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Aug 11

Aug 11Tue
  1. Rowan CheungAI score62

    Meta opens weights for Muse Glimmer 30B model, Muse Spark 1.2 to follow

    AIMeta announced it is opening the weights for Muse Glimmer, a 30B parameter dense model that can run locally. Muse Spark 1.2, described as its latest foundation model, will have its weights released soon. The author's interview with Mark Zuckerberg quotes him saying Llama 4 fell short of the trajectory he wanted and that the lab was rebuilt.

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  1. Ian JohnsonAI score46

    Ian Johnson on copying, remixing, and creating in the AI era

    AIIan Johnson argues that early creative work is often a copy or remix of earlier work, and that cheap copying will be unavoidable. He advises beginners to make things, focus on what they value, and connect with their audience rather than relying on distribution mechanics or artificial scarcity. The post is presented as a reply to a shadcn post about his component being quickly cloned by agents.

Aug 5

Aug 5Wed
  1. AI Futures ProjectAI score59

    AI Futures Project proposes four options for pacing the US AI frontier

    AIThe AI Futures Project proposes four options for domestically pacing frontier AI development to reduce existential risk, ordered from simplest to hardest to execute. The options include a temporary pause, minimum external-inference and transparent-safety compute allocations, a cap on the capability level of models used for AI R&D, and third-party safety-case risk assessments with a monthly risk threshold. The authors suggest starting with a 5-20% safety compute pilot and preparing verification tools in advance.

  2. Andrew NgAI score57

    Jeff Dean, Sanjay Ghemawat, Oriol Vinyals and Quoc Le found Discovery Loop

    AIJeff Dean, Sanjay Ghemawat, Oriol Vinyals and Quoc Le are founding Discovery Loop, a Public Benefit Corporation focused on automating machine learning, science, and engineering to accelerate discovery. The founders say they have worked together for 14 to 30 years and helped build widely used products, infrastructure and AI models. Andrew Ng's own text is a congratulatory message, and the company's mission statement appears in the attached image.

  3. koray kavukcuogluAI score38

    Koray Kavukcuoglu named SVP leading Google DeepMind's model development

    AIKoray Kavukcuoglu, who will become SVP of Google DeepMind, announced he will lead all aspects of model development, GDM research, and Gemini app and dev teams. He thanked Sundar Pichai and congratulated Demis Hassabis on becoming Chair of Google DeepMind and Chief Scientist of Alphabet while continuing to lead Isomorphic Labs. Kavukcuoglu said he will keep working with Hassabis as the teams pursue AGI.

Aug 4

Aug 4Tue
  1. Intern Large ModelsAI score26

    Shanghai AI Lab Chief Scientist and Nitzberg debate AI safety by design

    AIAt WAIC 2026, Shanghai AI Laboratory's Bowen Zhou asked whether external evaluations, red teaming, and third-party verification suffice to grant AI real-world authority, and Nitzberg answered no. Nitzberg compared AI to bridges, arguing that builders must carry the burden of proof through safety-by-design and pre-deployment evidence that powerful agents remain understandable and controllable.

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  1. Andrej KarpathyAI score66

    Karpathy tests Opus 5 by rendering Lord of the Rings opening in 3D

    AIAndrej Karpathy gave Claude Opus 5 the first paragraph of Lord of the Rings with a 1M token budget and asked for a Three.js render. Opus spent about two hours writing 5500 lines of code that procedurally renders the story, which Karpathy calls janky but fun. He notes the model struggled to audit its work because it cannot efficiently perceive video or play the resulting game, relying on slow screenshots that led to several errors.

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  1. Ahmad Al-DahleAI score52

    Ahmad Al-Dahle argues AI capex is both short on compute and overbuilt

    AIAhmad Al-Dahle argues that AI infrastructure faces both a compute shortage and overbuilding, with the four largest hyperscalers planning roughly $725 billion of capex in 2026, up 77 percent from last year. He describes a "mutually assured construction" dynamic in which every well-capitalized player buys the same insurance against falling behind, so the industry overbuilds by construction.

Jul 28

Jul 28Tue
  1. Tri DaoAI score42

    Putting LLM brains on robots yields 4x SOTA gains without extra training

    AITri Dao reports that connecting an LLM as the "brain" to robot control policies quadruples state-of-the-art performance with no extra training. He says he was surprised by how well it works and expects agents running on robots to arrive soon. Background from a quoted post reports real-robot success rising from 16.7% to 97.3% and simulated LIBERO-PRO success from 12.8% to 53.3%.

Jul 27

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  1. Alex AlbertAI score34

    Opus 5 now produces consultant-grade spreadsheets and slide decks, Alex Albert says

    AIAlex Albert, of Anthropic, says Opus 5 now produces near-superhuman spreadsheets and slide decks that match what a consultant would make, just over six months after its predecessor. He also notes that finance professionals are reporting strong reactions to Claude for Excel, and he expects agentic progress seen in coding to extend to other fields in 2026.

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  1. AI Snake OilAI score57

    Narayanan argues AI job change will unfold over decades, not with one model release

    AIArvind Narayanan's ICML keynote argues that AI's labor impact will depend on slow organizational adaptation rather than a single lab milestone. He cites reliability measurements showing agent accuracy rose much faster than reliability over the last 24 months, and points to software engineering and past technologies like electricity and ATMs. He concludes that evaluation work and human judgment will become more central as building tasks are increasingly automated.

Jul 10

Jul 10Fri
  1. AI Futures ProjectAI score38

    AI Futures Project Proposes Further Research Into Plan A and Alternative Scenarios

    AIAI Futures Project released AI 2040: Plan A and outlined further research areas, including building competing prescriptive scenarios such as Plan S, a domestic-first Plan A, GPU arms control, and CERN for AI. The group also flagged covert-project modeling and US domestic governance as areas of substantial uncertainty needing further work.

  2. Soumith ChintalaAI score29

    Thinking Machines outlines personalization, human participation, and decentralization goals

    AISoumith Chintala, a Thinking Machines figure, says the lab focuses on personalization and sovereignty, human participation, and decentralization to democratize AI. He argues these reduce society's dependence on centralized AGI companies, including his own. He points to Tinker, interaction models, and openly published research as previews, with more coming soon.

Jul 9

Jul 9Thu
  1. Thinking Machines LabAI score44

    Thinking Machines Argues the Future Worth Building Keeps Humans Central to AI Decisions

    AIThinking Machines Lab says AI should extend human will and judgment, with people shaping its goals through continuous feedback rather than relying on models trained once and frozen. The company outlines three technical directions: training strong models, building tools for customization including training model weights, and developing interfaces that let personal judgment influence AI work. It also says it will publish research for the scientific community.