Matthew Glickman, CEO of Genesis Computing, discusses the rise of AI data agents designed to automate complex data engineering workflows. He covers the "last 10%" problem in enterprise AI, the unique value of targeting the data engineer persona, and how these agents can tackle challenges like legacy system migration and knowledge capture, ultimately giving valuable time back to data teams.
Learn how Vercel's open-source Workflows platform simplifies deploying durable, observable, and long-running AI agents by abstracting away the infrastructure complexities of queues, databases, and error handling.
In this special year-end episode, experts from the Mixture of Experts podcast review the biggest AI moments of 2025 and predict what's next for 2026. The discussion covers the rise of "super agents" and the battle for the user interface, open source's breakout year and its remaining challenges, the AI hardware supply crisis and the push for efficiency, and the future of modular, multimodal AI systems.
Explore Jeff Crume's cybersecurity predictions for 2026 and beyond, detailing the dual impact of AI in security, the rise of autonomous AI agents, the futility of deepfake detection, and the critical importance of post-quantum cryptography and passkeys for future defense.
In a live demo, Poolside's CEOs showcase their second-generation model, the Malibu agent, by migrating a complex codebase from ADA to Rust, including automated testing and iterative feature development. They outline their vision for achieving AGI through a full-stack approach combining proprietary models, reinforcement learning, and massive-scale compute, with plans for a public model release in early 2025.
A panel of experts discusses the future of AI in software engineering, focusing on the "barbell effect" where AI excels at hyper-complex tasks but fails at simple ones. The conversation explores whether performance is driven by the model or the agent orchestration, the evolving role of the engineer as an architect, and the significant challenges open-source tools face against vertically integrated proprietary systems, particularly the high cost of inference.