Vanishing Gradients

Randy Olson, data-visualization coder who teaches agents Tufte-style judgment. Jeremiah Lowin, orchestration and context-engineering lead building Prefect and agent memories. Wes McKinney, pandas creator building RoboRev and parallel-agent factories. They demo agentic workflows: RoboRev’s multi-pass code reviews, memory-driven personal agents, verifier loops for charts, skills as thin drivers, and managing context and evals.

Eddie Landesberg, a staff data scientist who applies causal inference to AI evaluation, and Stella Wenxing Liu, Head of Applied Science and creator of an AI evals course, discuss using AI evals as a product compass. They cover team-centered evaluations, custom metrics and clear product constraints. They explore causal inference for policy-style evaluation and calibrating LLM judges. They emphasize hands-on data curiosity and statistical rigor.

Katharine Jarmul, privacy and AI expert, author of Practical Data Privacy and instructor of Practical AI Privacy courses. She unpacks why much AI privacy is theater. Short takes on treating prompts as public, tracking data flows with privacy observability, multimodal reidentification risks, tiered guardrails, and why federated learning needs extra protections.

Sebastian Raschka, independent AI researcher and author of practical, code-first LLM guides. He digs into what modern model architectures actually contain. Conversations hit inference-scaling tricks, hybrid transformer/state-space designs, KV-cache and long-context tactics, Multi-head Latent Attention, and the tradeoffs of running local vs. frontier models.

Bryan Bischof, Head of AI at Theory Ventures and long-time data scientist, explains why real-world data challenges reveal agent failures. He recounts a hackathon testing agents on SQL, logs, and 750,000 PDFs. Topics include failure funnels and binary checkpoints, why unlimited submissions encourage hill-climbing, DocETL for document extraction, MCP as a semantic layer, and when simple coding agents beat heavy frameworks.

Samuel Colvin, creator of Pydantic and lead of the Pydantic Stack, explains building durable AI agents with engineering-grade reliability. He discusses agentlets as small specialized building blocks, using Temporal for robust workflow durability, separating deterministic workflows from stochastic model calls, and making observability and type-safe validation central to production AI.

Alex Strick van Linschoten, ML engineer and curator of the LLMOps database, tracks real-world production AI deployments. He recounts a $50K infinite-loop cost and warns about silent failures. They discuss ripping out and rebuilding agent systems, extreme low-latency voice agents that toss context, three-tier agent architectures, the 100-to-1 token noise problem, and when simple tools beat complex stacks.

Alison Hill, product leader focused on multimodal UX and community learning. Marcel Kornacker, CTO and co-creator of Apache Parquet, expert in multimodal data systems. Wes McKinney, creator of pandas and data tooling veteran. They discuss agent ergonomics and language tradeoffs, adversarial AI code review, making image/video operations native to data platforms, and why taste and schemas matter for multimodal workflows.

Join search guru Doug Turnbull, who shaped systems at Reddit and Shopify, and John Berryman, the brain behind GitHub Copilot, as they dive into the future of agentic search. They explore the evolution from traditional search to agentic retrieval, spotlighting John's five-level maturity model for AI adoption. Learn why understanding user intent is paramount and discover practical steps to create your own agentic loops. They also share insights on avoiding common pitfalls in search design, emphasizing the importance of real user feedback.

Eleanor Berger and Isaac Flaath, co-founders of Elite AI Assisted Coding, delve into the future of software development with AI. They explore how agents can maintain living documentation using simple markdown files, enhancing ongoing learning. Discover the power of specification-first planning to define success, while automated tech debt audits keep projects in check. The duo emphasizes the importance of accountability and clear communication in teamwork. With insights on using agents for routine tasks, they reveal how to save developers hundreds of hours!