Use Cases

1. Solo Developer - Alex

Alex works on a side project after hours. No team, no Jira, no overhead.

Tools: Joy, Jot

Journey: Alex initializes Joy in the repo (joy init), adds an epic and a handful of stories, and works through them one by one. Daily tasks like "write tests" or "update README" go into Jot. Everything stays in Git. When Alex picks the project back up after a week off, joy ls and joy roadmap show exactly where things stand. No login, no expired trial, no forgotten password.

2. Startup Team - Five Developers

A small team building a SaaS product. They share a Git repo and need a lightweight backlog without the ceremony of enterprise tools.

Tools: Joy with AI integration

Journey: The team runs joy init in their monorepo. Everyone adds stories and tasks from the terminal. AI coding tools read the backlog, pick up tasks, and submit work for review. The team uses milestones to plan sprints and joy roadmap to track progress. Dependencies prevent anyone from starting work before prerequisites are done. The event log shows who did what - human or AI - without extra process.

3. Open Source Maintainer - Sam

Sam maintains a popular library with external contributors. Governance matters: not everyone should close issues or merge without review.

Tools: Joy with capabilities and gates

Journey: Sam configures Joy with member capabilities. Core maintainers have full access. External contributors can add ideas and bugs but cannot change status or close items. AI tools help triage incoming issues and draft responses, but Sam reviews every status change. The commit-msg hook ensures every contribution references a backlog item. The event log provides a transparent history for the community.

4. Enterprise Team - Maya, Compliance Officer

Maya's company operates across multiple regions - engineering teams in the EU, a development office in China, and partners in the US and Japan. Each jurisdiction has its own data protection requirements, and every region uses AI-assisted development.

Tools: Full Trust Model - Joy, Judge, Crypt

Journey: The team configures the five-pillar Trust Model. Trustship defines who can do what. Guardianship adds gates that require human approval before AI can close items or merge code. Judge provides independent audit reports. Every event is signed and append-only.

For the EU teams, GDPR compliance is built in: data stays in the Git repo, E2E encryption ensures the server never sees plaintext, and the event log provides the audit trail regulators expect. The China office operates under PIPL requirements - self-hosted deployment keeps all data within Chinese infrastructure, and Qwen Code integration provides AI assistance without routing data through foreign services. US partners work under CCPA and SOC 2 frameworks, addressed by the same capability-based access controls and independent audit via Judge. The Japan team's APPI obligations are met through the transparent data model and clear consent chains in the delegation system.

Maya can pull a compliance report for any time period, for any region, showing exactly which changes were made, by whom, and under whose authority. One governance framework, multiple regulatory jurisdictions. Crypt encrypts sensitive project data at rest with keys that never leave the device.

5. Agency / Freelancer - Chris

Chris juggles three client projects and personal tasks. Context switching is the enemy.

Tools: Joy (per client repo), Jot (personal)

Journey: Each client repo has its own Joy project with its own backlog. Chris uses joy ls --mine to see assigned work across the current project. Personal tasks - follow-ups, invoices, admin work - go into Jot. When a client task spawns personal todos ("send status update", "schedule demo"), those overflow from Joy into Jot. One terminal, multiple projects, zero browser tabs.