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Free Booklet: Monitoring And Auditing In AWS
Monitoring and Auditing in AWS is a practical guide designed to help developers, architects, and security professionals build robust visibility into their cloud environments. As organizations increasingly rely on AWS to power critical workloads, the ability to track activity, detect anomalies, and respond to potential threats becomes essential. This booklet explores the core services, patterns, and best practices that enable effective monitoring and auditing across AWS, providing a clear path to strengthening security posture, ensuring compliance, and maintaining operational resilience in modern cloud-native systems. Go to Post
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Eyes Everywhere: Secure Logging and Alerting for Modern Systems – Part III
Logging and alerting become truly powerful only when they are embedded into a well-designed security architecture. Individual applications emitting logs or isolated detection rules provide only partial visibility. Modern organisations operate distributed systems composed of microservices, cloud infrastructure, container platforms, and external APIs. In such environments, security visibility requires a unified architecture capable of collecting, analysing, and responding to telemetry across the entire system. Designing Systems That Detect and Respond to Threats A secure observability architecture combines several layers. Applications generate structured logs. Infrastructure produces telemetry about hosts, containers, and network activity. These signals are collected and aggregated through centralized… Go to Post
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Eyes Everywhere: Secure Logging and Alerting for Modern Systems – Part II
Logging is the foundation of security visibility, but logs alone do not defend systems. A modern production environment may generate millions or even billions of log entries per day. Hidden within this massive stream of telemetry are the signals that reveal active attacks, compromised accounts, and data exfiltration attempts. Without intelligent processing, these signals remain buried inside an ocean of noise. Alerting transforms raw logs into actionable intelligence. It is the mechanism through which suspicious events are detected, prioritized, and escalated to the people or systems capable of responding. In a mature security architecture, logging produces the raw telemetry, while… Go to Post
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Eyes Everywhere: Secure Logging and Alerting for Modern Systems – Part I
Modern software systems generate an enormous stream of operational data. Every authentication attempt, database query, API request, container deployment, and network connection leaves a digital trace somewhere inside the infrastructure. Historically, developers treated these traces primarily as troubleshooting aids—temporary clues to diagnose bugs when something went wrong. In contemporary security engineering, however, logs serve a far more profound purpose. Logs are now a primary sensor layer for detecting attacks. The Role of Logging in Modern Security In early software systems, logging existed primarily to support debugging. Developers would emit messages describing program execution so they could understand failures during development… Go to Post
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Free Booklet: Build And Deploy Anywhere With GPT-5 Codex
This interactive booklet is intended as an expanded version of the previous post. Due to the nature of code generation, the actual output and workflow will be different for each case. The code examples in this booklet are mainly for illustrative purposes. Go to Post
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Build and Deploy Anywhere with GPT-5.3-Codex
Software engineering has always evolved alongside its tools. Compilers turned human ideas into executable programs. Integrated development environments improved productivity and debugging. Version control systems enabled collaboration at scale. Continuous delivery pipelines made rapid and reliable deployment possible. In early 2026, another major step appeared: agentic coding systems capable of participating in the engineering process itself. One of the most advanced examples of this new class of tools is GPT-5.3-Codex, OpenAI’s latest coding-focused model designed to reason across repositories, plan multi-step changes, execute development tasks, and collaborate with engineers across the full software lifecycle. Unlike traditional autocomplete-style coding assistants, GPT-5.3-Codex… Go to Post
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The Art of Low-Level Memory: Mastering Span, Memory, and ref struct
This article introduces a powerful, modern C# toolkit designed to bypass traffic jams by writing allocation-free code. We will explore Span<T>, a type-safe “window” into existing memory that lets you parse and process data without creating copies. We’ll then cover its essential, heap-friendly counterpart, Memory<T>, which is crucial for asynchronous programming. Finally, we’ll dive into creating your own ref struct types to build custom, high-speed utilities that operate entirely on the stack. Throughout this guide, we will use the practical context of our car rental application to demonstrate how these features can be used to optimize critical code paths, delivering… Go to Post
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Decoding Ivy: A Deep Dive into Angular’s Compiler and Runtime
You run ng build –prod on your car rental application, and you notice the final bundle size is smaller than it used to be. When you fire up the development server with ng serve, changes to a component appear in your browser almost instantly. You’ve heard the word Ivy thrown around in conference talks and blog posts as the reason for these improvements. But what is Ivy, really? It’s not just a name or a version number; it’s a fundamental re-architecture of Angular’s core, a ground-up rewrite of the compiler and runtime. If you’ve ever wanted to look under the… Go to Post
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The Rise of “Vibe Coding” and Intuitive Software Development
The world of software development is being reshaped by a new, more intuitive approach: “vibe coding.” This method, fueled by advancements in artificial intelligence, is moving the focus from writing syntactically perfect code to expressing the desired outcome in natural language. This deep-dive article explores the essence of vibe coding, spotlights the pioneering tools enabling this shift, and provides a framework for its integration across the entire Software Development Life Cycle (SDLC). Deconstructing the “Vibe”: What is Vibe Coding? At its core, vibe coding is a development practice where a human developer collaborates with an AI-powered coding assistant to generate,… Go to Post
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Deep Dive into the Heart of Node.js
Every experienced Node.js developer has been there. An application runs smoothly in development, but under the strain of production traffic, a mysterious performance bottleneck appears. The usual toolkit of console.log statements and basic profilers points to no obvious culprit in the application logic. The code seems correct, yet the application slows to a crawl. In these moments, it becomes clear that the problem isn’t just what our code does, but how Node.js is executing it. This is where a surface-level understanding is no longer enough. To solve the hard problems and build truly high-performance applications, we need to look under… Go to Post
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The Scheduler, The Fiber, and The Reconciler: A Deep Dive into React’s Core
Most React developers are familiar with the concept of the Virtual DOM. We’re taught that when we call setState, React creates a new virtual tree, “diffs” it with the old one, and efficiently updates the actual browser DOM. While true, this high-level explanation barely scratches the surface of the sophisticated engine running under the hood. It doesn’t answer the critical questions: How does React handle multiple, competing updates? What allows it to render fluid animations while also fetching data or responding to user input without freezing the page? The simple diffing algorithm is only the beginning of the story. The… Go to Post
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Too many llamas? Running AI locally
In the rapidly evolving landscape of artificial intelligence, understanding the distinctions between various tools and models is crucial for developers and researchers. This blog post aims to elucidate the differences between the LLaMA model, llama.cpp, and Ollama. While the LLaMA model serves as the foundational large language model developed by Meta, llama.cpp is an open-source C++ implementation designed to run LLaMA efficiently on local hardware. Building upon llama.cpp, Ollama offers a user-friendly interface with additional optimizations and features. By exploring these distinctions, readers will gain insights into selecting the appropriate tool for their AI applications. What is the LLaMA Model? LLaMA (Large Language Model Meta AI) is… Go to Post
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The Essential Guide to Basic Data Types in C#: A Journey Through the Foundations
When diving into a new programming language, understanding its basic data types is like learning the alphabet before you write a novel. In C#, data types form the bedrock of how you work with data—whether it’s numbers, text, or more complex structures. But unlike some languages that prefer to keep things ambiguous (cough JavaScript cough), C# is strongly typed. This means every variable you declare has a specific data type, and the compiler insists you stick to it. No shortcuts. No funny business. It’s like having a very strict grammar teacher who loves semicolons. So, let’s begin our descent into the type system… Go to Post
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The Rise of the Chief AI Officer: Why Every Company Needs a Leader for the AI Revolution
In the ever-evolving landscape of modern business, one thing has become abundantly clear: artificial intelligence (AI) is no longer a futuristic concept or a niche tool reserved for tech giants. It is here, it is transformative, and it is reshaping industries at an unprecedented pace. From automating mundane tasks to unlocking insights from vast troves of data, AI is proving to be a game-changer. But with great power comes great responsibility—and complexity. This is where the role of the Chief AI Officer (CAIO) emerges as not just a luxury, but a necessity for any forward-thinking organisation. What is a Chief… Go to Post
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The Essential Guide to Basic Data Types in Python
Python is often celebrated for its readability, simplicity, and the fact that you can write code that looks suspiciously like English. But beneath this friendly facade lies a language built on a set of powerful, flexible data types that make everything tick—from the simplest “Hello, World!” script to complex machine learning models. Understanding these basic data types isn’t just about syntax; it’s about grasping the building blocks of how Python handles data. Numbers Let’s start with the most primitive of primitive types—numbers. In Python, numbers aren’t just numbers. They come with personalities, quirks, and, occasionally, the ability to break your… Go to Post
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Understanding Vector Databases in the Modern Data Landscape
In the ever-expanding cosmos of data management, relational databases once held the status of celestial bodies—structured, predictable, and elegant in their ordered revolutions around SQL queries. Then came the meteoric rise of NoSQL databases, breaking free from rigid schemas like rebellious planets charting eccentric orbits. And now, we find ourselves grappling with a new cosmic phenomenon: vector databases—databases designed to handle data not in neatly ordered rows and columns, nor in flexible JSON-like blobs, but as multidimensional points floating in abstract mathematical spaces. At first glance, the term vector database may sound like something conjured up by a caffeinated data scientist at 2… Go to Post
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A Programmer’s Guide to Types and Data Structures in JavaScript
Data structures are fundamental tools in programming, enabling us to efficiently store, manipulate, and access data. In JavaScript, a language known for its flexibility, mastering these structures can significantly enhance your ability to solve problems and write optimal code. In this blog post, we’ll explore commonly used data structures in JavaScript. By understanding both the “how” and the “why” of these data structures, you’ll be better equipped to tackle complex problems. As always, we start simple. Primitive Types JavaScript’s primitive types form the foundation of all data manipulation. We can consider that there are 3 main primitive data types: strings,… Go to Post
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Refactoring with GitHub Copilot: A Developer’s Perspective
Refactoring is like tidying up your workspace — it’s not glamorous, but it makes everything easier to work with. It’s the art of changing your code without altering its behavior, focusing purely on making it cleaner, more maintainable, and easier for developers (current and future) to understand. And in this day and age, we have a nifty assistant to make this process smoother: GitHub Copilot. In this post, I’ll walk you through how GitHub Copilot can assist with refactoring, using a few straightforward examples in JavaScript. Whether you’re consolidating redundant code, simplifying complex logic, or breaking apart monolithic functions, Copilot can… Go to Post
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Unlocking the Art of Prompting, Output Refinement and Creative Collaboration with Generative AI
To excel in crafting prompts for generative AI tools like ChatGPT, Claude, or Perplexity, you need to fundamentally shift your understanding of the interaction. While it might feel like you’re engaging in a conversation with an intelligent entity, what’s really happening is far more mechanical and mathematical. These tools are not conscious or sentient but are instead advanced predictive engines. Your prompts are not queries in the traditional sense—they are patterns that guide the AI to predict the next sequence of letters, spaces, or even conceptual elements in its output. The illusion of conversation, intelligence, and creativity is a result… Go to Post
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Harnessing Data Science in Microsoft Azure: A Practical Guide to Tools, Workflows, and Best Practices
Data science is an interdisciplinary field that involves the scientific study of data to extract knowledge and make informed decisions. It encompasses various roles, including data scientists, analysts, architects, engineers, statisticians, and business analysts, who work together to analyze massive datasets. The demand for data science is growing rapidly as the amount of data increases exponentially, and companies rely more heavily on analytics to drive revenue, innovation, and personalisation. By leveraging data science, businesses and organisations can gain valuable insights to improve customer satisfaction, develop new products, and increase sales, while also tackling some of the world’s most pressing challenges.… Go to Post
Professional Developer
by Rodrigo Silva
