Zli – A Batteries-Included CLI Framework for Zig

Hacker News

I built zli, a batteries-included CLI framework for Zig with a focus on DX and composability. Key features: - Typed flags with default values and help output - Rich formatting, and layout support - Command trees with isolated execution logic - It’s designed to feel good to use, not just to work. - Built for real-world CLI apps, not toy examples. Would love feedback, feature ideas, or thoughts from other Zig devs. repo here: https://github.com/xcaeser/zli

Visit product View on Hacker NewsMay 25, 2025

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

81%81% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

56%56% 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

55%55% 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: apps · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

32%32% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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.

Correct prediction on native model

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