Notes, experiments, and playbooks on building production LLM applications, orchestrating autonomous agents, and optimizing software delivery. Written for developers and practitioners.
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Calculate exact VRAM, GQA KV cache footprint, and hardware suitability across vLLM, SGLang, and Ollama.
Estimate token count and API costs across GPT-4o, Claude 3.5, Gemini, and Llama 3 models in real-time.
Audit articles and system prompts against 8 AEO signals to evaluate citation likelihood in ChatGPT and Perplexity.
Architect and deploy production AI agents using Model Context Protocol (MCP). Step-by-step implementation for LangGraph host integration, stdio vs SSE transports, and secure tool sandboxing.
Build a production-ready MCP server with FastMCP over stdio transport. Covers tool registration, schema validation, error handling, concurrency limits, and Claude Code integration.
Harden your MCP server against prompt injection, unauthorized access, and filesystem escapes. Covers OAuth token verification, Docker sandboxing, path allowlists, and rate limiting.
Build a CI/CD evaluation gate for AI agents using DeepEval and Ragas. Covers metric selection, dataset management, GitHub Actions integration, and threshold-based deployment blocking.
Build a fully local RAG pipeline using Ollama for embedding, LanceDB for vector storage, and hybrid BM25+vector search. Includes latency benchmarks, chunking strategies, and production integration.
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.