News Aggregator

Building a 3D WebXR Game with WASI Cycles: Integrating WasmEdge, Wasmtime, and Wasmer to Invoke MongoDB, Kafka, and Oracle

Aggregated on: 2025-08-25 13:14:43

Let's start with the basics. WASM and WASI Defined

Orchestrating Complex Workflows With XState

Aggregated on: 2025-08-25 12:29:43

XState is a state orchestration and management library designed for JavaScript and TypeScript applications. It approaches complex logic through an event-driven model that combines state machines, statecharts, and actors. This structure helps developers create clear, maintainable workflows and application states that behave reliably and are easy to visualize. Although XState is widely used in UI development, its declarative structure makes it an excellent choice for backend workflows (specially in cloud-native and/or event-driven systems). In this article, we’ll look at how XState can be leveraged to manage complex backend workflows using AWS Lambda and AWS ECS and draw some comparisons.

Toward Explainable AI (Part 2): Bridging Theory and Practice—The Two Major Categories of Explainable AI Techniques

Aggregated on: 2025-08-25 11:29:43

Series reminder: This series explores how explainability in AI helps build trust, ensure accountability, and align with real-world needs, from foundational principles to practical use cases. Previously, in Part I: Why AI Needs to Be Explainable: Understanding the risks of opaque AI.

Certificate Authorities: The Keystone of Digital Trust

Aggregated on: 2025-08-22 20:14:42

TLDR: Certificate Authorities (CAs) are the ultimate trust brokers online, issuing the digital certificates that make secure web browsing, e-commerce, and confidential communications possible. This article breaks down what CAs do, the nuances of public and private trust, role of browsers and global forums and how they enforce compliance, and why recent security incidents underline the critical responsibility of every CA. We’ll explore Certificate Transparency (CT), leading CAs and CT log providers, review high-profile failures, and explain where CA technology is headed next.

From History to the Future of AI Communication—IPC to MCP and A2A

Aggregated on: 2025-08-22 19:14:42

Google has explicitly positioned its A2A protocol as complementary to Anthropic's MCP, aiming to address different yet related aspects of building sophisticated agentic systems. The core distinction lies in the layer of interaction each protocol standardizes. MCP focuses on the connection between a single AI agent and its external resources (tools and data), while A2A focuses on the communication and collaboration among distinct AI agents. Architecturally, they operate at different levels. MCP governs the agent-to-resource interface, while A2A governs the agent-to-agent interface. In a typical multi-agent workflow, one agent (the client) might use A2A to request assistance from another specialized agent (the server). This server agent might then use MCP to interact with various tools or data sources required to fulfill the task requested via A2A, before sending the result back using A2A.

SHAP-Based Explainable AI (XAI) Integration With .Net Application

Aggregated on: 2025-08-22 18:29:42

Think of Explainable AI (XAI) as your friendly guide to a complex machine’s secret thoughts. Instead of leaving you guessing why an algorithm made a certain call, XAI opens the door, points out the important clues, and speaks plainly about what drove its decision. Explainable AI builds trust on the ML decision since it speaks how the decision made, makes the human to believe and to catch and fix the mistakes. Explanation from Explainable AI: “I have started at 0%—yet to know the prediction. Spotting that dog’s snout boosted my confidence by 45%, seeing its upright ears added 30%, the fluffy fur another 10%, and the collar a small 7%. A hint of grass slightly pulled me down by 5%. This is a dog and I'm 87% sure about this.”

Real-Time Model Inference With Apache Kafka and Flink for Predictive AI and GenAI

Aggregated on: 2025-08-22 17:14:42

Artificial intelligence (AI) and machine Learning (ML) are transforming business operations by enabling systems to learn from data and make intelligent decisions for predictive and generative AI use cases. Two essential components of AI/ML are model training and inference. Models are developed and refined using historical data. Model inference is the process of using trained machine learning models to make predictions or generate outputs based on new, unseen data. This blog post covers the basics of model inference, comparing different approaches like remote and embedded inference. It also explores how data streaming with Apache Kafka and Flink enhances the performance and reliability of these predictions. Whether for real-time fraud detection, smart customer service applications, or predictive maintenance, understanding the value of data streaming for model inference is crucial for leveraging AI/ML effectively.

Data Lake, Warehouse, or Lakehouse? Rethinking the Future of Data Architecture

Aggregated on: 2025-08-22 16:14:42

Editor's Note: The following is an article written for and published in DZone's 2025 Trend Report, Data Engineering: Scaling Intelligence With the Modern Data Stack. In the age of AI and ubiquitous data, the lines between traditional data architectures are blurring. Data lakes, warehouses, and lakehouses are no longer isolated strategies but are increasingly converging into unified, intelligent platforms. This article explores how modern data architectures are evolving to meet new demands for real-time insights, agility, and a single source of truth.

A Deep Dive into Behavior-Driven Development

Aggregated on: 2025-08-22 15:29:42

Behavior-Driven Development (BDD) fosters integration between developers, testers, product owners, and business analysts. Scenario participation ensures a common understanding of system functionality among all participants. In this article, we focus on BDD, its definition, importance, and strategies for implementing it in current projects. Understanding the Problem In software engineering, fulfilling both a product's technical requirements and the business objectives is essential.

Python Async/Sync: Advanced Blocking Detection and Best Practices (Part 2)

Aggregated on: 2025-08-22 14:14:41

If you're new to the challenges of mixing asynchronous and synchronous Python code, you might find it helpful to first read the first part, which focuses on understanding and solving asynchronous blocking and covers the foundational problems and initial solutions. This part will delve into advanced techniques for identifying and mitigating performance pitfalls. How to Detect Blocking Sync Code in Async Proactively identifying these hidden blockers is crucial for maintaining high-performance asyncio applications. Here are battle-tested methodologies:

Data Storage: The Foundation for Scalable Analytics

Aggregated on: 2025-08-22 13:14:41

In the last few years, cloud storage has become so inexpensive that most teams barely think about it. Services like S3 can store petabytes for pennies, and Glacier can archive data for less than the price of a coffee each month. We know how easy it is to spin up buckets and push data in, and it’s no wonder storage often gets treated as an afterthought. But here’s the catch: cheap doesn’t mean unimportant. With the rise of digital transformation, every company is turning into a data company, with its data volumes skyrocketing. For example, e-commerce sites track every click by customers, manufacturers stream IoT sensor feeds, storing every log, and banks store every transaction for years for audit and compliance reasons.

How to Create Ansible Users and Add Passwords

Aggregated on: 2025-08-22 12:29:41

Managing users efficiently is a key part of automating system administration with Ansible. In this guide, you’ll learn how to create users, set passwords, add users to groups, and configure remote access using Ansible’s powerful tools. What is the Ansible User Module? The Ansible user module is used to manage user accounts on Linux and UNIX-like operating systems on target systems. It can set user properties such as UID, home directory, login shell, and password hash. Ansible tasks are idempotent, so re-running the task will not create duplicate users.

Toward Explainable AI (Part I): Bridging Theory and Practice—Why AI Needs to Be Explainable

Aggregated on: 2025-08-22 11:29:41

Series reminder: This series explores how explainability in AI helps build trust, ensure accountability, and align with real-world needs, from foundational principles to practical use cases. In this Part: We lay the groundwork for explainable AI: what it means, why it matters, and what’s at stake when AI systems remain opaque.

Greenplum vs Apache Doris: Features, Performance, and Advantages Compared

Aggregated on: 2025-08-21 20:29:41

As organizations increasingly rely on data for real-time decision-making, the demand for scalable, high-performance analytical databases has never been higher. Among the contenders in the modern analytics space, Greenplum and Apache Doris stand out for their MPP (Massively Parallel Processing) architectures and ability to handle large-scale data workloads. While both are designed for analytics, they differ significantly in architecture, performance, and ease of management. This article provides a side-by-side comparison to help data teams evaluate which solution better aligns with their technical and business needs. 1. Overview and Architecture Greenplum Greenplum is an open-source distributed relational database based on PostgreSQL. It adopts an MPP (Massively Parallel Processing) architecture, designed specifically for large-scale data analytics. Its architecture consists of three main components:

Zero-Touch Patch Management With PowerShell and Intune: How We Automated Compliance at Scale

Aggregated on: 2025-08-21 19:14:41

Keeping hundreds of endpoints patched and compliant sounds easy on paper until you’re juggling different departments, conflicting maintenance windows, and manual tracking spreadsheets. We knew our approach had to change when a missed update led to a critical zero-day vulnerability exposure in one of our branch office servers. This article walks through how we transitioned from inconsistent, manual patching to a fully automated, audit-friendly system using Microsoft Intune, PowerShell, and scheduled compliance logic. No third-party tools. No more guesswork.

Comparing Cassandra and DynamoDB: A Side-By-Side Guide

Aggregated on: 2025-08-21 18:29:41

Database technologies have gone through revolutions in the last decade. With just a handful of databases before, there are now multiple options — selecting the right one for a new project is often a challenge. The last decade saw the rise in popularity of NoSQL databases, which remove some of the complexities of relational databases for use cases that don’t require structured queries. This article attempts to compare two popular NoSQL databases: DynamoDB and Cassandra. It highlights their features and compares database operations. Evolution Cassandra is an open-source database released under the Apache License. It was originally built by Facebook for internal use and open-sourced in 2008. Ongoing development and stewardship of Cassandra is handled by the Apache Software Foundation. The latest version available at the time of writing is 5.0.

Python Async/Sync: Understanding and Solving Blocking (Part 1)

Aggregated on: 2025-08-21 17:14:41

Note: This blog post is divided into two parts to provide a comprehensive guide to mastering asynchronous and synchronous code coexistence in Python. This first part focuses on understanding the core problems and initial solutions. The second part will focus on detecting blocking code and best practices. Introduction Modern Python applications increasingly leverage asyncio to build highly concurrent systems, from responsive APIs and intelligent bots to efficient data pipelines. However, a common challenge arises when integrating new asynchronous code with existing synchronous components. This fusion often leads to frustrating performance bottlenecks, including mysterious timeouts, blocked event loops, and unexpected slowdowns. The complexity can escalate further when multithreading enters the equation.

The Ultimate Guide to OCR Transcription Services

Aggregated on: 2025-08-21 16:14:41

Transcribing handwriting to text is standard among businesses that need to scan handwritten documents or convert old records into something accessible and editable online or in searchable databases. Not only can transcribing handwritten documents make data extraction easy, but it is also a way to go paperless. With OCR’s expanding role across industries, from healthcare and finance to logistics and legal, the global market reached a valuation of USD 12.56 billion in 2023 and is projected to grow at a CAGR of 14.8% through 2030 (Grand View Research). This surge is largely fueled by advancements in transcription services that enhance OCR accuracy and usability, ensuring high-quality text extraction from diverse sources.

Securing Cloud Applications: Best Practices for Developers

Aggregated on: 2025-08-21 15:29:41

Cloud computing offers unmatched scalability and flexibility, but it also introduces new security challenges. Developers must take proactive steps to secure applications, infrastructure, and sensitive data from cyber threats. In this tutorial, we will explore essential cloud security best practices covering:

Yet Another Servers in Go: Understanding epoll, kqueue, and netpoll

Aggregated on: 2025-08-21 14:29:41

Hi there! This article demystifies how Go’s standard net package handles thousands of connections under high load by leveraging non-blocking I/O through

AI-Powered Root Cause Analysis: Introducing the Incident Investigator

Aggregated on: 2025-08-21 13:14:41

Debugging cloud infrastructure problems can be time-consuming and stressful. Incidents rarely come with an obvious explanation. It usually takes digging through logs, comparing deployments, and searching through dashboards just to understand what changed. With Microtica’s AI Incident Investigator, that changes. This AI-powered agent helps DevOps and SRE teams find the root cause of incidents faster by providing natural language insights based on deployment context, change history, and system telemetry.

How to Build an AI-Powered Chatbot With Retrieval-Augmented Generation (RAG) Using LangGraph

Aggregated on: 2025-08-21 12:29:42

Why RAG? Large language models (LLMs) like GPT-4 can produce fluent, grammatically accurate text; however, without access to external, updated knowledge, they frequently hallucinate or fabricate facts. This turns into a prime issue in high-stakes environments — like legal, medical, or business enterprise contexts — in which accuracy and accept as true with are non-negotiable. Retrieval-augmented generation (RAG) resolves this problem by fetching relevant, trusted information from your own knowledge base (e.g., documents, PDFs, internal databases) and injecting it into the LLM prompt. This method grounds the model`s outputs, dramatically lowering hallucinations whilst tailoring responses to your domain.

Design Automation in Closure Engineering: Building Parametric Assemblies With CATIA and VB Scripting

Aggregated on: 2025-08-21 11:14:41

Modern closure systems in EVs and advanced vehicles demand more than just clean geometry; they require embedded logic, constraint-driven structures, and validation-aware modeling. While CATIA V5/V6 offers robust 3D capabilities, its true power emerges when engineers start treating CAD like code. With VB scripting, it is possible to encode design intelligence directly into the CAD model, enabling parametric automation across complex mechanical assemblies. This article breaks down how parametric automation can reduce review-cycle fatigue, enforce design intent, and enable a traceable, simulation-ready closure workflow.

Filtering Java Stack Traces With MgntUtils Library

Aggregated on: 2025-08-20 20:14:40

Introduction: Problem Definition and Suggested Solution Idea This article is a a technical article for Java developers that suggest a solution for a major pain point of analyzing very long stack traces searching for meaningful information in a pile of frameworks related stack trace lines. The core idea of the solution is to provide a capability to intelligently filter out irrelevant parts of the stack trace without losing important and meaningful information. The benefits are two-fold: 1. Making stack trace much easier to read and analyze, making it more clear and concise

Why Architecture Matters: Structuring Modern Web Apps

Aggregated on: 2025-08-20 19:29:40

Modern web applications have become fundamental to delivering seamless and efficient services, especially in the public sector. Local governments face increasing demand to provide responsive, user-friendly, and scalable digital solutions to the public. Leveraging a high-performing web application architecture using React.js and .NET Core This article serves as a comprehensive guide to modern high-performing web application architecture, specifically focusing on the integration of React.js for the front end and .NET Core 8 for the backend services empowering local government agencies to meet the growing state-of-the-art apps need by harnessing a contemporary tech stack that accelerates development, enhances maintainability, and optimizes user experience.

Operationalizing the OWASP AI Testing Guide: Building Secure AI Foundations Through NHI Governance

Aggregated on: 2025-08-20 18:14:40

Artificial intelligence (AI) is becoming a core component in modern development pipelines. Every industry faces the same critical questions regarding the testing and securing of AI systems, which must account for their complexity, dynamic nature, and newly introduced risks. The new OWASP AI Testing Guide is a direct response to this challenge. This community-created guide provides a comprehensive and evolving framework for systematically assessing AI systems across various dimensions, including adversarial robustness, privacy, fairness, and governance. Building secure AI isn't just about the models; it involves everything surrounding them.

MCP Client-Server Integration With Semantic Kernel

Aggregated on: 2025-08-20 17:14:40

Modern AI applications gain real popularity when they translate natural language prompts to execute external services. This article describes the basic understanding of the key components: semantic kernel, Azure OpenAI, and MCP Client-Server. It also describes the implementation to connect the Semantic Kernel to an Azure-hosted OpenAI resource so that an LLM can be queried directly. Additionally, you will learn how to create an MCP Client, run the MCP Server, and expose the MCP tools. The tools that are discovered can then be registered as kernel functions in the Semantic Kernel and thus, augment the LLM with the ability to execute external tools as a Service that are provided through the MCP Server.

Prompt Engineering Wasn't Enough; Context Engineering Is What Came Next

Aggregated on: 2025-08-20 16:14:40

Over the last few years, the conversation around AI has slowly shifted from prompt engineering to something more structured and more powerful: context engineering. When you are working on a chatbot that answers questions around a knowledge base or working on an agentic AI framework that is very complex, the way you architect context depends entirely on the problem you are solving. Simply put, context complexity scales with the task uncertainty. Simple, predictable tasks require minimal context structuring, while complex, multi-step tasks require sophisticated context orchestration.

Talk to Your BigQuery Data Using Claude Desktop

Aggregated on: 2025-08-20 15:14:40

Have you ever thought about talking to your data in Google Cloud BigQuery using natural language queries? If I told you a year ago that it was possible, you might think that I am out of my mind. But with MCP (Model Context Protocol), it is now totally possible. Before we get into the nitty-gritty details of how it is done, let us first look at a simple diagram explaining how we can connect and talk to our BigQuery data using natural language via MCP. We will then talk about each of the components and how they are set up, and then we will look at how the whole thing works.

Bridging the Gap: Integrating Graphic Design Principles into Front-End Development

Aggregated on: 2025-08-20 14:14:40

The line between design and development is becoming increasingly blurred. Websites and applications no longer compete solely based on functionality—they must also deliver intuitive, visually appealing user experiences. For front-end developers, understanding and applying basic graphic design principles is no longer a luxury but a necessity. This blog explores how developers can harness design fundamentals to create beautiful, effective user interfaces that not only function well but also delight users. The Need for Design Literacy in Development Traditionally, the design and development worlds were siloed. Designers handled aesthetics, while developers focused on code. But as agile workflows, collaborative tools, and lean UX practices became the norm, the need for developers to be visually literate grew.

Amadeus Cloud Migration on Ampere Altra Instances

Aggregated on: 2025-08-20 13:44:40

“You might not be familiar with Amadeus, because it is a B2B company [but] when you search for a flight or a hotel on the Internet, there is a good chance that you are using an Amadeus-powered service behind the scenes,” according to Didier Spezia, a cloud architect for Amadeus. Amadeus is a leading global travel IT company, powering the activities of many actors in the travel industry: airlines, hotel chains, travel agencies, airports, among others. One of Amadeus’ activities is to provide shopping services to search and price flights for travel agencies and companies like Kayak or Expedia. Amadeus also supports more advanced capabilities, such as budget-driven queries and calendar-constrained queries, which require pre-calculating multi-dimensional indexes. Searching for suitable flights with available seats among many airlines is surprisingly difficult.

Getting Started With PyIceberg: A Pythonic Approach to Managing Apache Iceberg Tables

Aggregated on: 2025-08-20 13:29:40

Modern data platforms are evolving rapidly—driven by a need for scalability, flexibility, and analytics at scale. Lakehouse architecture sits at the center of this evolution, combining the low-cost storage of data lakes with the reliability and structure of data warehouses. To power these lakehouses, organizations are turning to open table formats like Apache Iceberg. Originally developed at Netflix, Apache Iceberg was built to manage petabyte-scale analytics in cloud object storage. It brings database-style features—ACID transactions, schema evolution, partition pruning, and time travel—to large-scale files stored in systems like Amazon S3 or Azure Data Lake.

Containerized Intelligence: Running LLMs at Scale Using Docker and Kubernetes

Aggregated on: 2025-08-20 11:29:40

Large Language Models (LLMs) such as GPT, LLaMA, and Mistral have transformed the way applications interpret and generate natural language, driving innovation across a wide range of industries. Yet, operationalizing these models at scale introduces a host of technical challenges, including dependency management, GPU integration, orchestration, and auto-scaling. The rapid evolution of LLMs presents immense opportunities for building intelligent, language-aware applications. However, deploying and managing these compute-intensive models in production environments requires a reliable and scalable infrastructure. This is where containerization with Docker and orchestration with Kubernetes come into play—offering a powerful combination to streamline LLM deployment, ensure reproducibility, and support horizontal scaling.

How to Program a Quantum Computer: A Beginner's Guide

Aggregated on: 2025-08-19 20:29:40

Quantum computing might sound familiar, but have you ever tried using it yourself? Despite the reputation for complex math, the fundamentals of quantum computing are surprisingly easy. This guide offers a beginner-friendly walkthrough for working with qubits. You’ll learn how to build your first quantum program and see it generate numeric output, step by step.

Data Engineering for AI-Native Architectures: Designing Scalable, Cost-Optimized Data Pipelines to Power GenAI, Agentic AI, and Real-Time Insights

Aggregated on: 2025-08-19 19:29:40

Editor's Note: The following is an article written for and published in DZone's 2025 Trend Report, Data Engineering: Scaling Intelligence With the Modern Data Stack. The data engineering landscape has undergone a fundamental transformation with a complete reimagining of how data flows through organizations. Traditional business intelligence (BI) pipelines were built for looking backward, answering questions like "How did we perform last quarter?" Today's AI-native architectures demand systems that can feed real-time insights to recommendation engines, provide context to large language models, and maintain the massive vector stores that power retrieval-augmented generation (RAG).

Not only AI: What Else Drives Team Performance Today?

Aggregated on: 2025-08-19 18:14:40

Today’s world is obsessed with AI, and performance conversations often center on models, automation, and tooling. But when it comes to real, sustainable productivity gains, it’s not just about adding more AI. It's about designing better systems. In high-speed product environments (whether driven by AI or not) execution is urgent, but effectiveness depends on how you enable people. At GlobalLogic, I joined an early-stage GenAI product team. The stakes were high, timelines were tight, and yet, within three months, we boosted team performance by 20%. But we didn’t get there by chasing every shiny AI solution. We got there by doing the basics right: building clarity, creating smart feedback loops, and empowering decision-makers.

What We Learned Migrating to a Pub/Sub Architecture: Real-World Case Studies from High-Traffic Systems

Aggregated on: 2025-08-19 17:14:40

Modern e-commerce platforms must handle millions of users and thousands of simultaneous transactions. Our case study involves a large retail monolith serving millions of customers (~4,000 requests/s). The monolith struggled with scalability, so we re-architected it into microservices using Apache Kafka as the core Pub/Sub backbone. Kafka was chosen for its high throughput and decoupling: it “decouple[s] data sources from data consumers” for flexible, scalable streaming. For example, Figure 1 illustrates typical retail event-streaming use cases: real-time inventory, personalized marketing, and fraud detection. Major retailers like Walmart deploy ~8,500 Kafka nodes processing ~11 billion events per day to drive omnichannel inventory and order streams , while others (e.g. AO.com) correlate historical and live data for one-on-one marketing. These examples reflect Kafka’s strengths: massive throughput (millions of events/sec ) and service decoupling (Kafka can “completely decouple services” ). We set a goal to replicate these capabilities in our e-commerce migration. Figure 1: Business use-case categories enabled by Kafka event streaming in retail (source: Kai Waehner ). Kafka applications span revenue-driving features (customer 360, personalization), cost-savings (modernizing legacy systems, microservices), and risk mitigation (real-time fraud and compliance). In our migration, we similarly targeted these areas: for example, we replaced a monolithic order-flow (lock-step API calls) with independent services that exchange OrderPlaced, InventoryUpdated, etc. events via Kafka topics. This eliminated tight coupling between services, aligning with Kafka’s role as a “dumb pipe” where only endpoints enforce logic.

Building SQLGenie: A Natural Language to SQL Query Generator with LLM Integration

Aggregated on: 2025-08-19 16:14:40

SQL queries can be intimidating, especially for non-technical users. What if we could bridge the gap between human language and structured SQL statements? Enter SQLGenie—a tool that translates natural language queries into SQL by understanding database schemas and user intent. To build SQLGenie, I explored multiple approaches—from state-of-the-art LLMs to efficient rule-based systems. Each method had its strengths and limitations, leading to a hybrid solution that balances accuracy, speed, and cost-effectiveness.

Agile AI Agents

Aggregated on: 2025-08-19 15:29:40

TL; DR: Thinking About Use Cases I tried ChatGPT’s new Agent Mode: Is it really a new Agile AI Agent that autonomously identifies noteworthy signals in the daily communication and data noise? Or is it a glorified automated prompt execution device? Let’s find out. (Note: I only have a Plus account, which limits the experience.)

Building a Secure and Unified Data Platform

Aggregated on: 2025-08-19 14:14:40

Introduction I want to walk you through a detailed setup that combines a Compute Engine Virtual Machine (VM) with a custom Virtual Private Cloud (VPC), a managed PostgreSQL database using Cloud SQL, and the analytical prowess of BigQuery. We will complete setting up a secure, efficient, and interconnected environment for your data needs. Getting Started Create a new Google Cloud Project.

Quality Beyond Code: Holistic Quality Mindset in Agile Teams

Aggregated on: 2025-08-19 13:14:40

Quality is not just a function of technology and Product, but also encompasses every aspect of day-to-day project operations for efficient project delivery. Traditionally, the Cost of Quality (COQ) refers to costs associated with achieving and maintaining product or service quality. It comprises both the costs of good quality and the costs associated with poor quality. Reduced cost of quality increases project margin and efficiency.

Regex in Action: Practical Examples for Python Programmers

Aggregated on: 2025-08-19 12:14:40

Regex (Regular Expressions) is a powerful tool that is embedded inside Python which is a sequence of characters that define search patterns. Regex allows one to do string searching, string matching and manipulating strings based on the search pattern to do the operations like text extraction, data validation and search and replace functions. Regex is used whether we are processing large datasets, web scraping or parsing the logs. Let us explore some real-world examples and use cases to better understand Regex. Below are a few examples where Regex is greatly utilized:

A Retrospective on GenAI Token Consumption and the Role of Caching

Aggregated on: 2025-08-19 11:14:40

Caching is an important technique for enhancing the performance and cost efficiency of diverse cloud native applications, including modern generative AI applications. By retaining frequently accessed data or the computationally expensive results of AI model inferences, AI applications can significantly reduce latency and also lower token consumption costs. This optimization allows systems to handle larger workloads with greater cost efficiency, mitigating the often overlooked expenses associated with frequent AI model interactions. This retrospective discusses the emerging coding practices in software development using AI tools, their hidden costs, and various caching techniques directly applicable to reducing token generation costs.

What’s Wrong With Data Validation — and How It Relates to the Liskov Substitution Principle

Aggregated on: 2025-08-18 20:29:39

Introduction: When You Don’t Know if You Should Validate In everyday software development, many engineers find themselves asking the same question: “Do I need to validate this data again, or can I assume it’s already valid?” Sometimes, the answer feels uncertain. One part of the code performs validation “just in case,” while another trusts the input, leading to either redundant checks or dangerous omissions. This situation creates tension between performance and safety, and often results in code that is both harder to maintain and more error-prone.

Combine Node.js and WordPress Under One Domain

Aggregated on: 2025-08-18 19:29:39

I have been working on a website that combines a custom Node.js application with a WordPress blog, and I am excited to share my journey. After trying out different hosting configurations, I found a simple way to create a smooth online presence using Nginx on AlmaLinux. Important note: Throughout this guide, replace example.com with your actual domain name. For instance, if your domain is mydomain.com, you will substitute all instances of example.com with mydomain.com.

The Kill Switch: A Coder's Silent Act of Revenge

Aggregated on: 2025-08-18 18:29:39

In the age of code dominance, where billions of dollars are controlled by lines of code, a frustrated coder crossed the boundary between protest and cybercrime. What began as a grudge became an organized act of sabotage, one that now could land him 10 years in federal prison. Recently, a contract programmer was fired by a US trucking and logistics company. But unbeknownst to his bosses, he had secretly embedded a digital kill switch in their production infrastructure. A week later, the company's systems were knocked offline, their settings scrambled, and vital services grounded.

Expert Techniques to Trim Your Docker Images and Speed Up Build Times

Aggregated on: 2025-08-18 17:29:39

Key Takeaways Pick your base image like you're choosing a foundation for your house. Going with a minimal variant like python-slim or a runtime-specific CUDA image, is hands down the quickest way to slash your image size and reduce security risks. Multi-stage builds are your new best friend for keeping things organized. Think of it like having a messy workshop (your "builder" stage) where you do all the heavy lifting with compilers and testing tools, then only moving the finished product to your clean showroom (the "runtime" stage). Layer your Dockerfile with caching in mind, always. Put the stuff that rarely changes (like dependency installation) before the stuff that changes all the time (like your app code). This simple trick can cut your build times from minutes to mere seconds. Remember that every RUN command creates a permanent layer. You've got to chain your installation and cleanup commands together with && to make sure temporary files actually disappear within the same layer. Otherwise, you're just hiding a mess under the rug while still paying for the storage. Stop treating .dockerignore like an afterthought. Make it your first line of defense to keep huge datasets, model checkpoints, and (yikes!) credentials from ever getting near your build context. So you've built your AI model, containerized everything, and hit docker build. The build finishes, and there it is: a multi-gigabyte monster staring back at you. If you've worked with AI containers, you know this pain. Docker's convenience comes at a price, and that price is bloated, sluggish images that slow down everything from developer workflows to CI/CD pipelines while burning through your cloud budget. This guide isn't just another collection of Docker tips. We're going deep into the fundamental principles that make containers efficient. We'll tackle both sides of the optimization coin:

Prompt-Based ETL: Automating SQL Generation for Data Movement With LLMs

Aggregated on: 2025-08-18 16:14:39

Every modern data team has experienced it: A product manager asks for a quick metric, “total signups in Asia over the last quarter, broken down by device type,” and suddenly the analytics backlog grows. Somewhere deep in the data warehouse, an engineer is now tracing join paths across five tables, crafting a carefully optimized SQL query, validating edge cases, and packaging it into a pipeline that will likely break the next time the schema changes.

Real-Time Analytics Using Zero-ETL for MySQL

Aggregated on: 2025-08-18 15:14:39

Organizations rely on real-time analytics to gain insights into their core business drivers, enhance operational efficiency, and maintain a competitive edge. Traditionally, this has involved the use of complex extract, transform, and load (ETL) pipelines. ETL is the process of combining, cleaning, and normalizing data from different sources to prepare it for analytics, AI, and machine learning (ML) workloads. Although ETL processes have long been a staple of data integration, they often prove time-consuming, complex, and less adaptable to the fast-changing demands of modern data architectures. By transitioning towards zero-ETL architectures, businesses can foster agility in analytics, streamline processes, and make sure that data is immediately actionable. In this post, we demonstrate how to set up a zero-ETL integration between Amazon Relational Database Service (Amazon RDS) for MySQL (source) and Amazon Redshift (destination). The transactional data from the source gets refreshed in near real time on the destination, which processes analytical queries.

Logging MCP Protocol When Using stdio- Part II

Aggregated on: 2025-08-18 14:59:39

In Part 1, we introduced the challenge of logging MCP’s stdio communication and outlined three powerful techniques to solve it. Now, let’s get our hands dirty. This part provides a complete, practical walkthrough, demonstrating how to apply these concepts by building a Spring AI-based MCP server from scratch, configuring a GitHub Copilot client, and even creating a custom client to showcase the full power of the protocol. Copilot Conversation Illustration