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Daily word puzzle game based on polysemy

Hacker News

Daily word puzzle game based on polysemy

I built this daily puzzle game called "Omitten". The idea came from a compulsive habit I got from reading books on an e-reader and constantly looking up definitions (iykyk). Quite often you'd see a single word with 20 different meanings in different contexts, and I wanted to build a game around that. In the game you get a maximum of 4 clues. The first one will generally be an archaic form or an unusual context. The second and third pivot into entirely different domains to try and trick your brain, and by the fourth, it should be fairly obvious. You start with only the first clue, and as you make guesses or skip, the next clues unlock, but you lose 25 points per clue. Today is only Day 5 of the project, and it is currently a slaughterhouse. The win rate is sitting at 24% (you can see this yourself after completing) because today's word is a sneaky one. Typically the win rate sits at the 90-99% area, so yeah. The tech stack itself is fairly simple, it uses a fairly minimal svelte frontend — relies on their motion and transition APIs for most of the animations (combined with some simple CSS animations). On the backend it's a Go monolith running a [Connect RPC server]( https://connectrpc.com/ ), which was a super fun learning experience and using what is almost exactly GRPC directly on frontend feels magical. I tried to follow Google's AIP as closely as I could for the rpc methods, and I quite like the way it turned out. The backend is a simple pg database that's interacted with via sqlc and atlas for migrations. By day 3 people started asking about accounts so they could play across devices and keep their streak, so it was a fun chance to use PASETO for tokens and argon2 for password hashing. I'm excited to hear what people think and any feedback is much appreciated!

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Actual performance

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
93%93% 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.
TrustMRRFits verified-revenue profile · Strong signals: google, way · Missing: mobile apps, ios, personal
56%56% 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
49%49% 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: google, context, single · Missing: mac, agents, macos
37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% 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
19%19% 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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