docs: bootstrap tutor planning

openspec/changes/bootstrap-job-tutor-platform/design.md

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+# Design
+
+## Product Boundary
+
+The first user is a software job seeker. The product should begin with
+technical interview preparation because it gives clear task loops:
+
+- ask a question
+- receive an answer
+- grade against a rubric
+- ask follow-ups
+- extract memory
+- recommend review
+- show progress and next challenge
+
+General students remain a future expansion path, but the first requirements
+should not be diluted by full K-12 scope.
+
+## Architecture Boundary
+
+Use a Go backend as the product service boundary. Internalize `agent-farm-go`
+workflow patterns and contracts inside that backend boundary while keeping agent
+behavior configuration-first where possible. Use `third-one` as the LLM
+execution kernel with `deepseek-v4-flash` as the default configured model
+target.
+
+The product backend owns durable user, learner, memory, ontology, and asset
+records. External memory or graph projects may inform design or become adapters,
+but they should not own product privacy or tenant semantics.
+
+## Memory Boundary
+
+The platform should not model memory as a flat RAG corpus. It should keep
+structured learning state:
+
+- learner profile
+- concept mastery
+- misconception
+- evidence
+- intervention
+- review schedule
+
+Every durable memory update must include evidence so the product can explain why
+it believes a learner is weak or strong on a concept.
+
+## Ontology Boundary
+
+Uploaded materials should become source-backed learning graphs. Inferred gaps
+and generated explanations are candidates until reviewed or otherwise validated.
+
+## Visual Asset Boundary
+
+Image generation should support diagrams and slide-like learning slices, but the
+asset pipeline must preserve prompt lineage, source concept links, and review
+state. The desired image provider key is `gpt-image-v2`, but production
+implementation must verify the current OpenAI API model name before wiring.
+
+## Gamification Boundary
+
+Use game-design principles to create healthy persistence: adaptive challenge,
+visible growth, clear goals, strong session endings, and long-term readiness
+progress. Do not optimize for compulsion alone. Random rewards, punitive streaks,
+and shame-based leaderboards are out of scope for the first product baseline.
+
+The initial learning loop is:
+
+- readiness goal
+- interview question
+- answer
+- rubric feedback
+- follow-up or correction
+- memory update
+- visible progress
+- next best challenge