GeneGuessr – a daily biology web puzzle

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I made a web game inspired by Geoguessr and Wordle, where you get shown a 3D model of a random human protein each day, and you have to triangulate its gene name using similarity clues. My background is in wet lab molecular biology and I intend this game to be engaging mostly to other biologists. But if you're outside the field, I'm interested to know if you can still solve it with browser use LLMs, and if you learned something interesting doing so. Let me know what you think. I made it with Claude over the last 2 months. My coding experience is limited to basic python data analysis and figure making. I've seen people online asking, "Now that we have coding AI, why isn't there a deluge of awesome AI-generated apps made by non-coders?" - if this sounds like you, check out Geneguessr to understand what a web app by a non-coder looks like. I might write more about the process if there's a demand, but what really unlocked the project for Claude was Linear MCP, where it could put each individual issue on a shared Kanban board. This, and Playwright MCP for testing on live site, were the two workhorses that got me through this. For bugs Claude couldn't one-shot, Linear was great for consolidating issue information so that I could dump it into ChatGPT Codex - it would usually think for like half an hour, output very confusing explanations, but the bug was gone. Game is free, no log-in required, sorry if you run into any mobile bugs - didn't test it much there. https://geneguessr.brinedew.bio/

Visit product View on Hacker NewsDec 23, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, mcp · Missing: mac, agents, macos

93%93% 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.

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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, 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

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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