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SUMMARY:Before you open that OSS issue: evidence-first triage - Pavel Suli
 mov\, Sergey Eremeykin
DTSTART;TZID=Europe/Zurich:20261023T091000
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DTSTAMP:20261111T040115Z
UID:pretalx-swiss-python-summit-2026-AQXG8Q@talks.python-summit.ch
DESCRIPTION:You found a gap in a library you use. Should you open an issue
 \, write an RFC\, or spend a month on a pull request? Most teams answer th
 is in a one-off chat with an LLM\, get a confident two-sentence verdict\, 
 and only later discover the closed duplicate issue or the adjacent API tha
 t already solves the problem.\n\nThis talk is a case study\, not a product
  pitch. I walk through how we evaluated whether reviving PyTorch's prototy
 pe torch.masked module was worth a multi-month upstream contribution. The 
 decision changed once we gathered evidence: old and closed GitHub issues\,
  maintainer activity on the code path\, and alternatives such as NestedTen
 sor and FlexAttention. The useful output was not "yes" or "no" - it was a 
 short\, cited brief that a teammate could verify in ten minutes.\n\nFrom t
 hat workflow I distill a repeatable method any Python team can use before 
 writing a spec: what to retrieve (issues\, merged PRs\, module git history
 \, ecosystem docs)\, what not to trust (verdict-first LLM answers\, unvali
 dated "library X already does this")\, and how to structure the result so 
 humans keep the final call.\n\nI briefly show the shape of a Python CLI we
  are building to automate retrieval and synthesis with mandatory citations
  - Typer\, httpx\, GitHub API\, local indexing - but the talk stands alone
 : you leave with a checklist you can run tomorrow with ripgrep\, gh\, and 
 a markdown template.\n\nAudience: contributors\, maintainers tired of dupl
 icate feature requests\, and teams choosing where to invest OSS effort. In
 termediate level\; no PyTorch expertise required (since the Data Science D
 ay is only tomorrow).
LOCATION:Aula 4.101
URL:https://talks.python-summit.ch/swiss-python-summit-2026/talk/AQXG8Q/
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