My

My Spin on a Maze Generator – Looking for Feedback

Hacker News

My Spin on a Maze Generator – Looking for Feedback

Hey everyone, I've been following along, inspired by all the cool projects I've seen on HN. I decided to roll up my sleeves and code something myself – a maze generator! It's a fun little tool that lets you create, navigate, and download mazes of all shapes and sizes. I've thrown in some options for customizing the maze, like wall thickness and color schemes. Plus, there's an A* search algorithm for finding the quickest way out of the maze. Now that I've got it up and running, I'm looking for some honest feedback. What do you think? What would you change? How can I make it even better? If you've got a minute, check it out and let me know your thoughts: AI Maze Generator Thanks for taking a look, and I'm all ears for your suggestions!

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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: code · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
43%43% predicted probability of success on AppSumo, 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
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 · Strong signals: way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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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