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The story of the coder and the librarian. In this week's edition of datapro.news we are looking at a handoff pattern that changes how agents interact with your codebase: 1. Use NotebookLM to generate an Engineering Brief from your repo + docs + architecture decisions 2. Drop that brief into your project's CLAUDE.md 3. Point Claude Code at it: "Follow the Engineering Brief. If unsure, query NotebookLM." The result: your agent starts every session with grounded, cited context instead of guessing its way through your schema. With Claude 4.6's multi-agent setup, you can even split the work. A lightweight Researcher Agent handles NotebookLM lookups. A Builder Agent focuses purely on writing code. Verified information flows between them in parallel. It is the closest thing I have seen to giving an AI agent the institutional knowledge that usually lives in one person's head.

And buried inside it was something that every data engineer needs to understand right now. A basic content management misconfiguration exposed 3,000 unpublished assets to the open internet. Among them, details of Claude Mythos 5 — a 10 trillion parameter model that Anthropic hadn't announced, hadn't released, and clearly didn't want the world seeing yet. The fallout was immediate. $14.5 billion wiped from the cybersecurity sector in a single trading day. But here's the part that should concern this community most... Mythos 5 is reportedly capable of autonomous vulnerability discovery across production codebases at machine level speeds. The same multi-agent architecture that makes it a powerful engineering tool makes it a serious adversarial threat to the data pipelines you build and manage every day. The bitter irony? The most capable AI model ever leaked was exposed because of poor data governance. Not sophisticated hacking. A misconfigured data lake. This week's DataPro.news edition goes deep on what happened, what Mythos 5 actually is, and what it means practically for pipeline security. Check out the explainer video here 👇

Hey Everyone! Are you subscribed to any great newsletters or podcasts focused on Data & AI? I’m always on the lookout for high-quality sources—whether it's a daily or weekly tech update. Some News I Found Interesting: Databricks Serverless Compute: Databricks has rolled out serverless compute on AWS and Azure, simplifying infrastructure and making scalability easier. https://www.iavcworld.de/cloud-computing/10216-databricks-kuendigt-serverless-compute-auf-aws-und-azure-an.html Google Pixel 9: Google just launched its latest smartphone, the Pixel 9, with a heavy focus on AI and seamless user experience. It’s an exciting release that shows how AI is becoming more integrated into our daily lives. https://www.theverge.com/24218825/google-pixel-9-event-announcements-products?utm_source=tldrnewsletter AI Data Lakehouse: Hopsworks is making waves with the industry's first AI data lakehouse, which could revolutionize how data is stored and accessed in AI projects. Hopsworks wants to make a splash with the industry's first AI data lakehouse - SiliconANGLE Interested to hear what you say! :)

We ran every major agentic platform against a real marketing ETL schema drift scenario. Scored them on connectors, governance, PII handling, security and production readiness. The result is not a single winner. It is a map. Five platforms made the cut. One is still in alpha but has the most sophisticated data sovereignty architecture we have seen. One can detect a 50% row count drop in a pipeline that reported as "successful." One exists specifically because its predecessor had a 17% baseline defence rate against adversarial instructions. If your data team is still treating agentic AI as a future problem, this week's DataPro is worth 10 minutes of your time. Full bake-off at datapro.news 👇