From d0b18202978d31d6cbd5c7a2ddc33b35f613b990 Mon Sep 17 00:00:00 2001 From: oberon Date: Tue, 5 May 2026 00:46:14 +0300 Subject: [PATCH] Add vision.md --- vision.md | 44 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) create mode 100644 vision.md diff --git a/vision.md b/vision.md new file mode 100644 index 0000000..08cd4a3 --- /dev/null +++ b/vision.md @@ -0,0 +1,44 @@ +# Non-Linear: Product Vision + +## One-liner + +A graph-native issue tracker for small IT teams where both humans and AI agents navigate a shared decomposition tree of project structure. + +## Core Thesis + +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. + +## Why Now + +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*. This is a structural advantage, not a feature. + +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. + +3. **Agent ecosystems are emerging.** Building for AI-agent workflows is a bet on timing. Teams are beginning to use agents for code review, task decomposition, triage, and status reporting. An agent-native tracker is positioned for this shift. + +## Target Users + +- **Startup dev teams** (3-10 people) building software products +- **Freelancers managing multiple client projects** with cross-project dependencies +- **One-person teams with AI agents** where the agent acts as a focused collaborator + +## Competitive Landscape + +| Tool | Strength | Gap | +|------|----------|-----| +| Linear | Fast, opinionated, clean | Flat structure, AI retrofitted | +| Jira | Powerful, extensible | Heavy, complex, AI bolted on | +| GitHub Issues/Projects | Integrated with code | Minimal structure | +| Plane.so | Open-source Linear alternative | Same flat model | +| Trello | Simple, visual | No hierarchy, no agent support | +| Kumu / Obsidian Canvas | Graph modeling | Not issue trackers | + +**The gap:** No tool combines graph-native project modeling with AI-agent-first API design. + +## Design Principles + +1. **Graph is the spine.** The decomposition tree defines project structure. Everything else — views, permissions, agent navigation — derives from graph position. +2. **Depth is type.** Issues are untyped. A node's position in the tree implies its abstraction level. Root = project, children = components, leaves = tasks. Labels handle orthogonal concerns. +3. **Two graphs, separated.** The decomposition tree (strict parent→child hierarchy) and the association graph (lateral links like "blocks", "relates-to") are distinct relationship types. The tree is structural; links are annotation. +4. **Agents are first-class actors.** Not assistants bolted on — agents have accounts, roles, permissions, and can traverse the graph independently. +5. **Granular trust.** The permission system is policy-based from day one. Roles are convenience bundles over a granular engine, not hardcoded ceilings. \ No newline at end of file