Where AI Meets
Engineering
A showcase of production systems built through AI pair programming — not AI-assisted, but AI-native from architecture to deployment.
AI-Augmented Systems
Manual Excel download, no monitoring
Fully automated pipeline, runs twice daily
A fully automated data pipeline for the California WARN Act. Engineered for surgical precision, it transforms raw government filings into live actionable intelligence using ETag caching, MD5 verification, and GitHub Actions CI/CD — running twice daily with zero human intervention.
No automation, manual device control
Zero-touch IoT orchestration system
An advanced IoT orchestration layer for automated prayer-time notifications. Integrates Raspberry Pi with Sony Android TV via ADB, managing media states and low-level system commands. Built from scratch using AI pair programming—compressing a 3-week engineering cycle into 4 days.
T-Mobile Bill Automation
From manual PDF parsing to fully automated billing pipeline
Manual Python script, ran per request
Event-driven E2E billing automation
Originally a manual Python script to parse T-Mobile family plan PDFs and split costs, it was transformed into a fully automated, event-driven E2E system. The Mac Folder Action watches ~/Downloads and instantly processes new bills, calculates splits, and sends Zelle-ready summaries via email.
Portfolio: bilalahamad.com
Premium AI-native portfolio, built with Next.js + Framer Motion
Google DeepMind
Anthropic
Static HTML/CSS resume site
AI-native Next.js portfolio with analytics
A fully responsive, dark-mode portfolio website built end-to-end with AI pair programming (Antigravity + Gemini). Features glassmorphism design, live GitHub data, Vercel Analytics, Google Analytics with custom events, and an interactive Certifications gallery.
ai-metrics.jsonUpdated 2026-04-14Read the Full Whitepaper
A deep technical analysis on AI-native development methodology, metrics, and the framework I use to measure engineering velocity.