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Retail Mage – A multiplayer Unreal game where GenAI NPCs go to shop

Hacker News

Retail Mage – A multiplayer Unreal game where GenAI NPCs go to shop

Hey HN! For the past year, our small startup has been focused on a single question: can we use generative AI to create genuinely new kinds of gameplay? Retail Mage is our first attempt at an answer. I like to joke that Retail Mage is "late stage capitalism come for Diagon Alley." You and your friends are mages working the end of your shift at Mage Mart, seeking 5 star reviews from the wacky customers and trying not to burn the shop down, which sometimes happens. We've built what we think is one of the more sophisticated implementations of GenAI in games so far. NPCs are powered by embodied AI agents that can observe their environment, plan actions, remember interactions, and engage in natural conversation. Going beyond AI agents, we also introduce an AI-based interaction system: Players and NPCs can just type or say what they want to do, and we translate that into the 3D simulation. This approach lets us bring "rulings, not rules" style gameplay to video games for the first time. The technical journey has been... interesting! Some highlights: - All inference runs in the cloud. We're part of Amazon's Generative AI Accelerator (the only gaming company) - We manage inference ourselves, using some of the latest techniques in batching, forking and KV-cache reuse - Over the last year, we've reduced our inference cost by 3 orders of magnitude - After testing over two dozen models, we're currently using Google's Gemma - We have our own in-house AI tools for Unreal Engine, including in-engine/runtime inference testing - We developed our own natural language unit testing suite for our models and features And we launch today! Try it out: https://store.steampowered.com/app/3224380/Retail_Mage/ Retail Mage is just the start. We have so many other prototypes and experiments. As a game developer, I'm legitimately excited to bring a new genre of games to players. Happy to answer questions!

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video, google · Missing: mobile apps, ios, personal
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist
42%42% 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 · Strong signals: reviews · Missing: plus, platform, intuitive
22%22% 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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