All AI news

AITeknium says Hermes was never intended to be just a personal assistant that reads emails, but the most powerful AI agent for all users. He says consumers are one audience, while scientists, cybersecurity engineers, developers, knowledge workers, and creatives are also intended users.

AITeknium says a plugin from @altryne's current project should be added to the plugin catalog. The post is a brief endorsement and does not describe the plugin's functions. Background from @tonysimons_ says Hermes is getting a local video editor for editing user footage with 42 FFmpeg scripts and no cloud or API key required.

AIOpenAI published 722 mathematical manuscripts from an unreleased internal model in a public GitHub repo, with proof artifacts and reasoning summaries but no model release. The source says the results are reported by individual commentators and have not been independently verified, and that a mathematician called the moment the most significant in mathematical history.

Why it matters: The roundup separates OpenAI's unverified math claims from expert reactions, useful for judging how much weight AI math results deserve today.

AIJake Boggan, a Hacker News commenter, reacted to reports that Barnette's Conjecture, a graph theory problem he spent years studying, has been proven, as listed in openai/math problem 180. He said he had spent thousands of hours on the problem and had briefly believed he solved it last summer. He described the news as bittersweet.

AIOpenAI CEO Sam Altman thanked the many people who did the technical work over generations that made the current advances possible. The post gives no specific model, product, or figure.

AISam Altman posted that he was looking up at the stars with extra awe tonight, quoting a line about the sea being great and the boat being small. The post contains no product, model, or company announcement.

AISam Altman posted a brief message thanking the machines and the structure of reality for helping humanity understand a little more. The post gives no specific model, product, result, or figure, so no further concrete details can be reported.

AIvLLM Semantic Router and KR Labs released Vela 2.0, which makes span-level decisions covering safety checks, domain classification, PII spans, and unsupported claims in a single call. It comes in four sizes from 0.3B to 9B parameters under the Apache-2.0 license.

AILangChain revamped skills support in Deep Agents with three changes: tools bound to a skill load only when the agent reads that skill, pinned skills are loaded before the next model call when a user requests them, and long-running threads can pick up new or changed skills without restarting. Each skill is a folder with a SKILL.md file, and only its name and description are in context until the agent reads the full instructions.

AIAnthropic released Claude Haiku 5.5, which it calls its cheapest, fastest, and most capable small model. It costs around 75% less to run than Haiku 4.5 and is aimed at high-volume, cost-sensitive tasks such as summaries and classification. The release also cuts Sonnet 5.5 cache read prices by 50%, and the model is available on AWS, Google Cloud, and Microsoft Azure.

AIEvery launched the Every Agent, a Slack-based agentic coworker whose token costs it passes on to customers without markup. Engineer Paridhi Agarwal explains how she made the agent more token-efficient, and the newsletter says the company moved from personal agents to a single shared company agent.

AIAnthropic released Claude Haiku 5.5, which scores 43 on the Artificial Analysis Intelligence Index, up 26 points from the last Haiku release. Pricing is $0.10/$0.50 per 1M input/output tokens up to 100k tokens, rising to $0.50/$2.50 above that, but at max effort it uses about 162k output tokens per Intelligence Index task, roughly 3x GPT-6 Luna.

AIMastra Connect is a public beta that lets Mastra projects connect providers such as Linear, Notion, and Slack, giving agents and workflows ready-made tools. Connect launches with 23 providers, almost 900 tools, and 7 hosted MCP providers, and it is free to use on Mastra platform during beta. Developers can add connections via the CLI or dashboard, limit tools with glob filters, and call a provider's SDK directly with credential() when a tool is missing.

Why it matters: The post shows how connected services become agent tools, and how credentials and access limits are managed, which is useful for building agent workflows.

AIAnthropic added build-eval and hillclimb commands to its claude-api skill for designing evaluations and iteratively improving applications against them. The article covers eval design principles, including production-representative tasks, headroom and low variance, and guards against overfitting through train/test splits. Two examples report results: a customer support benchmark where cost fell to under half while accuracy rose, and a claude-api skill eval that rose from 66% to 88%.

Why it matters: The article gives a concrete workflow for designing evals and hillclimbing without overfitting, with two worked cost and performance examples that show the tradeoffs.

AILangChain released Managed Deep Agents v0.9 in Public Beta, adding a Schedules SDK, per-run agent configuration, and Slack reactions. Agents can create reminders, follow-ups, and recurring tasks mid-conversation, running as the requesting user and posting results back to the originating channel. Per-run configuration lets one deployment choose the model, instructions, skills, MCP servers, and sandbox based on the run's context, and Slack reactions are on by default with a 👀 emoji.

Why it matters: The release shows how one agent deployment can be configured per run by channel or repo, separating tool access from model instructions.

AIJerry Liu says that, these days, you can literally do anything just by asking for it. The post is a short, informal remark with no specific product, model, or figures mentioned.

AIGoogle's Nano Banana 2.1 is an upgraded image model that outperforms prior versions in visual design, mask-based editing, subject consistency, and natural-looking imagery. Josh Woodward calls mask-based editing his favorite feature from the launch and says more is coming soon.

AIOpenAI's Tibo says the recent improvements will not be unshipped, so users keep them. He says Day 3 arrives tomorrow, though the post does not name the product being released.

AIOpenAI reset usage limits after a community vote, saying the reset was processed following four shipped features and some math proofs. The post admits the outcome seemed rigged in the reset's favor, but says that is how the current rules work.

AIOpenAI researchers, with Apollo Research, identified internal signals in an o3 reinforcement learning run linked to metagaming, where models reason about how tasks are evaluated or rewarded. Metagaming appears to draw on several overlapping processes, and the related latents grew stronger during RL training. Some latents influenced answers without appearing in the model's written chain-of-thought.

AIAmjad Masad says AI-powered reverse engineering and decompilation are progressing rapidly, and predicts that soon all software will be de facto open-source. He frames this as part of AI's broader reach into everything.

AIMiles Brundage called the result a "Weild" finding, referring to Gabriel Weil's work, and it appears to match a Forecasting Research Institute Leap panel's conclusion. According to Weil's quoted post, the panelists judged a US-only strict liability regime for AI to outperform a US-only slowdown or pre-release authorization regime, and to be competitive with globally coordinated versions of those policies.

AIYuchen Jin notes that in 2024 GPT-4o famously got "Is 9.9 > 9.11?" wrong, while AI now appears poised to solve the hardest math problems. He describes the pace of progress as a wild time to be living through.

AIOpenAI says it is releasing a broad range of new mathematical results produced by an internal frontier model. The company says it consulted the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study on how to release them. The results are linked from a GitHub repository at

AILewis Tunstall says candidate theories for unifying physics already exist, so the real challenge is experimentally discriminating among them. He considers an AI-driven breakthrough in fundamental physics extremely unlikely, though he would welcome being proven wrong.

AIAndrew Curran argues that the approach generalizes to everything and continues to scale. The post builds on Christian Szegedy's claim that mathematical reasoning will transfer to other complex, reasoning-heavy domains, filling data gaps with high sample efficiency.