Notes, experiments, and playbooks on building production LLM applications, orchestrating autonomous agents, and optimizing software delivery. Written for developers and practitioners.
Learn how to architect, build, and deploy production-ready AI agents using the Model Context Protocol (MCP). Covers state management, security, and scalability.
Learn how to emulate the Amazon Bedrock Runtime API locally using MiniStack and Ollama. Continue using the standard Boto3 SDK while developing against local LLMs, then switch to Amazon Bedrock by removing a single endpoint_url override.
A definitive engineering guide to building, evaluating, and deploying production-grade AI agents using the NVIDIA NeMo Agent Toolkit and NVIDIA NIM.
An analysis of the state of enterprise AI agents. Covers the shift from single-agent to multi-agent architectures, the rise of MCP, and edge inference.
A definitive guide to defining, orchestrating, and executing AI workflows. Covers sequential chains, parallel execution, and human-in-the-loop architectures.
AI Engineer & Systems Practitioner
Building LLM products, researching multi-agent orchestration, and setting up secure deployment pipelines. Formerly writing DevOps playbooks, now mapping the production AI stack.
Learn more about my experiments arrow_forwardEverything published here is tested locally and run against real applications. We avoid corporate jargon, consultant hand-waving, and empty marketing hype.
Designing, optimizing, and operating reliable AI pipelines in production. Context truncation, pricing model controls, hybrid search architectures.
Orchestrating autonomous decision-making loops and workflows. Complex state machines, multi-agent frameworks, LangGraph orchestration.
Practical implementations, fine-tuning methodologies, model evaluation frameworks, and speech-to-text models on Azure and cloud environments.
Accelerating local development velocity, command-line interfaces, automated CI/CD pipelines, container optimizations, and secret scanners.
We send out weekly breakdowns of RAG implementations, agent logic flows, Docker optimizations, and code checklists. Straight code snippets, zero fluff.