Blog

I've spent the last month watching how teams use cto.new, and I've noticed a few patterns in what works and what doesn't. It's not really about the setup, most people figure that out fine. It's about how you think about delegating work to an AI agent.

Jeremy King, a veteran software architect near Raleigh, North Carolina, has turned solo development into a high-velocity operation through AI tools—particularly using cto.new via the GitHub interface. A longtime consultant and hands-on coder, Jeremy now builds full-stack projects like Leaderboard Fantasy and TutorPro by orchestrating a network of AI coding agents. His workflow runs almost entirely through GitHub: he uses Claude to write specs, breaks work into small issues, assigns tasks directly to cto.new for implementation, and uses Copilot for QA reviews.

An alloyed agent runs on more than one LLM in a single agentic loop with shared context.

There has been a lot of chatter in the coding agent world over the last few months about context engineering and managing long contexts. The defining work on this is probably...

The mission is straightforward: put our best in class coding agent behind a task orchestration agent we call cto.new, make it free and empower a generation of software engineers to break through the clouds and soar into wild new levels of productivity.

Wequity uses cto.new in their production codebase via the Linear interface. Learn how they have accelerated their development process using the two tools in tandem.

How to get started with cto.new

Engine Labs has posted a state of the art score on Terminal Bench - the top score with a Claude Sonnet 4 class model and #2 overall.

Researching how best to give LLMs terminal access and address the halting problem.