GPT-6 generates Python 3.15 profiler visualizations for web-edit app

Willison's experiment doesn't just show that GPT-6 Astra can generate flame graphs; it demonstrates a specific workflow choice: routing performance analysis through an LLM rather than building native tooling. The cost and latency implications of that choice remain unstated.

This connects directly to Asana's 76x cost reduction with GPT-6.1 Sol (OpenAI, October 9th), which proved that model selection and routing architecture matter more than raw capability for agent tasks. Willison's profiling workflow is a similar case: the question is whether LLM-assisted analysis justifies its inference cost versus traditional sampling profilers. The earlier research on coding-agent benchmarks (arXiv, October 7th) also applies here, since profiling performance is a task type that shifts within developer sessions but isn't typically measured in isolation. If LLM-assisted profiling becomes standard, evaluation frameworks need to capture whether it actually accelerates incident resolution in multi-turn debugging workflows, not just whether it produces correct flame graphs.

If Willison or others publish latency and cost comparisons between LLM-assisted profiling and native Python 3.15 profilers on the same workloads within the next two months, that determines whether this is a viable pattern. If no cost analysis surfaces, assume the workflow remains a one-off exploration rather than a replicable deployment model.