How spec-driven dev helped me ship “AI Math Tutor” from living-room idea to production

#kiro #edtech #productivity #ai

Why I built this

I built AI Math Tutor after mentoring my high-schooler and seeing how often students get stuck between steps. I combined a Python math engine (symbolic steps + visuals), a Go API gateway (auth, WebSockets), and a React frontend into a production-ready platform with observability baked in. The spec-driven flow took me from requirements → design → implementation without thrash.

Highlights

• Step-by-step solutions + AI explanations
• Realtime collaboration; JWT + RBAC
• Docker/K8s, PostgreSQL, Redis
• Roadmap: handwriting/voice input; dashboards; spaced repetition

What I learned

Specs reduce rework; separating I/O (Go) from math (Python) keeps performance predictable; pedagogy is a feature.

What AI Math Tutor does

• Step-by-step solutions (algebra, calculus, linear algebra, stats, AI/ML math)
• Interactive quizzes with targeted hints and feedback
• Visual intuition (2D/3D plots, vector fields, function graphs)
• Personalized learning paths with progress tracking
• Realtime collaboration (coach ↔ learner)
• Planned: voice/handwriting input (OCR/STT)

Architecture at a glance

We use a microservices approach:
• Python Math Engine (FastAPI) for symbolic computation, AI explainers, and visuals
• Go API Gateway (Gin) for high-throughput APIs, JWT auth, WebSockets, rate-limiting
• React Frontend for a clean, responsive UI
• PostgreSQL + Redis for durable data + fast sessions/caching
• Docker/K8s for local dev and production deployment

Spec-driven from day one

I ran a requirements → design → tasks flow and built in observability and security early: health checks, metrics, error tracking, JWT + RBAC, and session management. That discipline helped move from concept → stable production system without big-bang rewrites.

Challenges

• Grounding LLM explanations in exact symbolic steps
• Realtime stability at scale with WebSockets + sessions
• Consistent math rendering (LaTeX + plots) across devices
• Security (roles, tokens) while keeping UX friction-free

Results & what I’m proud of

• End-to-end auth + RBAC; secure session flows
• Health/observability wired throughout
• Realtime collaboration that behaves under load
• A symbolic engine paired with AI explanations for clarity

What’s next

• Mobile + handwriting/voice input
• Teacher/parent dashboards and mastery insights
• Spaced repetition for long-term retention
• Deeper AI/ML math tracks (optimization, spectral methods, backprop labs)
• Privacy by design (data minimization, audit trails)
• Open-source modules for community reuse