JSON Diff Tool – Compare JSON Documents in the Browser

Hacker News

Hi HN! I built a simple browser-based JSON diff tool after getting frustrated with existing options that were cluttered with ads. This tool: - Runs entirely client-side (your JSON never leaves your browser) - Shows visual side-by-side comparison - Handles nested objects, arrays, and type changes - Generates shareable links (data encoded in URL, no database) - No tracking, no ads, no account required I use it constantly for comparing API responses during development and debugging config file changes. The diff algorithm recursively walks both JSON structures and categorizes changes as additions, deletions, or modifications. Performance is decent up to ~10MB files. The shareable link feature encodes both JSON docs in the URL itself, so you can share comparisons with teammates without any server-side storage. Open source and completely free. Would appreciate any feedback on the UX or algorithm improvements! Link: https://jsondifference.com/

Visit product View on Hacker NewsJan 30, 2026

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

69%69% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released

57%57% 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

48%48% 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

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

14%14% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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