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✅ Phase 1-2 Complete: Modules 1-10 aligned with tutorial master plan ✅ CNN Training Pipeline: Autograd → Spatial → Optimizers → DataLoader → Training ✅ Technical Validation: All modules import and function correctly ✅ CIFAR-10 Ready: Multi-channel Conv2D, BatchNorm, MaxPool2D, complete pipeline Key Achievements: - Fixed module sequence alignment (spatial now Module 7, not 6) - Updated tutorial master plan for logical pedagogical flow - Phase 2 milestone achieved: Students can train CNNs on CIFAR-10 - Complete systems engineering focus throughout all modules - Production-ready CNN pipeline with memory profiling Next Phase: Language models (Modules 11-15) for TinyGPT milestone
TinyTorch .claude Directory Structure
This directory contains all guidelines, standards, and agent definitions for the TinyTorch project.
📁 Directory Structure
.claude/
├── README.md # This file
├── guidelines/ # Development standards and principles
│ ├── DESIGN_PHILOSOPHY.md # KISS principle and simplicity guidelines
│ ├── GIT_WORKFLOW.md # Git branching and commit standards
│ ├── MODULE_DEVELOPMENT.md # How to develop TinyTorch modules
│ ├── TESTING_STANDARDS.md # Testing patterns and requirements
│ ├── PERFORMANCE_CLAIMS.md # How to make honest performance claims
│ └── AGENT_COORDINATION.md # How AI agents work together
├── agents/ # AI agent definitions
│ ├── technical-program-manager.md
│ ├── education-architect.md
│ ├── module-developer.md
│ ├── package-manager.md
│ ├── quality-assurance.md
│ ├── documentation-publisher.md
│ ├── workflow-coordinator.md
│ ├── devops-engineer.md
│ └── tito-cli-developer.md
└── [legacy files to review]
🎯 Quick Start for New Development
-
Read Core Principles First
guidelines/DESIGN_PHILOSOPHY.md- Understand KISS principleguidelines/GIT_WORKFLOW.md- Learn branching requirements
-
For Module Development
guidelines/MODULE_DEVELOPMENT.md- Module structure and patternsguidelines/TESTING_STANDARDS.md- How to write testsguidelines/PERFORMANCE_CLAIMS.md- How to report results
-
For Agent Coordination
guidelines/AGENT_COORDINATION.md- How agents work together- Start with Technical Program Manager (TPM) for all requests
📋 Key Principles Summary
1. Keep It Simple, Stupid (KISS)
- One file, one purpose
- Clear over clever
- Verified over theoretical
- Direct over abstract
2. Git Workflow
- ALWAYS work on feature branches
- NEVER commit directly to main/dev
- Test before committing
- No automated attribution in commits
3. Module Development
- Edit .py files only (never .ipynb)
- Test immediately after implementation
- Include systems analysis (memory, performance)
- Follow exact structure pattern
4. Testing Standards
- Test immediately, not at the end
- Simple assertions over complex frameworks
- Tests should educate, not just verify
- Always compare against baseline
5. Performance Claims
- Only claim what you've measured
- Include all relevant metrics
- Report failures honestly
- Reproducibility is key
6. Agent Coordination
- TPM is primary interface
- Sequential workflow with clear handoffs
- QA testing is MANDATORY
- Package integration is MANDATORY
🚀 Common Workflows
Starting New Module Development
1. Create feature branch
2. Request TPM agent assistance
3. Follow MODULE_DEVELOPMENT.md structure
4. Test with TESTING_STANDARDS.md patterns
5. Verify performance per PERFORMANCE_CLAIMS.md
6. Merge following GIT_WORKFLOW.md
Making Performance Claims
1. Run baseline measurements
2. Run actual measurements
3. Calculate real improvements
4. Document with all metrics
5. No unverified claims
Working with Agents
1. Always start with TPM agent
2. Let TPM coordinate other agents
3. Wait for QA approval before proceeding
4. Wait for Package Manager integration
5. Only then commit
📝 Important Notes
- Virtual Environment: Always activate .venv before development
- Honesty: Report actual results, not aspirations
- Simplicity: When in doubt, choose the simpler option
- Education First: We're teaching, not impressing
🔗 Quick Links
- Main Instructions:
/CLAUDE.md - Module Source:
/modules/source/ - Examples:
/examples/ - Tests:
/tests/
📌 Remember
"If students can't understand it, we've failed."
Every decision should be filtered through:
- Is it simple?
- Is it honest?
- Is it educational?
- Is it verified?
If any answer is "no", reconsider.