Zrc - a Unix shell without fi, esac or done

Hacker News

Welp, second time posting this project! Bourne shell's ALGOL-inspired syntax seems a bit verbose, and while `rc` is quite nice, it lacks some features I'd prefer in a shell (this is probably by design). Though don't let the name I chose fool you, this shell is probably more related to `tcl` or `csh` than `rc`, except in some small places. It's been a while since my last post. Notable new features include a stack trace (via `caller`), `try`/`catch` (but only with user-supplied strings), `fc`, `disown`, login shell detection, and a shorter argv alias (`${argv number}` becomes `$number`). I'd love some feedback on the design. (Also yes, I'm aware the line editor can't wrap that well. Probably WONTFIX)

Visit product View on Hacker NewsOct 29, 2025

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

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.

Hacker NewsStrong engagement from HN community · Strong signals: 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.

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

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

30%30% 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

16%16% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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