Simple Sprite Sheet Generation

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Games got me into programming and the love of computer science (think Commander Keen, Monkey Island, DOOM). They embody everything I love - programming, design, architecture, complex state management, art, music, narrative/story telling. I recently started experimenting with creating games using coding agents such as CC and Codex. What I found worked very well is if you take the pre-made assets (sprites, tilemaps, sounds) and then create a spec/harness together with game engine documentation, Codex (or CC) will do a great job in putting it all together - even play-testing the game for you. Then I thought...why not take it even further? Create game assets as well! And here my little project was born - "AI Game Studio". Currently it only supports sprite sheet creation, but I will evolve it into a full-fledged game studio. I don't want to replace game designers - I've been following many for years and the art and craft to what they do is incredible. The idea here is simple - if you are a small indie developer, you could use a tool such as this to rapidly prototype some concepts before deciding on a direction. Or, you can create a basic/skeleton assets and enhance them manually. The project uses Grok/xAI - simply because I tried over a dozen different models - from WAN to Seedance - local and API based - and Grok Imagine is the only one that was able to produce good quality results, consistently. I would love for you to try this out, and leave comments and constructive feedback. The tool is available here: https://github.com/acatovic/ai-game-studio Video demo is also linked in the repo.

Visit product View on Hacker NewsMay 24, 2026

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created

95%95% 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: agents, agent, model · Missing: mac, macos, cursor

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, game engine, io · Missing: https docs, excited, just released

47%47% 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 · Strong signals: video · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

13%13% 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.

Correct prediction on native model

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