DevToys.app – A Swiss Army knife for developers

Hacker News

With a friend from the university, we realized that many developers are pasting sensitive data on random websites in order to decode some Base64, JWT, compress some images...etc without wondering what the website does with this data. Therefore, we developed a free, open source and completely offline Windows app designed to help with these little common tasks. While we released multiple updates these past months, we finally reached v1.0 today. https://devtoys.app Feedback appreciated :-) Thanks!

Visit product View on Hacker NewsJan 19, 2022

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

81%81% 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.

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

53%53% 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 · Strong signals: month · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

16%16% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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