Writing tests with AI, but not LLMs

Ryan sits down with Dimitri Stiliadis, CTO and co-founder of Endor Labs, to talk about how AppSec is evolving to address AI’s use cases. They discuss the implications of AI-generated code on security practices, the importance of human oversight in managing vulnerabilities, and ho ... Show More

Gil Feig, co-founder and CTO of Merge, joins the show to explore Merge’s approach for reducing third-party APIs to a single call, the complexities of and need for data normalization, and the role that AI and MCP plays in the future of API functionality. Episode notes: Merge conne ... Show More

In the second part of this two-part Leaders of Code episode, Peter O'Connor, Director of Platform Engineering, and Ryan J. Salva, Senior Director of Product at Google Developer Experiences, dive beyond AI hype to explore the shifts reshaping how engineering teams operate and scal ... Show More

Machine learning pipeline orchestration tools, such as SageMaker and Kubeflow, streamline the end-to-end process of data ingestion, model training, deployment, and monitoring, with Kubeflow providing an open-source, cross-cloud platform built atop Kubernetes. Organizations typica ... Show More

Machine learning models need updating - what's the reliable way to do it? While in Romania, Richard sat down with Annie Talvasto to talk about her work helping to build DevOps practices around machine learning: Building repeatable processes for data ingestions, cleaning, organiza ... Show More

Tool use in code AI agents allows for both in-editor code completion and agent-driven file and command actions, while the Model Context Protocol (MCP) standardizes how these agents communicate with external and internal tools. MCP integration broadens the automation capabilities ... Show More

Ian Miell is a partner at consultancy Container Solutions, and an author of books on Bash, Git, Terraform and Docker. He explains to Craig how writing - whether runbooks, blog posts, training courses, or “real” books, can help you learn and make your team more effective. Do you h ... Show More

The role of the data scientist is changing. Some organizations are splitting the role into more narrowly focused jobs, while others are broadening it. The latter approach, known as the Full Stack Data Scientist, is derived from the concept of a full stack software engineer, with ... Show More

We are at GenAI saturation, so let’s talk about scikit-learn, a long time favorite for data scientists building classifiers, time series analyzers, dimensionality reducers, and more! Scikit-learn is deployed across industry and driving a significant portion of the “AI” that is ac ... Show More

Every PM has to build AI features these days. And with that means a completely new skill set:- AI prototyping- Observability, Akin to Telemetry- AI Evals: The New PRD for AI PMs- RAG v Fine-Tuning v Prompt Engineering- Working with AI EngineersSo, in today’s episode, I bring you ... Show More

In this episode of the AWS Developers Podcast, Seb and Ran Isenberg discuss the automation of IAM policies across multiple AWS accounts. They explore the challenges faced in managing security and access in a multi-account environment, the design and implementation of an automatio ... Show More

Want our guide to master AI Agents? Get it here: https://clickhubspot.com/bka Episode 76: What actually makes something a real "AI Agent"—and how close are we to AI handling complex work entirely on its own? Matt Wolfe (https://x.com/mreflow) is joined by Deepak Singh (https://x. ... Show More

Simon is joined by Stephen Liedig to discuss the evolution of serverless technology and its impact on application development, exploring benefits like scalability, cost optimization, and faster dev cycles. They delve into key services and concepts in serverless design, including ... Show More