Building SDKs in the Agentic Era
In the time it takes to train a frontier model, the open source libraries we rely on can undergo significant changes. This creates an ongoing delta between what an LLM coding agent suggests and what the best practices are, or what even works. For the team at Google DeepMind, this is an ongoing challenge as we publish both models and open-source SDKs.
This talk will share some of the challenges that we, as SDK maintainers face, and we’ll share some results from our experiments. We’ll focus primarily on the “training cutoff knowledge gap”, and how it is applicable for users and owners of open source projects, but we will also discuss some of the other challenges maintainers face in a world where producing code is trivial.
Mark McDonald
Mark is an engineer in the Google DeepMind developer experience team, and is based in Perth, Australia.
He co-wrote the (Guinness World Record holding) Kaggle 5-day Generative AI course, introducing hundreds of thousands of developers to building with GenAI.
But normally he works with DeepMind research teams to bring cutting-edge technology to developers of all kinds, and works with GenAI builders to bring Gemini to apps of all kinds.
He’s worked on a range of Google products, including the Gemini API, PaLM API, TensorFlow, Google Maps and even Santa Tracker.