Software engineer working on large-scale distributed systems, big data, and AI infrastructure. Currently at Microsoft.
There is a popular narrative right now that AI is bottlenecked by model capabilities — that we need bigger models, smarter agents, better reasoning. After several years of building data infrastructure in logistics and now enterprise AI, I have come to a different view: the real bottleneck is data infrastructure, and most companies will never get the full value of AI until they fix it.
A junior engineer reached out to me recently. They were stuck in a job with bad management, mentally fried, and wanted out — but the idea of grinding interviews on top of a draining day job felt impossible. They asked how I balanced shipping work, prepping for interviews, and actually passing the loops. I’ve also sat on the other side of the table as an interviewer, so I have opinions about what works and what gets people rejected.
My previous post ran the math on why investing matters. My full strategy is what I actually do, but I got feedback that it’s dense for someone just getting started. Fair. This is the simple version — concepts and behavior only, country-agnostic. For tickers and account names, the full post has them.
My previous post showed the math: a Seattle engineering income at ~7% real returns reaches the rat-race escape zone (FIRE) in about a decade. This is the operating manual that turns that math into a portfolio.
Many teams shipping retrieval-augmented generation today started from the same five-line tutorial: chunk your documents, embed them into a vector store, embed the user’s question, fetch the nearest hits, hand them to an LLM. It works beautifully in a notebook. It also has almost nothing to do with what a real RAG product looks like once it has users.
One reason — if you can see the world a little more broadly, can calculate risk, and can plan long-term, you can see that this rat race actually has an exit.
A friend recently read my last post on why data infrastructure is the real bottleneck for enterprise AI, and asked exactly the right question: what is a data pipeline, and why does AI need one in the first place? If you have used ChatGPT, Gemini, or Copilot but never built a model or a pipeline yourself, that question deserves a proper answer — and I realized I had never written one.
GitHub Pages on the Free plan only builds from public repos. To keep your source private, GitHub charges $4/month for Pro. Here’s how to get the same result for $0 by splitting source and built output into two repos.
Gig-economy platforms — Uber, Lyft, DoorDash, Instacart, Amazon DSP, and many smaller ones — share a deep operational problem: they need to evaluate the performance of large numbers of independent workers using objective data, but the data is full of factors the worker cannot control.
A pattern I have seen in nearly every company I have worked at, regardless of size or sophistication: somewhere in the engineering org, there is a critical data pipeline that is “automated” only in the loosest sense. An engineer runs a script. The script does some things. Sometimes it works. Sometimes it produces malformed output and somebody has to spend two hours figuring out what went wrong, manually cleaning up, and re-running.