Vibe coding is the new normal, but high performing teams are the ones that outgrow the vibes and engineer around autonomy. That was the big lesson from the "Claude Code Incident" If your team is experimenting with agents, here are 4 changes that are becoming non negotiable. ⚜️ Treat agent design as architecture. 👎🏼 Downgrade trust by default. 😖 Make failure boring and recoverable. 🤔 Invest in senior judgement. This week’s DataPro News explains why data engineering feels this shift first and what to change. What is the one area you think most teams are underestimating, security, rollback, permissions, or observability?

Whilst everyone was debating the latest LLM benchmarks, Origin Quantum quietly released Origin Pilot V4.0 - the world's first full-scale quantum computing OS built for local, on-premises deployment. No cloud lock-in. No proprietary access model. Just a production-grade quantum OS you can actually deploy in your own environment. 📹 View the explainer below and 📖 the full breakdown this week at datapro.news Here is what that means for us as data engineers right now. The access model that kept quantum computing out of enterprise infrastructure just broke. Both IBM and Google built their quantum strategies around cloud control. Origin Pilot V4.0 runs on your hardware, in your environment, on your terms. For anyone working in regulated industries, financial services, defence, or pharma - this is the data sovereignty conversation you have been waiting to have. The question is: Is your organisation treating quantum readiness as an architecture concern yet, or is it still filed under "future problem"? Drop your thoughts below. Genuinely curious where people are on this one...

Use this open source alternative instead. Dia is a free modelling tool. I migrated the data vault icons to use them in Dia. Instructions here: https://www.danielfritzler.de/visual-data-vault-dia.html

Accuracy went up, not down. That is not a typo. That is what good context engineering looks like in practice. Most data engineers are still thinking about AI in terms of prompts. The teams pulling ahead have moved on to something far more powerful: systematically managing what their agents know, when they know it, and how much of it they actually need. This week's DataPro explainer covers the four pillars of context engineering, translated specifically for data engineers — with real numbers, real frameworks, and three concrete things you can start building this week.

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! :)