The credentials behind the release
LiteLLM’s root-cause report, task-level adoption, and the operating choices behind local inference on a Mac.
Every edition, in context
The archive
A day-by-day record of what changed in enterprise AI, and why it mattered.
Know when to defer
Abstention, evaluation wording, executable policies, and a focused test of model boundaries.
The interface becomes the decision
A voice SDK, typed decisions, and research into what AI systems recommend and verify.
Capability needs a boundary
Authorization, memory, recoverability, and the tradeoffs inside an agent review team.
Faster models meet harder operating questions
New vendor economics, hidden instructions, and the evidence an agent needs to keep.
Make the model call understandable
A quieter edition with one operational question: can you trace the AI work from end to end?
Structure, data, and the terms of use
Better inputs for materials AI, and a practical response to changing platform terms.