The fastest path to reading code well is usually:
1. Read tests before reading implementation
2. Read small amounts of code
3. Verbalize your prediction before running
4. Run it
5. Compare reality vs prediction — precisely, not approximately
6. Deliberately induce a failure and predict how it breaks
7. Modify it surgically — one change, one observation
8. Find the code that caused the gap and read it again
That loop builds a causal model of execution, not just familiarity with syntax.
A major mistake in programming education is emphasizing:
- large-scale code production,
- syntax memorization,
- or blank-page coding.
Those often create shallow "token generation" ability without deep execution understanding.
A second mistake is optimizing for learner comfort:
- writing feels productive,
- prediction feels uncertain,
- so prediction gets skipped.
But discomfort before running is where the model gets built.
This is why:
- debugging teaches more than tutorials,
- tracing teaches more than copying,
- modifying existing systems teaches more than greenfield building,
- reading well-written code can outperform writing lots of mediocre code,
- and corpus quality matters as much as volume — a bad model of what code looks like is worse than no model.