I Built a Debugging Challenge for the AI Coding Age

Hacker News

Hi all, I'm sure some of the best engineers out here are having a hard time standing out nowadays. It's hard to evaluate and improve your skills, when AI is writing the code. Especially when a junior dev is sitting by your side and "accomplishing" 2x more than you. I didn't like this reality where the line between real talent and AI slop is blurring, so I decided to create a challenge, purely for the community, that is made to truly give a stage for talented devs to stand out in the age of AI. We encourage devs to bring their agents with them, because the challenge is built to not be solvable only with AI agents. today the challenge is live for the next 24 hours. couldn't be more excited for some of you to check it out and give some honest, no-filter feedback. head-up - it is hard :)

Visit product View on Hacker NewsMay 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, coding · Missing: mac, macos, cursor

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best fitHighest predicted score across all platforms for this description.

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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

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: excited, lua, ide · Missing: https docs, just released, exist

41%41% 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 · 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

23%23% 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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