Problem
Most chess apps optimize for play, content feeds, or subscriptions. I wanted a focused post-game review loop that stays useful as a free local-first utility.
Back to earlier work
Earlier project
ChessIQ
Review your games with Stockfish in the browser, then turn what you missed into focused practice.
Built for repeated post-game use as a free local-first utility: fast first feedback, deeper analysis over time, and follow-up practice from your own positions.
Who it is for
Players who care about improving through their own games, not generic puzzles detached from recent mistakes.
What I built
I built a browser-native analysis workflow: import games, get progressive engine feedback, inspect mistakes, then keep useful follow-up practice close to the review.
Why it matters
ChessIQ closes the loop from review to practice in one place, with no account required and on-device analysis by default.
How I built it
Technical choices that support the review loop.
Browser-native engine
Stockfish runs in-browser with WebAssembly and workers, keeping core review on the player’s device instead of depending on a remote engine service.
Progressive analysis
ChessIQ gives quick first feedback, then refines deeper analysis over time without treating early shallow results as final.
Trustworthy review state
The app separates decision-time and post-move evaluations, guards against stale engine output, and avoids noisy label changes during live refinement.
Local study memory
Reviewed games, analysis history, training state, and preferences persist locally so repeated study becomes more useful over time.
Real-world imports
The importer handles PGN paste/upload, Chess.com and Lichess sources, and imperfect export data without requiring account setup.
Technical notes
- Next.js + TypeScript app for dense analysis and study flows
- WebAssembly worker orchestration for browser-native engine execution
- IndexedDB-backed persistence strategy for larger local datasets
- Responsive UX tuned for desktop review and mobile post-game sessions
What I learned
- A clear review loop beats feature sprawl.
- Trust comes from stable behavior, not just strong engine depth.
- Data edge cases are product work, not cleanup work.