Clean Code Katas – Learn How to Write Clean and Maintainable Code

Hacker News

Last week I posted about an Idea I had across different sites. I got a lot of positive response so I decided to work on it. The idea is simple, weekly programming challenges aimed towards learning how to write clean and maintainable code. Each challenge is short, between 30-60 minutes. You can then submit the challenge to get feedback from others and also help others. There will also be a leaderboard of some of the best submissions and top users. I appreciate any feedback and please share it with people you might think would be interested. Please sign up if you are interested: https://www.cleancodekata.com/

Visit product View on Hacker NewsAug 13, 2018

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Did not reach leaderboard

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

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, code · 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 · Strong signals: users · Missing: plus, platform, intuitive

29%29% 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

18%18% 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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