A Tiling Window Manager for Windows, Written in Janet

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Hi HN! I read[1] about Janet[2] some time ago, then immediately got impressed by the enthusiasm of its community, and by the language itself, so I started playing with it. At the time I was searching for a tiling window manager for Windows, and unavoidably the idea of scratching my own itch with Janet got hold of me, so Jwno was born. Simply put, Jwno is a keyboard-driven tiling window manager for Windows, scriptable with Janet. But since it has a complete Lisp runtime, and a thin wrapper library for Win32 APIs[3], you can certainly do much more with it. I hope you'll enjoy playing with it as much as I enjoyed building it. And yes, I use StumpWM on the Linux side, by the way. [1]: https://ianthehenry.com/posts/why-janet/ [2]: https://janet-lang.org/ [3]: https://github.com/agent-kilo/jw32

Visit product View on Hacker NewsMay 20, 2025

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Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

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

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% 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

33%33% 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

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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