Büşra Şeker

After months of growth in @ViralSort , MRR now looks stable and has started slightly declining.

Have you faced this stage before?

What did you do to restart growth?

Hey PH mates!

I ve been tracking my own workflow lately while chatting around with ChatGPT and Gemini, and I noticed a painful pattern: I had the whole picture in my mind, but I constantly forget some edge cases or tiny technical constraints on my first try.

I hit send, wait 30 seconds, realise the AI guessed wrong, tweak it, and retry. It s an absolute token-waster and a massive productivity killer.

I'm trying to prototype a lightweight extension to intercept these "blind spots" in real-time right inside the input box before hitting send. But I'm curious about how you lot deal with this:

On a Tuesday, the first enterprise question is usually not can you capture AI code? It s who can see the records, how long do they live, and what happens when a policy blocks a change?

That s the part LineageLens is built for. Base gives you local capture. Lite gives a shared team record. Plus and Max move the data into a backend where auth, permissions, retention, and policy live next to the provenance records instead of around them.

The useful thing here is not another dashboard. It s a self-hosted record of prompt, model, tool, file, and outcome that engineering, security, and platform teams can actually govern on their own infrastructure.

I keep seeing AI governance tools start with visibility, then discover that the real enterprise questions are identity, retention, and review. If the record cannot be scoped, retained, and exported on your side, it is not really governable.