Reduce time debugging AI slop in prod

Hacker News

Lets be honest, poor code slips into production far too often. Increasingly, that code is some untested auto generated AI code. And is ALWAYS a pain in the ass to fix! So I built a simple tool to make your debugging routine easier and quicker. The tool catches your production issues, raises the bug, traces the root cause and suggests a fix via a small UI. It clicks into your logs, infra and git to get full observability of your stack and is ready to use with only 2 heml commands. This is the first iteration of what will become a more rounded product which i'd love to steer from feedback and questions! Link: https://github.com/dingus-technology/DINGUS

Visit product View on Hacker NewsNov 23, 2025

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

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

58%58% 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 · Strong signals: way · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

49%49% 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

41%41% 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.

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