The personal AI assistant pattern wants to read everything you have. The honest engineering pattern is the opposite, design the assistant to be useful while structurally unable to see the data that shouldn't go to it. Worth being concrete about how.
What to actually buy if you're starting a home AI setup in mid-2025. Not the maximalist build; the right-size build for the workloads that justify owning the hardware.
The conversation history with your AI tools is a piece of infrastructure now. Treating it like ephemeral chat is fine for the casual case; treating it that way for daily work means you lose context, repeat yourself, and slowly poison your own retrieval.
Twelve months in, the home setup has changed shape: a Mac mini for the always-on lighter services, a Synology underneath for storage and platform services, fast portable SSDs at the working ends, and since March a Mac Studio for inference.
An M4 Max with 64 GB of unified memory comfortably runs open-weights models that needed a serious GPU rig a year ago. The price-per-capability curve crossed something quietly, and the consequence for who can run frontier-adjacent inference at home is bigger than it looks.
It isn't a cluster in the data-center sense. It's a small, deliberate setup, a Mac Studio for inference, a Mac mini for the always-on services, fast portable SSDs at the working ends, and a real NAS underneath. Worth being honest about what it is and what it isn't.
The case for running AI locally is louder than the math justifies for most workloads. Worth being explicit about which workloads it actually wins, and which ones the cloud still owns.
Three open-source vector stores cover most of the self-hosted RAG surface area in 2025. Worth being concrete about which one fits which workload, because the trade-offs matter and the docs won't tell you.
The legal frameworks around AI training data are still arguing about the wrong layer. The interesting ownership question isn't whether your text was scraped. It's whether the captured shape of how you reason is yours.
Llama 4 is the first time Meta has shipped a mixture-of-experts open-weights model at frontier scale. The release is more interesting for what it implies about Meta's strategy than for its benchmark wins.
The headlines say GPUs are unobtainable. The headlines are about the hyperscalers. For everyone else, the picture in early 2025 is more interesting and more usable than it gets credit for.
A framework I sketched a couple of years back: capture the priors that shape how someone reasons, ship the result as a portable artifact. Worth revisiting now that the substrate has landed.
