Financial institutions have traditionally relied on fragmented AI models for tasks like fraud detection and credit scoring, but siloed systems hinder a unified understanding of consumer behavior. With 65% of institutions now using AI, complexity is rising. Leading firms are adopting transformer-based transaction foundation models that learn a single representation from proprietary data, improving performance across multiple tasks. Examples include Revolut's PRAGMA, Mastercard's large tabular model, and Stripe's fraud detection. NVIDIA's new developer example enables any institution to build such models.

Simon Willison built a prototype tool that automatically converts large pasted text into file attachments, inspired by Claude's paste detection. It supports direct file opening, drag-and-drop, and image thumbnails.

A new agentic system called RocketSmith automates design, manufacturing, and optimization of high-powered rockets, using subagents and skills for iterative flight parameter optimization. Four rockets were 3D-printed and tested, with all achieving stable launches and two recovered flight-ready. Flight simulations achieved 84% accuracy in apogee prediction.

This paper reviews the emerging safety problem of 'silent failures' in physical AI systems, where black-box models appear confident but produce physically invalid actions. It proposes a bounded problem formulation, defines silent physical-action failure, and presents a taxonomy of runtime guardrail functions.