Sarah Sachs, Head of AI Engineering at Notion, discusses the economic traps of AI model contracts and advocates for a "win on product" strategy. She details how Notion maintains optionality and leverage by treating suppliers as competitors, implementing a model-agnostic "AI Switzerland" approach with an auto model, leveraging open-weight models, and prioritizing data flywheels and orchestration over token economics to build sustainable AI products.
A speed-run through the fundamentals of graphs for AI engineers, covering how to build effective graphs from unstructured text using schema-first approaches and entity resolution, and then exploring powerful graph-native algorithms like Personalized PageRank, Shortest Path, and Subgraph Matching, all illustrated with real-world applications to create smarter, cheaper, and more reliable AI solutions.
The episode explores Anthropic's "advisor strategy," a novel AI agent pattern that combines a fast, cheap "executor" model with a frontier-class "advisor" model. This allows for mid-task consultation within a single API call, resolving the tension between cost and capability. Benchmarks show simultaneous improvements in quality and cost reduction, demonstrating that AI progress is shifting towards intelligent model composition rather than just larger models.
This talk introduces Recursive Language Models (RLM) as a solution for coding agents struggling with large codebases. RLM externalizes context management into a programmable REPL, allowing the model to write code, inspect the repository, and recursively delegate sub-questions via `llm_query` to curate relevant context. The speaker demonstrates `RLM Code`, an independent implementation, showcasing its operational loop, observability features, and practical applications for AI engineers dealing with complex software projects.
This panel discussion explores the inflection point of Local AI, driven by advanced models, improved hardware, and a robust ecosystem. Experts discuss how this shift addresses critical concerns around privacy, cost, sovereignty, and resilience, emphasizing the pivotal role of open-source AI and specialized models. They delve into technical optimizations, the evolution from generalized to specialized AI, and the challenges of making local AI accessible and performant for both enterprise and individual users.
Alex Volkov introduces the "Z/L Continuum," a framework for navigating the tension between rapid AI-generated code production and the critical need for human review. He argues that the key lies in understanding that the continuum applies to tasks, not individuals, and presents a pragmatic routing table for verifying changes based on their criticality, highlighting the shift towards engineering systems that build and verify code, rather than meticulously inspecting every line. The talk also touches on emerging capabilities like Fable and "loops" and the importance of flexibility and human judgment in the evolving AI engineering landscape.