Distributed State in Large-Scale LLM Orchestration
How we held sub-50ms consistency across regions for ephemeral model state — and why most teams reach for consensus protocols they do not need.
A clinical examination of applied AI, software architecture, neurodiversity and human performance — every entry, in one place.
How we held sub-50ms consistency across regions for ephemeral model state — and why most teams reach for consensus protocols they do not need.
Offline eval scores that climb while production quality flatlines are the default failure mode of applied AI. Here is how the gap opens, and how to close it.
Microfrontends promise team autonomy. In a regulated finance product they quietly traded one shared codebase for a distributed governance problem nobody owned.
Raising an autistic child taught me to treat the home like a system with interfaces and failure modes. Here is what that reframe changed, honestly.
After fifteen years at a desk, I started treating training like infrastructure for cognition. The protocol is boring, measurable, and it works.
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