Quality

Definition

Quality coverage in this archive spans 7 posts from Nov 2017 to Mar 2026 and leans into practical engineering craft: interfaces, testing, and maintainable implementation details. The strongest adjacent threads are ai, testing, and code review. Recurring title motifs include ai, code, evaluation, and testing.

Working claims

  • The through-line is clarity first: simple designs that survive change beat clever abstractions.
  • Early posts lean on code and stop, while newer posts lean on ai and evaluation as constraints shifted.
  • This topic repeatedly intersects with ai, testing, and code review, so design choices here rarely stand alone.

How to apply this

  • Keep interfaces small, automate regressions early, and make operational assumptions explicit in code.
  • Start with the newest post to calibrate current constraints, then backtrack to older entries for first principles.
  • When boundary questions appear, cross-read ai and testing before committing implementation details.

Where teams get burned

  • Abstracting before usage patterns are stable enough to justify indirection.
  • Treating style consistency as optional until quality and velocity both degrade.
  • Applying guidance from 2017 to 2026 without revisiting assumptions as context changed.

Suggested reading path

References