zlacker

The "local models got good, but cloud models got even better" section nails the current paradox. Simon's observation that coding agents need reliable tool calling that local models can't yet deliver is accurate - but it frames the problem purely as a capability gap.

There's a philosophical angle being missed: do we actually want our coding agents making hundreds of tool calls through someone else's infrastructure? The more capable these systems become, the more intimate access they have to our codebases, credentials, and workflows. Every token of context we send to a frontier model is data we've permanently given up control of.

I've been working on something addressing this directly - LocalGhost.ai (https://www.localghost.ai/manifesto) - hardware designed around the premise that "sovereign AI" isn't just about capability parity but about the principle that your AI should be yours. The manifesto articulates why I think this matters beyond the technical arguments.

Simon mentions his next laptop will have 128GB RAM hoping 2026 models close the gap. I'm betting we'll need purpose-built local inference hardware that treats privacy as a first-class constraint, not an afterthought. The YOLO mode section and "normalization of deviance" concerns only strengthen this case - running agents in insecure ways becomes less terrifying when "insecure" means "my local machine" rather than "the cloud plus whoever's listening."

The capability gap will close. The trust gap won't unless we build for it.