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Learntris, a test-driven programming challenge

Hacker News

Learntris, a test-driven programming challenge

I'm making a platform for programming challenges, and this is one where a Tetris clone. To use the test server and get the full experience, you need to sign in with a google account, but if you want to try it the "old" way, there's a console-based version here: https://github.com/tangentcode/learntris Please let me know what you think, even if it's just feedback about the website/concept. Thanks!

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2points
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
69%69% 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: io · Missing: https docs, excited, just released
48%48% 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, code · Missing: mac, agents, macos
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal
29%29% 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
10%10% 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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