Find out how your AI agent fails. Before your users do.
Almost every team now runs AI in production, but few know how it behaves once real users show up. The failures are quiet: a wrong refund policy, a made-up order status. Nothing crashes, so you hear about it from your users. three.dev finds those failures in your live traffic on its own and ranks them by severity. Setup is one prompt to Claude Code, Cursor, or Codex. Then your coding agent gets what it needs to write the fix.
Hi Product Hunt, I'm Bryan, product lead at three.dev.
AI features can fail quietly. A support assistant tells a customer the refund window is 60 days when the policy says 30. Nothing crashes and no alert fires. You only find out when a customer complains, or when you happen to open the right request at the right time.
Most teams we've talked to have no way to see this. They read a few conversations by hand after a complaint. Evals don't close the gap, because you can only write an eval for a failure you already know about.
Get started by pasting one setup prompt into your coding agent.
We score your live requests in the background, with no added latency.
Issues are found automatically and grouped into failure modes, ranked by severity, each with the real conversations and evidence behind it.
Hand the top one to your coding agent over MCP. It reads the examples and proposes a targeted fix.
I'd love to hear from anyone running AI in production:
How do you find out today when your AI gets something wrong?
What's the worst quiet failure you've caught, and how long did it run before you saw it?
For the Product Hunt community: sign up by Nov 22, send traffic within a week, and the Starter plan is free for six months. That's $474 off ($79 a month).
I'm here all day to answer questions. Try it on your own traffic and tell us what you find!
About three.dev on Product Hunt
“Find out how your AI agent fails. Before your users do.”
three.dev launched on Product Hunt on November 12th, 2026 and earned 112 upvotes and 13 comments, placing #9 on the daily leaderboard. Almost every team now runs AI in production, but few know how it behaves once real users show up. The failures are quiet: a wrong refund policy, a made-up order status. Nothing crashes, so you hear about it from your users. three.dev finds those failures in your live traffic on its own and ranks them by severity. Setup is one prompt to Claude Code, Cursor, or Codex. Then your coding agent gets what it needs to write the fix.
On the analytics side, three.dev competes within Analytics, Developer Tools and Artificial Intelligence — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how three.dev performed against the three products that launched closest to it on the same day.
Who hunted three.dev?
three.dev was hunted by Bryan Lee. 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 three.dev including community comment highlights and product details, visit the product overview.
Bryan Lee
Hi Product Hunt, I'm Bryan, product lead at three.dev.
AI features can fail quietly. A support assistant tells a customer the refund window is 60 days when the policy says 30. Nothing crashes and no alert fires. You only find out when a customer complains, or when you happen to open the right request at the right time.
Most teams we've talked to have no way to see this. They read a few conversations by hand after a complaint. Evals don't close the gap, because you can only write an eval for a failure you already know about.
So three.dev starts from your live traffic:
Get started by pasting one setup prompt into your coding agent.
We score your live requests in the background, with no added latency.
Issues are found automatically and grouped into failure modes, ranked by severity, each with the real conversations and evidence behind it.
Hand the top one to your coding agent over MCP. It reads the examples and proposes a targeted fix.
I'd love to hear from anyone running AI in production:
How do you find out today when your AI gets something wrong?
What's the worst quiet failure you've caught, and how long did it run before you saw it?
For the Product Hunt community: sign up by Nov 22, send traffic within a week, and the Starter plan is free for six months. That's $474 off ($79 a month).
I'm here all day to answer questions. Try it on your own traffic and tell us what you find!