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CodeSwissKnife – Free Developer Toolkit for macOS and Windows

Hacker News

CodeSwissKnife – Free Developer Toolkit for macOS and Windows

I’d like to share CodeSwissKnife 1.1.0, a free all-in-one developer toolkit I built for macOS and Windows. It’s designed to help developers simplify everyday tasks like encoding, decoding, text formatting, and more—fully offline for privacy and speed. This toolkit was born out of the frustration of relying on scattered online utilities. With CodeSwissKnife, everything is in one place, ad-free, and secure. Key features: - Text converters (e.g., CamelCase, KebabCase) - Encoding & decoding (Base64, URLs, etc.) - JSON and XML formatting - UUID and hash generation - Regex testing and Markdown preview Feel free to check it out and share any feedback or ideas for improvement. Happy holidays! https://codeswissknife.com/

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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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: ide, io · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: mac, macos, tasks · Missing: agents, agent, cursor
37%37% 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
12%12% 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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