RoboLearn: AI-Native Textbook Platform

The Thesis

The software industry has disrupted itself. Spec-Driven Development with Reusable Intelligence (SDD-RI) transforms what traditionally takes months into days—not through faster coding, but through compounding intelligence.

This hackathon isn't just about winning 300 points. It's about launching a platform.

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What We're Actually Building

Traditional Timeline	Our Timeline
Book content: 6-18 months	48 hours
Author platform: 3-6 months	Week 1-2
Multi-book infrastructure: 6-12 months	Month 1
Institutional features: 12+ months	Month 2

Why? Because every hour invested in reusable intelligence compounds. The lesson-writer agent that creates Module 1 creates Module 4 at the same speed. The skills that power RoboLearn power the next ten books.

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Platform Vision

┌─────────────────────────────────────────────────────────────┐
│                    RoboLearn Platform                        │
├───────────────────┬───────────────────┬─────────────────────┤
│     STUDENTS      │      AUTHORS      │    INSTITUTIONS     │
│                   │                   │                     │
│ Personalized      │ AI-assisted       │ White-label         │
│ Hardware-aware    │ Days not months   │ Analytics           │
│ Multilingual      │ Revenue share     │ Curriculum control  │
│ Interactive       │ Agent workforce   │ Bulk licensing      │
└───────────────────┴───────────────────┴─────────────────────┘

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Hackathon Deliverables (Sunday 6 PM)

Scoring Target: 300/300

Requirement	Points	Deliverable
Book + RAG Chatbot	100	4 modules, context-aware chat
Reusable Intelligence	50	Skills, agents, knowledge, MCP configs
Auth + Onboarding	50	Better-Auth, hardware survey, profile-based filtering
Personalization	50	AI rewrites content for user context
Urdu Translation	50	Toggle between English/Urdu
Total	300

Student Experience

Signup → Hardware Survey → Personalized Content
                ↓
    ┌────────┬────────┬────────────┐
    │ Learn  │Visualize│Personalize │  ← 3-Tab UI
    │ (MDX)  │(Diagram)│   (AI)     │
    └────────┴────────┴────────────┘
                ↓
    ┌─────────────────────────────┐
    │    Interactive Python Lab    │
    │  Pyodide + MockROS + Robot   │
    └─────────────────────────────┘
                ↓
    ┌─────────────────────────────┐
    │      RAG Chat Sidebar        │
    │  Context-aware • Select-ask  │
    └─────────────────────────────┘
                ↓
           🔄 EN ↔ UR

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Technical Architecture

Stack

Layer	Choice	Why
Frontend	Docusaurus	MDX-native, fast builds
Hosting	GitHub Pages → Cloudflare	Free, global CDN
Backend	FastAPI + Cloud Run	Serverless, scales to zero
Database	Neon Postgres	Profiles, hardware configs
Vector DB	Qdrant Cloud	RAG embeddings
Auth	Better-Auth	Modern, official MCP server
AI	OpenAI Agents SDK	Chat, personalization

Reusable Intelligence Structure

.claude/
├── skills/                           # HOW (reusable patterns)
│   ├── authoring/                    # Content creation skills
│   │   ├── lesson-generator/         # Generate lessons (4-layer framework)
│   │   ├── assessment-builder/       # Create quizzes and assessments
│   │   ├── learning-objectives/      # Design learning objectives
│   │   ├── mermaid-diagram/          # Generate educational diagrams
│   │   ├── urdu-translator/          # Translate content to Urdu
│   │   ├── quiz-generator/           # Generate quiz questions
│   │   ├── summary-generator/        # Create lesson summaries
│   │   ├── notebooklm-slides/        # Generate slide decks
│   │   └── concept-scaffolding/      # Scaffold complex concepts
│   │   └── visual-asset-workflow/    # Create educational visuals
│   └── engineering/                  # Platform development skills
│       ├── pyodide-exercise/         # Browser-based Python exercises
│       ├── docusaurus-deployer/      # Deploy to GitHub Pages
│       ├── frontend-design/          # UI component design
│       ├── image-generator/          # Generate images with Gemini
│       ├── mcp-builder/              # Create MCP servers
│       ├── skill-creator/            # Create new skills
│       ├── chatkit-integration/       # ChatKit framework integration patterns
│       └── session-intelligence-harvester/  # Capture learnings
│
├── agents/                           # WHO (autonomous workers)
│   ├── super-orchestra.md            # General workflow orchestration
│   ├── authoring/                    # Content creation agents
│   │   ├── content-implementer.md    # Implement lessons from specs
│   │   ├── chapter-planner.md        # Plan chapter structure
│   │   ├── educational-validator.md  # Validate constitutional compliance
│   │   └── validation-auditor.md     # Quality validation before publish
│   └── engineering/                  # Platform development agents
│       ├── spec-architect.md         # Design specifications
│       └── chatkit-integration-agent.md  # ChatKit integration workflow
│
├── commands/                         # Slash commands (/sp.*)
└── .mcp.json                         # MCP server configuration

# Domain knowledge lives in authoritative sources:
# - requirement.md (course structure, hardware specs)
# - .specify/memory/constitution.md (principles, tiers)
# - README.md (platform vision)

MCP Strategy

Server	Use	Rationale
Better-Auth MCP	Auth implementation	Active introspection — generates schemas, supersedes docs
Context7	Library docs	Generalist for React, FastAPI, Pyodide
Tavily	Research	Synthesized answers for content generation
DeepWiki	Repo understanding	Understand panaversity base template

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Execution Plan (10 Hours)

Phase 1: Foundation + Intelligence (Hour 0-2) ✅ COMPLETE

Task	Deliverable	Status
1.1	Fork repo, rename to robolearn, verify build	✅
1.2	Create folder structure (skills, agents, knowledge, mcp)	✅
1.3	Write knowledge files (vocabulary, hardware-tiers, course-structure)	✅
1.4	Write skill files (lesson-generator, hardware-filter, urdu-translator)	✅
1.5	Write agent files (lesson-writer, rag-builder)	✅
1.6	Configure MCP servers	✅
1.7	Content cleanup, rebrand, navigation	✅
1.8	Component stubs, first deploy	✅
1.9	Homepage redesign (Industrial Confidence design system)	✅

Exit: ✅ Live at username.github.io/robolearn with intelligence infrastructure + redesigned homepage

Phase 2: Content Generation (Hour 2-4) ✅ COMPLETE

Task	Deliverable	Status
2.1	Module 1: ROS 2 Foundations (7 chapters, 25 lessons)	✅
2.2	Module 2: Gazebo/Unity Simulation (6 chapters, 22 lessons)	✅
2.3	Module READMEs and chapter structure	✅
2.4	8 authoring skills created	✅

Exit: ✅ 47 lessons across 2 modules with complete skill infrastructure

Extensions (moved to Phase 5):

• Create Mermaid/React Flow diagrams
• Add hardware-filtered sections

Phase 3: Auth + Profiles (Hour 4-5)

Task	Deliverable
3.1	Better-Auth setup (use official MCP)
3.2	Neon Postgres schema
3.3	Survey component

Exit: Users can signup, complete survey, see filtered content

Phase 4: Backend + RAG (Hour 5-7) ✅ COMPLETE

Task	Deliverable	Status
4.1	FastAPI app structure	✅
4.2	Qdrant collection setup	✅
4.3	Embedding pipeline (content → vectors)	✅
4.4	OpenAI Agents SDK config	✅
4.5	Deploy to Cloud Run	✅

Exit: ✅ RAG chatbot answering questions with book context + visual enhancements

Extensions Completed:

• ChatKit server integration with PostgreSQL persistence
• Context injection (user profile, page context, conversation history)
• Streaming responses for real-time UX
• Complete specifications reverse-engineered (specs/007-chatkit-server/)

Phase 5: Chat UI (Hour 7-8) ✅ COMPLETE

Task	Deliverable	Status
5.1	ChatKit widget component	✅
5.2	Current page context injection	✅
5.3	Select-to-ask functionality	✅
5.4	Hardware-aware responses	✅
5.5	User authentication integration	✅
5.6	Personalization menu	✅
5.7	Script loading detection	✅

Exit: ✅ Functional ChatKit widget with context awareness, text selection "Ask", and user personalization

Extensions Completed:

• ChatKit React component integration
• Custom fetch interceptor for auth and metadata
• Page context extraction (URL, title, headings, meta tags)
• User profile context transmission
• Complete specifications reverse-engineered (specs/008-chatkit-ui-widget/)
• Reusable intelligence harvested (skill + agent)

Phase 6: Bonus Features (Hour 9-9.5) ✅ COMPLETE

Task	Deliverable	Status
6.1	Urdu translation or Multi Language	✅
6.2	LanguageToggle component	✅

Exit: ✅ Full 300-point feature set

Completed Features:

• LanguageToggle component with locale switching (/en/ and /ur/ routes)
• Docusaurus i18n plugin for auto-translation (Gemini API)
• RTL (Right-to-Left) CSS support for Urdu content
• Language preference persistence (localStorage)
• Complete specification (specs/006-i18n-auto-translate-gemini/)

Phase 7: Ship (Hour 9.5-10) ✅ MOSTLY COMPLETE

Task	Deliverable	Status
7.1	End-to-end testing	✅
7.2	90-second demo video	🟡 Pending
7.3	README with setup instructions	✅
7.4	Submit	🟡 Pending

Exit: ✅ Hackathon submission ready (demo video pending)

Completed:

• End-to-end testing completed for ChatKit integration
• Comprehensive README with setup instructions
• Complete documentation (specs, ADRs, PHRs)
• Reusable intelligence infrastructure

Phase 8: Interactive Lab (Hour 8-9)

Task	Deliverable
6.1	PythonRunner component (Pyodide)
6.2	MockROSBridge class
6.3	RobotViewer component
6.4	Wire up: code → mock ROS → robot responds
6.5	Personalization endpoint
6.6	Personalize tab in content

Exit: Students write ROS-like code, see robot react

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Post-Hackathon Roadmap

Week 1-2: Author Platform

Feature	Description
Author Dashboard	Book management, chapter organization
Agent Studio	Configure lesson-writer, review AI drafts
Analytics	Reader engagement, chat queries, hardware distribution

Month 1: Multi-Book

Feature	Description
Book isolation	Separate knowledge folders per book
Shared infrastructure	Common auth, RAG, components
Second book	"CoLearning Python: The AI-Driven Way"

Month 2: Institutional

Feature	Description
White-label	Custom branding per institution
Bulk licensing	Student seat management
LMS integration	Grade passback, SSO

Month 3+: Scale

Feature	Description
Mobile app	React Native
Offline mode	Downloaded content
Real ROS 2	Beyond MockROS for advanced users
Marketplace	Third-party authors

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Revenue Model

Individual Learners

Tier	Price	Features
Free	$0	Read content, basic exercises
Professional	$49/month	RAG chat, personalization, multilingual, certificates
Annual	$399/year	Professional features + 30% savings

Corporate Training

Tier	Price	Features
Team	$199/month	10 seats, progress tracking, admin dashboard
Department	$799/month	50 seats, custom learning paths, analytics
Enterprise	Custom	Unlimited seats, SSO, dedicated support, SLA

Institutional (Universities, Bootcamps)

Tier	Price	Features
Academic	$2,500/year	200 students, LMS integration, grade passback
Campus	$10,000/year	1,000 students, white-label option, curriculum control
Enterprise	$50,000+/year	Unlimited, custom development, dedicated success manager

Author Revenue

Model	Split
Free Books	0% platform fee
Paid Books	70% author / 30% platform
Enterprise Licensing	60% author / 40% platform (includes support overhead)

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Competitive Moat

Why this is hard to copy:

Layer	Moat
Skills	Compound with every book authored
Knowledge	Domain expertise encoded
Agents	Workflows refined through use
Network	Zia Khan's 50K+ student distribution
Data	Chat queries reveal what students struggle with

Every book makes the platform smarter. Every student interaction improves the RAG. The intelligence compounds.

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Risk Mitigation

Risk	Mitigation
Deadline pressure	Phases ordered by point value, cut from bottom
RAG quality	Start basic, iterate
Live demo fails	Pre-recorded backup video
Content thin	3 polished lessons > 10 rough ones
MockROS feels fake	Frame as "pedagogical simulation" — judges evaluate concept

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Success Metrics

Phase	Metric	Target
Hackathon	Score	300 points
Week 1	Author dashboard	MVP live
Month 1	Books	2 live
Month 1	Students	500 active
Month 3	MRR	$2,000
Month 6	Students	10,000

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The Bottom Line

We don't just submit a hackathon project. We launch a platform.

The same intelligence that builds RoboLearn builds the next ten books. The same agents that write Physical AI lessons write Python lessons. The same infrastructure that serves 100 students serves 100,000.

This is what AI-driven development with Spec-Driven methodology and Reusable Intelligence makes possible.

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