Over the past month, the Chalk team has been actively engaging in a series of events across the United States to showcase their latest innovations in the fintech and machine learning sectors. At the NexGen Banking Summit in NYC, discussions revolved around overcoming the latency challenges in fraud detection and optimizing financial institutions' data infrastructure for multiple applications, emphasizing the importance of modern inference architectures and infrastructure decisions. In Menlo Park, at VeloxCon, the team provided insights into optimizing Python for enhanced performance, highlighting the challenges of integrating traditional data engineering with machine learning demands. The virtual Agents & GenAI Summit focused on blending structured data with large language model outputs for sophisticated fraud detection, illustrating Chalk's role in streamlining these processes. At OptimizedAI in Atlanta and the Data Council in San Francisco, discussions centered on the balance between flexibility and complexity in data ecosystems, with a particular focus on the adoption of open standards like Apache Arrow and Iceberg. Across these events, a consistent theme was the emphasis on simplifying the modern data stack by leveraging familiar tools and languages, aligning with Chalk's philosophy of evolving existing technologies to meet current needs while maintaining speed and simplicity in AI-native systems.