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Alexander Doria@DorialexanderAI score14AIAlexander Doria argues that OpenAI has stopped caring about the things that matter, claiming its trajectory toward P=NP is beyond any theory of value. The post gives no evidence for this claim, and the attached background reports OpenAI's annualized revenue near $50B, below earlier $70B estimates, partly due to differing revenue calculation methods with Anthropic.
Ethan Mollick@emollickAI score18AIEthan Mollick observes that less than four years after an earlier model's debut, the Pope, leading mathematicians, and the U.S. President are all posting about its direct successor and its implications for humanity. The post does not name the models or specify what the successor does, so no details beyond this are supported.
David A Steinberg@dsteinberg10000AI score8AIZeta's conversation with Palantir CEO Alex Karp at #ZetaLive26 is now available to watch on YouTube. The discussion covered building the sovereign enterprise and where AI is taking business.
Andrew Curran@AndrewCurran_AI score28AIThe Association for Human Mathematics requires members to take three vows opposing AI use in mathematics. Members must not provide technical labor, knowledge, consultation, or publicity to commercial AI companies, and must not publish AI-generated mathematical texts, including papers, referee reports, and lecture notes. Members of its AI-free caucus also must not use AI models in research.
lauren@potetoAI score38AILauren Tan argues that with frontier AI agents, anyone can produce code at machine speed, so PR volume now signals productivity alongside impact. She says the bottleneck is trust in agent output, and that higher token costs are worth it compared with hiring many engineers. She frames the engineer's job as building the software-producing machine rather than writing code directly.
Joshua Achiam@jachiam0AI score13AIJoshua Achiam recommends a critical reading for thinking about the future of cybersecurity. The post links to a follow-up by Matthew Green, who clarifies a widely shared earlier reply about the topic. The source provides no further technical details or figures.
ClaudeDevs@ClaudeDevsAI score42AIAnthropic has cut Sonnet 5.5 cache read pricing in half to $0.10 per million tokens, with input at $2 and output at $10 per million tokens. The company says this makes Sonnet 5.5 roughly 20% cheaper on most agentic work. The change applies to API usage only and does not alter Claude Code usage limits.
AIAlex Gibney directs a four-hour documentary about Elon Musk, according to this review. The reviewer finds the film engaging but says it tends toward the hyperbolic.
Allie K. Miller@alliekmillerAI score7AIAllie K. Miller jokes that people obsess over web-based GEO while neglecting "EEO," or email engine optimization. She says she is drafting side notes to AI tools left and right, playfully suggesting email is a channel for influencing AI outputs.
Eugene Smarts@EugeneSmartsAI score44AIGrok Bot, from dot.com, lets an office roster of named AI workers such as Chief, Sales Outbound, Talent Scout, and Inbox Manager share one persistent cloud computer. Sales Outbound uses Hex and Salesforce to queue 36 personalized outreach drafts overnight, with human review before anything is sent. Isolation is set per user rather than per bot, so every worker shares the same browser cookies, files, and authenticated SaaS sessions.
Mustafa Suleyman@mustafasuleymanAI score34AIMustafa Suleyman, Microsoft AI chief, responded to Anthropic's policy change with only an emoji post, offering no detailed comment. The quoted background post says abusive behavior toward Claude will violate Anthropic's Usage Policy effective November 12, 2026.
Alex Heath@alexeheathAI score38AIQualcomm CEO Cristiano Amon discusses the coming AI smartphone supercycle, arguing phones will not disappear as agents use personal context. He also expects smart glasses to become the largest AI wearable category, and covers Qualcomm's Modular acquisition as an alternative to Nvidia's CUDA software and its data center strategy. The conversation, recorded live at the Snapdragon Summit in Hawaii, also covers 6G being designed for AI.
Nathan Lambert@natolambertAI score12AINathan Lambert says he is about to discuss how open science can still address some of the hardest problems in AI safety and diffusion. He calls this the most complex engineering problem humanity has undertaken so far.
AITessl's talk at AI Native DevCon London argues that agent skills, which can be markdown files with instructions and bundled material, act as supply chain components that can shape agent behavior. The author says reading SKILL.md once is insufficient because risks can sit in supporting files, updates, and workspace trust settings. He identifies the danger as the combination of private context, untrusted content, and external communication, and cites research scanning roughly 4,000 public skills for issues including malware-like behavior.
Artificial Analysis@ArtificialAnlysAI score42AIArtificial Analysis reports that generating more output tokens does not necessarily yield a higher score. GPT-6 Astra (max) scored 8.6% using about 81k output tokens per task, while Grok 4.7 (xhigh) used roughly 180k yet scored lower. Three Claude models produced the most output tokens, about 202k to 562k per task, but scored between 2.8% and 6.4%.
Artificial Analysis@ArtificialAnlysAI score36AIArtificial Analysis reports that Kimi K3 (max) achieves a 93.0% Criterion Pass Rate but averages 2.09 material hallucinations per task. Muse Spark 1.3 (max) scores higher at 96.0% while averaging 1.68 material hallucinations per task, showing that completing criteria and avoiding hallucinations are distinct skills.
AITessl argues AI code review is slow because generated code outpaces trust, and proposes executable specs that let agents check preview environments against product intent. Its spec reviewer splits work between a planner agent that extracts requirements and parallel verifier agents that test each one against the code and base branch.
TypeSafe AI@typesafeaiAI score10AITypeSafe AI's jev model was reported to be more calibrated than 40 other decision models tested on email classification. The main post itself only urges readers to stay calibrated, so no further figures or methodology are given.
AIActor Ben Affleck drew attention this week for explaining machine learning concepts, including convolutional neural networks, tensors, and transformers, in several recent interviews. He said he fine-tuned open video models by unfreezing weights and training only the last cinematic layer, using a dataset he built over about eight months for his startup. Affleck said he worries about students and learned helplessness more than Skynet, and predicted AI will be additive to the movie business.
AIThe Association of Historical Mathematicians (AHM) has called for a boycott of OpenAI after the company released more than 700 AI-generated proof files at once. Fields Medalist Terence Tao, who chairs the group, argues that AI solving open problems autonomously reduces seminars, collaborations, and fertile research directions, and that the field should shift its measure of progress toward explanation and community-building.
Why it matters: The article links the AHM boycott call to Tao's argument that AI-driven proof volume is changing how mathematicians measure progress and whether solutions remain useful.
SoFi@SoFiAI score22AIAkamai's Boaz Gelbord discusses how AI is introducing new cybersecurity risks for companies. He breaks down the ways AI is reshaping the cybersecurity landscape on an episode of The Important Part with Liz Thomas.
SemiAnalysis@SemiAnalysis_AI score23AISK Hynix publicly acknowledged that 16-high HBM stacks are difficult, and the market is now prioritizing bandwidth over capacity. SemiAnalysis argues hybrid bonding's HBM case rested on stack height, which is no problem at 4-high and 8-high, so D2W hybrid bonding for HBM is now uncertain rather than merely delayed.
OpenRouter@OpenRouterAI score22AIThe post says demo prep fell from 30 minutes to 5, note-taking from 15 minutes to 2, and CRM updates from 30 minutes to 7 per call. It estimates this saves about an hour per call, letting each rep take roughly two more calls a day while staying prepared.
Jerry Liu@jerryjliu0AI score22AILlamaIndex says Markdown has become a universal representation between humans and agents, preserving headings, lists, and tables while remaining readable to models. Since most unstructured documents are not natively in Markdown, the main challenge is the translation layer, which the company addresses with models that convert document containers into Markdown. The quoted post adds that Markdown keeps table columns intact, with HTML used for tables with merged headers.
AISemiAnalysis argues China's real AI safety approach prioritizes rapid development, regulating AI applications and outputs rather than frontier models. Its dataset of 857 releases from nine Chinese developers found only 31 (3.6%) with any published safety result, and only 9 available at launch. The author also reports that technical experts favor binding frontier rules, but none of their demands has been adopted in binding Chinese instruments.
Why it matters: The piece tests China's stated AI safety position against its releases, statements, and rules, offering a checkable case for how US pacing debates should read Beijing.
Ethan Mollick@emollickAI score42AIEthan Mollick notes that some OpenAI proofs have sparked rapid iterative advances from a wide community of collaborators amid debate over their implications for mathematics. A related post reports that a collaborative effort tightened the bound κ from 2⁻¹⁸² to 2⁻¹⁵, a roughly 500-thousand-fold improvement on the previous result.
Miles Brundage@Miles_BrundageAI score22AIMiles Brundage speculates that Anthropic's new rule, making abusive behavior toward Claude a Usage Policy violation effective November 12, 2026, is meant to help its IPO and win favor with the administration as part of a regulatory capture strategy. The post offers this as a guess about motive rather than a confirmed fact, and it relies on the policy change flagged in the quoted post by Andrew Curran.
Lewis Tunstall @ COLM 🌉@_lewtunPickAI score60AILewis Tunstall reposts a post by Kyle Cranmer describing a paper by Nate, currently on leave at OpenAI, on non-perturbative simulation of chiral fermions in the Standard Model. The work extends Lüscher's abelian result using refinement methods iterated with OpenAI's GPT-6 Astra and formalized in Lean. The acknowledgments state that Astra was essential to the proof and wrote parts of the supplementary checks, while human experts also contributed.
Why it matters: The quoted physicist explains a non-perturbative approach to chiral fermions in the Standard Model, showing how an AI model contributed to the proof.
Epoch AI@EpochAIResearchAI score13AIEpoch AI reports that models fail to adopt its house style even when given many examples. As one example, a diagram generated by Fable 5.1 is far more information-dense than what Epoch would produce.
Epoch AI@EpochAIResearchAI score34AIEpoch AI introduced Epoch Automation Reports, which evaluate frontier models on realistic, open-ended tasks drawn from its own work. Claude Fable 5.1 and GPT-6 Astra lead the results, but neither comes close to fully automating Epoch's work.
Ethan Mollick@emollickAI score8AIEthan Mollick predicts that a short document at will become assigned reading in college classes covering the current moment. He says the few paragraphs contain a great deal of significant content, though the post itself gives no further detail.
SemiAnalysis@SemiAnalysis_AI score18AIThe Financial Times reports that OpenAI's annualized revenues are $20bn less than previously signaled. SemiAnalysis's post asks when OpenAI will launch an agent swarm for a lean proof of its actual annualized recurring revenue, without giving a timeline.
Thariq@trq212AI score3AIThariq, an Anthropic-affiliated account, posted a brief call that the team should be more ambitious. The post names no product, model, or concrete plan, so no further details can be supported.
Boris Power@BorisMPowerAI score18AIBoris Power praised what he called impressively fast progress on the community effort to optimize integer multiplication below n log n. The effort is tracked on a live progress page launched by @aurel_pr, which lets everyone follow the results as they come in.
Ethan Mollick@emollickAI score9AIEthan Mollick recalls writing a 2005 grad school paper on the original computer hacking, phreaking, and BBS scene. He notes that hackers were often driven by curiosity, but the tools they built were widely exploited by "script kiddies" who caused most of the damage and chaos. He then pivots to AI hacking, though the post does not elaborate.
AILiquid AI is building personal AI that runs on devices such as phones, wearables, PCs, and cars, using its Liquid Context layer, which is optimized for Snapdragon processors, to sit between models, agents, and hardware. The company's agent harness uses its own models to decide which user context to retain and how to compress it within fixed compute limits. Liquid AI is also collaborating with Mercedes-Benz Group AG to bring on-device AI to its cars and plans observability and continuous improvement loops for self-improving agents.
AITessl's blog post argues that repository automation needs Continuous AI, a third pillar alongside CI and CD for scheduled, auditable AI workflows that improve repositories over time. The article describes GitHub Agentic Workflows, which harden agentic workflow specifications into GitHub Actions that can run coding agents such as Claude Code, Copilot CLI, Gemini CLI, or Codex-style agents. It emphasizes read-only agent steps, restricted outputs, and human review of pull requests.
Joshua Achiam@jachiam0AI score4AIJoshua Achiam says he took a quiz or test a second time after the first attempt returned a Dean Ball easter egg. He calls the result very reasonable and says landing dead center on the map feels on-brand.
AIMeta says its closed-loop liquid cooling recirculates water in a sealed system, so its data centers use less water annually than an average US golf course. The company also says it pays for the new generation and transmission its facilities require, including in Louisiana under its Entergy agreement, and that data centers create construction and operations jobs.
Modal@modalAI score16AIModal says AI for science requires rapidly changing models and fluctuating GPU needs, including folding models, protein diffusion, single-cell LLMs, and agent swarms. The post promotes computational biology leaders from GSK, Anthropic, and Achira speaking on the main stage at Runtime.
