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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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Bloomberg AI / 7:28 PM

Why Bridgewater’s CIO Says AI’s Human Extinction Risk Is Real

As one of the earliest backers of both OpenAI and Anthropic, Greg Jensen has seen AI development up close. Now, he's among the tech insiders more worried than ever about AI's human extinction risks. The managing CIO of Bridgewater joins Joe Weisenthal and Tracy Alloway on the Odd Lots podcast to discuss how AI discourse is starting to resemble the start of the Covid-19 pandemic in 2020 and why the time for governments to put meaningful regulations in place is now. (Source: Bloomberg)

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The Decoder / 12:58 PM

Iris-mini and Iris-pro are the strongest open-weight search agents in their class

The AllSpark team has released Iris-mini and Iris-pro, two open-source search agents built on Qwen models that lead benchmarks among open-weight models in their size classes. According to the paper, the training data and models also improved performance on tasks they were never trained for, including general tool use and office work. The article Iris-mini and Iris-pro are the strongest open-weight search agents in their class appeared first on The Decoder .

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