Tina Kim

This feels like a hard balance, especially for products that need a bit of context.

If the landing page explains too much, it can start to feel heavy. If it explains too little, people may sign up without really understanding the problem, the use case, or why the product matters.

I ve seen products where the tool itself is good, but the first reaction is still: I don t get it yet.

For makers here, what do you try to make clear before signup, and what do you leave for users to discover inside the product?

Hi everyone!

It's Tony

Most enterprise AI governance conversations focus on the wrong layer.

The hard part is not showing a dashboard with model usage. The hard part is building a control plane that still makes sense when someone joins, leaves, changes teams, or works in a different workspace. If the system cannot handle first boot safely, cannot revoke access cleanly, and cannot keep provenance inside your own infrastructure, then it is not really governing anything.

That is why the current LineageLens direction feels more like infrastructure than analytics. The backend now has a setup guard so the product stays locked until the first admin exists. It supports workspace-scoped invites, registration can be disabled, and token rotation means old sessions can be invalidated instead of lingering forever. On the capture side, even the free local extension preserves confidence and source, so evidence is not flattened into a raw diff.

I think that is the right shape for enterprise AI provenance. The important question is not what model wrote the code? It is who had access, what workspace was it in, and can we prove that the evidence still means something after access changes?