Full-stack engineering capabilities, AI integration, and technical innovation
End-to-end development covering modern frontend frameworks, AI u0026 LLM tooling, scalable backend APIs, e-commerce, cloud infrastructure, and enterprise CMS.
What I engineer
Full-stack development, AI integrations, modern frontend frameworks, and cloud architecture built to production standards.
AI u0026 LLM Solutions Engineering
01Core Innovation Layer
Autonomous AI agents, RAG search pipelines, custom prompt chaining, vector embeddings, and OpenAI/Claude API integrations.
- AI Agents
- RAG Architecture
- Vector Search
- OpenAI u0026 Claude APIs
- LangChain
Modern Full-Stack Engineering
02JavaScript / TypeScript / Python
Performant single-page u0026 server-rendered applications with clean state management and modular component architecture.
- React.js
- Next.js App Router
- TypeScript
- Node.js
- Python
Automation u0026 Workflow Systems
03Process Acceleration
Eliminating manual data transfer with resilient automation pipelines, webhook handlers, and AI-driven data triage.
- n8n
- Make.com
- GoHighLevel
- Webhook Architecture
- Automated Triage
eCommerce u0026 Custom SaaS Apps
04High-Volume Systems
Shopify themes u0026 custom Remix apps, high-throughput WooCommerce setups, custom checkout flows, and payment integrations.
- Shopify Custom Apps
- WooCommerce at Scale
- Stripe u0026 Payment APIs
- SaaS Platforms
Enterprise CMS u0026 Headless Architecture
05Content Infrastructure
Enterprise content models using custom Gutenberg u0026 ACF blocks, Contentstack, HubSpot, and Headless CMS backends.
- Gutenberg Blocks
- ACF Development
- Headless CMS
- Contentstack
- HubSpot
API u0026 Microservices Engineering
06Contracts u0026 Integration
Designing REST and GraphQL APIs with strict typing, idempotency, failure retries, and comprehensive documentation.
- REST API Design
- GraphQL APIs
- Microservices
- Idempotency u0026 Retries
DevOps u0026 Infrastructure
07Delivery u0026 Observability
CI/CD pipelines, containerization, serverless edge deployment, and hosting infrastructure that keeps deployments boring.
- GitHub Actions
- CI/CD Pipelines
- Docker
- Vercel
- AWS u0026 Cloudflare
Web Performance u0026 Security Engineering
08Speed u0026 Hardening
Core Web Vitals optimization, server-side caching, security hardening, and query budgets measured by real-user data.
- Core Web Vitals
- SSR Optimisation
- Redis Caching
- Security Hardening
The workshop: experiments, prototypes, and tools
Personal research rather than client work. Each entry documents the technical problem, the approach, the stack, and what it taught me.
half of these started as a bad idea at 11pm. two of them still are.
-
Gutenberg Blocks
A schema linter for custom Gutenberg blocks
A small Node utility that validates block.json files against a house schema before they reach review.
Node.js block.json Gutenberg CI/CD→ -
Performance Testing
Query budget probe for template rendering
A must-use plugin that records query counts and timings per template and warns when a page exceeds its budget.
PHP WP-CLI MySQL SAVEQUERIES→ -
WooCommerce Customizations
Checkout customization sandbox
A local sandbox for exercising checkout field logic, validation, and gateway edge cases without touching a real store.
WooCommerce PHP WP-CLI Mock Gateway→ -
API Integrations
Contract tests for WordPress REST endpoints
Lightweight tests that assert the shape of REST responses so consumers do not break silently.
WordPress REST API PHP PHPUnit JSON Schema→ -
Developer Utilities
Content audit toolkit in WP-CLI
Commands that report orphaned meta, unused terms, oversized options, and blocks no longer registered anywhere.
WP-CLI PHP MySQL→ -
Automation
Release automation for internal plugins
A pipeline that lints, analyses, versions, and packages internal plugins on tag.
GitHub Actions Composer PHPCS PHPStan→
Technology ecosystem
Full-stack development, AI capabilities, and modern tooling grouped by architectural layer.
Questions about the stack and the lab
- Does "AI engineering" here mean more than calling an API?
- Yes. Calling a model is the easy part. The engineering is everything around it: retrieval quality over a vector index, tool definitions with schema validation so the model cannot return unusable output, retry and fallback routing when a provider degrades, token cost budgets, and observability so you can tell why a given answer was produced. Without those layers an AI feature is a demo, not a system.
- Gutenberg blocks or a page builder – which do you recommend?
- Native blocks, in almost every case. Page builders trade short-term editor convenience for long-term lock-in: markup you do not control, upgrade paths that break, and a rendering layer you cannot profile. Custom Gutenberg or ACF blocks give editors the same flexibility while keeping the output, the performance characteristics and the migration path in your hands.
- Which e-commerce platforms do you engineer on?
- WooCommerce and Shopify. On WooCommerce the work is usually checkout and product logic extended without forking core behaviour, so upgrade paths stay intact. On Shopify it is custom themes and app functionality against the Admin and Storefront APIs. Both frequently involve inventory synchronisation and webhook pipelines between the store and the systems behind it.
- What is in the Lab section?
- Working tools rather than portfolio pieces: a schema linter for custom Gutenberg blocks, a query budget probe for template rendering, contract tests for WordPress REST endpoints, a WP-CLI content audit toolkit, and release automation for internal plugins. They exist because the same problem kept recurring across projects.
- How do you approach API and integration work?
- Defensively. Every outbound call assumes the other side will be slow, wrong or absent: timeouts, bounded retries with backoff, idempotency keys where the operation writes, and failure modes that surface in monitoring rather than in a support ticket three days later. Integrations do not usually break loudly – they break quietly, which is the harder problem.