tetradoom - from dos to desktop using only host system libraries

Hacker News

Hey HN! Wanted to share with you what my friend nullgemm was working on for the past several years. Those are a set of multi-platform libraries built from scratch using only system APIs and are supporting X11, Wayland, Windows and Mac OS Windowing library (Globuf): https://github.com/nullgemm/globuf Keyboard & mouse handling library (Willis): https://github.com/nullgemm/willis Cursor setting library (Cursoryx): https://github.com/nullgemm/cursoryx Screen resolution and density detection library (DPIshit): https://github.com/nullgemm/dpishit Gamepad handling library (PunKnobs): https://github.com/nullgemm/punknobs

Visit product View on Hacker NewsSep 24, 2025

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

74%74% 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

63%63% 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: platform, host · Missing: plus, intuitive, reviews

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

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

45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

42%42% 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

15%15% 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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