Rechain – A daily word puzzle about semantic bridges

Hacker News

Hi HN! I built Rechain ( https://rechain.me ), a daily word puzzle where you connect two seemingly unrelated words through a chain of logical associations. Example: OCEAN → WAVE → HEAT → GLASS. Each word relates to the one before it (ocean wave, heat wave, heat + glass = melting, etc.). Stack: React, Firebase, Gemini API for puzzle generation, Vercel hosting. No login required—you can play anonymously and optionally sign in to preserve stats. Why I built it: I wanted a word game that felt more like solving a logic puzzle than a vocabulary test. The challenge isn't knowing obscure words—it's finding the conceptual path between two ideas. Looking for feedback on: Puzzle difficulty curve (too easy? frustrating?) Mobile UX The "reveal letter" hint system—useful or too hand-holdy? This is a solo side project and I'm iterating quickly based on feedback. Code isn't open source yet but happy to discuss the architecture.

Visit product View on Hacker NewsJan 31, 2026

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

50%50% 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.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

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

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: gemini, code, open · Missing: mac, agents, macos

30%30% predicted probability of success on Product Hunt, 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 · 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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