AI

AI Game Playtester

Hacker News

AI Game Playtester

Hey everyone! Wanted to show off a little side project I've been working on. A lot of people have been using AI to one-shot games, but they're often quite buggy - glitchy graphics, collisions aren't setup, or regressions in earlier levels. With an AI playtester in the loop, I've been able to one-shot games like Candy Crush or Crossy Road that would previously have bugs like cut-off UIs or stuck retries. The way it works basically goes like this: 1/ The playtester starts by reading the code to understand what its parent AI is asking it to play, and to decide what breakpoints, hooks and logging to add 2/ The playtester sets up the scene with the needed environment 3/ The playtester sends a sequence of inputs, then pauses the game 4/ The playtester then uses a vision model to understand the scene, queries the hooks it initially set up, and reads the logs to decide what to do next After doing a few input loops, it then determines if the game is working, the game is broken, or if the test failed. It is a paid offering so I'm not expecting anyone to try it out, but I wanted to share it nonetheless ^^. Lmk if you have other ideas on how you think it can be improved

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
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: model, using, code · Missing: mac, agents, macos
77%77% 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
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
30%30% 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
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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