Alex Danilowicz, co-founder of Magic Patterns (YC W23), walks through the company's origin as an attempt to make design accessible to front-end engineers and non-designers, demos the new MagicOS canvas and its MCP integration with coding agents, and reviews several AI-built startup landing pages to explain what makes a site feel vibe coded and how to make AI-generated design more intentional.
Anekha Sokhal, founder and CEO of Moshi, explains how her background in energy derivatives trading and machine learning research led to a real-time intelligence platform for financial traders. The discussion covers how Moshi structures public, alternative, and private data into a connected graph, why recommendation and UI design matter as much as modeling, how trusted sources and conflicting reports are handled, and the pressure on AI engineers to become the whole team.
Joon Sung Park, creator of generative agents and CEO of Simile, explains how foundation models trained on real human behavior can simulate people, from a 25-agent town that organized its own Valentine's Day party to 85%-plus accurate predictions of how real participants respond in surveys, behavioral games, and randomized trials.
George Kurtz, CEO of CrowdStrike, discusses how agentic AI has flattened the adversary pyramid, putting nation-state-grade tradecraft in the hands of low-skill attackers, and why open-weight models have become the tip of the spear. He covers CrowdStrike's Safe Mind lab, built with NVIDIA around red and blue security models trained on fifteen years of telemetry, the lessons from the Hugging Face incident, the case for defense dominance, and why he sees AI as a net creator of opportunity.
Vishal Srivastava and Tanmay Sah of New York Life analyze 1,368 public AI incidents to show what real failures reveal about how AI systems should be tested. They organize failures into three dimensions (technical strength, deployment controls, business impact) and 13 sub-dimensions covering about 98% of cases, find that weak deployment controls are most closely tied to harm, and present a materiality-based ranking of use cases, a failure-driven build-break-fix evaluation loop, and an airline chatbot refund case as an example.
Max Kanat-Alexander reframes the familiar "it depends" answer around two factors that govern all software work: understanding a system and safely changing it. He explains why code quality matters for durable software but not for a new, rapidly growing category of disposable software created by AI, and shows how to make systems more disposable and make rigor cheap through standardization, inward-flow tooling, easier testing, and maximum strictness.