SuperOpt – Linux Shortcuts on Mac

Hacker News

so as a long time Fedora user I just got a macbook for work. while it's so much faster than my old Fedora-based work laptop, it's also running macOS... which I really don't want to learn or get used to. i tried a Fedora VM on the Macbook and even then the modifier keys are still garbled. my friends kept telling i'd simply get used to macOS but i'd like to stick to what i know.. instead of spending some time adjusting to macOS, i spent a few days with an LLM to create a program which uses macOS accessibility features as well as private APIs to make the experience as close to my Fedora computer as possible. it adds some features like a close button in mission control, "snap assist" from windows 10, and the option to remap various keyboard shortcuts to their "normal" equivalents (e.g., ctrl+c -> cmd+c). of course everything can be turned individually on or off. i feel a lot more confident using macOS now after this now that the majority of my muscle memory can transfer over to macos. since the code is written by AI it's licensed under WTFPL. (are AI-generated works even copyrightable?) source code is here: https://github.com/logonoff/superopt

Visit product View on Hacker NewsSep 15, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, user · Missing: agents, agent, cursor

98%98% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

71%71% 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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

25%25% 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

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.

Incorrect prediction on native model

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