Codenaturally.net, a website for building manual programming fluency

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I made this website to be an environment for graduated recall practice and testing of programming fluency with various languages/tools. I write a lot less manual code these days, but I don't like being too reliant on agents to do everything for me; I like to keep my fluency with some of the languages / tools I value the most. So this website helps me practice in those languages and retain and build my fluency. Hoping it might help others who feel similarly do the same.

Visit product View on Hacker NewsSep 11, 2026

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3points

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Did not reach leaderboard

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

79%79% 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: agents, agent, code · Missing: mac, macos, cursor

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

54%54% 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

33%33% 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

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.

Incorrect prediction on native model

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