When comms drop out, the stack has to keep working. > Telemetry has to be trusted. > Anomalies have to be prioritised. > Decisions have to be made with partial information. That is distributed systems, observability, and reliability engineering under the harshest constraints imaginable. Artemis is basically a masterclass in building pipelines that survive latency, disruption, and zero tolerance for bad data. This week’s edition of Datapro.news goes deep on the Data and AI leaps that made Artemis-level ambition possible, and what it tells us about the future of data engineering on Earth. Full investigation in this week's DataPro.news 👇

I've seen this framing everywhere this quarter. And it's not wrong, exactly. It's just incomplete in a way that's genuinely misleading to early-career practitioners. The layoff waves of 2024 and 2025 was mostly a correction. Tech companies overhired by 25-50% during the pandemic and spent two years slowly unwinding that. AI got the credit (or the blame) because it made better copy for investors. The genuine AI-driven displacement is happening now, in 2026, and it is real. But it is not evenly distributed. It is concentrated in execution-heavy, process-repetitive roles. The engineers being squeezed are the ones whose primary value was running pipelines someone else designed. The engineers thriving are the ones who design them. Who govern them. Who can look at what a machine produced and say whether it is correct. That distinction matters enormously if you are deciding where to invest the next two years of your career. Full investigation in this week's DataPro.news 👇

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! :)