Ask HN: What is your (AI) dev tech stack / workflow?

Hacker News Discussion ↗

Common Tools and Stacks

  • Most use a small core: Claude Code, Codex (OpenAI), OpenCode, Pi, VS Code/Zed/Neovim, Git, Docker, FastAPI/TypeScript/Python.
  • Several build custom harnesses or terminal-first setups (tmux/zellij/Ghostty, TUI launchers, custom Vim/Neovim integrations).
  • Others rely on cloud agent platforms and orchestrators (Conductor, exe.dev, Pi, Mecha-AI, various MCP/skills-based systems).
  • A minority stick to traditional IDEs (PyCharm, IntelliJ, plain vim) or refuse AI tools entirely.

Workflows and Roles for AI

  • Common patterns: architect → implementer → reviewer agents; or “sword and shield” (one model codes, another audits).
  • Many use AI mainly for debugging, explanation, refactoring, and test generation, not raw code writing.
  • Some go “full factory”: multi-agent swarms, worktrees per feature, CI/CD-heavy pipelines, and continuous automated test runs.
  • Others deliberately keep it simple: one agent, one repo, request minimal diffs, manual review and testing.

Context, Specs, and Planning

  • Context loss is widely seen as a bigger problem than model quality.
  • Popular mitigations: spec-driven development, detailed markdown requirements, Gherkin stories, todo.md / ticket hierarchies, and immutable phase docs (discovery → plan → implementation → verification → review).
  • Several keep separate repos or folders just for plans and AI artifacts to preserve context across sessions.
  • Persistent “memory” features are often disabled due to unpredictability; explicit written artifacts are preferred.

Automation vs Control & Quality

  • Strong emphasis on TDD, linters, type systems, and CI as guardrails against sloppy or unsafe AI output.
  • Some run agents in VMs/containers with restricted credentials to avoid destructive actions.
  • Views differ on full automation: some claim thousands of AI-driven commits; others only trust AI for adjacent tooling or small utilities.

Teaching and Newcomers

  • For beginners, many recommend: minimal tooling (Claude/Codex + simple editor), tight feedback loops, and focus on learning to describe problems precisely.
  • Several warn that over-complex multi-agent setups cause confusion; start with “slow code,” strong specs, and manual review.

Skepticism and Concerns

  • A visible minority avoid AI on principle or for learning-quality reasons, stressing the value of struggle and deep understanding.
  • Others worry about “vibe-coded” messes, context mismanagement, and long-term maintainability of AI-generated code.