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CodeSwissKnife – Free Beta Developer Toolkit for macOS and Windows

Hacker News

CodeSwissKnife – Free Beta Developer Toolkit for macOS and Windows

Hi HN! I’m excited to introduce CodeSwissKnife, a free beta version of a comprehensive developer toolkit that I’ve built for macOS and Windows. It’s designed to simplify everyday tasks like encoding, decoding, text formatting, and more—all without needing an internet connection. I created this tool because I was tired of searching online for quick utilities, only to be bombarded by ads and security concerns. With CodeSwissKnife, everything runs offline, keeping your data secure on your device. Key Features: Text case converters (UpperCase, CamelCase, KebabCase, and more) Encoding & decoding (Base64, URLs, HTML entities) Parsing & generation (UUIDs, URI parsing) Cryptography tools (hash generation, JWT validation) Conversion & formatting (JSON, XML, color formats) I’d love for you to try out the beta, give feedback, and suggest any features you’d like to see added! Feedback and bug reports are very welcome as I continue to improve the app. Thank you!

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
65%65% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, tasks · Missing: agents, agent, cursor
57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, io · Missing: https docs, just released, exist
26%26% 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
20%20% 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
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto, introduce · Missing: web3, chat, cryptocurrency
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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