- How is AI project planning for developers different from generic AI planning tools?
- Generic AI planning tools (ChatGPT, Notion AI, Taskade) generate plans based on your description. Developer-specific planning tools read the codebase before planning — they know your existing database schema, API patterns, and service architecture. The output format is also different: developer-focused tools produce subtasks with file references and acceptance criteria at the code level, not milestone lists and stakeholder deliverables.
- How does Tekk.coach work with Cursor?
- Tekk generates the structured spec for what you're building. Cursor executes it. You describe the feature in Tekk, the agent reads your codebase and produces a plan with subtasks, acceptance criteria, and file references. You open Cursor, paste or reference the spec, and the agent has the precision instructions it needs to implement correctly. The difference between a Cursor session with a Tekk spec and one with a paragraph prompt is the difference between clean first-pass execution and a rework cycle.
- How does Tekk.coach work with Claude Code?
- Same principle as Cursor. Tekk generates the codebase-grounded spec; Claude Code executes it. Because Tekk reads your codebase and Claude Code executes against it, the spec Tekk produces is in the same language as the codebase Claude Code will touch — right files, right patterns, right acceptance criteria.
- Does Tekk replace Linear or Jira for developer task planning?
- Tekk is not a Jira replacement for teams that need Jira-style workflow management, approval chains, or process governance. For teams that want AI-assisted planning grounded in their codebase, connected to a visual task board, Tekk is the primary workspace. The Kanban board in Tekk shows To Do / In Progress / Done; each card links to the full codebase-aware planning session. It's planning + tracking for builders who don't need enterprise workflow tooling.
- What makes codebase-aware project planning better than generic AI planning?
- When the plan doesn't reflect the actual codebase, two things happen. First, the spec contains implementation directions that conflict with the existing architecture — requiring an engineering translation pass before the plan is usable. Second, AI coding agents executing the spec produce code that works in isolation but doesn't fit the system — triggering rework. Codebase-aware planning eliminates both failure modes. The spec reflects reality before any code is written.
- How does Tekk handle domains where I have limited expertise?
- When you're building something outside your core domain (a data pipeline, an AI agent integration, a security-sensitive feature), Tekk searches the web for current best practices during planning. The spec it produces reflects both your specific codebase architecture and current external standards for the domain. You don't need to research the domain separately and then incorporate the findings manually — the plan already contains that knowledge.
- Is AI project planning for developers just for solo builders?
- No — small teams (1-10 people) get significant value from a shared codebase-aware planning workspace. Context doesn't fall through the cracks between chat threads, scattered markdown files, and issue tracker descriptions. Plans are generated, stored, and connected to the Kanban board in one place. Everyone on the team works from the same codebase-grounded specifications.
- What does the output of Tekk's developer planning look like?
- A complete spec with: a TL;DR summarizing what's being built and why; an explicit Building / Not Building scope section; subtasks described as behavioral slices ("user can now do X"), each with acceptance criteria, file references, and dependencies; assumptions with risk levels and consequences if wrong; and validation scenarios for end-to-end testing. This is a living document in the Tekk editor — editable, persistent, connected to your Kanban card.
