2025 - Nov/Dec
Paul Sheriff modernizes JavaScript with ES6 classes, while Sahil Malik implements AI-powered threat detection using machine learning anomaly detection. Sonu Kapoor advances Angular development with reactive signals for smarter inputs and routing. Joydip Kanjilal demonstrates seamlessly integrating ML.NET for intelligent .NET applications. The collaborative team of Medancic, Grubisa, and Kaplan introduces CSCS Web for dynamic scripting in ASP.NET Core. Finally, Jason Murphy presents Model Context Protocol as the essential bridge connecting AI models to real-world data and systems.
-
Reading for Fun and Profit
Rod Paddock reflects on how a curated library of timeless software books has profoundly shaped his coding practice and view of the field. He explains why classic titles—from Weinberg’s psychology of programming to Brooks’s Mythical Man-Month, Game Developer postmortems, Kocienda’s Creative Selection, and Programmer at Work interviews—remain inspirational because they illuminate teamwork, real-world project challenges, and the social nature of software.
-
Add Some Class to Your JavaScript
Paul introduces the ES6 JavaScript class keyword and shows you how to pass in arguments, create public read-only properties, and make private fields. You’ll learn how to override an inherited method in an extended class, call methods in the parent class, and extend the built-in classes.
-
Threat Detection Using Anomaly Detection
Sahil Malik demonstrates how developers can use local, unsupervised machine learning—specifically Isolation Forest—to detect anomalous system behavior as a practical security measure in an AI-driven development world; he provides step-by-step code to generate synthetic logs, train and run the model, visualize results, and suggests real-world enhancements for monitoring, tuning, and alerting.
-
Angular Signals in the Real World: Smarter Inputs and Reactive Routing
In this second installation of his series on Angular Signals, Sonu designs Angular Signals applications that replace lifecycle-bound input handling, remove boilerplate queries, update two-way binding, use signals and route parameters to improve navigation decisions, and connect to signal-powered API calls.
-
Integrating AI and ML in .NET Core with ML.NET
ML.NET empowers .NET developers to seamlessly integrate machine learning and artificial intelligence capabilities into their applications without leaving the familiar .NET ecosystem. Joydip demonstrates how to build intelligent applications using ML.NET's cross-platform framework, covering everything from product price prediction APIs to sales forecasting systems in ASP.NET Core. The article provides practical, step-by-step guidance for implementing real-world machine learning scenarios, including data preparation, model training, and deployment strategies. Readers will learn to leverage ML.NET's automated machine learning features and Model Builder tool to create powerful predictive analytics solutions that enhance business decision-making and customer experiences.
-
Custom Scripting for Web Applications
This article by Nevio Medancic, Enzo Grubisa, and Vassili Kaplan argues for using CSCS Web, a lightweight, JavaScript-like scripting language, to extend ASP.NET Core web development with server-side endpoints, templates, and dynamic content without recompiling. The authors demonstrate how CSCS Web can implement endpoints, access request data, render HTML templates, manipulate JSON, and mix SSR and CSR techniques (with HTMX) to build interactive web applications. The article emphasizes the language’s openness and ease of extending functionality by registering new CSCS functions at runtime, and showcases a complete Employee List sample to illustrate practical, script-driven APIs and templating workflows.
-
MCP: Building the Bridge Between AI and the Real World
Jason Murphy argues for Model Context Protocol (MCP) as a foundational open standard that unifies how AI models access and use external context. He contends that current ad hoc solutions—plug-ins, vector stores, and RAG—are brittle, siloed, and non-transferable, and thus inadequate for scalable, secure real-world use. MCP provides a structured, interoperable, permissioned bridge between AI models and diverse data sources and tools, enabling context requests, controlled access, and auditable provenance. Jason also addresses security, governance, and risk, stressing that MCP is a governance-forward, adaptable infrastructure for reliable AI collaboration with the real world.
-
Fixing Security Issues in Seconds, Not Sprints, Powered by GenAI
Gaurav Mittal writes about SecureCodeAgent, a GenAI-powered approach (using Azure OpenAI) that shifts security left by delivering real-time, in-editor code scans that identify vulnerabilities, suggest fixes, assign severity, and integrate into pre-commit/pre-push and CI workflows—reducing cost, context-switching, and post-deployment remediation compared with traditional static analyzers while improving developer education and faster, safer delivery.