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Fleek – Own Your $Home

Hacker News

Fleek – Own Your $Home

Fleek is an all-in-one management system for everything you need to be productive on your computer. Functionally, Fleek is a user-friendly wrapper around Nix and Nix Home Manager. It aims to provide users with a simple way to add packages, add shell aliases, and manage the shell's PATH. It was born from the question "What if we had the power of Nix and the simplicity of HomeBrew?" We built Fleek because we love Nix and Home Manager, but wanted an easier way to get started. Fleek intentionally hides the complexity of Nix, offering commands like "fleek add neovim" to add a package to your system. You can use Fleek iteratively to make changes to your $HOME config and apply them. You can also use Fleek as a one-off generator to create a lightly-opinionated Home Manager flake that you can modify as you like. Fleek is easy to share between computers, giving you an uncomplicated way to get the exact same configuration on Linux, Mac & WSL. Website: https://getfleek.dev Code: https://github.com/ublue-os/fleek Example Generated Configuration: https://github.com/ublue-os/fleek/tree/main/examples/high My Personal Fleek Configuration: https://github.com/bketelsen/fleek We'd love to get your feedback on the app, the process, and the generated configurations.

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Actual performance

20points
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
78%78% 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, user, computer · Missing: agents, macos, agent
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: friendly, users · Missing: plus, platform, intuitive
59%59% predicted probability of success on AppSumo, 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
50%50% 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: personal, users, way · Missing: mobile apps, ios, entrepreneurs
44%44% 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
11%11% 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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