Production systems, live demos, and open source.

Live Cloudflare Worker implementing the Model Context Protocol (MCP). Exposes three tools (time_now, worker_info, and echo) demonstrating MCP server patterns, Workers AI binding, and edge AI deployment. A reference implementation for agentic tool-use architecture.

Autonomous email-summarization agent using Claude Haiku with function calling for tool-based auto-reply. Demonstrates Claude tool-use patterns, serverless deployment to Vercel, and supply-chain auditability through JFrog Artifactory proxying 900+ packages. Includes 5-point DX friction report with actionable fixes.

The portfolio assistant on this site, grounded in resume, projects, and background. Deployed as a Cloudflare Worker using Workers AI and Llama 3.3 70B: system prompt grounding, edge inference, rate limiting, and conversational UX without a RAG layer. Open the chat widget to try it.

Fully offline, beginner-friendly lab for evaluating RAG systems. Includes synthetic datasets, embeddings, vector search, and retrieval metrics (Recall@k, Precision@k, MRR, and groundedness scoring), built for reproducible RAG evaluation without API dependencies.

Enterprise AI operating system orchestrating multi-agent workflows with Claude as the default LLM provider. Features a 22-case CI-gated evaluation harness running on every commit, a tool-loop safety circuit breaker preventing runaway agent behavior, and strict output validation requiring cited sources or failing at parse time. Demonstrates production patterns for Claude tool-use and agentic safety.

Black-box pairwise evaluation baseline for comparing chatbot versions using LLM-as-judge. Supports fake and edge judge paths, audit metadata, and CI integration, with a roadmap toward conversation-level multi-turn evaluation.

Tutorials and demos for building real-world applications with OpenAI APIs, covering function calling, RAG, embeddings, and multimodal applications through four progressive labs from API basics to production patterns.

Lakehouse-native evaluation framework measuring regional Spanish LLM performance (El Salvador vs Peru) using Delta tables, Spark, and Databricks. Applies Bronze/Silver/Gold data architecture to LLM benchmarking at scale.

End-to-end ML platform built on Kubernetes and microservices, including containerized model serving, automated retraining pipelines, CI/CD for ML, and production monitoring. Stack: Kubernetes, Docker, FastAPI, MLflow.

Distributed ML pipeline predicting flight delays across 30M+ records using MapReduce, Hadoop, and Apache Spark on Databricks. Applied ensemble methods (GBT, Random Forest) with feature engineering on temporal and weather data.

Applied linear regression and TensorFlow to identify socioeconomic and demographic predictors of hate crime rates across U.S. counties. Surfaced statistically significant correlations to inform policy research.

Multi-database recommendation engine using Neo4j (graph traversal), MongoDB (document store), and Redis (caching) to generate personalized store location suggestions at low latency.

Exploratory data analysis on NFL tracking data using Python, NumPy, and Pandas. Analyzed player movement patterns and derived game-level insights from raw positional data.

Designed and executed a research study on movie revenue predictors using OLS regression, hypothesis testing, and diagnostic analysis to identify drivers of box office performance.

Interactive Tableau dashboard reimagining travel data as a visual guide, layering geographic, seasonal, and sentiment data to surface non-obvious destination insights.

iOS app leveraging real-time safety data and ML-based route scoring to recommend safe running routes. Full mobile + backend stack built as UC Berkeley MIDS capstone project.