Learn about AI technologies and implementations
A verified guide to Qwen3.8-Max Preview, GLM-5.2, and DeepSeek-V4-Flash: model IDs, compatible coding agents, setup paths, caveats, and fair repository evaluation.
A practical control architecture for separating independent evidence, AI-influenced decisions, synthetic content, and production feedback before the next model learns from them.
A practical method for tracing personal data through AI pipelines, choosing deletion, rebuild, retraining, or unlearning, and proving that derived artifacts stay clean.
A practical guide to inventorying models, data, prompts, tools, evidence, and provenance so an exact AI release can be assessed, promoted, and rolled back.
Before launch, an AI feature needs an owner, evaluation gates, security boundaries, observability, cost limits, fallback, and controlled change.
AI procurement should test the service behind the interface: data handling, evaluations, security, operations, cost, portability, and exit.
Audit-ready AI links every consequential output to its inputs, policy checks, model and tool versions, approvals, actions, and observed outcome.
A browser agent operates inside pages that may be misleading, compromised, or designed to redirect its behavior. The web must remain data—not authority.
Human-in-the-loop design works when approval is reserved for consequential uncertainty and presented with enough evidence to make a real decision.
How to build a small, trustworthy model context from policy, task state, evidence, memory, and tools—and test it under conflict, noise, and attack.
A production evaluation framework for computer-use agents that measures final state, side effects, recovery, evidence, safety, and performance under real interface variation.
A practical guide to measuring and reducing AI latency across UI, context, retrieval, queues, model prefill, decoding, tools, and final side-effect verification.