I've been on both sides of the data engineering hiring table for years. I've written interview loops, failed interview loops, and watched candidates ace screens that told me absolutely nothing about whether they could debug a silent data loss bug at 2am. The signal was always thin. Now it's basically noise. Here's the situation in 2026: 64% of companies ban AI in interviews. Candidates use it anyw
Generative AI is no longer just an emerging technology. It is becoming a core business capability across software development, customer support, analytics, content generation, automation, knowledge management, and enterprise productivity. For cloud professionals, developers, data teams, and solution architects, learning Generative AI on AWS is now a high-value career move. AWS provides a growing e
The Model Context Protocol has transformed how we connect AI to tools. But connecting agents to tools is only half the battle — connecting agents to each other is where the real challenge begins. I recently read @raviteja_nekkalapu_'s excellent article "I built an AI security Firewall and made it open source because production apps were leaking SSNs to OpenAI" and it resonated deeply with challeng
When stepping into the world of data engineering, Apache Airflow is likely one of the first tools you will encounter. It is the industry standard for programmatically authoring, scheduling, and monitoring workflows. Before building our first DAG, it's important to know what has changed in Airflow 3.1.0. Initially, Airflow users imported DAGs and tasks from airflow.models and airflow.decorators. I
A deeply-synthesized, opinionated reference distilled from five canonical sources: donnemartin/system-design-primer · ByteByteGoHq/system-design-101 · karanpratapsingh/system-design · ashishps1/awesome-system-design-resources · binhnguyennus/awesome-scalability Use it as: a study guide for interviews, a checklist for design reviews, and a vocabulary for cross-team discussions. 📖 How to Use This
Your requests may look like a real browser, but they’re still getting blocked. Even when requests include realistic headers, they can still be detected if HTTP/2 behavior, such as header ordering, pseudo-header structure, and frame sequencing, does not match real browsers. These low-level inconsistencies reduce stability and reliability, making automated traffic easier to identify. In HTTP/2, head
I opened IBM Course 4 — Python for Data Science, AI and Development — fully expecting to breeze through it. I'd used Python before. In college. In personal projects. It was supposed to be the comfortable one. Then **kwargs showed up. My previous post went up on May 2. After that, I finished IBM Course 3 on Prompt Engineering. May 3 — started Course 4. Finished a major chunk of it the same day. May
Two and a half months ago we published Why We Built UCP Playground, which closed on 114 agent sessions and an honest acknowledgement that the dataset was thin — most models had single-digit sample sizes, store coverage was uneven, and the headline rates moved meaningfully with every new run. A month later we crossed a different threshold: the first fully autonomous AI agent purchase through UCP —