JS

JSONXMLKit – Format JSON/XML and Decode Base64 in the Browser

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JSONXMLKit – Format JSON/XML and Decode Base64 in the Browser

I built this because I was constantly switching tabs to format JSON or XML, or decode Base64 — and many of the tools I found were either bloated, sent my data to a server, or just didn’t support things like XPath or JSONPath. JSONXMLKit runs 100% in-browser, and includes: JSON and XML formatters Path search with JSONPath and XPath File-based Base64 encoder/decoder (images, PDFs, etc.) No login, no uploads. Just a set of quick, client-side tools I needed and decided to share. Feedback welcome!

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Actual performance

3points
4comments
Did not reach leaderboard

Launch Intel predictions

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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, 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
46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released
34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
21%21% 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
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
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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