Project Overview: ai-appointment-setter
I decided to anchor this project around one operational rule: shared memory in Obsidian is the source of truth, and product work should stay lightweight and iteration-friendly. That decision shows up both in AGENTS.md (explicit QMD memory workflow) and in execution themes: stabilize tests first, then improve the user-facing product surface.
What We Built
- A Node.js project with a clear implementation center in
app/, supported bydocs/andskills/. - A polished UI pass across the core product surface, reflected in recent commits: “Create polished landing page with shadcn/ui components” and “Rebuild dashboard with professional shadcn/ui layout.”
- Test reliability fixes in webhook and integration flows before/alongside UI work, including repeated ConversationEngine mock corrections.
- A working dashboard path actively touched in
app/src/app/dashboard/layout.tsxandapp/src/app/dashboard/page.tsx.
Why We Built It
- We needed continuity across agent and human operators; the Obsidian rule prevents decision drift and preserves context between sessions.
- We prioritized practical delivery over platform expansion: improve trust and usability in the existing app instead of adding new infrastructure.
- Session focus confirms this direction: recent work emphasized project health verification and quality checks, not feature sprawl.
- The sequencing in commits suggests an intentional strategy: fix brittle test surfaces, then invest in UX quality and rebrand alignment (“Rebrand from AI Appointment Setter to Dial AI”).
How It Works
- Operational governance is explicit: before substantial work, we prefetch project memory from the canonical Obsidian location via the documented QMD script flow.
- Day-to-day implementation happens inside
app/, where API, middleware, tests, and dashboard UI live together as the active surface. - The current repo root has no package scripts, so execution is driven from subproject context (not a monorepo-wide task runner contract).
- The practical loop is: recover memory context, run health/quality checks, then ship scoped UI or reliability improvements with clear commit intent.