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AI Lab

Where I learn AI by building it

No black boxes. Each experiment here builds a layer of the modern AI stack from first principles — embeddings, retrieval, agent loops, and the data engineering underneath.

Agents & tool calling

Reasoning loops that retrieve context, decide when to invoke tools, execute them, and synthesize answers — built by hand to understand every step of the agentic pattern.

RAG & vector search

PDF ingestion → chunking → Sentence Transformer embeddings → ChromaDB semantic retrieval. The full retrieval-augmented generation pipeline, no framework magic.

Local-first LLMs

Running llama3 on Ollama for private, zero-cost inference — because understanding deployment constraints is part of AI engineering, not an afterthought.

AI-assisted engineering

Spec-driven development with AI pair programming in production since 2022: write the contract, direct the model, verify rigorously. Applied daily at enterprise scale.

Experiments

quantedge

Private

PythonUpdated Sep 2026

webjson

Private

TypeScriptUpdated Aug 2026

ai-resume-builder

Private

TypeScriptUpdated Aug 2026Live demo

Why this matters

Production AI systems fail in the plumbing: retrieval quality, context management, latency, cost, observability. 9+ years of building enterprise systems taught me to engineer for those failure modes — the AI Lab is where I apply that discipline to LLM-based products. The goal isn't demos; it's understanding deep enough to ship AI features that survive real users.