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Projects I've Built with AI
Over the past year I've been building full-stack products end-to-end using agentic AI— GitHub Copilot agent mode paired with Claude to handle architecture, backend, frontend, infrastructure, and deployment. These are real, live products built from scratch.
🏠 homecardiy.com
● Live at www.homecardiy.com
Full-stack AI home & car inventory platform. Track every asset you own with photos, serial numbers, repair history, AI repair guidance, warranty alerts, and document storage.
- Agentic repair engine: Claude uses tool calling to check your repair history, open recalls, and overdue maintenance before answering — autonomously, per query
- 7 tools in the repair agent: repair history, safety recalls, maintenance status, YouTube, Amazon, local stores, contractors
- Claude Vision API extracts appliance/vehicle details from photos automatically
- 1,200+ unit tests, schema contract validation, staging gate before every prod deploy
- AWS Lambda + Aurora DSQL + API Gateway + S3/CloudFront — built with GitHub Copilot agent mode
🗾 Japan Trip Companion
● Live at tlacy.github.io/tokyo-trip
Interactive travel app built for an 8-day Tokyo family trip (May 2026). Converts a static itinerary into a full mobile web app in a single HTML file.
- Day-by-day itinerary with Google Maps & numbered stops
- 70+ Japanese phrases across 7 categories with native speech synthesis (ja-JP)
- Live speech-to-text translation via microphone + MyMemory API
- All booking refs, emergency contacts, and transit tips in one place
- Proof-of-concept for myitineraryai.com (see below)
🌐 myitineraryai.com
⏳ In Development — myitineraryai.com
Upload any trip itinerary (PDF, Word doc, or text) and get back an interactive mobile web app—personalized maps, phrases, tips, and booking refs—ready in minutes.
- Claude extracts structured trip data from unstructured itinerary documents
- Generates a bespoke trip companion app, hosted on S3
- Stripe-gated: review your parsed itinerary first, then pay $7 to generate
- Same Lambda + API Gateway stack as homecardiy.com
🔌 MCP Server (Professional Context)
An MCP (Model Context Protocol) server that exposes my professional background as structured resources for AI assistants.
- Career data, metrics, and writing available to Claude Desktop and other MCP clients
- AI tools for bio generation, career queries, and engineering metrics
- View on GitHub →
🍽️ weeklymealsai.com
● Live at www.weeklymealsai.com
AI weekly meal planner. Set your household size, diet, and budget—Claude generates a full week of meals plus a consolidated grocery list.
- Claude generates structured meal plans from natural-language preferences
- Auto-built grocery lists with reliable recipe search links
- Same serverless stack: Lambda + Aurora DSQL + API Gateway + CloudFront
🌱 elawnaustin.com
● Live at www.elawnaustin.com
Multi-tenant field-service invoicing SaaS. eLawn Austin is tenant #1; the platform is built from day one to support any field-service contractor (lawn, sprinkler, HVAC, pool).
- Strict multi-tenant data isolation—every query scoped by provider, cross-tenant tests required
- Customer request → appointment → invoice → payment lifecycle
- Reliability and security as the product—no AI, just rock-solid UX
📷 PhotoCraft
● Live at tomlacy.net/photocraft
RAW photo compositing platform. Upload NEF/DNG/CR3/ARW files and build HDR, focus-stack, best-blend, and panorama composites in the cloud.
- Python Lambda containers running rawpy + OpenCV for real image science
- Direct-to-S3 presigned uploads; async compositing with job-status polling
- Solved real production hard problems: memory caps, linear-vs-sRGB gamma, OOM stale-job reapers
🚀 AI Zero to One
● Live at www.aizero2one.com
The productized engine behind these apps: a production-grade AWS serverless SaaS starter that takes you from idea to deployed product, with the agent guardrails baked in.
- One-command provisioning of the full stack (Lambda, DSQL, API Gateway, S3/CloudFront, SES)
- Tests, schema contracts, staging gate, and 100+ documented agent pitfalls included
- Refined across six shipped products—every hard-won lesson rolled back into the template
AI Leadership & Approach
My philosophy on AI in engineering: the fastest way to learn what's real vs. hype is to build with it. That's what I've been doing.
🤖 Agentic AI Development
Building products using GitHub Copilot agent mode—autonomous multi-step coding that handles planning, implementation, testing, and deployment.
- Agent writes tests first, catches its own errors, iterates without prompting
- Full-stack apps (DB schema → API → frontend → CI/CD) in days, not weeks
- Deep familiarity with where agents excel and where human judgment is irreplaceable
- Documenting pitfalls and patterns in 100+ documented agent instructions per project—a living playbook that gets tighter every session
👁️ Multimodal AI (Vision)
Production use of Claude Vision API to extract structured data from photos, documents, and insurance cards—no forms required.
- Photo → appliance details (brand, model, serial number) automatically
- Insurance card → policy number, carrier, vehicles extracted in one call
- Client-side resize before upload to stay within Lambda's 6MB invocation limit
- HEIC handling, format validation, graceful degradation for unsupported types
☁️ Serverless AI Infrastructure
Deploying AI-powered APIs on AWS serverless stack built for minimal ops overhead and cost efficiency at startup scale.
- Lambda (Node.js) + API Gateway HTTP v2 + Aurora DSQL (IAM-auth PostgreSQL)
- S3 + CloudFront for global frontend delivery
- AWS Secrets Manager for zero-plaintext credential management
- Staging gate in deploy pipeline: smoke tests run against staging before production
⚡ Engineering Productivity at Scale
At the enterprise level: establishing AI-assisted development practices that improve velocity without sacrificing quality or security.
- AI-assisted code review, test generation, and documentation
- DORA metrics to measure the actual impact of AI tooling on delivery
- Security-first AI adoption: OWASP compliance, prompt injection defense
- Build vs. buy framework for AI capabilities (when to use APIs vs. fine-tune)
Engineering Leadership Principles
- Build to learn: The fastest way to have an informed AI opinion is to ship something with it
- Agents need guardrails, not leashes: Agentic AI is most powerful with tight feedback loops (tests, contracts, staging)
- Parity over convenience: Dev/prod environment gaps cause more production bugs than bad code
- Scalable architecture: Design for 10x growth, optimize for today's constraints
- Security first: Zero-trust, defense in depth, OWASP compliance by default—not afterthought
Lessons Learned Building Six Products with AI
- The model is the easy part: the hard part is the infrastructure, security, and test discipline around it
- Capture every pitfall: a documented mistake becomes a guardrail—the agent instruction files now prevent classes of bugs across every new project
- Test before browser: a 0.2s unit test beats a 10-minute deploy-and-check loop every time
- Own the mistake, fix it, write it down: Christopher Avery's Responsibility Process applies to humans and agents alike
- Reuse the engine: each product feeds lessons back into a shared starter, so the next build starts further ahead
Influences & Staying Current
I keep this practice sharp by learning from the people pushing AI-augmented leadership and engineering forward:
- Geoff Woods — The AI-Driven Leader (and his work on AI leadership): using AI as a thought partner for better executive decisions, not just a productivity tool
- Gene Kim — Vibe Coding: building software through intent and conversation with AI rather than line-by-line authoring
- Plus the daily feed above—I read the news, but I learn by shipping