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Mystery House (Remodeled) – a ground-up rebuild for the Pico-8

Hacker News

Mystery House (Remodeled) – a ground-up rebuild for the Pico-8

After bringing Zork to the Pico-8 via Status Line (a z-machine interpreter for the system), I had been thinking about bringing a graphic text adventure to the system within the single-cart limitation of 64K. Today I have released Mystery House (Remodeled), which brings all graphics, text, *and bugs* (intentionally so) of this public domain classic to the Pico-8. It is GPLv3 and available on GitHub: https://github.com/ChristopherDrum/mystery_house_remodeled Two graphics tools, Versawriter-8 and 1 Bit Wonder, were also built to support this project. More details about those, and their source code, will be released in the coming weeks. These were instrumental in helping me squeeze the 140K original down to just 60K (.p8.png cart size). Blogs which address the interactive fiction (software archaeology POV) and Pico-8 dev ("making your own graphic adventure" POV) communities are in the works. I hope this project is of interest to the HN community.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
66%66% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, single · Missing: agents, macos, agent
50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% 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
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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
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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