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
I'm working on an AI Data Analyst in MLJAR Studio. The idea is simple: you ask a question in natural language, AI writes Python code, executes it, and shows the result. But recently I found a small example that reminded me why AI data analysis needs more than code generation. I was testing a medical data analysis use case with a diabetes CSV file. The first task was simple: load data from this URL
Vendredi matin, 9 h 15. Françoise est dans son cockpit — trois écrans, à gauche l'Excel-pointeuse qu'elle tient à jour depuis quinze ans, à droite Sage, et au milieu Rembrandt depuis trois semaines. Sa tasse à la main, celle avec sa tête imprimée dessus que quelqu'un lui a offerte à Noël. Elle pivote sur sa chaise et me lance depuis son bureau : « Michel, combien on a d'inscrits pour la rentrée, d
« Hold on, we need to talk, this doesn't add up » Friday morning, 9:15 AM. Françoise is in her cockpit — three screens: on the left the Excel attendance sheet she's kept up to date for fifteen years, on the right Sage, and in the middle Rembrandt for three weeks now. Cup in hand, the one with her face printed on it that someone gave her at Christmas. She swivels in her chair and calls over from