# I open-sourced the cockpit we used to understand huge engineering departments in days

> Source: <https://dev.to/yaniv_zalevas_a91253c56bb/i-open-sourced-the-cockpit-we-used-to-understand-huge-engineering-departments-in-days-4h6m>
> Published: 2026-09-19 09:07:13+00:00

My name is Yaniv, and I run Codpal, a CTO-as-a-service company. Over the years we've walked into a lot of new companies, and we always needed the same thing: a fast understanding of how engineering actually runs — what's in the roadmap, what the developers are focused on, who the main developers carrying the work really are.

Every engagement started with weeks of meetings to map what the data could have told us in hours.

Deckgauge pulls Jira, GitHub, GitLab and Azure DevOps into a single data warehouse of development information, and gives you the cockpit picture:

It let us sit with division leads on day one and speak their language — with real data instead of guesses.

Deckgauge was largely built by AI agents (Claude Code with an agent workflow) — human-reviewed on every merge. The tool that measures engineering is itself an experiment in AI-built engineering. Scroll the commit history; it's the artifact.

The Next New Thing (56K subscribers), a weekly show covering the best GitHub repos, featured Deckgauge — demoing the board live and taking on the "why not just Jira?" question:

🎬 [https://youtu.be/1fHsIveXRa8?t=1445](https://youtu.be/1fHsIveXRa8?t=1445)

It worked so well we decided it's a gift for everyone to use:

Fully **self-hosted** — single Docker deploy, nothing leaves your infrastructure. In a world where every engineering-metrics SaaS wants your ticket data in their cloud, we think that matters. License is FSL-1.1 (free for any use except offering it as a competing product; converts to Apache-2.0 in September 2028).

If you're an engineering leader who wants visibility without the vendor cloud, or you just want to see what an AI-agent-built codebase looks like — take it for a spin. Feedback welcome, especially the critical kind.

*Cross-post note: this continues the AI-agents journey from my earlier posts here — the tool that measures engineering is itself an experiment in AI-built engineering.*