1 link tagged with all of: iteration + adjacent possible + ai
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Exploring the effectiveness of coding agents hinges on effective user input, constraints, and context. By applying Steven Johnson's patterns for generating ideas, the article demonstrates how to enhance coding agent outputs through structured prompting and feedback mechanisms. This approach encourages incremental development, reuses existing solutions, and fosters a collaborative environment between humans and AI.
- Coding agents produce better output when users apply Steven Johnson's "good ideas" patterns (adjacent possible, liquid networks, slow hunches) as prompting strategies rather than treating the agent as a one-shot answer machine.
- Structured constraints and iterative feedback loops—not just clearer instructions—are what actually improve agent performance, mirroring how innovation historically emerges from recombination rather than isolated genius.
- Treating coding agents as collaborators to think alongside, rather than tools to extract finished answers from, leads to more incremental, reusable, and higher-quality code.