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AI-driven Kanban: AI proposes subtasks, estimates and dependencies; humans review. Next.js 16, Supabase, AI SDK.
Translation pending
Taken from the repository README
README
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A Kanban board where AI works like a teammate under human supervision: when you create a task,
a language model proposes technical subtasks, estimates and dependencies. You review every
proposal (accept, edit or reject) before anything is saved. Blocked tasks, dependency cycles and
bottlenecks are detected with deterministic, tested logic and surfaced in real time.
Status: v0.1 is live: boards, auth, RLS, drag and drop and a one-click demo. Next up: v0.2
(AI task decomposition).
Live: ai-driven-kanban.vercel.app (try the demo, no sign-up needed)
▶ From a card to an acked plan in 28 seconds. Click to watch on YouTube.
Why ackboard
AI that writes straight to your board is a liability. Auto-generated tasks look productive until
someone has to clean them up.
ackboard keeps the AI in draft mode. It suggests and you decide, so your board only holds
work a person signed off on. Ack is engineer shorthand for "seen it, approved", and that's the
whole product: nothing the AI suggests is saved until you ack it.
How it works
The review is the product
Everything on the board
🔗 Dependencies that mean something
Blocked-by links, cycle checks, blocked badges and a bottlenecks panel.
👥 Built for the whole team
Live sync and presence for everyone on the board; invite with single-use links.
📝 Cards with depth
Markdown, labels, due dates, members and subtask checklists.
✋ Move fast
Reorder cards and columns with mouse, touch or keyboard.
🔒 Private by default
Owner, editor and viewer roles, enforced by Row Level Security in Postgres.
🧪 Try it in one click
Demo boards with no sign-up, deleted automatically after 7 days.
Engineering highlights
Designed, built and shipped end to end by one person, in production at
kanban.utitytools.com.
AI you can trust: versioned prompts, streaming structured output validated with Zod, user
text delimited as untrusted input, an eval suite for quality, and per-call
observability (cost, latency, tokens, outcome).
Security in depth: Row Level Security on every table, tested with pgTAP; column-level
grants; a nonce-based Content Security Policy; and AI calls routed only to providers that don't
train on your data (ADR 0025).
Deterministic where it matters: cycle detection, blocked tasks and bottlenecks live in pure,
unit-tested TypeScript, not in the model.
Cost under control: daily quotas per account and per demo session, plus a global budget cap
(ADR 0016,
ADR 0024).
Tested on every PR: unit tests (Vitest), end-to-end tests (Playwright) and database tests
(pgTAP). Production deploys only after migrations succeed.
What is sent: when you click Suggest with AI (subtasks), the card's title, description
and existing subtask titles; when you click Suggest blockers with AI, the card's title,
description and column, plus the titles, columns and done state of up to 100 other cards on
the board. Nothing else. It goes through Vercel AI Gateway to
the model provider (Anthropic, Claude Haiku 4.5 by default). Don't put secrets or personal
data in cards on a board where you use the AI.
No training: every call asks the Gateway to route only to providers that don't train on
the data you send; if none is available, the call fails instead
(ADR 0025).
What is stored: nothing from the model until you accept it. Accepted subtasks and blockers
are saved like any others. For quotas and monitoring, the app records metadata about each call
(who, when, which feature, model, tokens, cost, duration and how it ended), never the card
text or the response (observability).
Limits: 20 suggestions a day per account and 3 per demo session (subtasks and blockers
combined), plus a global daily budget. All reset at midnight UTC
(ADR 0016,
ADR 0024).
Roadmap
v0.1 Board, auth, RLS, drag and drop, demo mode, deploy
v0.2 AI task decomposition with streaming, human review and daily quotas
Context for agents:AGENTS.md holds the stack, commands and conventions;
CLAUDE.md adds Claude Code specifics.
Specialised subagents in .claude/agents: DB architect, frontend, AI,
QA and a read-only code reviewer.
Enforced workflow: feature branches, Conventional Commits, PRs squash-merged into main
with CI green. Enforced by Claude Code hooks, git hooks and branch rules, not just by convention.
Decisions are recorded as ADRs in docs/adr, incidents as blameless
post-mortems in docs/incidents.
