# d3 15 Published articles for d3 15. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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