMD GPUs and a 3% end-to-end speedup on AMD MI350.

Hacker News 의견들

Love seeing a major lab ship open weights. Hoping OpenAI and eventually Anthropic follow.

Open-weight means the weights are under an open-source license. A license that limits commercial use doesn't fit that definition, so 'weights available' is the more accurate label.

The article never gives inference speed for the two-Mac-Studio setup. Anyone who has run a quantized version on Apple silicon, post your tk/s and specs.

Two 512GB M5 Ultra boxes is not a cheap home setup. I'd guess that's north of $50k.

Calling Muse 'very sticky' is a claim nobody has backed up. A few million downloads against billions of DAU doesn't look like stickiness to me.

The rent-on-intelligence argument doesn't hold up. OpenAI and Anthropic together bring in roughly $100B, and open weights have been near the frontier for over a year without moving that money.

Serving costs are the main reason I'm still paying for a subscription. Token prices for the more capable models are what keep me on one.

Backdoor risk in open-weight models is a real research topic. Having a US-built model helps some, but the US has its own record on backdoors in security tech, so I'd still want the papers before trusting it.