56 lines
3.9 KiB
Markdown
56 lines
3.9 KiB
Markdown
# Non-Linear: Product Vision
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## One-liner
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A graph-native issue tracker for small IT teams where both humans and AI agents navigate a shared decomposition tree of components and issues, with first-class multi-repo integration.
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## Core Thesis
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Software projects are easier to conceptualize top-down using graphs. Traditional issue trackers (Jira, Linear, GitHub Issues) are flat or loosely hierarchical — they force teams to reverse-engineer structure from ticket lists and labels. Non-Linear makes the graph the first-class data model, so both humans and AI agents can reason about project topology directly.
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## Why Now
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1. **AI agents need topology.** Current tools bolt on AI after the fact. An agent that wants to understand "what should I work on next?" or "what's blocked?" has to reverse-engineer structure from flat ticket lists. A graph-native model gives agents *rails to traverse*.
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2. **Small teams are underserved.** There's a gap between "too simple" (Trello, GitHub Issues) and "too heavy" (Jira, Azure DevOps). Small teams need more structure than a Kanban board but can't justify Jira administration overhead.
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3. **Agent ecosystems are emerging.** Teams are beginning to use agents for code review, task decomposition, triage, and status reporting. An agent-native tracker is positioned for this shift.
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4. **Code structure is the natural skeleton.** Most projects already have a structure — it's in their repos. Connecting code and inferring the project skeleton eliminates the cold-start problem that kills adoption of structured tools.
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## Target Users
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- **Startup dev teams** (3-10 people) building software products
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- **Freelancers managing multiple client projects** with cross-project dependencies
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- **One-person teams with AI agents** where the agent acts as a focused collaborator
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## Competitive Landscape
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| Tool | Strength | Gap |
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|------|----------|-----|
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| Linear | Fast, opinionated, clean | Flat structure, AI retrofitted, no code-aware skeleton |
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| Jira | Powerful, extensible | Heavy, complex, AI bolted on |
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| GitHub Issues/Projects | Integrated with code | Minimal structure, single-repo |
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| Plane.so | Open-source Linear alternative | Same flat model |
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| Trello | Simple, visual | No hierarchy, no agent support |
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| Kumu / Obsidian Canvas | Graph modeling | Not issue trackers |
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**The gap:** No tool combines graph-native project modeling, multi-repo code integration, and AI-agent-first API design.
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## Design Principles
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1. **Graph is the spine.** The decomposition tree defines project structure. Everything else — views, permissions, agent navigation — derives from graph position.
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2. **Two node types.** Components are the skeleton (stable, map to code). Issues are the work (flow through statuses, get assigned). Cleaner than untyped depth-as-type.
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3. **Two graphs, separated.** The decomposition tree (strict parent→child) and the association graph (lateral links) are distinct. The tree is structural; links are annotation.
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4. **Code is the skeleton.** Connect your repos, infer the component tree. The fastest path from "nothing" to "structured project" is through code you already have.
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5. **Agents are first-class actors.** Not assistants bolted on — agents have accounts, roles, permissions, and can traverse the graph independently.
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6. **Granular trust.** The permission system is policy-based from day one. Roles are convenience bundles over a granular engine, not hardcoded ceilings.
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7. **Keyboard-first.** Every action has a shortcut. The command palette is the primary navigation method.
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8. **Plan-then-apply.** Structural changes show a preview of consequences before committing.
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## Three Fast-Start Paths
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1. **Connect your code:** OAuth to GitHub/GitLab → select repos → AI infers component skeleton → adjust → start adding issues
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2. **Clean start:** Create project → root node → add components and issues manually
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3. **Import from tracker (v0.2+):** Import from Linear/Jira/GitHub Issues → infer hierarchy → adjust
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