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Course Launch Checklist

Complete guide to launching qwed-learning to the world.

✅ Pre-Launch (Before Announcement)

Repository Polish

  • All modules completed and tested
  • README.md has clear value prop
  • CONTRIBUTING.md guides community
  • LICENSE is correct (CC0-1.0)
  • All links work (test locally)
  • Code examples run without errors
  • Typography and formatting consistent

GitHub Settings

  • Enable GitHub Discussions
    • Go to Settings → Features → Discussions
    • Create categories: General, Module 1, Module 2, Module 3, Module 4, Show Your Projects
  • Add topics/tags: ai, llm, verification, course, neurosymbolic
  • Set description: "Free course on fail-closed AI verification and trust-boundary engineering"
  • Add website: https://github.com/QWED-AI/qwed-learning

Content Final Check

  • Module 0 complete (for beginners)
  • Modules 1-4 complete
  • Examples work (healthcare, ecommerce, financial)
  • Notebooks open correctly
  • Capstone project structure ready
  • Positioning guides clear

🚀 Launch Day (Coordinated Announcement)

Morning (9-10 AM)

Twitter Thread:

  • Post main thread using template
  • Pin to profile
  • Tag relevant accounts (@OpenAI, @AnthropicAI if appropriate)

Afternoon (2-3 PM)

LinkedIn:

  • Post long-form announcement
  • Share in relevant groups (optional)

GitHub:

  • Star your own repo (to seed stars)
  • Post in GitHub Discussions Welcome thread

Evening (6-7 PM)

Reddit:

  • r/MachineLearning (check subreddit rules first)
  • r/learnmachinelearning
  • r/artificial (if allowed)

Hacker News:

  • Submit as "Show HN"
  • Keep title concise: "Show HN: Free course on AI verification"

Next Day

Dev.to:

  • Publish full article
  • Cross-post to Medium (optional)

📊 Post-Launch (First Week)

Community Engagement

  • Respond to ALL GitHub issues within 24h
  • Answer discussions promptly
  • Thank people for stars/contributions
  • Share student projects

Monitoring

  • Track GitHub stars daily
  • Monitor discussions for common questions
  • Check social media mentions
  • Respond to technical questions

Content Updates

  • Fix any bugs/typos reported
  • Add FAQ if questions repeat
  • Update README based on feedback

🎯 Growth Strategy (Ongoing)

Week 1-2

  • Daily engagement on GitHub Discussions
  • Share 1-2 tweets from course content
  • Respond to all mentions

Week 3-4

  • Feature student projects (if any)
  • Write follow-up blog post
  • Consider guest posts on popular blogs

Month 2+

  • Add community contributions
  • Create "Success Stories" section
  • Plan course improvements based on feedback

📈 Success Metrics

Track these weekly:

GitHub:

  • Stars: Goal 100 (week 1), 500 (month 1), 1000 (month 3)
  • Forks: ~10% of stars
  • Discussions: 5+ per week
  • Issues: Aim for <48h resolution

Social:

  • Twitter impressions
  • LinkedIn engagement rate
  • Reddit upvotes
  • Dev.to views

Course Completion:

  • Capstone projects submitted
  • Student feedback
  • Portfolio additions

🐛 Common Issues & Solutions

"Code doesn't run"

  • Check Python version requirements
  • Verify API keys are set
  • Test examples yourself before launch

"Too advanced for me"

  • Point to Module 0 (Prerequisites)
  • Suggest starting slower
  • Offer to help in discussions

"Need video tutorials"

  • Acknowledge request
  • Add to roadmap
  • Focus on written content quality first

"Certification?"

  • Explain it's self-paced
  • Portfolio project is the "certificate"
  • Consider quiz system later

🔄 Continuous Improvement

Monthly Review

  • Check analytics
  • Read all feedback
  • Plan next improvements
  • Update roadmap

Quarterly Goals

  • Add requested features
  • Improve weak modules
  • Expand examples
  • Consider translations

🤝 Community Building

GitHub Discussions Structure

Categories:

  1. 💬 General - Course feedback, general questions
  2. 📚 Module Discussions
    • Module 0: Prerequisites
    • Module 1: The Crisis
    • Module 2: Theory
    • Module 3: Hands-On
    • Module 4: Advanced
  3. 🏗️ Show Your Projects - Student implementations
  4. 🐛 Help Needed - Debugging assistance
  5. 💡 Ideas - Course improvement suggestions

Seeding Discussions

Post these to start conversations:

  1. "What brought you to this course?"
  2. "Share your first verification success!"
  3. "Which mental model resonates most? (Proof vs Confidence, Draft vs Decision Boundary, etc.)"
  4. "Capstone project progress thread"

📧 Email Templates (If Building List)

Welcome Email

Subject: Welcome to AI Verification! 🎓

Thanks for your interest in the qwed-learning course!

Here's where to start:
→ Module 0 if you're new to LLMs
→ Module 1 if you know the basics

Join the community:
→ GitHub Discussions
→ Twitter: @rahuldass29

Questions? Just reply!

- Team QWED

Week 1 Follow-up

Subject: How's the course going?

Hey!

Have you started the AI Verification course?

Quick wins:
✅ Module 1: See the $12,889 bug
✅ Module 3: Build your first verifier
✅ Capstone: Portfolio project

Stuck? Ask in GitHub Discussions!

- Team QWED

🎉 Milestone Celebrations

100 Stars:

  • Thankyou tweet
  • Update README with badge

500 Stars:

  • Blog post: "What we learned from 500 students"
  • Feature top contributors

1000 Stars:

  • Major announcement
  • Plan course v2.0
  • Consider swag for top contributors

Final Go/No-Go Checklist

Before hitting "Publish":

  • All modules readable and error-free
  • Examples tested and working
  • README compelling and clear
  • No broken links
  • Social posts drafted
  • GitHub Discussions enabled
  • You're ready to engage with community
  • Week 1 time blocked for support

If all checked: LAUNCH! 🚀

Remember: Course quality > Launch hype. Better to delay and ship excellent than rush and ship mediocre.

You've got this! 💪