This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
When you paste a stack trace and ask an AI to fix it, it usually patches wherever the error surfaces not where the bug originates. I built a prompt that forces the model to work the problem in order: read the symptom literally, rank hypotheses by likelihood with confidence levels, identify the actual origin (not just the throw site), then propose a fix and a way to verify you actually found the root cause not just made the error move.t: instantly share code, notes, and snippets.
This started from a frustration I kept running into: paste a stack trace into an AI assistant, get a fix, apply it, and the error moves somewhere else. Repeat until you've wasted an afternoon.
The problem isn't the model it's that "fix this bug" is the wrong prompt. It skips straight to a solution before anything is actually understood. The crash site and the bug origin are often two completely different places, and a patch at the throw site just hides the symptom.
So I built prompts that enforce an order diagnosis before fix, ranked hypotheses before commitment, and a verification step so "the error went away" isn't mistaken for "I found the cause." Six prompts in total covering the full lifecycle: code review, bug triage, refactor planning, test generation, PR descriptions, and incident postmortems.
The bug triage prompt is free here: https://gist.github.com/chatterj...
Happy to answer any questions about how any of the prompts are structured or what I was trying to solve with each one.
AI Bug Triage Prompt was submitted on Product Hunt and earned 5 upvotes and 1 comments, placing #81 on the daily leaderboard. When you paste a stack trace and ask an AI to fix it, it usually patches wherever the error surfaces not where the bug originates. I built a prompt that forces the model to work the problem in order: read the symptom literally, rank hypotheses by likelihood with confidence levels, identify the actual origin (not just the throw site), then propose a fix and a way to verify you actually found the root cause not just made the error move.t: instantly share code, notes, and snippets.
On the analytics side, AI Bug Triage Prompt competes within Software Engineering, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how AI Bug Triage Prompt performed against the three products that launched closest to it on the same day.
Who hunted AI Bug Triage Prompt?
AI Bug Triage Prompt was hunted by Chatter Jay. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of AI Bug Triage Prompt including community comment highlights and product details, visit the product overview.
Chatter Jay