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.
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.
Beyond chatbots: How to build agentic AI systems
AI is moving from chatbots to agents: systems that plan, use tools, and act autonomously. Why 2025 marks the real inflection point.
Building enterprise AI agents: Frontline lessons with TrueFoundry
Lessons from enterprise teams working with TrueFoundry on what it really takes to deploy agentic AI at scale.
AIAI London
Stream every session from AIAI London, with sessions from OpenAI, Synthesia, Wayve, Databricks, Financial Times, BBC and more.
How to turn shadow AI into a safe agentic workforce: Lessons from Barndoor AI
Enterprises struggle with AI not from a lack of capability, but from missing control, visibility, and trust. Barndoor aims to close that gap.