Mae Twyman

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.

For the first year of Murror, every landing page, every ad, every App Store screenshot led with the same thing: "AI-powered self-compassion."

It checked all the boxes. AI was the hot keyword. Investors loved it. It felt modern.

I've been using Claude Code heavily for the past few months while building my startup (12k+ line Python codebase), and it's genuinely changed the way I develop. It navigates large codebases with context that I haven't seen from any other tool: understanding file relationships, catching upstream/downstream effects of changes, and reasoning about architecture-level decisions, not just autocompleting lines.

But what really caught my attention is the business side. Anthropic is forecasting cash-flow positive by 2027, while OpenAI is projected to burn through $115B+ through 2029. Claude Code alone reportedly crossed $1B in annualized revenue. Their API revenue is estimated at double OpenAI's. And they're doing all of this with significantly lower burn.