● 45 posts
Essays and field notes on agentic engineering, RAG, and running AI in production.
I hit my Claude Code rate limit three times last Tuesday. Each time, I had to stop what I was doing, log out, log into a different subscription, and pick up where I left off. The context was gone. The
I was in the middle of a refactor — removing dead code from a shared SDK module — when my edits vanished. No error. No warning. Just gone.
I'd been building my AI infrastructure for months before I found a name for it. I had a CLAUDE.md with operating modes, a folder of skills, a deploy script that pushed everything to multiple machines.
I kept writing skills. New skill for code review. New skill for deployment. New skill for browser automation. Each one added a capability, and each one stayed a capability — a one-off that I had to co
I had four machines. My AI assistant behaved differently on each one.
The first real sign I had a system rather than a workflow was when I noticed the assistant failing gracefully.
I had a 2,000-word CLAUDE.md in one of my repos. It covered architecture, directory structure, coding conventions, style rules — the works. Every time I ran Claude Code or Codex against the codebase,
Three months ago, I was letting my AI agent write code directly on the production server. No branches, no CI, no tests between "idea" and "live in production." If the agent broke something at 2 AM, th
Unit tests tell you if the code you wrote works. They don't tell you about the code you forgot to write. After shipping an alternate phone numbers feature that passed all 17 unit tests, a review agent
BugBot finds bugs in code that's already written. But what about the bugs that exist because the architecture is wrong — where the code does exactly what it says, but "what it says" is inconsistent ac
Over the past three posts, I've covered the individual pieces: DevFlow for CI/CD enforcement, BugBot for adversarial review, and ArchReview for architectural tracing. But skills don't live in isolatio
Every code review I've ever done was a single pass. You open the diff, read through it, maybe catch a few things, leave some comments, approve. The problem is bugs don't care about your review flow. T
After running BugBot across several real codebases, the result that surprised me most wasn't the bugs it found. It was which iteration found them. The same files, reviewed from a different angle in a
I run AI coding agents across multiple machines, multiple sessions, sometimes for days at a time. The biggest frustration isn't capability — it's amnesia. Every new session starts from zero. The agent
When I checked our mobile Lighthouse scores last month, both of our clinic websites — arcs.health and covenant.clinic — were in rough shape. Not broken, but mediocre. The kind of scores that quietly c
I run multiple Claude Code sessions in the same project all the time. One session handles a long-running task via a Ralph Wiggum loop (a self-referential iteration technique), while I open another ses
I've been running OCR on insurance cards at our urgent care clinics for a few months now. Patients hand over their card at check-in, staff snaps a photo, and our system extracts member IDs, group numb
Synology Active Backup for Business is one of the best self-hosted backup solutions out there — agent-based, centralized, bare-metal restore capable. There's just one problem: the Linux agent only off
Insurance card data entry is one of the most tedious bottlenecks in patient onboarding. Commercial OCR services charge per page and require sending patient data to third-party servers. I wanted to see
Urgent care operates in a constant state of tension. Providers spend more time documenting visits than examining patients. Front desk staff juggle phone calls, check-ins, and insurance verification wh
When someone asks you what makes life meaningful, the reflexive answer is usually something about happiness, fulfillment, or pleasure. But Jordan Peterson argues we're asking the wrong question. The b
Claude Code's entire architecture is fundamentally just one while loop. Not as a simplification—literally. While competitors build orchestration frameworks with task queues, agent hierarchies, and com
When patients call or message an urgent care clinic, they're usually asking the same questions: "What are your hours?" "Do you take my insurance?" "How long is the wait right now?" These repetitive in
Every urgent care provider knows the pressure. A parent brings in a child with an ear infection. The exam is ambiguous — it could be viral, could be bacterial. You know antibiotics probably won't help
The urgent care industry is undergoing a quiet revolution in how patients are seen. The traditional walk-in-only model — once the defining feature of urgent care — is giving way to hybrid scheduling s
Niccolo Machiavelli has been misunderstood for 500 years. His name has become synonymous with manipulation and ruthlessness, but his actual writings contain some of the most pragmatic leadership insig
In October 2025, I deployed a production RAG system that would have cost $39,600 annually using OpenAI's APIs. My actual cost? $2,300 per year. That's 94% cost savings while maintaining comparable per
The story of Abraham waiting 100 years for a son, only to be asked to sacrifice him, seems absurd on its face. Why would ancient cultures preserve such a psychologically brutal narrative? Dr. Jordan P
The best productivity system is the one you'll actually use. After years of experimenting with todo apps, kanban boards, and elaborate task managers, I found something that works: a hybrid of Tiago Fo
If you're a developer, you probably use tmux or screen. You might even consider it essential infrastructure. But according to Kovid Goyal, creator of the Kitty terminal emulator, terminal multiplexers
Every January, millions of people set ambitious goals. By February, 80% have abandoned them. The conventional wisdom blames willpower, discipline, or commitment. But the real problem runs deeper: most
What if one algorithm could handle everything—from fixing a typo to architecting a distributed system to building an entire company? Not a vague philosophy, but a precise, verifiable framework with me
I got a 4-star Google review last week that bothered me more than it should have.
After spending months helping enterprises build AI systems, I noticed a pattern: everyone wanted powerful RAG capabilities, but few were comfortable shipping their proprietary data to external APIs. T
I used to spend 6+ hours writing Analysis of Alternatives reports. Last week, I built an AI agent that does it in minutes - and you can too, without writing complex code.
I've been working on a project to support SSE for MCP. Couldn't find any good example on the client in Python for supporting SSE with FastAPI MCP. So I wrote it up myself and thought I will share ever
Learn how to architect AI agent systems with a modular, skill-based approach and implement real-time communication using Server-Sent Events (SSE).
Learn how to implement robust evaluation for AI agents using Azure AI Evaluation SDK when working with Azure API Management (APIM), overcoming authentication and integration challenges.
Learn how to transform an open-source AI research agent into an enterprise-ready solution using Microsoft's Semantic Kernel framework with Azure OpenAI.
Learn how to leverage GPU acceleration to significantly improve document processing speed in Retrieval-Augmented Generation (RAG) systems.
This blog post details the technical architecture and innovations that make this system particularly effective for enterprise use cases.
- Amazon Bedrock Guardrails are a robust feature within the Amazon Bedrock service designed to enhance the safety, compliance, and overall quality of interactions with AI models. Guardrails provide a
Microsoft Phi-2 Fine Tuning - Learn how to adapt this powerful small language model for sentiment analysis of employee performance data using LoRA and quantization.
Learn advanced techniques to tackle the performance challenges of processing skewed data distributions in Apache Spark, backed by a real-world case study with 5x performance improvement.
A comprehensive comparison of sentiment analysis capabilities across Azure Cognitive Text Analytics, AWS Comprehend, and custom fine-tuned models like RoBERTa and Phi2.