Reproducible,Unix-Like,84KB FOSS tasks alternative that respects you

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Karui is a lightweight (84KB) to-do list android app with a Unix-inspired aesthetic, using AlpineJS. My goal was simple: create a privacy-friendly app that isn't bloated with too many dependencies, or unnecessary complexity, make it as light as possible while it still fits my usecase, have a terminal-esque design so it stands out from others, and add tons of themes so that you can waste time changing themes of your todolist as much as you do changing themes of your code editor. The project started as I tried polishing a really old android app from android 4.4.2 which had its code on Github under GPLv3 license. But the app just wont build. I walked into some forums with questions, got rejected from some (the question was listed as too niche), but in the end the app won't build. Still it was an interesting observation. Even if the source is available, if you can't run it without closed-source dependencies, is it really free? But it was a gateway to looking into how to make a truly free software for android. In most android projects on GitHub there are proprietary dependencies like analytics scripts, or reliance on Google's Play Store APIs, and even encrypted blocks that can only be read by Google, which gets them a non-free or Anti-Features tags even if they make it to f-droid, which is of particular interest to me as I am looking into a qr library to share and save notes data between devices. This ensures your notes never have to connect to the internet. But nevertheless the AlpineJs route felt the best for this app, its unlikely that the support for webview will go away at any point, and refactoring the code slightly to fit the future changes would just be a breeze, atleast that's what I hope. I considered native approache with Jetpack Compose, but it didn’t align with Karui’s minimalism-first philosophy. The app’s focus is on simplicity, reproducibility, and long-term support—WebView ensures future-proofing without locking into Google’s UI frameworks.

Visit product View on Hacker NewsMar 27, 2025

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

95%95% 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: google, tasks, using · Missing: mac, agents, macos

85%85% predicted probability of success on Product Hunt, 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

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: google, way · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

21%21% 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

18%18% 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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