In my journey furthering AI-amplified engineering, one major theme is becoming more and more obvious to me: Engineering context is infrastructure. Let me explain. Giving AI access to your code and letting it do work is simply not good enough. It needs to understand the intent of what the software
Let’s be honest: tools like Cursor and Copilot have changed the game. Watching one turn a single comment into full-blown code is a genuine jaw-dropper. Suddenly, side projects that used to swallow up half a year feel like weekend experiments. Got a random idea for a data pipeline that
AI agents are booming, no doubt. But getting them to play nicely with our existing microservice world often feels like forcing a square peg into a round hole. We use wrappers, translators (like MCP servers), and tool definitions – it works, kind of, but feels complex and brittle. Current Integration Pattern
In the tech world, too often platform engineering teams are viewed as simply, "nice to have". Sometimes the function isn't considered or we just get too heads down in our daily work that we don't see the pain their absence may be causing. I&
Just wanted to say Happy New Year everyone, and hope you all have an amazing 2025. My resolution is to write more, and just live life a little more. Be more in touch with my family, my friends, and my hobbies. Maybe not stress too much about work. I started
So this has turned out to be somewhat of a controversial subject. Some of the most heated discussions I've had with peers have been on this very topic. "Leetcode" style interviews for senior engineering candidates (think architects, staff engineers, etc). I've been on both
Domain-Driven Design (DDD) gets a lot of attention, and rightfully so, but I've often seen conversations, exercises, and other activities related to trying to implement it become so heavy and taxing that we tend to lose steam, get frustrated, and end up not getting to the outcome we
In modern systems architecture, choosing the right approach to data management can significantly impact your system's performance, scalability, and maintainability. In this post, I'll write about the different types of data and workloads you'll encounter, and explore when to use each one. Quick Overview
I've seen my share of organizations struggle with defining and implementing effective architecture roles, especially in regards to defining the distinctions between architects and staff engineers. In this post, I'll discuss my thoughts, based on real-world experience and patterns I've seen succeed (and fail)
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