... locking in the launch window for LineageLens (open-source AI code provenance "git blame for AI-generated code") and I want to time it for when the most people are actually around, not just follow a generic playbook. Trying to get this right, so I'd value real input from this crowd: - Day of week which days do you see the most activity here? Mid-week, or does that just mean more competition for attention? - Time of day is there ...

... started building LineageLens because I kept asking a question that nobody on my team could answer: if a CVE gets traced back to an AI-generated line in production next week, what evidence do we actually have? Not "we use Copilot and review everything." Machine-readable evidence: which model generated which lines, with what prompt, at what confidence, and whether a human reviewed the AI context before it shipped. For most teams, the answer is nothing. The Cloud Security Alliance ... ... number on it this month: 81% of organizations have no complete visibility into where AI-generated code lives in production. That gap grows larger every sprint. LineageLens

... assistant message. The filename is on the line before the fence, not inside it. Cline and Continue use proper tool calls. A response from some backends carries both. The adapter we shipped this week (`openai_ chat.py ` in the LineageLens proxy) handles all three patterns: tool-call edits, text-content edits (Aider SEARCH/REPLACE, unified diffs, fenced blocks), and mixed responses where both appear together. It covers Aider, Cline, Continue, Copilot CLI, Goose, Windsurf, and any OpenAI-compatible backend Azure, groq, fireworks ... ... mistral, together.ai . The hardest design decision was not the parsing. It was fail-open vs

... probably saw the VS Code 1.117 story: "Co-authored-by: Copilot" silently added to commits by default, including for code that Copilot never wrote. Microsoft reverted it in 1.119. Good. But the revert is the wrong ending to the right story. The developer anger was about consent and accuracy both valid. What did not get picked up in the discourse was the architectural question underneath: even when Copilot does write code, a git trailer tells you almost nothing useful ... ... carry the prompt, the model version, the temperature, the insertion timing, the correlation confidence, or any risk context. It just says "an AI tool existed in this editor." That is not an audit trail. I have been building LineageLens

... Something I built led to a design decision I want to get feedback on. LineageLens is a free VS

... hundred milliseconds in transit. After the model returns its response and the editor applies the change, that prompt is gone. Git records the diff. Nothing else records the origin by default. There is no reconstruct operation. I built LineageLens partly because I ran into this problem in my own workflow traced a bug to a block I had accepted from Cursor weeks earlier and had no way to recover what I had asked for. The architecture reflects this constraint directly ... ... Base extension (free, zero config, VS

... this: if someone asks you six months later whether a block of code was AI-generated, can you prove the record still means what it said when it was created? That is why we added two things in LineageLens: a provenance hash chain and a signed AI BOM export. Each record gets a deterministic hash linked to the previous record, so tampering becomes visible. The export carries prompt hashes instead of raw prompts, plus summary fields like disclosure coverage ... ... transformations. I also agree the endpoint/version specificity matters a lot. Model family provenance is often too vague to support meaningful verification later. Comment from Praveen(@praveen62): @ari_heljakka  That overlap is very interesting. I think provenance and evaluator lineage

... been working with LineageLens and just added a custom agent adapter so our internal CLI tool is attributed with prompts, model metadata, and confidence evidence. The registry design makes this surprisingly low-friction: implement a detect(input) that returns a NormalizedAgentContext (tool name, model, session ids, confidence, and evidence), register the adapter, then run the quickstart proxy to validate captures. Why this matters : your team can capture private or bespoke tools without sending data to a vendor ... ... recent repo changes (custom agents landed in late May) and found the adapter API predictable: detection should be conservative, emit evidence items, and choose appropriate ordering so your specialist adapter wins over the fallback. If you ve extended LineageLens

... trap in "single unified status view," and the thing I'd watch for with LineageLens: a view people have to watch becomes another dashboard they forget to check. The value evaporates if it's pull instead of push. It should exist for when you want it, but it should tap you, not wait for you. Genuine question, not a pitch: where's LineageLens

... 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 ... ... 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? There is a live demo at https://lineage-website.vercel.app

... People often think LineageLens is just about capturing AI-generated code , but the real value is what happens on Day 2. The dashboard turns those intercepted edits into a searchable audit trail. Instead of blindly digging through `git blame`, you can search past AI edits, trace the prompt-to-code workflow, and instantly answer why a specific model wrote a function that way. Comment from B ra eker(@busra_seker1): @praveen62  The why a model wrote it that way part ... ... dashboard search is implemented under the hood (SQLite/pgvector) or how it handles the parsed state machine data. Comment from Praveen(@praveen62): @busra_seker1  We handle the noise in two specific ways: Agent-Level Interception: LineageLens

... 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 ... ... operations teams, the biggest governance gap isn't tooling. It's that developers treat audit trails as overhead, not as protection. A review policy only works when the team feels it protects them, not just the company. Curious, does LineageLens

... 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 ... ... praveen62 This is such a nuanced problem, Praveen. When different layers (extension, backend, MCP) start interpreting the same AI event differently, the 'single source of truth' completely breaks down. My question is: How are you enforcing this agreement within LineageLens

... 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

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