Thinking in Code
20 days ago
- Solving coding puzzles (loops, conditions, pseudocode, debugging) improved the author's use of AI coding agents.
- Programming is about making vague ideas precise, not just remembering syntax; LLMs handle syntax but precision remains key.
- Core programming concepts (loops, conditions, while loops, if/else, debugging) teach thinking habits applicable beyond coding.
- LLMs fill gaps well, but we must be aware of implicit decisions we hand over when leaving things undefined.
- Better prompts are those with more structure, not just better wording; they are programs expressed in prose.
- Natural language is becoming an interface to computation, making computational thinking valuable for more people.
- Understanding primitives like loops, conditions, state, and edge cases helps in everyday problem-solving.
- Learning to think in code is becoming more useful even as LLMs reduce the need to write code.