Llm

I was building a GPU recommendation engine — one that maps workload descriptions to specific configurations, primary recommendations, and cost ranges — and kept hitting the same wall: getting the recommendations right meant going deep on every constraint that determines whether a deployment actually works. Not whether it’s affordable. Whether it works at all. VRAM has to fit the full training state, not just the model weights. Training data has to be where the GPUs are. The interconnect has to support the parallelism strategy. None of that shows up in a $/hr comparison. Here are the five calculations that come before it. ...

Over the last 18 months I have been in a lot of conversations about AI PCs — with enterprises evaluating fleet upgrades, with device vendors making the case for their hardware, and with IT leaders trying to figure out what their employees actually need. The consistent signal: everybody agrees AI PCs matter. Purchases are happening — Windows 10 end-of-support has accelerated that — but the buying is cautious and uneven. Two reasons come up every time: the AI landscape is moving fast enough that enterprises are not confident their requirements will look the same in 12 months, and they do not have a reliable way to evaluate what they are being sold. Most are not even sure what the right criteria should be. ...

I’ve spent the last several months building agentic AI systems — not demoing them, building them. And I want to share something that took me longer than I’d like to admit to fully internalize. The hype is real. The gap is also real. And the gap is closing — but not in the way most people think. This reflects where I am in March 2026, building on roughly 18 months of hands-on agentic work. The field is moving fast and I expect some of this to age. ...