Cs

Csv.repair – Free browser-based tool to fix broken CSV files

Hacker News

Csv.repair – Free browser-based tool to fix broken CSV files

I built csv.repair a free, open source browser-based tool for analyzing, querying, and repairing broken or oversized CSV files. No file uploads - everything runs locally in your browser. Your data never leaves your machine. What it does: - Handles CSV files with millions of rows (virtual scrolling + Web Workers) - Inline cell editing (double-click to edit, Tab to move between cells) - Run SQL queries directly on your data (powered by AlaSQL) - Auto-repair: trim whitespace, remove empty/duplicate rows, standardize dates, fix encoding - Health diagnostics that flag malformed rows, inconsistent columns, encoding problems - Column statistics and distribution charts - Undo/redo, search & replace, keyboard shortcuts - Export the cleaned file when you're done - Tech stack: React + TypeScript + Vite + PapaParse + AlaSQL + Recharts + Tailwind CSS. Installable as a PWA. Live: https://csv.repair I'd love feedback especially from people who regularly deal with messy CSV files.

Visit product View on Hacker NewsFeb 27, 2026

Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, coding, open · Missing: agents, macos, agent

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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