Yakari – Interactive TUIs for CLI tools

Hacker News

Hi HN! I wanted to share Yakari, a tool I built to make command-line interfaces more approachable through interactive TUIs. If you've ever forgotten CLI flags or needed to look up command syntax, this might help. Yakari turns complex commands into interactive menus. Users can navigate through options with simple key presses instead of memorizing complex command structures. If you've used Emacs and Magit (or any other Transient) before, the interface will feel familiar. Features: - Transform CLIs into guided menus - Create custom menus for any CLI - Support for flags, named parameters, choices, and interactive inputs - Command history and contextual help You can try it out without installing thanks to uv [1]: uvx --from yakari ykr demo # Play with a demo showcasing different argument types uvx --from yakari ykr git # Try the git menu in any git repo The project is built with Python using Textual and is heavily inspired by Emacs' Transient. I'd love feedback from both CLI users and developers. What tools would you find most useful to have menus for? How could this make your terminal workflows easier? [1] https://docs.astral.sh/uv/

Visit product View on Hacker NewsDec 18, 2024

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

89%89% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

62%62% 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

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: interface, users · Missing: plus, platform, intuitive

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users, para · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

13%13% 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.

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