# December 2024 Release Checklist

## Pre-Release (Complete Before Pushing)

### Code & Documentation
- [ ] All 20 modules have complete implementations
- [ ] All inline tests pass when running modules
- [ ] README.md updated with December release notice
- [ ] STUDENT_VERSION_TOOLING.md created (explains untested tooling)
- [ ] DECEMBER_2024_RELEASE.md created (announcement template)
- [ ] Academic Integrity section added to README

### Repository Cleanup
- [ ] Remove any temporary/debug files
- [ ] Update .gitignore if needed
- [ ] Verify no sensitive data in commits
- [ ] Clean up any TODOs or FIXMEs in visible code

### Testing
- [ ] Run key module tests: `pytest tests/01_tensor tests/05_autograd tests/09_spatial`
- [ ] Verify book builds locally: `cd book && jupyter-book build .`
- [ ] Check that setup script works: `./setup-environment.sh`
- [ ] Test at least one milestone: `python milestones/03_1986_mlp_revival/mlp_mnist.py`

---

## Release Day (Execution)

### 1. Merge to Main (30 minutes)
```bash
cd /Users/VJ/GitHub/TinyTorch

# 1. Ensure you're on your working branch
git status

# 2. Commit all changes
git add README.md STUDENT_VERSION_TOOLING.md DECEMBER_2024_RELEASE.md RELEASE_CHECKLIST.md
git commit -m "Prepare December 2024 release with complete implementations"

# 3. Switch to main and merge
git checkout main
git merge optimization-tier-restructure --no-ff -m "Release December 2024: Complete 20-module implementation"

# 4. Push to GitHub
git push origin main

# 5. Verify GitHub Actions triggered
# Go to: https://github.com/mlsysbook/TinyTorch/actions
# Confirm "Deploy TinyTorch Jupyter Book" workflow started
```

### 2. Verify Deployment (10 minutes)
```bash
# Wait 5-10 minutes for GitHub Actions to complete

# Check deployment status
open https://github.com/mlsysbook/TinyTorch/actions

# Verify book is live
open https://mlsysbook.github.io/TinyTorch/

# Test these critical pages:
# - Home page loads
# - Chapter navigation works
# - At least 3 module chapters render correctly
```

### 3. Create GitHub Release (15 minutes)
```bash
# Go to: https://github.com/mlsysbook/TinyTorch/releases/new

Tag: v0.1.0-alpha
Release title: TinyTorch December 2024 Release - Community Review
Description: Copy from DECEMBER_2024_RELEASE.md

Key sections:
- What's released
- What we're seeking feedback on
- How to review
- What's not ready yet
- Links to book and discussions

Mark as: "Pre-release" (this is alpha quality)
```

### 4. Enable GitHub Discussions (5 minutes)
```bash
# Go to: https://github.com/mlsysbook/TinyTorch/settings

# Enable:
- Discussions tab
- Issues (should already be enabled)
- Wiki (optional)

# Create initial discussion categories:
- 💬 General Feedback
- 📚 Pedagogy & Learning Design
- 💻 Implementation Quality
- 🐛 Bugs & Issues
- 💡 Feature Suggestions
- 🎓 Classroom Use (Future)
```

---

## Announcement (1-2 hours)

### Prepare Announcement Text

**Short Version (Twitter/LinkedIn - 280 chars)**:
```
🚀 TinyTorch December Release: Build ML systems from scratch!

20 modules: Tensors → Transformers → Optimization
Goal: CIFAR-10 CNNs @ 75%+ with YOUR code (no PyTorch!)

Seeking feedback on pedagogy & implementation.

📚 Book: https://mlsysbook.github.io/TinyTorch/
💻 Repo: https://github.com/mlsysbook/TinyTorch

#MachineLearning #MLSystems #Education
```

**Medium Version (Blog post - 500 words)**:
Use DECEMBER_2024_RELEASE.md sections:
- What is TinyTorch
- What's released
- What we're seeking feedback on
- How to review
- Links

**Long Version (Academic announcement - 1000 words)**:
Full DECEMBER_2024_RELEASE.md content

### Distribution Channels

#### Academic
- [ ] Harvard SEAS mailing list
- [ ] ML education forums (e.g., MLSys community)
- [ ] Academic Twitter/X

#### Technical Community
- [ ] Hacker News (Show HN: TinyTorch - Build ML systems from scratch)
- [ ] Reddit r/MachineLearning (appropriate day/time)
- [ ] LinkedIn post (tag relevant educators/engineers)
- [ ] Twitter/X thread (break down into tweet storm)

#### Direct Outreach
- [ ] Email to ML educator colleagues (personal note)
- [ ] Reach out to PyTorch/FastAI communities
- [ ] Contact MiniTorch maintainers (Cornell) - as peer project
- [ ] Share with Karpathy, George Hotz communities (related projects)

---

## Post-Release Monitoring (First Week)

### Daily Tasks
- [ ] Check GitHub Issues (respond within 24 hours)
- [ ] Monitor Discussions (participate actively)
- [ ] Track analytics (GitHub stars, book views if available)
- [ ] Respond to Twitter/LinkedIn comments
- [ ] Collect feedback in organized notes

### Weekly Tasks
- [ ] Summarize feedback themes
- [ ] Identify critical bugs vs. enhancements
- [ ] Prioritize based on community input
- [ ] Update project roadmap based on feedback

### What to Look For
1. **Critical bugs** - breaks setup or core modules → fix immediately
2. **Pedagogical gaps** - unclear instructions, missing context
3. **Technical issues** - implementation problems, incorrect code
4. **Feature requests** - nice-to-have but not blocking

---

## Success Metrics (First Month)

### Quantitative
- [ ] GitHub Stars: Target 100+ in first month
- [ ] Issues/Discussions: Active engagement (20+ threads)
- [ ] Book views: Analytics showing page visits
- [ ] Forks: Community interest in contributing

### Qualitative
- [ ] Positive feedback on pedagogical approach
- [ ] Constructive technical feedback incorporated
- [ ] Interest from other instructors
- [ ] Community contributions (PRs, issues)

---

## Contingency Plans

### If Book Doesn't Deploy
```bash
# Manual deployment
cd book
jupyter-book clean . && jupyter-book build .
# Upload _build/html/ to GitHub Pages manually
```

### If Critical Bug Found
```bash
# Hot fix workflow
git checkout main
git checkout -b hotfix-issue-123
# Make fix
git commit -m "Fix critical bug in tensor operations"
git push origin hotfix-issue-123
# Merge immediately to main
```

### If Negative Reception
- Don't panic
- Listen to feedback
- Acknowledge legitimate concerns
- Focus on improvement, not defense
- Remember: this is alpha, feedback is the goal

---

## After First Month

### Review & Plan
- [ ] Compile all feedback into summary doc
- [ ] Identify patterns in feedback
- [ ] Create prioritized improvement roadmap
- [ ] Decide on timeline for next release

### Next Steps Decision
1. **If feedback is mostly positive**: 
   - Focus on polishing existing modules
   - Begin student version testing
   
2. **If significant issues found**:
   - Address critical problems first
   - Delay student version work
   
3. **If little engagement**:
   - Re-evaluate announcement strategy
   - Reach out to specific communities
   - Consider why adoption is slow

---

## Notes

**Philosophy**: Ship early, get feedback, iterate based on real use.

**Goal**: Not perfection, but improvement through community input.

**Timeline**: December release → January-March refinement → Spring validation → Fall classroom (maybe)

**Mindset**: Academic software development is iterative. First release exposes blind spots.

---

**Ready to ship?** Check off items above and execute! 🚀


