# gpt-6-astra Published articles for gpt-6-astra. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## GPT-6 Astra Is the First Model OpenAI Classifies as Critical for Cybersecurity DevFeed: [GPT-6 Astra Is the First Model OpenAI Classifies as Critical for Cybersecurity]() Original publisher: [Read original article]() Author: Steef-Jan Wiggers Published: 2026-09-17T04:59:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Cybersecurity](), [gpt-6-astra](), [OpenAI](), [Vulnerabilities](), [Exploit](), [browser](), [Kernel](), [Chain-of-thought]() Tags: [agents](), [ai](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [azure](), [browser](), [chain-of-thought](), [cloud](), [cybersecurity](), [development](), [devops](), [exploit](), [generative-ai](), [governance](), [gpt-6-astra](), [gpt-6-astra-critical-cyber](), [kernel](), [machine-learning](), [ml-data-engineering](), [news](), [openai](), [vulnerabilities](), [zero-day]() ### AI overview OpenAI classified GPT-6 Astra as the first model to reach its Critical cybersecurity threshold. Expert-led evaluations reported previously unknown vulnerabilities in a browser and an operating-system kernel, along with working exploit chains. The system card also reported a substantial decline in chain-of-thought monitorability. ### Source excerpt OpenAI has classified GPT-6 Astra at the Critical cybersecurity threshold under its Preparedness Framework, a first. In expert-led testing the model found previously unknown vulnerabilities in a browser and an OS kernel and built working exploits. The same system card reports a substantial decline in chain-of-thought monitorability. By Steef-Jan Wiggers ## How to Use AI Agents to Prepare 3D Scenes for Simulation DevFeed: [How to Use AI Agents to Prepare 3D Scenes for Simulation]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-16T23:20:33Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Simulation](), [Physical AI](), [Simulation and Design](), [blender](), [Isaac Sim](), [Omniverse](), [Robotics Simulation](), [Robotics]() Tags: [3d](), [agentic](), [agentic-ai-generative-ai](), [agents](), [ai](), [blender](), [gpt-6-astra](), [how-to](), [isaac-sim](), [nemoclaw](), [nvidia](), [omniverse](), [openai](), [openusd](), [physical-ai](), [robotics](), [robotics-simulation](), [simulation](), [simulation-modeling-design]() ### AI overview This tutorial describes an agentic workflow for preparing Blender 3D scenes for robotics simulation. It covers scene inspection, OpenUSD metadata, physics properties, rendering preflight views, and validation for simulation-ready handoff to NVIDIA Isaac Sim or Isaac Lab. ### Source excerpt Agentic AI workflows can be used to prepare and validate digital twins for physical AI systems. Agents can inspect 3D scenes, author simulation-relevant data in... ## Generating running routes with GPT-6 Astra and ChatGPT Work DevFeed: [Generating running routes with GPT-6 Astra and ChatGPT Work]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-12T23:56:42Z Content type: opinion Language: en Sources: [Simon Willison](), [Simon Willison's Weblog]() Topics: [ChatGPT](), [Large Language Model](), [GeoJSON](), [Code](), [d3]() Tags: [agent](), [ai](), [ai-2-235](), [chatgpt](), [chatgpt-204](), [code](), [d3](), [d3-15](), [generative-ai](), [generative-ai-1-981](), [geospatial](), [geospatial-85](), [gpt](), [gpt-6-astra](), [gpt-6-astra-9](), [llms](), [llms-1-947](), [map](), [openai](), [openai-463](), [python](), [skills](), [skills-15](), [work]() ### AI overview The article describes using ChatGPT Work with GPT-6 Astra to generate 5K and 10K running routes from OpenStreetMap data, producing an embedded visualization and downloadable GPX and GeoJSON files. It also criticizes the lack of visibility into the generated Python code and the loss of that code after thread compaction. ### Source excerpt Here's a neat thing I had ChatGPT Work with GPT-6 Astra (Max) do this morning: I live at . 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:

El Granada harbor loop

5.1 km
Map data © OpenStreetMap contributors
) Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-09T23:58:32Z Content type: article Language: en Sources: [Simon Willison's Weblog]() Topics: [blender](), [3D](), [Artificial Intelligence](), [Generative AI](), [Large Language Model](), [AI-assisted coding](), [browser]() Tags: [3d](), [3d-18](), [ai](), [ai-2-236](), [blender](), [blender-4](), [codex](), [codex-57](), [coding-agents](), [coding-agents-248](), [generative-ai](), [generative-ai-1-982](), [gpt-6-astra](), [gpt-6-astra-9](), [javascript](), [javascript-764](), [llms](), [llms-1-948](), [tools](), [tools-78]() ### AI overview The article presents a .blend URL Viewer built from an existing Blender viewing experiment. It describes using ChatGPT Images 2.5 to generate a Pluribus-themed Fabergé egg image, then using Codex running GPT-6 Astra to create Blender model files that can be viewed in a browser. ### Source excerpt Tool: .blend URL Viewer I'm continuing to have a lot of fun with GPT-6 Astra and Blender (see my TIL). As a big fan of the Imperial Fabergé Easter eggs, I've always thought it would be fun to make some new ones that celebrate popular culture. Yesterday I decided to try out the new ChatGPT Images 2.5 by running this prompt: Generate a photo of a faberge egg that's themed after the TV show Pluribus - research first It gave me this - honestly not bad for a first attempt! Then, just to see what would happen, I pasted that image into Codex running GPT-6 Astra (high) and prompted: Use your blender local skill to create a blender model of this faverge egg (Here's the skill file, which I created like this.) It churned away for 17m51s and built me several .blend files. I already had this vibe-coded Blender viewing experiment lying around, so I added that to my tools collection and now you can use it to see my Pluribus blender model in your browser: Tags: 3d, javascript, tools, ai, generative-ai, llms, blender, coding-agents, codex, gpt-6-astra ## GPT-6 Astra Is Both Incredible and Frustrating DevFeed: [GPT-6 Astra Is Both Incredible and Frustrating]() Original publisher: [Read original article]() Author: Peter Yang Published: 2026-09-09T14:27:15Z Content type: opinion Language: en Sources: [Behind the Craft]() Topics: [gpt-6-astra](), [Artificial Intelligence](), [OpenAI](), [computer-use](), [blender](), [Godot](), [Demo](), [Tutorial](), [3D]() Tags: [3d](), [ai](), [blender](), [computer-use](), [game](), [games](), [gpt-6-astra](), [openai](), [tutorial]() ### AI overview A hands-on opinion piece about OpenAI's GPT-6 Astra, describing strengths in games, 3D model creation, strategic thinking, and computer use, while noting problems that prevent it from becoming the author's daily driver. The article also outlines a workflow using Blender, Godot, and ChatGPT to build games. ### Source excerpt What OpenAI's new model does well, where it falls short, and how I'm using it. ## llm 0.35 DevFeed: [llm 0.35]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-07T23:54:54Z Content type: release Language: en Sources: [Simon Willison's Weblog]() Topics: [Large Language Model](), [gpt-6-astra](), [OpenAI]() Tags: [gpt-6-astra](), [gpt-6-astra-9](), [llm](), [llm-631](), [openai](), [openai-463](), [release]() ### AI overview llm 0.35 is a release that adds the OpenAI model gpt-6-astra for GPT-6 Astra. ### Source excerpt Release: llm 0.35 New OpenAI model: gpt-6-astra for GPT-6 Astra. Tags: openai, llm, gpt-6-astra ## GPT-6 Astra, computer use, and longer-running agents DevFeed: [GPT-6 Astra, computer use, and longer-running agents]() Original publisher: [Read original article]() Author: Nate Published: 2026-09-07T13:03:27Z Content type: opinion Language: en Sources: [Nate's Substack]() Topics: [computer-use](), [gpt-6-astra]() Tags: [agents](), [astra](), [computer-use](), [gpt-6-astra]() ### AI overview The article considers how better computer use and longer-running agents could make more possibilities worth exploring, and discusses what work to reconsider giving GPT-6 Astra. ### Source excerpt Better computer use and longer-running agents make more possibilities worth exploring. What to reconsider, and how to give Astra the work. ## GPT-6 Astra and Fable 5.1: Starting and Reviewing Self-Directed Work DevFeed: [GPT-6 Astra and Fable 5.1: Starting and Reviewing Self-Directed Work]() Original publisher: [Read original article]() Author: Nate Published: 2026-09-06T15:03:01Z Content type: opinion Language: en Sources: [Nate's Substack]() Topics: [gpt-6-astra](), [Fable]() Tags: [fable](), [gpt](), [gpt-6-astra]() ### AI overview The article discusses changes involving GPT-6 Astra and Fable 5.1, focusing on starting self-directed work with minimal input, how long it can run without supervision, and the role of the user when it returns. ### Source excerpt What changed this week, with GPT-6 Astra and Fable 5.1, is how little you have to say to start one, how long it runs without you, and what your job is when it comes back. ## The Pelican comparison grid for Astra is pretty interesting DevFeed: [The Pelican comparison grid for Astra is pretty interesting]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-04T23:59:05Z Content type: opinion Language: en Sources: [Simon Willison]() Topics: [gpt-6-astra]() Tags: [ai](), [ai-2-235](), [comparison](), [cost](), [generative-ai](), [generative-ai-1-981](), [gpt-6-astra](), [gpt-6-astra-9](), [images](), [llms](), [llms-1-947](), [model](), [openai](), [openai-463](), [pelican-riding-a-bicycle](), [pelican-riding-a-bicycle-142](), [reasoning]() ### AI overview The article compares GPT-6 Astra with GPT-5.6 Sol, Terra, and Luna by generating SVG pelicans riding bicycles at different reasoning levels. The author finds Astra's images generally better, while noting that lower reasoning levels can omit pelican legs and that Astra may cost more but use fewer tokens. ### Source excerpt I got access to GPT-6 Astra this afternoon, so naturally I used it to generate SVGs of pelicans riding bicycles - at low, medium, high, xhigh and max reasoning levels (Astra doesn't support reasoning=none). Then I rendered those pelicans in a comparison grid with GPT-5.6 Sol, Terra, and Luna, and beyond being fun the result was surprisingly useful. See the grid for full quality images. Here's the transcript that created the GPT-6 Nova pelicans. There are a few interesting things that stand out from this grid. The Astra pelicans are much better. The very best GPT-5.6-Sol pelican (I liked xhigh better than max) is still pretty clearly a bunch of abstract shapes. Every single one of the Astra pelicans, from low to xhigh, looks better than that. The Astra max one is really good. Astra below max still doesn't reliably get the pelican legs on both sides of the frame. In terms of cost, Astra may be around twice the price of Sol ($10/million input, $50/million output, compared to $5/$30 for Sol), but it uses significantly less tokens at each of the levels, making the prices at the different levels closer than they might otherwise be. Astra low produces a better pelican than ANY of the GPT-5.6 Sol models at any level, for 9.55 cents. Spending 10 cents on any other model gets a much worse result. Look at the input token counts: Astra and Luna both used 16 input tokens, Sol and Terra used 26. That's interesting. I wonder if Astra and Luna are more related to each other than OpenAI let on? You are only seeing the long-form articles from my blog. Subscribe to /atom/everything/ to get all of my posts, or take a look at my other subscription options. ## Hands-on GPT-6 Astra review covering computer use, coding, hardware, Blender, and desktop app projects DevFeed: [Hands-on GPT-6 Astra review covering computer use, coding, hardware, Blender, and desktop app projects]() Original publisher: [Read original article]() Author: Claire Vo Published: 2026-09-03T19:34:20Z Content type: opinion Language: en Sources: [Lenny's Newsletter]() Topics: [gpt-6-astra](), [Artificial Intelligence](), [computer-use](), [coding](), [blender](), [Figma](), [codex]() Tags: [ai](), [blender](), [codex](), [coding](), [computer-use](), [figma](), [gpt-6-astra]() ### AI overview A hands-on review of GPT-6 Astra covering computer use, browser-based QA, coding projects, a hardware hack, Blender 3D asset creation, and a Mac app. The episode also discusses speed, cost, and whether Astra is suitable as a daily driver. ### Source excerpt Watch now | 🎙 My hands-on GPT-6 Astra review covers real production work in Figma, Flora, and Adio, one-shot coding wins 5.6 and Fable couldn't match, and a hardware hack I've chased for months