Bug-to-file prediction as a triage API

What to build

A CLI/IDE extension for solo developers and small teams that takes a bug report or stack trace and predicts which files in the repo need to change, using a bug-localization model — shippable as a VS Code extension or GitHub Action that comments on issues with suggested files.

Bug triage eats hours hunting through unfamiliar codebases; a model trained on real-world fix history can shortcut that search and turn a bug report straight into a ranked file list.

Demand

Solo devs and small teams maintaining large or inherited codebases want faster triage, and AI coding assistants have primed users to expect this kind of automated code navigation.

• Hacker NewsAnnouncement

  Show HN: Wn – find the files a bug touches, trained on 1.1M real fixes — 6 points, 0 comments on HN.

Stack

• OpenAI/Claude API for report parsing
• Sentence embeddings (e.g. sentence-transformers)
• GitHub Issues/Actions API
• VS Code Extension API
• Postgres or SQLite for repo file index
• Supabase

Solo + AI difficulty

The hard part is the ML model itself (already built by the original project, so a builder could fork/wrap it rather than retrain); the easy part is wiring a GitHub Action or VS Code extension around an existing API. Time-to-MVP is roughly 1-2 weeks if reusing the open-source model, versus months if training a proprietary one.

Entry threshold

Model training on 1.1M fixes is already done by the original author, so a builder would need to wrap it as a hosted API, VS Code extension, or GitHub App with repo indexing; a usable MVP wrapper is a few weeks of work but real integration into CI or issue trackers to be reliable takes more iteration.

Window

3-6 months

Where to find first users

• Show HN follow-up post
• GitHub Marketplace listing for the Action
• r/programming and r/devops
• Indie hacker / build-in-public communities

Competitors

• Wn (where-next, the original open-source project)

Counter-signals & risks

• Bug localization accuracy trained on public/open-source fix datasets may not generalize well to proprietary, large, or monorepo codebases with different architectures and conventions

• Existing bug tracker and IDE ecosystems (e.g., GitHub Copilot, Sentry, Jira integrations) may add similar file-prediction features natively, reducing the market for a standalone tool

• Show HN traction and a novel model do not guarantee enterprise adoption, since engineering teams may be cautious about trusting automated triage suggestions without strong precision/recall guarantees