Codev OS

The easiest way to get started with Codev OS 3.0 isn't a long installation process—it's just one command. If you want to see how the agentic workflow handles your own codebase, you can run the setup directly via npx: >npm install -g @cluesmith/codev Then, if you want to start a new project, do >codev init or for an existing project >codev adopt Everything is open-source and ready to go. Check the README for the full breakdown of the new 3.0 features: https://lnkd.in/gnVnrHnb

ICYMI: We put Claude Code and Codev OS head-to-head to build the same app using the same model (Claude Opus). The results highlight why a "multi-agent" protocol matters for production code. The Breakdown: ✅ Claude Code: 8 bugs (including 1 Critical re-render loop). ✅ Codev: 6 bugs (0 Critical). ✅ The Advantage: A +1.3 lead in overall code quality. It was the same base model — Opus 4.5. However, by (a) having a consistent protocol for specifying and planning projects, (b) having Google's Gemini and OpenAI's Codex review the specs, plans, and code, we caught many bugs (including a critical security bug) before the code went into production. If you're interested in the full bug-by-bug comparison, we’ve published the research report here: https://lnkd.in/gGEVBZpD

🚀 ICYMI: We ran a 14-day sprint with Codev OS (https://codevos.ai/) earlier this year, and a single developer was able to ship 106 PRs merged in those two weeks on a project that already had 90,000 lines of code. 🚀 Against industry benchmarks, that matches the output of a 3-4 person elite engineering team. We didn’t achieve this by adding headcount or by vibecoding, but by using a disciplined approach to human AI joint development. The Highlights: ✅ 85% Autonomy: 22 out of 26 feature projects were completed from start to finish with zero human intervention. ✅ Built-in Quality: Our multi-agent review protocol caught 20 pre-merge bugs, including a security-critical socket permissions gap that could have been disastrous if it had hit production. ✅ The Bottom Line: We saw a 3.4x ROI, saving roughly 33 hours of manual engineering time in just two weeks. This was done based on a few key principles: treating natural language design conversations as first-class citizens, enforcing phased quality checks, and using multiple model reviews to catch bugs much earlier 🏛️ As we launch Codev 3.0, we’re looking back at these results as the foundation of what "Context-Driven Development" can actually do. If you’re interested in the math behind the efficiency, the full value analysis is here: https://lnkd.in/gv83PZBW

We’re soft-launching Codev OS 3.0 today. The goal for this release was to move beyond the friction of "AI in a separate window" and treat agents as a standard part of the dev loop. Key updates in 3.0: ✅ VS Code Extension: Now, in addition to using Codev purely from the terminal and the web-based Tower UI, you can use Codev inside Visual Studio Code and its derivatives (like Cursor Cursor and Windsurf). ✅ Pluggable Architecture: Support for swapping between different forges ( GitHub, GitLab, Gitea) and different CLI harnesses (e.g., Claude Code, Google's Gemini CLI, OpenAI's Codex CLI, OpenCode). Also, more flexibility in how models are used (e.g., you can select just Codex for code reviews, but use Codex and Gemini for spec reviews). ✅ Team Coordination: Builders can now manage multiple PRs simultaneously and suggest specific human reviewers based on the nature of the changes. We’ve also added a Team tab in Tower to keep track of concurrent agentic and human tasks. The system is designed for a spec-first workflow in which natural language drives the creation process, and the code serves as the compilation target. 🏛️Watch M Waleed Kadous's overview of 3.0 below:

ICYMI: When you build software through ephemeral AI chats, you’re essentially deleting your source files. In a recent piece, Codev OS's Waleed Kadous dives into why the industry is moving toward Context-Driven Development. The core shift? Treating context as a managed artifact. Relying on ephemeral AI chat logs to build software is like compiling your C++ code to assembly and then deleting the source files—you lose the reasoning and the decisions that actually make the code work. Whether you're using tools from Google, GitHub, or our own Codev OS, the goal is the same: Write your context down, check it into git, and let it compound. Read the full deep dive here: https://lnkd.in/gBiF8xRb

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