Why an AI agent's tools, context and checks can matter as much as its model, with recent research on harness engineering and its limits.
AI releases include more than code. How SDLC practices extend to models, prompts, retrieval and runtime—and how CID connects execution, policy and evidence.
Quantization went from compression detail to model-governance problem. The history in four squeezes, the KV-cache math nobody runs, and how CID gates quantized checkpoints like the new models they are.
Data poisoning is a supply-chain attack that ships in your weights. What it does, the modern defense stack, and how CID makes it somebody else's problem.
The AI stack is closing below the model line — chips, racks, clouds — and opening above it. The model layer is the only layer an enterprise can own. Here's how to own it properly.
Fine-tuning turned every enterprise into a model builder — and almost none of them keep a build record. Why the fine-tuning boom is a lineage problem, and how STEAV solves it.
Sovereign AI means more than data residency. What real AI sovereignty requires — owning the model, the build record, and the exit — and how STEAV builds it.
Centralized training wins whenever the data can move. Federated learning is what you reach for when it can’t — and most of it doesn’t work yet. An honest cost sheet for both, and what closing the gap actually takes.
Model-build lineage is a signed, verifiable record of how an AI model was built — an AI bill of materials. What it proves, and how STEAV produces it.
Nothing to read yet. Plenty to watch. A live pipeline run says more than a post.