Editorial desk
Core AI concepts without the jargon or inflated claims.
09 · Explainer
MCP standardizes how an AI application discovers and uses external tools and context. It can reduce custom integration work, but it does not remove the need for authentication, authorization, trust, and careful tool design.
9 min read
10 · Explainer
A model can suggest structured arguments for one function while an agent system chooses goals, loops through steps, selects tools, observes results, and decides what to do next. The control surface grows with orchestration.
10 min read
11 · Explainer
An evaluation combines a dataset, task definition, model configuration, tools, sampling, grader, aggregation rule, and reporting choice. A score only has meaning inside that design.
7 min read
12 · Explainer
A cryptographically bound credential can show that provenance assertions have not been altered and that a known signer made them. It cannot independently decide whether the signer was honest or the depicted event was staged.
8 min read
13 · Explainer
Useful AI literacy includes human agency, ethics, foundational concepts, evidence, data protection, application skills, evaluation, disclosure, and knowing when not to use a system.
9 min read
14 · Explainer
A trace records the sequence of model turns, tool choices, arguments, results, timing, errors, and sometimes approvals. It helps teams understand how a final outcome was produced.
10 min read