Bynary
AI made building easy. Bynary makes building right.
The live demo is briefly offline while it moves to new hosting.
What it is
Bynary is an end to end product lifecycle harness. A feature starts as a card on a roadmap, goes through an AI planning conversation where a human makes the calls, then Claude Code builds it inside a cloud sandbox. Bynary tests it, reviews it, deploys it to a preview URL, records a walkthrough video, writes the docs and opens the pull request.
- 5phases per cycle: plan, build, review, document, ship
- 714commits over five months
- 64tables, with 186 row-level security policies
- 3AI review agents: security, accessibility, design
Key features
Ideation and roadmap
A product-manager persona helps shape features, but a feature can only reach "Ready" with a persona and a user-value statement. Now, Next, Later kanban that refuses to promote a feature ahead of its dependencies.
Planning with consent
An engineering-lead persona plans each feature in chat, with interactive pickers for stack, fonts, colours and infrastructure. Tech decisions need explicit sign-off before anything is written.
UI generation
HTML wireframes per feature, stitched into one click-through preview, plus a generated design system and component library that later builds must reuse.
Autonomous build
Each feature gets a fresh Claude Code session in an E2B sandbox, with the whole roadmap in its context. A type-check gate runs between features and a git push checkpoints each one.
Auto test and preview
Runs the project's own build, lint and tests, checks every acceptance criterion with AI, deploys a preview on Coolify and fixes failed deploys itself, up to three rounds.
AI review, then human review
Security (OWASP), accessibility (WCAG 2.2 AA) and design review agents raise issues before a person reviews each task with diffs, logs and recorded Playwright walkthroughs.
Docs and merge
Generates user docs, release notes and technical docs, then opens a GitHub pull request with the review outcome, file stats and a manual-steps checklist.
Traceability and cost
Audit log, decision log, a knowledge map from task to file and function, and the cost of every AI call, per cycle and per stage.
In action
The parts that aren't obvious
The database is the queue
API routes only set pending flags. A separate worker claims jobs with an atomic UPDATE so only one worker wins, with heartbeats, stale-job recovery and graceful shutdown. The same loop runs builds, docs, previews and GitHub sync.
A scope guard inside the sandbox
A Claude Code PreToolUse hook blocks edits to files outside the task, and a git pre-commit hook catches anything that slips through Bash.
Sandboxes that cannot leak money
Each sandbox is registered in the database before it is created, so a crash still leaves a record for the reaper. Pooled sandboxes rotate before the one-hour cap, and long builds resume from git checkpoints.
Regressions, not noise
A toolchain baseline separates errors that were already there from ones the agent introduced, so fix rounds are not wasted on old problems.
A supervisor for the agents
An always-on monitor spots stuck or looping builds, injects hints, and pauses for a human when it hits something it cannot fix, like a missing secret.
Built with
- App
- Next.js 16
- React 19
- TypeScript
- Tailwind CSS v4
- shadcn/ui
- Plate editor
- Mermaid
- AI
- Claude Sonnet 4.6
- Claude Opus 4.6
- Claude Haiku 4.5
- Claude Code
- MCP
- Infrastructure
- E2B sandboxes
- Supabase
- Coolify
- GitHub App
- Docker
- Testing
- Vitest
- Playwright
- Playwright MCP