From Idea to Production
In 7 Days
186K+ lines of TypeScript. 303 test files. 258 documentation files. Comprehensive guardrails system. This is what AI-augmented engineering maturity looks like.
From Rapid Commits to Guardrails & Quality
The evolution from prototyping velocity to production excellence
✓ AI Guardrails Enforcement
- • Icon usage validation
- • Enterprise compliance checks
- • Schema and data integrity
- • Test framework consistency
✓ Security & Quality Gates
- • Secrets detection & scanning
- • Mock data prevention
- • Automated test execution
- • Icon and asset validation
💡 Real Impact
Prevents 90% of common coding errors before they reach code review. Enforces consistent standards across codebase. Automated security checks catch vulnerabilities early.
🔐 Security Patterns
- • Authentication & authorization
- • Secrets management workflows
- • Security audit procedures
🤖 AI Guardrails
- • LLM integration best practices
- • Prompt engineering standards
- • AI provider abstraction
📊 Database Workflows
- • Migration and schema evolution
- • Data validation patterns
- • Repository pattern implementation
⚡ Development Standards
- • Code review checklists
- • Error handling patterns
- • Performance optimization
✅ Knowledge Transfer Impact
New developers onboard in hours, not days. Consistent implementation patterns across the team. Self-service troubleshooting and debugging guides reduce tribal knowledge.
Midway through development, spun up a private repository for enterprise-level product features while keeping core platform public.
✓ Public Repo (This Repo)
- • Core SaaS platform
- • Engineering best practices
- • Portfolio showcase code
- • Open source workflows
✓ Private Enterprise Repo
- • Proprietary business logic
- • Enterprise integrations
- • Revenue models & pricing
- • Advanced MCP servers
💡 Strategic Thinking
Recognized partway through that some features should remain proprietary for competitive advantage. Refactored to separate public platform (portfolio showcase) from private enterprise product. Both repos maintain same engineering standards and guardrails.
The Development Journey
From prototype to production-ready SaaS
Built by Donnie Laur - Engineering Leader & AI/SaaS Architect
Available for Engineering Manager, Director of Engineering roles, or consulting on AI integration into developer workflows
Started with Google's brand new AI Studio (vibe coder) to validate the concept before writing any code.
✓ What Worked
- • Fast validation (no code yet)
- • Brainstormed features
- • Generated initial prompts
💡 Key Insight
AI tools perfect for ideation. Validate before building.
Moved to Cursor + Claude. Focus: Ship in minutes, not hours.
⚡ Why TypeScript Files, Not Database?
Speed over perfection. TS files: 5 min to add 10 prompts. Database: 2 hours for schema + migrations. Ship fast, migrate later if needed.
Can't demo AI features without protecting API keys. This had to be production-ready.
45 minutes for UI that looks like a funded startup.
OpenAI + Google AI integration with tracking and analytics.
Notice there are minimal tests in this repo. This is intentional, not lazy. Here's why:
❌ Don't Test First When:
- • APIs are still changing daily
- • User feedback hasn't validated features
- • You're in rapid prototyping mode
- • The code might be thrown away tomorrow
✅ Add Tests When:
- • APIs stabilize (we're almost there)
- • Users validate the features
- • You move from prototype → product
- • Bugs start appearing in production
💡 The Real Lesson:
Writing tests for code you'll delete is waste. Ship fast, validate with users, THEN add tests when patterns emerge. This is how modern teams move fast without breaking things.
✅ UPDATE: Testing Journey Complete
1,357 total commits. Not sloppy - strategic. Small, atomic, revertable. Each commit ships working code.
🔥 This is NOT "vibe coding"
This is: Try → Ship → Learn → Iterate. If something doesn't work? Delete it and rebuild. Modern development is about velocity AND quality, not one or the other.
⚡ Why so many commits?
- • Each commit is functional and deployable
- • Small commits = easy to revert if wrong
- • Commit often = never lose work
- • Shows thinking process, not just final result
✅ The Point:
This is rapid prototyping at its finest: build fast, validate fast, pivot fast. If it works, keep it. If not, delete and start over. No ego, just results.
1. AI-Augmented, Not AI-Generated
AI suggests, human decides. AI writes boilerplate, human writes logic.
2. Ship Fast, Iterate Faster
TypeScript files → Database (when needed). Mock → Real APIs (when validated).
3. Production Quality from Day One
Real auth, real APIs, real error handling. No shortcuts on fundamentals.
4. Progressive Enhancement
Start simple, add complexity as needed. Pre-commit hooks added when patterns emerged.
Google AI Studio (Vibe Coder)
Initial concept validation - brand new tool, perfect for ideation
Windsurf + Claude Sonnet 3.5
Today's main development - 350+ commits with this combo
Tomorrow: Maybe Cursor?
Not locked in - use whatever works best for the task
💡 The Point:
I'm not married to any tool. Google AI Studio for ideation, Windsurf for today, Cursor tomorrow if it's better. Use the right tool for the job, not the trendy one.
AI Models Used
- • Claude Sonnet 3.5 (primary)
- • Google Gemini (testing)
- • GPT-4 (validation)
Development Tools
- • Windsurf (today)
- • Google AI Studio (ideation)
- • Cursor (maybe tomorrow)
The Evolution: From "ship fast" (early prototyping) to "ship right" (comprehensive guardrails, workflows, and quality gates). Both velocity AND quality, not one or the other.
Day 1-2: MVP Sprint (Rapid Prototyping)
500+ commits | TypeScript-first | Real auth & AI from day 1
Philosophy: Validate concept fast. TypeScript files for prompts (5min) vs database schema (2hr). Ship working features, refactor when patterns emerge.
Day 3-4: Business Patterns (Scaling Foundation)
Repository Pattern | AI Provider Factory | Enterprise RBAC
Added structure: 91 repository tests, 5-provider abstraction, 6-role RBAC with 13 permissions, OpsHub admin dashboard. Multi-tenant architecture emerged.
Day 5: Production Hardening (Enterprise Ready)
620 tests | Observability | Security | Cost controls
Bulletproofed for customers: Budget enforcement ($0.75-$2.50 per tool), RED metrics (p50/p95/p99), PII redaction (GDPR/SOC2), rate limiting, replay protection, 3 incident playbooks, 4 ADRs.
🎯 The Progression Pattern
Week 1: Ship fast, validate concept, get real usage data
Week 2: Add patterns when APIs stabilize, structured testing
Week 3: Production-harden with security, observability, ops maturity
This isn't "move fast and break things" - it's move fast, then make it bulletproof.
Day 6: Content Hardening (Real Data Migration)
Patterns to MongoDB | TODO resolution | Real data migration
Migrated all content to MongoDB, resolved 39 critical TODOs (auth adapter, audit logging, tRPC), added career recommendations API, site stats from database.
Key Docs: DAY_6_CONTENT_HARDENING.md, TODO_RESOLUTION_DAY6.md
Day 7: QA & Polish (Trust Signals)
Mock data removal | UI/UX polish | Enterprise guardrails
8 phases: Mock data audit (removed || 76, || 23 fallbacks), fixed 12 QA issues, improved prompt filters, enhanced UI readability, added pre-commit hooks for mock data detection, Red Hat trust audit.
Key Docs: DAY_7_QA_FRONTEND_IMPROVEMENTS.md, RED_HAT_TRUST_AUDIT.md, ADR-009 (Mock Data Removal)
🎯 Development Workflow
From prototype to production-ready enterprise platform
Day 4 (Nov 2)
- SEO Foundation: 19 role-specific landing pages (CTOs, VPs, Directors, Engineers, PMs, Designers, QA, DevOps, Security)
- Pillar Pages: 3 comprehensive SEO-optimized articles (AI upskilling, AI-first orgs, AI-assisted development)
- Content Generation: Gemini Deep Research integration for automated content creation
Day 5 (Nov 3)
- AI Model Catalog: 200+ AI models from OpenRouter with automated sync scripts
- AI Tools Directory: Curated catalog of development tools (Cursor, Copilot, Windsurf, etc.)
- Collection Pages: Filterable catalogs for AI Models, AI Tools, Patterns, Prompts
Day 6 (Nov 4)
- RAG Chatbot: Context-aware AI assistant with MongoDB vector search
- UI Polish: Hero sections for prompts/patterns pages, improved card readability
- SEO Optimization: Meta tags, structured data, sitemaps, internal linking
Day 7 (Nov 5)
- Security Audit: Comprehensive security scan (10/10 score) - all secrets externalized
- Repository Organization: Strategic content protection, professional structure
- Quality Audit: 95/100 quality score (up from 92/100)
🔄 Process Improvements & Automation
🎯 Key Achievements
Interested in This Approach?
Donnie Laur - Available for Engineering Manager, Director of Engineering roles, or consulting on AI integration into developer workflows.
Every commit documented. Every architectural decision explained with ADRs. Production-ready code with 620 tests and operational runbooks.
Or try the live B2B SaaS platform free at engify.ai/signup