I made a site for practicing front end debugging with real-world bugs

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I am excited to introduce my solo project, a platform built with the frontend development community in mind. It's an interactive environment aimed at refining debugging skills through exposure to real-world bugs. This project stems from the need for a hands-on, practical method of learning to debug. The site features a variety of intentional bugs for users to solve, mirroring the types of challenges faced in professional settings. Over the coming weeks, I will be adding many more exercises and new features to enhance your learning experience further. Check out CodeMender at https://www.codemender.io

Visit product View on Hacker NewsApr 26, 2024

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

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

63%63% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, code · Missing: mac, agents, macos

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

54%54% 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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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