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Rhyme or Reason – Open Ended Word Puzzle with LLM Judge

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Rhyme or Reason – Open Ended Word Puzzle with LLM Judge

I finally built a little app around a word puzzle that my brother and I created a few years back. You start with two random words and goal is to connect the two words with alternating RHYMES and "REASONS" (similar words). Here are two examples of valid solutions for starting words (peach, chair): peach --(rhyme)--> teach --(reason)--> school --(rhyme)--> stool --(reason)--> chair peach --(reason)--> pit --(rhyme)--> sit --(reason)--> chair In the app, every placed word is judged by an LLM on whether it rhymes or is "commonly related". There are definitely bugs here in terms of different pronunciations and pop-culture references, so I'm excited to see what the edge cases are. The selected two words reset daily and theres a lightweight sharing function to show your friends your solution. It's a surprisingly challenging game and there are virtually infinite solutions so I figure it'd be a fun thing to share with folks here! Would love some feedback!

Visit productView on Hacker NewsFeb 18, 2025
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Indie HackersFits the IH revenue-focused audience · Strong signals: created · 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.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, io · Missing: https docs, just released, exist
43%43% 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
35%35% 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
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos
24%24% 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
16%16% 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.

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