43,146 Lines in Two Days: A Transparent Case Study

Git-verified analysis of what one founder achieved across three projects in ~14 hours. Includes honest caveats and limitations.

On January 9-10, 2026, we documented a real development session. What you're about to read isn't marketing—it's a transparent analysis of what one non-developer founder achieved using AI-native development methodology.

Important disclaimer: This case study presents real results but acknowledges significant caveats. We're not snake oil salesmen—our goal is honest demonstration, not hyperbole.

The Numbers (Git-Verified)

All metrics derived from Git commit history:

• 43,146 net lines of code added
• 179 files changed
• 41 commits across 3 repositories
• ~14 hours of active work
• ~$30 in attributable costs

What Was Built

SCAINET Website (+25,436 lines)

A complete company website with:

• 10+ pages with custom design and animations
• Investor Portal with Firebase authentication
• Team Portal with role-based dashboards
• CI/CD pipeline (GitHub Actions → Vercel)
• Error tracking (Sentry) and analytics
• Custom email system via Firebase Functions
• SEO optimization, legal pages, and more

Family Hub Mobile App (+13,791 lines)

Parallel development on our flagship product:

• 4 new games: Space Blaster, Marble Maze, FlipCards, Memory Match
• Complete Chess.com-quality upgrade
• Premium board themes and sound effects
• Removed 3 low-quality games (quality curation)

Chazwazza Templates (+3,919 lines)

• 9 new enterprise templates
• Agent Excellence Guide updated to v3.1
• CI/CD patterns documentation

The Honest Caveats

We believe in transparency. Here's what these numbers include and don't include:

What's Included

• Generated/Template Code: Some output is templated or generated (HTML documents, configs)
• AI-Written Code: The majority was written by AI, reviewed by human
• Copy Operations: Some files copied between directories

What's NOT Captured

• Testing Time: Significant time spent testing and debugging
• Decision Making: Human direction not captured in LOC
• Learning Curve: Founder has months of AI tool experience
• Existing Foundation: Family Hub already had 137K+ lines

Realistic Expectations

We claim that AI-native development is significantly faster, that non-developers can ship production software, and that parallel AI agents multiply output.

We do NOT claim that anyone can replicate this on day one, that AI replaces human judgment, that every project sees 100x gains, or that this works for all software types.

Reproducibility

Based on our experience, here's what others might expect:

• Week 1: 2-3x productivity (learning curve)
• Month 1: 5-10x productivity (developing skills)
• Month 3+: Significant gains (methodology internalized)

The methodology is teachable, but requires investment to master.

Conclusion

This session demonstrates the potential of AI-native development. The results are real and verifiable. But they required experience, clear requirements, and a proven methodology.

The SCAINET methodology isn't magic—it's a learnable system that, when mastered, enables radical efficiency in software development.

The full case study with detailed timeline and verification instructions is available in our Investor Data Room.