Su

Sudo16 Fantasy Computer

Hacker News

Sudo16 Fantasy Computer

Hey everyone! I've been a lurker here for quite a while, and thought I'd share my latest project: the sudo16. The sudo16 is a fantasy 16 bit computer with a limited instruction set. How many instructions? That's for you to find out! The "game", if you will, is to reverse engineer the CPU and create complex programs in the limited system specifications (65,536 bytes of ROM, and 256 bytes of RAM). The getting started page has a complete list of instructions if you want to jump straight into development. The emulator backend is written in rust ( https://github.com/RhinoCodes/sudo16/ ) compiled to WASM, and the web frontend is vanilla js ( https://github.com/RhinoCodes/sudo16-web/ ). It should be possible to run the WASM binary outside of the browser with different frontends (rust in rust?). Happy Hacking (and Thanksgiving)!

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
70%70% 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 HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: computer, code · Missing: mac, agents, macos
62%62% 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
49%49% 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
44%44% 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
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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