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!
user-facing agents have enough speed and scope to do the job... that said, there is still softness around accuracy and MTTR for accuracy-related issues. I like that this is drop-in and does that one thing very well... much better in the long run than bringing in another "platform" or trying to whitelabel the entire agent from soup to nuts.
This is the failure mode I actually worry about. My agents don't crash — they confidently give a wrong answer and I only find out when a user tells me. The proxy setup is what makes it realistic for me to try: no SDK, just point the base URL at gate.three.dev and add a few headers. And failure modes ranked by severity with real examples, then handing the top one to a coding agent over MCP for a fix — that's a tight loop. Curious how noisy the severity ranking gets in the first week, before it has enough traffic to know what's actually common versus a one-off?
Really useful to catch AI mistakes in real conversations before customers start reporting them
Verifying that your agent is doing what it’s supposed to be doing it’s still an unsolved problem. Exciting to see y’all tackle this!
I love this idea and they've implemented it great. Evals are hard, each time I've built evals for a production app they've ended up abandoned and sadly end up spending my time being reactive when the model is acting strangely or different in prod. Love what the team here has built to solve this!
Congrats on the launch three.dev team! How much work is the setup? How much would I have to change the code of an existing project? Also, which languages or SDKs do you support?
I went through our website agent's chats and nothing looked broken. the miss was quieter: right answers, but it never offered the next step, so people just left. could three.dev flag a chat that's correct but goes nowhere?
the refund window example (60 days when the policy says 30) is exactly the kind of failure nobody catches, no error no alert, just a confident wrong answer
how do you decide an answer is wrong without the source of truth??? do you pull in the docs whcih the agent used, or is it judged from the conversation alone???
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.
three.dev was featured in Analytics (174.5k followers), Developer Tools (523k followers) and Artificial Intelligence (483.6k followers) on Product Hunt. Together, these topics include over 248.8k products, making this a competitive space to launch in.
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.
Want to see how three.dev stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
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!