JSONVault Pro – JSON/YAML/XML Viewer After the Give Freely Incident

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Hi HN, In January, JSON Formatter (the 2M+ user Chrome extension) pushed an update that injected a "Give Freely" donation popup on checkout pages. Without warning. Users thought they were being hacked. Hundreds of 1-star reviews followed overnight. I'd been wanting to build a better JSON viewer for a while. That was the signal. 3 months later: JSONVault Pro. What it does: - Auto-detects and formats JSON, YAML and XML on any URL - JWT auto-decode: eyJ... strings show the decoded payload inline — no more jwt.io - Side-by-side diff with LCS engine (handles key-order, type coercion, null vs absent) - JSONPath and JMESPath queries directly in the browser - DevTools panel with network request capture - Web Worker parsing for files up to 50MB - 21 premium themes (Dracula, Catppuccin, Nord...) - Zero tracking. Zero analytics. Data never leaves your machine. The free tier is genuinely useful — dark mode, collapsible tree, YAML/XML, search, sort, copy path, basic diff, DevTools panel. Not a crippled demo. Pro adds the power features: JWT decode, advanced diff, JSONPath, export (CSV/YAML/XML/TS), history, themes, virtual rendering for huge files. Chrome Web Store: https://chromewebstore.google.com/detail/ipghignebclihkckdpc... Website: https://jsonvaultpro.com I'd love feedback on: - The UX when switching between tree, code and raw views - YAML/XML detection — does it catch your files reliably? - Anything missing that would make you switch from your current tool

Visit product View on Hacker NewsMar 24, 2026

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best fitHighest predicted score across all platforms for this description.

Product HuntUnlikely to reach the leaderboard · Strong signals: mac, google, user · Missing: agents, macos, agent

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AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, users · Missing: plus, platform, intuitive

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

TrustMRRLess likely to generate early MRR · Strong signals: month, google, users · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, 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

22%22% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

19%19% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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