GitSignal

Measure true engineering impact, not lines of code.

5 followers

Measure true engineering impact, not lines of code.

5 followers

Evaluate developer performance accurately in the AI era. GitSignal uses a fine-tuned model to analyze actual code diffs, estimating the true human effort (in hours) behind every commit—making LOC and commit counts obsolete. 🤖 AI Assistant: Query your org's historical knowledge 🛑 Auto-Standups: Generated from merged code 📊 PM Dashboard: Track feature timelines instantly ⚖️ Fair Metrics: Measure impact vs. AI leverage Cancel your status meetings and get true engineering visibility.

Free Options

Launch tags:Productivity•Developer Tools•Artificial Intelligence

Launch Team

Pioneer — Fine-tune any LLM in minutes, with one prompt

Fine-tune any LLM in minutes, with one prompt

Promoted

Hunter

📌

Hey Product Hunt! 👋 I'm Hooman, the founder of GitSignal. As a tech lead, I used to spend a lot of time in status meetings trying to translate raw code into business impact. I realized that evaluating engineering output today is completely broken. When a developer can generate 1000 lines of boilerplate in minutes with Claude Code, metrics like LOC or commit counts are completely dead. Furthermore, while any generic AI can summarize what a dev did today, summarizing today doesn't tell you if your team is actually productive over a year, what they're burning time on, or who your true experts are. I built GitSignal to fix this. Instead of just scraping GitHub metadata, I fine-tuned a model that read your actual code diffs to provide realistic insights from every code commit, like estimates of the human effort (in hours) or the true impact of the commit. So 1 hour spent updating docs is not equal to 1 hour spent on a major feature. We translate that raw code into an always-on Engineering Intelligence Platform: 🛑 The Meeting Killer: Automated daily standups and PM feature timelines generated directly from code, not self-reports. 📉 True Effort & Trends: Spot productivity dips instantly and see where your engineering hours are actually going, bypassing "Claude Code inflation." 🧠 Historical Knowledge Graph: Ask our AI Assistant questions like, "Who on the team has the most experience integrating Stripe?" and get an answer based on years of actual code contributions, not resumes. ⚖️ Fair Metrics: We map developers on an "Impact vs. Leverage" matrix, rewarding deep problem-solving over noisy commits. It takes about 5 minutes to connect your repos. We have a 7-day free trial, and I would love for you to see how accurate our effort estimations are for your team's historical commits. I'll be hanging out here all day—drop your questions below or let me know how you are currently measuring AI leverage on your teams! 👇

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1mo ago

Hunter

You use the promo code "PRODUCTHUNT" on the checkout to get 1 month of Hobby plan for free.

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1mo ago

Hunter

Please see the live demo at : demo.gitsignal.ai

It is tracking Gemini CLI which is an open source project by Google. It updates every 2 hours with real data.

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1mo ago

jared.so

The "Impact vs Leverage" matrix for developers is a really smart framing. How does the effort estimation model handle commits that are mostly AI generated vs hand written? Great concept.

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1mo ago

Hunter

@mcarmonas Thanks! We actually don't try to detect "AI vs human" code line-by-line. Instead, our model estimates the effort twice for every commit: once with AI tools, and once without. The model is fine-tuned to understand the things AI is great at (like huge blocks of boilerplate or tests). The ratio between these two estimates becomes the "AI Leverage" metric.

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1mo ago

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