Get more useful work from AI.

Compare models and effort settings against the tasks you need to complete.

What a harness is, how it works and the five parts around the model.

Route each step of a workflow to the lightest model and effort setting that clears your quality bar.

Build deliberate practice loops with AI—testing recall, loading context and sharpening judgement.

How much model does this task need? Explore smaller models for focused work, lower resource use and local deployment.

Fast, typed judgement primitives that turn natural language and application state into calibrated probabilities.

Speak to the model. Explore voice as an interface for thinking aloud, drafting and working away from a keyboard.

Buy capacity for the work you need to complete. Check the plan limits and keep the required quality in view.

Choose what you can run, adapt and inspect. Compare the licence, hardware needs and quality required by the task.

Why end-to-end workflow design, bottlenecks and feedback loops matter more than isolated benchmark gains.

A dated sample of builder posts on harnesses, skills, workflows and local inference.

A dated sample on Chinese models, agents, compute infrastructure and token procurement.

Tracked statements, keynote claims and supply-chain signals on compute, inference economics and industrial AI.

A live gauge of where frontier capability, pricing and deployment consensus sit across the current release cycle.

Judge a model by your work, not the industry’s benchmarks. Turn your corrections into checks.