Why AI can't be debugged like traditional software
Boyd Kane explains why treating AI like debuggable code misses the fundamental difference in how these systems work.
A collection of interesting articles, tools, and ideas I've stumbled upon shared here as bookmarks and notes.
Boyd Kane explains why treating AI like debuggable code misses the fundamental difference in how these systems work.
The first community-driven policy I've seen that treats AI as a tool requiring human accountability, not a shortcut.
Meeting creep, quick questions and context switching crush flow.
Use the native HTML output element to announce dynamic results — semantic, accessible, and simple.
Anthropic argues that “context engineering” — not prompt tricks — is how agents stay accurate at long horizons.
Fabrizio Ferri Benedetti highlights docs-driven development and the rise of technical writers as “context curators” so AI can truly RTFM.
Miriam Suzanne from OddBird explains how combining CSS units can lead to more robust and user-friendly typography.
Lullabot's thoughts on 'How to build your AI integration strategy right'.
Printed direct from AllAboutKen.com