Open source Kanban desktop app that runs parallel agents on every card

Original Article ↗ Hacker News Discussion ↗

Overview of the Project

  • Open-source desktop Kanban app where each card can run its own coding agent in parallel.
  • Emphasis on “local-first”: data lives in a .kanbots/ folder alongside repos, with SQLite and worktrees; no servers or telemetry for the desktop edition.
  • Intended as an orchestration layer for agents using familiar project-management metaphors (cards, columns, boards).

Comparisons to Other Tools

  • Compared to Windsurf, Linear’s agent work, Vibe Kanban, Cline’s Kanban, OpenAI Symphony, Multica, Platespinner, Agent Kanban, and several smaller projects.
  • Some see it as “just another” Kanban→agent orchestrator; others argue overlap is natural and multiple competitors are expected.
  • Vibe Kanban is cited as feature-rich but effectively abandoned; several people suggest copying its best ideas (remote support, “Open in VS Code”).
  • Some ask how this differs from wiring agents directly into Jira, GitHub boards, ClickUp, etc., via existing APIs/CLIs.

Local-First vs. Cloud Account

  • For some, local-first with no mandatory cloud account is “table stakes” for adoption.
  • Conflicting reports: one commenter says a cloud login is required even for local use, another says they ran it locally without signing up. Status is unclear.

UI, UX, and Landing Page Feedback

  • Strong criticism of the marketing site: looks like generic AI-generated SaaS, “vibe coded,” slow on mobile, and choppy on WebKit; comparisons to other Claude-designed pages.
  • Some argue many AI-designed frontends feel homogenous and soulless, even when technically polished.
  • Suggestions that better visual design could be a differentiator among similar tools.
  • Kanban board on the landing page reportedly renders poorly on mobile.

Parallel Agents, Review Load, and Workflow Concerns

  • Interest in Kanban-as-orchestrator but skepticism about unsupervised agents; many report poor experiences when not closely supervising.
  • Core tension: agents can run many tasks overnight, but humans must review sequentially; more parallelism means more diffs to inspect.
  • Several admit they often do not review all generated code, especially for one-off tools; others insist full review is essential for serious or production systems.
  • Worry that organizations are shipping “AI slop” without real engineering discipline; others counter that code quality was often poor even before LLMs.

Worktrees, Infrastructure, and IDE Integration

  • Some want “1 task = 1 worktree = 1 full IDE instance,” not just “1 task = 1 chat,” including dedicated local URLs and infra per worktree.
  • Various homegrown scripts/tools (shell, bun CLIs, direnv, port management) are described; several say their custom setups are so tailored that GUI orchestrators struggle to compete.
  • Questions about how the app handles dependent cards, shared state, and conflict resolution remain largely unanswered in the thread.