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I made a LittleAlchemy clone but with infinite combinations

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I made a LittleAlchemy clone but with infinite combinations

As a kid I played Little Alchemy - a combination game where you start with 4 basic elements and can combine them to create new ones. The combinations were ~600 in total, so I asked myself why can't we have infinite amount. That's how I built this project. You can prompt and combine anything. Whether it's a "Rust Developer" and a "Girlfriend" - that works too I guess :) How to play: Start with 2 random elements Drag & drop to mix and discover Combine your way to new ideas GitHub: https://github.com/alexander-gekov/infinite-alchemy

Visit productView on Hacker NewsJul 28, 2025
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
62%62% 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: new · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, 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
45%45% 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: way · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% 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
20%20% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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