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