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GameScout AI – AI-powered game recommender

Hacker News

GameScout AI – AI-powered game recommender

I built GameScout AI because I never really liked the recommendations sites like Steam propose. It uses natural language to find games based on specific mechanics or moods, like "cozy farming sims with a bit of action" or "something like Dark Souls but funnier". The Stack: - Next.js & Tailwind: For a responsive, gaming-focused UI. - Groq (Llama 3): I’m using Groq for inference because sub-second latency makes semantic search feel like a local DB query. - Prompt Engineering: Optimized to explain why a game was picked based on actual mechanics, not just metadata. Any feedback is appreciated!

Visit product View on Hacker NewsFeb 26, 2026

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, plain · Missing: mac, agents, macos

75%75% 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.

Hacker NewsStrong engagement from HN community · Strong signals: llama, io · Missing: https docs, excited, just released

56%56% 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 · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

33%33% 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

12%12% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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