Technical Writing
Notes on distributed systems, AI infrastructure, databases, and observability. Written for engineers who want depth, not surface-level takes.
Building AI Systems: What Actually Happens When You Send a Prompt
A walkthrough of what actually happens between a prompt going in and an answer coming out — prompt engineering, tokenization, RAG retrieval, planning, ReAct agents, tools, and MCP — drawn from building a vulnerability scanner and an autonomous red-teaming orchestrator.
Building an Observability Dashboard for AI Agents at Scale
How we turned six AI coding agents' disconnected request logs into searchable, navigable conversations — identity resolution across Claude, Cursor, Copilot, Codex, Gemini and LangChain, Kafka-backed ingestion at scale, and session reconstruction at query time in Elasticsearch.
I Built a Red Team Orchestrator for AI Agents. Here's What Actually Made It Hard.
How I built a multi-agent orchestration pipeline to automatically red-team any AI agent or MCP server — and what surprised me along the way.
Building an AI Security Engineer Before Coding Agents Could Do It
How I built a hybrid static-analysis + semantic-search + LLM pipeline to automatically find vulnerabilities in source code — and why none of it was really a model problem.
Swagger Knows the Endpoints. It Doesn't Know the Workflow.
How I turned a static OpenAPI specification into realistic, stateful API traffic — building a dependency graph across hundreds of endpoints so a security-testing pipeline could replay requests that actually pass validation.