Ask HN: Is anyone experimenting with different ways of using LLMs for coding?

Hacker News Discussion ↗

Spec-driven vs. agentic/orchestrated coding

  • Many argue the biggest win is clearer specs, not complex orchestration: single intent, explicit I/O contracts, constraints, and preconditions, plus progressive clarification.
  • Preference by some for “clean slate” use: minimal or no long-term memory to avoid “context rot” and intent dilution; use external markdown/docs that the model can grep.
  • “Skills” (narrow, reusable instructions and tools) are seen as underrated; “agents” mostly help with context management and constraints.
  • Others report success with multi-agent teams, graph-based workflows, and role-pinned models (cheap for mechanics, strong for planning/review), but acknowledge high token costs and complexity.

Flow state and human experience

  • Common complaint: chat-style prompting and waiting breaks flow; users feel like managers supervising a junior dev instead of coding.
  • Some reclaim flow by:
    • Doing deep planning/spec work, then letting agents run unattended and reviewing later.
    • Using pair-programmer-style tools where the human drives and AI assists in small increments.
    • Running multiple parallel tasks/agents and treating “orchestration” itself as the flow activity.
  • Others feel multitasking across many agents is draining, not flow-inducing.

Practical workflows and harnesses

  • Frequent patterns:
    • Issue/PR-centered workflows where LLMs implement tickets, generate plans, and iterate via comments.
    • Three-tier documents (philosophy → spec → design) before code generation.
    • “Workboxes” / sandboxes per feature with automated branches, PRs, and deployable test instances.
    • Hermetic agents: separately generating code and tests from the same spec to avoid confirmation bias.
  • Custom harnesses span terminals, browser-based sandboxes, DAG/graph engines, and local VMs/containers, often with strict permissions and tooling (e.g., bug-hunting loops, security sandboxes).

Autocomplete, pairing, and editor integration

  • Strong interest in editor-centric flows: inline autocomplete, edit-completion, multiple anchored conversations per code region, and terminals where agents work in parallel panes.
  • Some see autocompletion harness and UX as more important than squeezing out slightly better models.

Learning, quality, and safety

  • Concern that fully agentic coding harms understanding and learning, especially for juniors; alternatives include having the LLM produce detailed instructional docs or act as a tutor/reviewer.
  • Many stress using LLMs to improve human understanding (diagrams, summaries, explanations) because human code comprehension is now the bottleneck.
  • Security and guardrails are recurring themes: sandboxed agents, restricted tools, and automated checks/tests between iterations.