docs: bootstrap tutor planning

.planning/PROJECT.md

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+# Tutor Platform
+
+## What This Is
+
+Tutor Platform is a web service for software job seekers preparing for
+technical interviews. It combines adaptive interview practice, evidence-backed
+learner memory, source-backed learning ontology, and game-inspired progression
+to make interview readiness visible and repeatable.
+
+The backend is Go because the product will internalize `agent-farm-go` workflow
+patterns and call `third-one` with `deepseek-v4-flash` through typed service
+boundaries.
+
+## Core Value
+
+The user should feel and prove that they are becoming more interview-ready after
+each short practice loop.
+
+## Requirements
+
+### Validated
+
+(None yet; ship to validate.)
+
+### Active
+
+- [ ] Developer job seekers can complete a diagnostic technical interview.
+- [ ] Answers are graded with rubrics and preserved as evidence.
+- [ ] Learner memory tracks concept mastery, misconceptions, evidence, and
+  interventions.
+- [ ] The system selects the next best interview challenge from learner state.
+- [ ] The user sees a readiness map and meaningful progression after each loop.
+- [ ] Uploaded learning materials can become source-backed ontology candidates.
+- [ ] Generated learning assets preserve prompt, source, and review lineage.
+- [ ] Backend implementation uses Go and keeps `agent-farm-go` workflow patterns
+  internalized behind typed interfaces.
+
+### Out of Scope
+
+- Full school LMS replacement; the first product target is job seekers.
+- Marketplace course publishing; not needed to prove the learning loop.
+- Automatic certification or hiring decisions; readiness is advisory.
+- Unreviewed generated canonical content; generated ontology and assets require
+  provenance and review state.
+- Gambling-like rewards or shame-based leaderboards; progression must be tied to
+  learning evidence.
+
+## Context
+
+- Product planning lives in `docs/planning/`.
+- OpenSpec change baseline lives in
+  `openspec/changes/bootstrap-job-tutor-platform/`.
+- The service should use a Go backend.
+- Workflow behavior should be configuration-first and inspired by
+  `agent-farm-go`.
+- LLM execution should use `third-one`, defaulting to `deepseek-v4-flash`.
+- Memory should be structured learner state, not a flat RAG transcript.
+- Gamification should use Flow, adaptive difficulty, growth lines, and strong
+  session endings without exploitative mechanics.
+
+## Constraints
+
+- **Backend stack**: Go, to align with internalized `agent-farm-go` workflow
+  patterns.
+- **File size**: manually authored source files must stay at or below 600 lines.
+- **Design principles**: SOLID, KISS, and YAGNI govern implementation.
+- **Workflow state**: product state changes should use typed contracts, not
+  freeform prose parsing.
+- **Privacy**: learner memory and evidence may become sensitive, especially for
+  future student/school expansion.
+
+## Key Decisions
+
+| Decision | Rationale | Outcome |
+|----------|-----------|---------|
+| Start with software job seekers | Clear, testable interview-practice loop | Pending |
+| Use Go backend | Aligns service with internalized `agent-farm-go` substrate | Pending |
+| Use `third-one` and `deepseek-v4-flash` by default | Matches current local model/runtime direction | Pending |
+| Structured learner memory, not RAG-first | Product value is learner modeling and readiness | Pending |
+| Game-inspired progression must be evidence-backed | Creates retention without empty rewards | Pending |
+| 600-line source limit | Forces responsibility boundaries early | Pending |
+
+---
+*Last updated: 2026-04-26 after Go backend and GSD planning direction were set.*