LineageLens

AI wrote it. LineageLens proves it.

Design Tools

▲ 56 votes1 commentsLaunched May 14, 2026

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Been deep in AI-assisted dev work and kept hitting the same wall: something breaks in AI-generated code and there's no way to answer "which prompt wrote this, which model, and what was the context?" Root cause analysis becomes guesswork. Built LineageLens to fix that. It runs a local proxy on port 8788 that sits between your AI tools and their providers. Every time an AI writes ≥4 lines into your codebase, it captures the exact prompt, the model and tool that produced it (Cursor, Claude Code)

AI Analysis

📝 Summary

LineageLens is a local proxy tool (running on port 8788) that sits between AI coding assistants like Cursor and Claude and their API providers. It automatically captures the exact prompt, model, tool, and context whenever AI generates 4 or more lines of code. This solves the critical pain point in AI-assisted development where developers cannot trace which AI prompt or model produced specific code, making debugging and root cause analysis guesswork. Unique selling point is providing full lineage and provenance for AI-generated code. Overall value proposition: brings transparency, accountability, and maintainability to AI-driven coding workflows.

📈 Market Timing

In 2025-2026, AI coding tools like Cursor and Claude are seeing explosive adoption, with growing focus on AI governance, compliance, IP ownership, and code maintainability. User demands are shifting from pure generation to responsible and debuggable AI integration. Regulatory emphasis on AI transparency aligns perfectly. This is an ideal window before standards solidify. Excellent Timing.

✅ Feasibility

Technical implementation of a local MITM-style proxy for API interception is achievable with existing libraries, though maintaining compatibility across evolving AI tools (Cursor, Claude, etc.) adds maintenance effort. Low operational costs as it runs locally; minimal supply chain or compliance risks. Strong scalability per developer/user. Overall High feasibility for a skilled solo or small team with proxy and devtool experience.

🎯 Target Market

Primary users: Individual developers, engineering teams, and tech companies actively using AI coding assistants (Cursor, Claude Code). Demographics: Professional software engineers aged 25-45. Industries: Software development and IT. Geographic: Global with concentration in US and Europe tech hubs. Market size: Part of the rapidly expanding AI developer tools sector with strong demand. Core pain points: Lack of visibility into AI code origins hindering debugging and compliance. High willingness to pay for productivity and risk-reduction tools.

⚔️ Competition

Low. Direct competitors: 1. LangSmith (smith.langchain.com), 2. PromptLayer (promptlayer.com), 3. Helicone (helicone.ai), 4. Phoenix by Arize (arize.com/phoenix). Advantages: Highly specialized for code lineage in IDE tools like Cursor with local privacy-focused proxy and event-triggered capture (≥4 lines); tighter integration for dev workflows. Disadvantages: Newer entrant with potentially fewer broad LLM observability features and integrations compared to established platforms.

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