Co

Compare Word documents in the browser (client-side only)

Hacker News

Compare Word documents in the browser (client-side only)

I built this after almost losing a client because we couldn't agree on which contract version was "final." The problem with existing tools: • Word's "Track Changes" only works if someone remembered to turn it on. • Online comparison tools often require uploading sensitive legal/financial docs to their servers. • Desktop software is often bloated, expensive, or platform-specific. My solution: • 100% Client-Side: Your files never leave your device. No server uploads, no database storage. • Format Support: Works with Word (.docx), PDF, Excel, PPT, TXT, and CSV. • Smart Parsing: I wrote custom parsers to preserve table structures and layouts during comparison (so complex contracts don't turn into a mess). • Zero Install: Works on any device with a modern browser. Technical stack: • Next.js & React for the frontend. • Web Workers for handling heavy parsing tasks (like PDF rendering) without freezing the UI. • Mammoth.js & PDF.js for document text extraction. • Pure client-side diffing algorithms optimized for performance. I'd genuinely appreciate feedback on: Parsing Accuracy: How well does it handle your complex Word tables? (I spent a lot of time on the rowspan/colspan logic!) UI/UX: Is the side-by-side comparison intuitive? Performance: How does it feel with larger documents? Happy to answer questions about the implementation or discuss the tradeoffs of building a serverless document tool!

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4points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, intuitive · Missing: plus, reviews, host
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: tasks · Missing: mac, agents, macos
49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
26%26% 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
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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