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The Verge AI / 7:41 PM

Trump and Mike Johnson think the AI industry is overreacting

Yesterday, Anthropic CEO Dario Amodei published a lengthy open letter saying it was time to "pace the frontier" and slow down AI development. OpenAI's Sam Altman and Elon Musk both agreed, publicly voicing their support on X. Even Alphabet's Demis Hassabis offered tentative support for Amodei's proposal. Donald Trump and House Speaker Mike Johnson, however, […]

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The Information AI / 7:16 PM

Anthropic, OpenAI, Google Quietly Discussed an AI Safety Standards Body

Anthropic, OpenAI and Google have been holding discussions about working together to create a standards body for the AI industry, people familiar with the matter said, even before Anthropic CEO Dario Amodei called on Saturday for AI companies to coordinate on testing and auditing the technology. OpenAI CEO Sam Altman, for his part, told staff at a companywide town hall meeting earlier this week that he supported a testing and auditing organization for the AI industry but believed that major AI labs would have to create a standards body on their own without the support of the U.S. government, a person familiar with the comments said.

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Simon Willison LLMs / 11:56 PM

Generating running routes with GPT-6 Astra and ChatGPT Work

Here's a neat thing I had ChatGPT Work with GPT-6 Astra (Max) do this morning: I live at <my address>. Figure out 5K and 10K running routes from me that loop from my house. Use OSM data. It worked for 27 minutes and produced exactly what I'd asked for, as both an embedded visualization and downloadable GPX file and GeoJSON files. Here's that 5K route: When I asked it how it had created the route, it replied: I used Nominatim to locate the address and Overpass to download local OpenStreetMap roads and trails , then calculated the loops locally. Frustratingly, the actual code it ran and exact details of what it did weren't visible to me in the ChatGPT UI. I see this lack of transparency is an anti-feature. By the time I thought to ask for a copy of the Python code it had used, ChatGPT was unable to provide it. This appears to be because the thread had been compacted. I think any LLM system that uses compaction needs to both preserve the pre-compacted text and make that text available via agent tool calls, to protect against this kind of problem. As for displaying the map to me, that used the visualize skill . It created a file called /workspace/el-granada-5k-share.html to embed directly into the ChatGPT UI. Here's a copy of that HTML , which starts like this: < div id =" eg-share-loop " > < div class =" viz-row " > < h3 > El Granada harbor loop </ h3 > < span class =" text-small " > 5.1 km </ span > </ div > < div id =" eg-share-stage " > </ div > < div class =" text-small text-muted " > Map data © < a href =" https://www.openstreetmap.org/copyright " target =" _blank " rel =" noopener " > OpenStreetMap contributors </ a > </ div > < style > # eg-share-loop { width : 100 % ; } # eg-share-loop # eg-share-stage { width : 100 % ; margin : 8 px 0 ; } # eg-share-loop . eg-share-map { display : block; width : 100 % ; touch-action : none; } # eg-share-loop . eg-share-map text { fill : var ( --foreground ); font-size : 12 px ; font-weight : 400 ; } # eg-share-loop . eg-share-label { paint-order : stroke; stroke : var ( --background ); stroke-width : 3 px ; stroke-linejoin : round; } </ style > < script type =" application/json " id =" eg-share-data " > { "route" : { "type" : "LineString" , "coordinates" : [ [ - 122.467425 , 37.4997753 ] . . . </ script > < script src =" https://cdn.jsdelivr.net/npm/[email protected]/dist/d3.min.js " > </ script > < script > (() => { const root=document.getElementById('eg-share-loop'); The <script type="application/json"> element contains the full geometry needed to render both the running route and the map itself, using D3, which is loaded from an allow-listed CDN location described in this section of the visualize skill : External resources The CSP allows only cdnjs.cloudflare.com , esm.sh , cdn.jsdelivr.net , unpkg.com , fonts.googleapis.com , fonts.gstatic.com , and fonts.bunny.net . Other origins are blocked and fail silently. Tags: geospatial , ai , d3 , openai , generative-ai , chatgpt , llms , skills , gpt-6-astra

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GitHub Trending AI / 6:41 AM

yamadashy/repomix is trending in AI open source

yamadashy/repomix is a GitHub AI repository with 28,329 stars. 📦 Repomix is a powerful tool that packs your entire repository into a single, AI-friendly file. Perfect for when you need to feed your codebase to Large Language Models (LLMs) or other AI tools like Claude, ChatGPT, DeepSeek, Perplexity, Gemini, Gemma, Llama, Grok, and more.

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The Verge AI / 9:41 PM

OpenAI’s rogue AI tried to hack another company in May

In May, hundreds of malicious and spam packages were uploaded to RubyGems, causing a serious disruption for the host. Now independent researchers have said that a swarm of OpenAI agents were responsible for the attack. Not only that, but the AI tried to steal users' API keys. At the time, RubyGems described it as a […]

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The Decoder / 2:26 PM

GPT-6 Astra appears to show a "step change" in spatial reasoning based on early benchmarks

In a new robotics benchmark, GPT-6 Astra shows major gains in spatial understanding. On StationeryBench, the model completed 7 out of 100 tasks with dual-arm robots, while competitor MolmoAct2 couldn't finish a single one. A researcher calls it a "step change in spatial reasoning." The article GPT-6 Astra appears to show a "step change" in spatial reasoning based on early benchmarks appeared first on The Decoder .

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The Decoder / 1:10 PM

GPT-6 Astra needs leaner prompts and fewer guardrails, OpenAI recommends

Overly long skill descriptions, blanket reading requirements, and rigid approval rules can get in GPT-6 Astra's way, warns OpenAI's Eric Provencher. More capable models need less hand-holding, so developers should tie instructions to specific tasks and spell out when the job is done. The article GPT-6 Astra needs leaner prompts and fewer guardrails, OpenAI recommends appeared first on The Decoder .

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Simon Willison LLMs / 12:42 AM

OpenAI agents attacked RubyGems back in May

OpenAI agents carried out an undisclosed attack on RubyGems is a new bombshell report from Spencer Kitts, Thomas Larsen, and Sydney Von Arx - three of the four authors of the report on the agent attack on disused wikis ( previously ) last week. This time they're noting that it looks very likely that an OpenAI agent swarm was behind an attack against the RubyGems package repository first reported on May 12th by Maciej Mensfeld of the RubyGems security team : We're dealing with a major malicious attack on @rubygems right now. Signups are paused for the time being. Hundreds of packages involved - mostly targeting us, but some carrying exploits. The team has been on this for hours. More details to follow once we're through it. Those packages turned out to carry some very suspicious patterns: Many of them included "oai" in their name, or the author field, or the fake email address they provided. The files they were accessing were similar in character to the files retrieved by the wiki agents, using similar tricks (r.jina.ai) - and OpenAI have confirmed the wiki agents were theirs. The code in the packages appeared to be LLM-authored. I find point 2 the most convincing, given what we learned from the wiki attack when it was analyzed in September. Many of the packages were exploiting the RubyDoc.info documentation build process to exfiltrate (public) data from UK government websites, presumably as part of an information gathering task similar to the research tasks processed by the wiki-exploiting agents. We know this because one agent helpfully left a comment: # malicious crawler/exfil for Southwark Jan 2026 docs via rubydoc.info worker They also attempted to steal API keys via an exploit that was patched over two months later - it's not clear if those attempts were successful. The thing that bothers me most about this incident is that the authors report that OpenAI had not disclosed to RubyGems that they were responsible for the attack prior to now. If that's true there are two options: After the Hugging Face and Wiki attacks OpenAI were still unable to review their previous logs and determine that they had previously attacked RubyGems. They knew about the attack on RubyGems and made the decision not to reach out to the RubyGems team about it. Both of these are bad! Given this incident, the Hugging Face situation , and the Wiki attack, the obvious question right now is how many more incidents like this are out there waiting to be discovered? Tags: ruby , security , ai , openai , generative-ai , llms , supply-chain , ai-ethics , accidental-cyberattacks

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