News Aggregator

How To Build an AI-Powered Search Bar With Vector Embeddings and OpenAI

Aggregated on: 2025-08-27 14:14:44

When you search for something in a search bar, but the results seem off from what you wanted to find, you join many others who have experienced this. We've all been there before — you search for "cost" only to come away with nothing because the doc only says "price." That's the pitfall of traditional keyword search — it matches words, not meaning.

Understanding Memory Page Sizes on Arm64

Aggregated on: 2025-08-27 13:44:44

One of the ways that the Arm64 architecture is different from x86 is the ability to configure the size of memory pages in the Memory Management Unit (MMU) of the CPU to 4K, 16K, or 64K. This article summarizes what memory page size is, how to configure page size on Linux systems, and when it might make sense to use a different page size in your applications. Introduction to Memory Page Size As we previously discussed in Diagnosing and Fixing a Page Fault Performance Issue with Arm64 Atomics, operating systems present a virtual memory address space to applications, and map physical memory pages to virtual memory addresses using a page table. The CPU then provides a mechanism called the Translation Lookaside Buffer (TLB) to ensure that recently accessed pages of memory can be identified and read faster using L1 or L2 CPU cache.

Apigee Edge to Apigee in GCP Migration—Replacing ExtensionCallout policy With MessageLogging Policy for Logging

Aggregated on: 2025-08-27 13:29:44

As more companies migrate their APIs to cloud, Apigee on Google Cloud provides a reliable solution to manage and secure APIs. For Apigee Edge (a SaaS platform) users, this migration allows them to leverage the cloud-native capabilities of Google Cloud to improve scalability, performance, and security. Benefits of Migration Cloud-Native Benefits: Apigee on Google Cloud provides seamless integration with applications hosted in GCP, making it easier to manage APIs. Scalability and Performance: Running on Google Cloud’s infrastructure, Apigee gains from its scalability, reliability, and strong performance. Security Features: Apigee integrates with Google Cloud Armor to provide enhanced protection against threats and DDoS attacks. Integrated with GCP Services: Apigee connects with other Google Cloud services like IAM, Logging, and Monitoring. Enhanced Features: Apigee provides various new features that were not available in Apigee Edge.

Data Splits in Machine Learning: Training, Validation, and Test Sets

Aggregated on: 2025-08-27 12:29:44

In machine learning, the integrity of your data pipeline is foundational. How you split and utilize your data impacts model performance as much as the algorithms themselves. Decisions made early, for data partitioning, inform not just development but deployment and ongoing monitoring. Effective data splitting separates model development from validation and performance assessment, ensuring reproducibility and meaningful results. This article explores the principles behind data splitting in machine learning. We’ll clarify why splits matter and examine core concepts: training, validation, and test sets. We then discuss advanced splitting strategies and present practical code samples and visualizations. Finally, you’ll find actionable guidelines for robust, production-ready machine learning workflows.

Toward Explainable AI (Part 3): Bridging Theory and Practice—When Explaining AI Is No Longer a Choice

Aggregated on: 2025-08-27 11:29:44

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 II: The Two Major Categories of Explainable AI Techniques. How XAI methods help open the black box

Benchmarking Storage Performance (Latency, Throughput) Using Python

Aggregated on: 2025-08-26 20:14:44

Understanding the performance of your AWS S3 storage specifically, how quickly you can read and write data is essential for both cost optimization and application speed. By running Python scripts that measure latency and throughput, you can compare different S3 storage classes, identify hidden bottlenecks, and make data-driven decisions about where and how to store your data. This article breaks down the fundamentals of S3 benchmarking, provides working Python examples, and shows how to interpret the results even if you’re not a cloud infrastructure expert.

From Simple Lookups to Agentic Reasoning: The Rise of Smart RAG Systems

Aggregated on: 2025-08-26 19:29:44

Retrieval-Augmented Generation (RAG) is a technique in large language models (LLMs) that enhances text generation by incorporating external data retrieval into the process. Unlike traditional LLM usage that relies solely on the model’s pre-trained knowledge, RAG allows an AI to “look things up” in outside sources during generation. This significantly improves the factual accuracy and relevance of responses by grounding them in real-time information, helping mitigate issues like hallucinations (fabricated or inaccurate facts) and outdated knowledge. In essence, RAG gives AI a dynamic memory beyond its static training data. However, the story of RAG doesn’t end with the basic idea of retrieval + generation. Over time, a series of RAG architectures have emerged – each one introduced to solve specific shortcomings of the earlier approaches. What began as a simple concept has grown into a sophisticated ecosystem of patterns, each designed to tackle real-world challenges such as maintaining conversational context, handling multiple data sources, and improving retrieval relevance. In this article, we’ll explore the major RAG architectures in an evolutionary sequence. We’ll see how each new architecture builds upon and resolves the limitations of its predecessor, using visual diagrams to illustrate the problem each one tackles and the solution it provides.

Building AI-Driven Anomaly Detection Model to Secure Industrial Automation

Aggregated on: 2025-08-26 18:14:44

Introduction In modern industrial automation, security is a primary requirement to keep the regular operation of industrial connected devices without disruption. However, the rise of cyber risks also significantly impacts the industry’s sustainable operation. The evolving cyberattacks can affect the overall industrial systems that control industrial processes and systems. Modern attacks are more targeted and designed to evade detection by traditional defensive approaches. A proactive approach is necessary, rather than a defensive strategy, to tackle these evolving cyber threats. This article presents a use case for building an anomaly detection framework using artificial intelligence (AI). More specifically, a hybrid learning model consisting of a deep learning LSTM model for feature extraction and a machine learning (ML) classifier to detect and predict anomalous behavior in industrial automation. The evolution of next-generation technologies, also known as Industry 4.0, has evolved to meet the challenges and requirements of optimal operations and efficient sustainability in industrial automation networks. In this modern era, the development of advanced mobile networks (5G), big data analytics, the Internet of Things (IoT), and Artificial Intelligence (AI) provides excellent opportunities for better and more optimal industrial operations. The integration of Mobile Network, for example, enables the seamless operation of millions of IIoT devices connected simultaneously with minimal bandwidth and low latency. However, apart from excellent opportunities, these technological paradigms also open a new door to cyber-criminals that can affect the sustainability and operations of industrial networks.

Oracle Standard Edition vs PostgreSQL (Open Source): Performance Benchmarking for Cost-Conscious Teams

Aggregated on: 2025-08-26 17:29:44

Relational databases sit at the core of nearly every application stack—powering everything from customer transactions to critical business reporting. Choosing the right database is no small decision: it influences application performance, scalability, maintenance overhead, and ultimately, total cost of ownership. Two of the most popular contenders in the OLTP (Online Transaction Processing) space are Oracle Standard Edition (SE) and PostgreSQL (open source). Oracle SE has a long-standing reputation in the enterprise world for its transactional integrity, advanced concurrency controls, and rock-solid durability. It’s a go-to choice for industries that demand reliability, like finance, healthcare, and manufacturing. On the other hand, PostgreSQL has emerged as a developer favorite in recent years, celebrated for its performance, extensibility, and, of course, its open-source licensing model that removes vendor lock-in.

A Comparative Analysis of GitHub Copilot and Copilot Agent: Architectures, Capabilities, and Impact in Software Development

Aggregated on: 2025-08-26 16:29:44

Artificial intelligence (AI) is rapidly reshaping how software is built, tested, and maintained. GitHub Copilot leads this shift as a smart coding assistant that suggests real-time code completions by learning from billions of lines of public code. As the complexity of development work continues to grow, the need for an AI tool that extends beyond code completion will arise. Enter GitHub Copilot Agent, a more autonomous assistant that can comprehend natural language, traverse multiple project files, and perform more advanced development tasks such as refactoring, debugging, and generating unit tests.

Java 21 Virtual Threads vs Cached and Fixed Threads

Aggregated on: 2025-08-26 15:29:44

Introduction Concurrent programming remains a crucial part of building scalable, responsive Java applications. Over the years, Java has steadily enhanced its multithreaded programming capabilities. This article reviews the evolution of concurrency from Java 8 through Java 21, highlighting important improvements and the impactful addition of virtual threads introduced in Java 21. Starting with Java 8, the concurrency API saw significant enhancements such as Atomic Variables, Concurrent Maps, and the integration of lambda expressions to enable more expressive parallel programming.

Zero-Latency Data Analytics for Modern PostgreSQL Applications

Aggregated on: 2025-08-26 14:29:44

On July 23, 2025, AWS announced Amazon Relational Database Service (Amazon RDS) for PostgreSQL zero-ETL integration with Amazon Redshift, enabling near real-time analytics and machine learning (ML) on petabytes of transactional data. With this launch, you can create multiple zero-ETL integrations from a single Amazon RDS PostgreSQL database, and you can apply data filtering for each integration to include or exclude specific databases and tables, tailoring replication to your needs. You can also use AWS CloudFormation to automate the configuration and deployment of resources needed for zero-ETL integration. Zero-ETL integrations make it simpler to analyze data from Amazon RDS to Amazon Redshift by removing the need for you to build and manage complex data pipelines and helping you derive holistic insights across many applications. Within seconds of data being written to Amazon RDS for PostgreSQL, the data is replicated to Amazon Redshift. Using zero-ETL, you can enhance data analysis on near-real-time data with the rich analytics capabilities of Amazon Redshift, including integrated ML, Spark support, and materialized views.

The Benefits of AI Micromanagement

Aggregated on: 2025-08-26 13:29:44

TL; DR: AI Micromanagement Has Its Merits The benefits of AI micromanagement show up when you feed ChatGPT 5 progressively more context about your actual situation. I tested five prompts for a retrospective design: from zero context to full team background with extended reasoning time. Case 1 produced generic “Scrum Oscars” nonsense. Case 5 delivered sophisticated root-cause analysis targeting chronic top-down thrash, dependency gridlock, and psychological safety erosion. The difference? Strategic context curation. More context created better solutions, but only when that context was relevant and structured.

Pulumi: Modern Infrastructure as Code With Real Programming Languages

Aggregated on: 2025-08-26 12:59:44

After a long journey with Terraform, when Terraform introduced HCL2, I started exploring for an alternative IaC tool to write code in my programming language of choice, and that's when I found Pulumi. Founded in 2017, Pulumi has emerged as a powerful alternative to traditional IaC tools by bridging the gap between software development and infrastructure management. Pulumi is a modern Infrastructure as Code (IaC) platform that enables developers and infrastructure teams to create, deploy, and manage cloud resources using familiar programming languages instead of domain-specific languages (DSLs) or YAML templates.

How to Implement Kill Switch Feature Flags in a Spring Boot Application

Aggregated on: 2025-08-26 11:29:44

Kill switches are a type of feature flag that allows you to shut off features in your application quickly. They are useful for: Emergency shutoffs of external APIs and services. Responding to unexpected spam or traffic spikes. Other operational incidents where you need to quickly put the brakes on without causing additional disruption. In this tutorial, you will learn to add a kill switch to a Spring Boot application, using the LaunchDarkly Java SDK. This example will utilize the Motivational Messages API as a data source here since it’s free, doesn’t require authentication, and gives us the message we might need to read today.

Transforming Data into Decisions: Crafting Generative AI That Delivers Accurate Intelligence

Aggregated on: 2025-08-25 20:14:43

Introduction: Generative AI, driven by advancements in machine learning (ML), has transformed various industries by enabling machines to create text, images, music, and even code. However, developing robust, reliable, and personalized generative systems involves more than just large language models. Crucial components include data validation, thorough testing, personalized ranking, and structured reasoning (for example, chain-of-thought prompting). These elements are essential for improving the accuracy, relevance, and adaptability of generative AI systems. This article will examine how integrating rigorous data practices, machine learning techniques such as personalized re-ranking, and reasoning strategies can improve the performance of generative AI systems. We will also introduce visual aids to clarify concepts such as linear classification, validation pipelines, and customer-centric ranking systems.

Debugging Distributed ML Systems

Aggregated on: 2025-08-25 19:14:43

My ML model for categorizing suddenly started classifying groceries as entertainment expenses. But why? What happened? I was looking at my personal finance dashboard and noticed something was completely off. The logs from each service looked normal. The health checks were green. Yet somehow, my grocery store purchases were being flagged as entertainment, and my restaurant bills were showing up as utilities.

A Beginner’s Guide to Hyperparameter Tuning: From Theory to Practice

Aggregated on: 2025-08-25 18:14:43

There are many ways to approach machine learning, and selecting the right algorithm is just the first step. What a model can truly offer in terms of performance can be distilled to how well it is fine-tuned. Here, the analogy is the adjusting of dials on a supercharged engine, which is otherwise called hyperparameters. Hyperparameter tuning is the act of modifying the parameters of a model — that is, the parameters defining the model's architecture — to achieve optimal performance. Choose it wisely and your project will achieve optimal efficiency and flexibility. Oppositely, if it’s screwed up, the model may underperform or overlearn.

AI Data Security: Core Concepts, Risks, and Proven Practices

Aggregated on: 2025-08-25 17:29:43

AI is everywhere now, and cybersecurity is no exception. If you’ve noticed your spam filter getting smarter or your bank flagging sketchy transactions faster, there’s a good chance AI is behind it. But the same tech that helps defend data can also become a liability. Today, we want to talk about AI data security and why it matters; how AI is changing the way we protect information, where things can go wrong, and what steps actually make a difference.

Agent-to-Agent Protocol: Implementation and Architecture With Strands Agents

Aggregated on: 2025-08-25 16:29:43

The future of AI lies not in isolated agents but in collaborative networks of specialized agents working together. The Agent-to-Agent (A2A) protocol defines how AI agents discover, communicate, and coordinate to solve complex problems that exceed individual agent capabilities. This technical guide explores implementing multi-agent systems using the Strands Agents SDK, an open-source framework that takes a model-driven approach to building AI agents with seamless collaboration capabilities.

Modernizing Chaos Engineering: The Shift From Traditional to Event-Driven

Aggregated on: 2025-08-25 15:14:43

Imagine you're a car manufacturer. Traditionally, you schedule crash tests every few months using standard scenarios — front impact, side impact, and rollover. These tests are helpful, but they don’t guarantee how the car will perform with actual drivers, under real conditions, during unexpected events like icy roads or sudden brake failures. Now imagine that instead of static crash tests, your vehicles have smart sensors that simulate critical failures at the moment drivers make changes, like switching to sport mode, engaging cruise control, or driving in a snowstorm. These real-time, event-triggered safety checks provide far more relevant insights, helping you design safer cars for real-world situations.

The Ephemeral Cloud: A New Blueprint for Infrastructure Efficiency With Crossplane and kube-green

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

We were all sold a compelling vision of cloud computing: one filled with agility, endless scalability, and remarkable cost savings. Yet, for many of us in the trenches, the daily reality looks quite different. We find ourselves wrestling with an infrastructure model built on long-lived, static environments for development, testing, and staging. This old way of working has quietly become a massive drain on our resources, creating financial waste, operational headaches, and a growing list of security and environmental debts. This isn't just one problem; it's a vicious cycle. The friction in our daily operations directly fuels the financial, security, and environmental burdens. To break free, we need more than just a new tool; we need to fundamentally rethink how we provision, manage, and consume infrastructure.

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.