Tim David

First: thank you.

It's our first launch. Appreciate all the kind support and sharp comments. We learned a lot.

One thing that surfaced while tightening LineageLens this week: capture is not the hard part. Agreement is.

If the extension, backend, and MCP server describe the same AI edit with slightly different field names or status values, you do not have provenance, you have three believable stories about the same event. That matters because reviewers and assistants start trusting whichever surface they looked at last.

The question I keep coming back to is simple: if a record can look applied in one place and accepted in another, is that still a single source of truth?

Thanks for the massive support on the last update! I ve been heads-down rewriting the core engine of LineageLens, and I'm stoked to share the v1.1.5 release.

When building an audit trail for AI-generated code, the default approach is to try and regex markdown blocks out of the LLM s text response. It is incredibly brittle. For this release, we ripped that out entirely.

The LineageLens proxy now natively parses the underlying structured protocols:

• Anthropic s tool_use blocks (handling the streaming JSON assembly)

• OpenAI s apply_patch DSL (via the newer Responses API)

• Gemini s functionCall arrays