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Scaling long-running autonomous coding

simonwillison.net

January 20, 2026

3 min read

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35/100

Summary

Wilson Lin at Cursor has experimented with a large fleet of autonomous coding agents, coordinating their work to generate over a million lines of code and trillions of tokens. The process involved running planners and sub-planners to create tasks for workers to execute.

Key Takeaways

Cursor ran hundreds of autonomous coding agents for nearly a week to build a web browser from scratch, resulting in over 1 million lines of code across 1,000 files.
The agents utilized planners and sub-planners to create tasks, with a judge agent determining project completion.
The FastRender repository includes build instructions and uses Git submodules to access various web specifications, enhancing the agents' coding capabilities.
Initial skepticism about the project was addressed after the inclusion of build instructions, leading to a functioning browser window capable of rendering web pages.

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Community Sentiment

Mixed

Positives

AI significantly reduces the cost of remixing existing solutions, allowing for rapid exploration of uncharted territories in coding and development.
The ability of LLMs to pattern match through codebases showcases impressive capabilities, enabling developers to leverage AI for more efficient coding.

Concerns

LLMs often mimic intelligence rather than exhibit true understanding, leading to a reliance on human judgment to assess their outputs.
Concerns about the environmental impact and costs associated with AI models, such as token and electricity costs, raise important questions about sustainability.