AI and Machine Learning
Large language models (OpenAI, Anthropic, open-source via Ollama), retrieval-augmented generation (RAG), vector databases (Pinecone, Weaviate, Chroma), prompt engineering, model evaluation, and fine-tuning.
Technology
The tools and technologies I use to build AI systems and websites.
My tech stack is chosen for what ships, not what is trending. I focus on technologies that are reliable, well-documented, and proven in production.
Everything from model selection and retrieval pipelines to frontend frameworks and deployment infrastructure. Chosen for reliability, not hype.
What this includes
Every technology is vetted for production use - no black boxes, no vendor lock-in.
Web Development
PHP (Laravel), Python (FastAPI, Flask), JavaScript/TypeScript (React, Next.js), HTML/CSS, modern CSS frameworks. Fast to develop with, fast to serve.
Infrastructure and Deployment
Docker, nginx, Linux server administration, CI/CD pipelines, cloud hosting (AWS, DigitalOcean), managed databases, and monitoring.
Design and UX
Figma for design, modern CSS with custom properties, responsive design, accessibility (WCAG), performance optimization (Core Web Vitals).
Approach
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Right tool for the job
No technology for technology sake. Every tool is chosen based on the specific problem, team constraints, and production requirements.
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Keep it simple
The best architecture is the simplest one that handles the requirements. Complexity is the enemy of reliability.
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Built for production
Every project is built with deployment, monitoring, and maintenance in mind from day one.
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Transparent stack
Clients always know exactly what technologies are in their stack and why.
Outcomes
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Reliable systems
Built with technologies that are well-supported, well-documented, and battle-tested in production.
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Fast delivery
Familiar toolchains mean faster development, fewer surprises, and quicker time to market.
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Maintainable code
Clean architecture, clear documentation, and technologies that your team can own long-term.
Let's work together