Building Trust with Agentic AI UX Patterns: A Dev’s Guide

Building Agentic AI UX Patterns requires shifting from ‘magic’ to control. This guide covers 6 essential patterns—including Intent Previews, Autonomy Dials, and Action Audits—to ensure autonomous AI systems build user trust rather than destroying it. Learn how to architect relationships where users always hold the ultimate authority.

WordPress Content Guidelines: Ending Editorial Chaos

WordPress is introducing Content Guidelines as a new Gutenberg experiment to centralize editorial standards. This machine-readable foundation aims to solve “editorial drift” by providing a single source of truth for brand voice, tone, and structural rules, making them accessible to both human authors and AI content assistants within the admin UI.

Mechanistic Interpretability: Peek Inside the LLM Black Box

Mechanistic Interpretability is the ‘Xdebug’ of the AI world, allowing developers to reverse-engineer LLMs. By tracing ‘circuits’ and the ‘residual stream,’ we can understand why models hallucinate or reason. This post explores the technical tools like TransformerLens and how to debug neural networks like a senior software engineer.

Google Interactions API: Ending the Everything Prompt Chaos

The ‘Everything Prompt’ is dying. Google’s new Interactions API introduces persistent, stateful AI sessions and background agentic workflows. Learn why moving beyond ephemeral chat loops is essential for building reliable, high-performance WordPress integrations and how to manage long-running deep research tasks without timing out your server.