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Scaling long-running autonomous coding
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Overview
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.
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Scaling long-running autonomous coding
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- Praise: AI significantly reduces the cost of remixing existing solutions, allowing for rapid exploration of uncharted territories in coding and development.
- Praise: The ability of LLMs to pattern match through codebases showcases impressive capabilities, enabling developers to leverage AI for more efficient coding.
- Concern: LLMs often mimic intelligence rather than exhibit true understanding, leading to a reliance on human judgment to assess their outputs.
- Concern: Concerns about the environmental impact and costs associated with AI models, such as token and electricity costs, raise important questions about sustainability.
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