JSON Tapose – A Simple, Client-Side JSON Diff Viewer

Hacker News

Hi HN! I built a simple JSON diff viewer that runs entirely in your browser. I needed a tool to compare JSONs that was both visually appealing and easy to use, so I made one. Key features: - Side-by-side JSON comparison with syntax highlighting - Works with differently ordered fields (unlike most text editors) - Client-side only - your data never leaves your browser - JSON beautify/formatting - Dark/light themes - No signup required Tech stack: - React + TypeScript - DaisyUI for styling - Vite for build Try it out: https://www.jsontapose.com Source: https://github.com/handsupmin/json-tapose I'd love to hear your feedback and suggestions for improvement!

Visit product View on Hacker NewsMay 21, 2025

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

73%73% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: visual · Missing: mac, agents, macos

33%33% 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

25%25% 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

13%13% 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.

Correct prediction on native model

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