LineageLens
AI wrote it. LineageLens proves it.
What it does
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)
Does the same job
all alternatives →
- KAKeelTest – AI-driven VS Code unit test generator with bug discoveryJan 2026 · keelcode.dev · ▲30
I built this because Cursor, Claude Code and other agentic AI tools kept giving me tests that looked fine but failed when I ran them. Or worse - I'd ask the agent to run them and it would start looping: fix tests, those fail, then it starts "fixing" my code so tests pass, or just deletes assertions so they "pass". Out of that frustration I built KeelTest - a VS Code extension that generates pytest tests and executes them, got hooked and decided to push this project forward... When tests fail, it tries to figure out why: - Generation error: Attemps to fix it automatically, then tries again -…
- PIPrompt Inspector – A browser-like inspect tool for prompts2025 · inspectmyprompt.com · ▲23
I was working on a project that involved indexing GitHub repos that used really long prompts. Iterating over each section and figuring out which parts of the prompt led to which parts of the output was a quite painful. As a frontend dev, I kept thinking it would be nice if I could just 'inspect element' on particular sections of the prompt. So I built this prompt debugger with visual mapping that shows exactly which parts generate which outputs. Now that context windows are getting huge, working with massive prompts should not be as difficult. Planning to open source this soon, but I'd love…
- HWHow well do you use Claude Code?Jul 2026 · ▲21
A bunch of companies that I spoke to had their own claude & codex OTel dashboards that showed spend + seats per month. However, none of the dashboards actually analyzed how the engineers worked with the tools and if there were any areas for improvement! That's why I created https://www.promptster.ai. Managers get aggregate level view of code quality and how that ties with team workflows (nothing on a per-engineer level). While engineers get personalized coaching on how they can save tokens while keeping output high. We also have a tool built for individuals to test their local…
More work this month
the category →
The app store for voice native apps that lives in your notch
Work · 28d ago · voiceos.com
Mac-native app for screenshots, recordings and collaboration
Work · 5d ago · cleanshot.com
Launched alongside, May 2026
the whole month →
Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com