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Ündes
Make AI answers honest, grounded, and reviewable
Ündes shows how far AI-generated engineering work can be trusted. It proposes a solution or code candidate and produces a reviewable artifact: evidence used, files checked, assumptions, what could not be proven, critique, open risks, and a trust verdict before merge.
I build with AI coding tools every day. They are great at getting to a draft fast: code, migrations, auth flows, architecture ideas, technical fixes.
But the expensive part usually comes later. What did the model actually check? Which files did it use? Which assumptions were hidden? What could not be proven? Is this safe enough to put into a real PR?
The hard question is no longer just: “Can AI write code?”
It is: “How far can I trust the engineering work AI just produced?”
Ündes is my answer to that problem.
It proposes an engineering solution or code candidate and, in the same run, produces a reviewable artifact: evidence used, files checked, critique, rejected hypotheses, unresolved assumptions, open production/PR risks, and a trust verdict such as safe to apply, needs review, or insufficient evidence.
It uses a multi-agent review process under the hood: propose, critique, consensus, devil’s advocate, and synthesis.
But the multi-agent workflow is the engine, not the point.
The point is to make AI-generated engineering work inspectable before you merge it, deploy it, or base an architecture decision on it.
Ündes does not replace engineering judgment and does not promise that generated code is automatically correct.
Sometimes the most useful answer is: “I could not prove this. Here is exactly what still needs to be checked.”
That is intentional. I would rather have an AI tool expose uncertainty than hand me a confident, unverified patch.
AI can generate the code. Ündes shows how far you can trust it.
I would appreciate feedback from engineers, tech leads, founders, and anyone already shipping with AI coding tools.
What would you need to see before trusting AI-generated engineering work in a real PR or architecture decision?
About Ündes on Product Hunt
“Make AI answers honest, grounded, and reviewable”
Ündes was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #112 on the daily leaderboard. Ündes shows how far AI-generated engineering work can be trusted. It proposes a solution or code candidate and produces a reviewable artifact: evidence used, files checked, assumptions, what could not be proven, critique, open risks, and a trust verdict before merge.
On the analytics side, Ündes competes within Software Engineering, Developer Tools and Artificial Intelligence — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how Ündes performed against the three products that launched closest to it on the same day.
Who hunted Ündes?
Ündes was hunted by Kair Akhmettayev. 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.
Kair Akhmettayev
Hey Product Hunt,
I build with AI coding tools every day. They are great at getting to a draft fast: code, migrations, auth flows, architecture ideas, technical fixes.
But the expensive part usually comes later. What did the model actually check? Which files did it use? Which assumptions were hidden? What could not be proven? Is this safe enough to put into a real PR?
The hard question is no longer just: “Can AI write code?”
It is: “How far can I trust the engineering work AI just produced?”
Ündes is my answer to that problem.
It proposes an engineering solution or code candidate and, in the same run, produces a reviewable artifact: evidence used, files checked, critique, rejected hypotheses, unresolved assumptions, open production/PR risks, and a trust verdict such as safe to apply, needs review, or insufficient evidence.
It uses a multi-agent review process under the hood: propose, critique, consensus, devil’s advocate, and synthesis.
But the multi-agent workflow is the engine, not the point.
The point is to make AI-generated engineering work inspectable before you merge it, deploy it, or base an architecture decision on it.
Ündes does not replace engineering judgment and does not promise that generated code is automatically correct.
Sometimes the most useful answer is: “I could not prove this. Here is exactly what still needs to be checked.”
That is intentional. I would rather have an AI tool expose uncertainty than hand me a confident, unverified patch.
AI can generate the code. Ündes shows how far you can trust it.
I would appreciate feedback from engineers, tech leads, founders, and anyone already shipping with AI coding tools.
What would you need to see before trusting AI-generated engineering work in a real PR or architecture decision?