Rokz: a browser port of a 1990 ASCII dungeon crawler

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For those younger than me, Kroz was Apogee's first game, a text-mode dungeon crawler from 1987. Apogee released the Turbo Pascal source under GPL in 2009. I ported the 1990 episode (Lost Adventures of Kroz) to the browser. No dependencies typescript. Some interesting tidbids I learned: The game's clock is its sound :-) Turbo Pascal's delay() blocks, and the sound effects are built out of delays, so a footstep is an 8 ms tone, 120 ms of silence, another 8 ms tone. That silence is the turn. You can't outrun Kroz by mashing keys because every move costs the length of its own beep. In the port a square-wave oscillator reproduces this (no samples etc), and playing a sound returns its duration whether or not it is audible. DOS Kroz asked at startup: "Slow or Fast PC" - and repeated sound chunks according to this. All the hidden stuff, easter eggs, and even most of bugs are reproduced. You can play it just like original. Oh, one thing - instead of ASCII letters I used tiles (from Dungeon Crawl Stone Soup (CC0)). I started with Lost Adventures because Apogee's source was most complete. I will continue with rest of the episodes. AI Disclose: I used Claude to help me extract levels from the Pascal source and to test it by taking over chrome session and measure miliseconds :D You can play it here: https://taler.pl/rokz/ source code here: https://github.com/htaler/rokz

Visit product View on Hacker NewsSep 8, 2026

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83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

60%60% 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 · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% 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

18%18% 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.

Incorrect prediction on native model

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