Brute forcing solutions through persistence and automation
Core Idea: Sometimes seemingly superhuman results can be achieved simply by applying brute force approaches and learning from the process, especially when AI can handle repetitive tasks.
Key Elements
- Popularized by Dan Luu, the method suggests that persistence with brute force approaches can yield impressive results
- AI coding excels at handling repetitive, brute force work that humans would find tedious
- This approach becomes viable for problems previously dismissed as "too much work"
- The method reveals opportunities for optimization through accumulated experience
- Unlike humans who seek efficiency when bored, LLMs will continue repetitive tasks indefinitely
Application Strategies
- Use LLMs to tackle large refactoring problems that would overwhelm human patience
- Build workflows that automate repetitive tasks using AI assistance
- Continuously monitor AI work to identify optimization opportunities
- Look for previously abandoned "too much work" problems as candidates
- Apply learning from brute force solutions to create more elegant approaches later
Connections
- Related Concepts: Requirements Not Solutions (focusing on what needs to be done), Scientific Debugging (systematic problem-solving)
- Broader Context: Automation Principles (leveraging technology for repetitive tasks)
- Applications: Type-Driven Refactoring (using compiler errors to guide changes), Test-Driven Development (incremental improvements through testing)
References
- Dan Luu (2022). "The Bulldozer Method." https://x.com/danluu/status/1570298241681616897
- Edward Z. Yang (2025). "AI Blindspots" collection, March 2025.
#problem-solving #productivity #ai-development #refactoring
Connections:
Sources:
- From: AI Blindspots