Topic hub · Software engineering and LLMs

Software engineering and LLM investigations

Software engineering and LLM investigations on maintainability, architecture, security, programming practice, AI systems and code quality.

About this collection

LLMs change the cost of producing code and prose, but they do not remove the cost of understanding, verification or ownership. This hub brings those concerns together with older software-engineering questions: what makes a change safe, when an abstraction clarifies a boundary, how dependencies expand a threat model, and why readable output can still be structurally wrong.

The selected path begins with maintainability and learning, then connects model-assisted work to security, framework design and philosophical accounts of AI. The full list is intentionally broader. It includes service objects, callbacks, language syntax, functional abstractions and adversarial protection for artists because the practical consequences of generative systems reach beyond code completion. These investigations do not assume that more automation is either progress or decline. They ask which evidence survives the demo, where review must remain human, and how a team can preserve a coherent system when plausible output becomes cheap.

Start here

  1. Good code after LLMs: maintainability was never just readability What maintainability means after LLM agents, and why explanation speed does not replace design integrity.
  2. If LLMs can write code, why learn programming from books? Whether programming concepts still matter in a world where LLMs and coding agents can generate production-grade code.
  3. Supply-chain attacks, npm, Laravel, and Ruby teams Npm and Laravel-style supply-chain attacks, and how Ruby and Rails teams can reduce risk in production, CI, and local development environments.
  4. APoSD and Clean Code from a Ruby point of view A Ruby-focused synthesis of John Ousterhout and Robert C. Martin on method length, comments, TDD, deep modules, and practical team policy.
  5. LLM evolution through a phenomenological lens LLM evolution read as a change in meaning, action, time, trust, and the lifeworld rather than as a brain, tool, or species metaphor.

All investigations

  1. Ghostty, agent spinners, and the Spotlight loop on macOS 27
  2. AI-guided compiler optimization: search, semantics, and trust
  3. How Clojure complements Ruby
  4. Music discovery by conversation
  5. Ruby and Rails hiring trends in Hacker News, 2019-2024
  6. APoSD and Clean Code from a Ruby point of view
  7. Go semicolon insertion: why opening braces stay on the same line
  8. Callbacks, invariants, and durable events in Rails
  9. SICP ideas in practical Ruby
  10. Supply-chain attacks, npm, Laravel, and Ruby teams
  11. The Rails job that ran before checkout committed
  12. Brotli, gzip, and Propshaft in Rails
  13. Ruby, Go, Rust, and Zig for simple CLI apps
  14. On zygohistomorphic prepromorphisms
  15. LLM evolution through a phenomenological lens
  16. Good code after LLMs: maintainability was never just readability
  17. Optimistic locking for editing a CRUD app
  18. If LLMs can write code, why learn programming from books?
  19. sqlc, iBATIS, and SQL-first data access
  20. Propshaft versus Sprockets in Rails assets
  21. Private and protected methods in Ruby
  22. Service objects and Rails conventions
  23. Handling Server-Sent Events from a Rails backend
  24. Glaze vs Nightshade: style cloaking, data poisoning, and limits
  25. Relational algebra, SQL, and Arel
  26. SQLite, PostgreSQL, and the smallest useful database choice
  27. Arel versus Sequel: an AST inside Rails and a toolkit outside it

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