Show HN: Stage – Putting humans back in control of code review

Original Article ↗ Hacker News Discussion ↗

Chapters and PR Workflow

  • Many reviewers like “chapters” that auto-split large PRs into logical groups, often matching what should have been several smaller PRs.
  • Some see chapters as complementary to stacked PRs and good commits; others argue this just compensates for bad practice and misbehaving teammates.
  • Users want manual control (editing chapter splits, CHAPTERS.md config), and chapter-level actions (mark as viewed, comment on chapter).

Context, Intent, and “Why”

  • Several commenters say the core problem in review is understanding intent, requirements, and acceptance criteria, not just “what changed.”
  • Suggestions: pull context from tickets (GitHub issues, Linear), embed agent context into git, map changes to specs/ACs/tests, and ensure PRs explain why and how to verify.
  • Some tools and workflows mentioned try to distill review learnings back into agents and docs (LESSONS.md, BUGBOT.md, agents.md).

Human vs AI Review

  • Product positions itself as “human-in-the-loop,” using AI to guide attention, not replace review.
  • Skeptics counter: if AI can reliably summarize and flag focus areas, why not let it just do the review? Others fear humans will only read AI’s “what to review” list.
  • Some see human review as essential for design tradeoffs, knowledge sharing, and onboarding, even if AI can mechanically check correctness.

Commits, Git, and Abstraction Level

  • Debate over whether chapters duplicate what good commits should already provide.
  • Critics argue tools like this discourage disciplined commit hygiene, which hurts bisect/blame/history.
  • Others say topic-grouped commits are hard and costly to maintain, especially for teams and AI-generated code; PR-level grouping is seen as more practical.

Trust, Deception, and AI Framing

  • Strong concern that AI narratives “spin” changes, making slop look polished and discouraging deep scrutiny.
  • Question whether a separate “narration” agent can truly be trusted not to mislead when operating on AI-generated PRs already optimized to appear good.

Business Model, Local vs Cloud, and OSS

  • Some want this as OSS or a local-first tool; distrust long-term SaaS for core dev workflows.
  • Pricing is criticized as high relative to general LLM subscriptions, and lack of upfront pricing info is a turnoff.
  • Others note incumbents (GitHub/GitLab) could integrate similar features; moat is questioned.