Technical essays, architecture notes, and failure analyses for reliable AI systems.
AI scores keep climbing. Here is why the number on the slide and the number in your business are never the same one — and what that gap costs.
AI agents do not get tired. They get convinced—and that quiet failure mode is becoming the reliability tax of long-running AI systems.
Why you can't tell two models apart in chat — and why that gap decides whether your agent finishes its task or dies quietly at step fourteen.
The deepest question in AI — can a model recognize its own ignorance? — is quietly being reduced to infrastructure. Here's what's shipping, what's still hard, and where practitioners should focus.
Reliability in LLM systems is not the elimination of probabilistic behavior — it is the architectural containment of it.
I ranked them by automation resistance, talent scarcity, pay ceiling, and one harder question: do they survive models 100× more capable?
The LLM Top 10 tells you where the system can be compromised. The Agentic Top 10 tells you how that compromise propagates once the system can act.
Cost per token is an infrastructure metric. Cost per successful task is a business metric. Here's the one equation that connects them—and why it flips the model you should deploy.