I put ProductChaengi in front of real users and watched the first two people verify their email and create a project.
Then both stopped before connecting an AI.
That is a tiny sample, so I am not going to pretend it proves why they left. But it did show me exactly where I still have no evidence: the jump from "I have a project" to "I should connect another tool."
I had been leading with the most technical part of the product. An AI can reuse work it has already checked, changed files can invalidate only the related checkpoints, and the next session can start with a much smaller slice of context.
That is useful later. It is not a good reason to begin.
The actual problem starts earlier.
A PRD gets written. A feature spec changes a week later. The user flow still reflects the old decision. Screens are drawn from a mixture of both. Test cases arrive at the end, and implementation becomes another interpretation of what the team meant.
Nothing is exactly "missing." The problem is that nobody can tell which decision is current, how one artifact led to the next, or where a change should travel.
ProductChaengi is meant to keep that chain together:
PRD → feature specs → personas → user flows → screens → design system → test cases → implementation review.
Each step can be drafted with the tools people already use, but a person reviews it before it becomes the current version. If an earlier decision changes, the downstream work should show what needs another look instead of quietly drifting apart.
The code checkpoint feature belongs at the end of that story. Once someone is working with an AI on implementation, it can avoid re-reading unchanged work and focus on the features affected by a change. Helpful, yes. But it only matters after the product decisions are worth carrying forward.
So I am changing how I explain and measure the product.
The first useful moment should not be "AI connected." It should be something closer to: "I can see one reviewed product decision and what it affects next."
Only after that should connecting an AI feel like a shortcut rather than setup homework.
The next thing I am watching is not raw sign-up count. It is whether someone can create one artifact, review it, and understand why the next artifact is connected to it. If that does not happen, more automation will not rescue the onboarding.
If you build products with AI tools, where does your context usually break first: PRD to design, design to tests, or tests to implementation?
I am building this in public here: https://product.chaengi.com/r/dev
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