AI hallucinations: Understanding why sometimes machines get it wrong
Why AI systems hallucinate, what causes these failures in practice, and how teams can reduce the risk in production.
Why I’m (hopefully) never building another agent
Practical lessons from building AI agents at scale, from tool design and evals to UX, rollout strategy, and what’s next.
Boston’s healthcare AI: Past changes and what’s next
Boston’s healthcare AI ecosystem has moved from cautious pilots to real-world impact. Here’s what’s changed, and what comes next.
Austin’s AI & tech landscape: How it’s evolved
Silicon Valley still sits at the center of the AI conversation, not because it has a monopoly on ideas, but because so many of the forces shaping AI’s future collide here.
40 companies shaping Silicon Valley’s AI landscape in 2026
Silicon Valley still sits at the center of the AI conversation, not because it has a monopoly on ideas, but because so many of the forces shaping AI’s future collide here.
Reimagining UI/UX education for AI and neuroinclusion
Design education must evolve beyond polished screens toward neuroinclusive cognition, AI-as-infrastructure, and uncertainty-aware governance.
How UK AI expertise meets Saudi retail ambition
The UK can unite its advanced AI expertise with its strict regulatory framework and its high-quality product development with the rapid expansion of Saudi retail operations.
AI agents struggle with “why” questions: a memory-based fix
LLMs forget context and fail at “why” reasoning. MAGMA fixes this with multi-graph memory across time, causality, entities, and meaning.
Fast-track product validation using AI
A key challenge of product management is reducing the time between idea generation and gaining validation to move forward (or kill it).
A new framework for keeping AI accountable
A new accountability framework treats AI responsibility as a continuous control problem, embedding values into systems and monitoring harm over time.