Codev OS

Have you ever shipped code with Claude only to find out it didn’t make its way through your CI pipeline (it was stuck), but your AI didn’t do anything about it, and you only found out at the end of the day when you got an email? CI Channel is a tool that uses the brand new Claude Code Channels feature to prevent that from happening. As soon as CI fails in your favorite forge ( GitHub, GitLab, Gitea), it sends a message to your Claude Code agent. It investigates the issue and can suggest a fix before you’ve even realized there’s a problem. While most of the attention has gone to how to hook up agents to Discord, there are lots of cool, practical things you can do with channels. The project was built using Codev OS, so it’s fully documented and tested. It’s a showcase of how our agentic architecture enables us to ship modular, high-impact tools that solve real developer friction points. To use it, just do [npx -y ci-channel setup --repo owner/project] in your favorite Claude Code workspace. ⬇️ Repo: https://lnkd.in/grBZcJW8

Codev OS, the human-AI co-development operating system for creating production quality code now has its own Linkedin page if you'd like to follow along.

Closing the gap between AI demos and production-quality codebases. There is a significant gap between building a quick AI demo and maintaining a production-quality codebase. AI-generated code is rarely tested or documented by default. The architecture is often ad hoc rather than intentional. As you add features, the system becomes increasingly difficult to maintain. We built Codev OS (https://codevos.ai/) to help close that gap. Codev is an operating system for humans and AI agents to build production-quality code together. It’s the layer that augments agent harnesses like Claude Code. It replaces the "honor system" of prompting with a deterministic state machine that enforces the rigor required to mitigate unintended consequences in complex systems. The Architecture of Discipline ✅ The Architect-Builder Pattern: You work with an Architect agent to define specifications and implementation plans. Builder agents then execute these plans in isolated git worktrees, ensuring the human remains the director of the system. ✅ Multi-Model Consultation: Every phase is reviewed by three independent models ( Claude, Google's Gemini, and OpenAI's Codex). During our 2.0 sprint, no single model caught more than 55% of the bugs; the combined consensus caught security-critical flaws—including an SSRF bypass—before they could ship. ✅ Context as Code: Specs and plans are version-controlled alongside the source code. This hierarchy enables progressive disclosure, meaning that a new builder agent understands the architecture and intent before it ever touches a file. ✅ Enforced Protocols: Using the Porch orchestrator, Codev ensures that agents cannot skip the specification, planning, or testing phases. In our head-to-head testing, this methodology produced 2.9x more test lines and a significantly higher "deployment readiness" score. The role of the software engineer is evolving from a hands-on coder to a system architect. CodevOS is built to support that shift, providing the framework to manage autonomous agents with the same rigor you’d apply to an elite human engineering team. Explore the open-source repo and the technical tour: https://lnkd.in/g9aJyJrW #CodevOS #OpenSource #AgenticAI