News Aggregator

Optimizing Java Applications for Arm64 in the Cloud

Aggregated on: 2025-12-26 21:14:54

Java remains one of the most popular languages for enterprise applications running on the cloud. While languages like Go, Rust, JavaScript, and Python have a high profile for cloud application developers, the RedMonk language rankings have ranked Java in the top three most popular languages throughout the history of the ranking. When deploying applications to the cloud, there are a few key differences between deployment environments and development environments. Whether you’re spinning up a microservice application on Kubernetes or launching virtual machine instances, it is important to tune your Java Virtual Machine (JVM) to ensure that you are getting your money’s worth from your cloud spend. It pays to know how the JVM allocates resources and to ensure you use them efficiently.

The Architecture of Prompts: Designing Reliable, Deterministic AI Systems

Aggregated on: 2025-12-26 20:14:54

If 2025 was the year organizations discovered prompt engineering as a discipline, 2026 will be when we establish it as standard practice. Prompts aren't clever text tricks—they're software components with architectures, failure modes, and lifecycle requirements. As production LLM deployments multiply, consequences escalate from "weird response" to "compliance violation" or "incorrect customer data." This demands a fundamental shift: we must think about prompts like APIs or microservices. A prompt isn't a sentence—it's an engineered system designed for consistent outputs under varying conditions.

Inside a Large Retailer’s Web Architecture: Data Extraction and Analytical Insights

Aggregated on: 2025-12-26 19:14:54

Recently, I was trying to identify and understand my daily sugar and other nutrient intake. So, I was trying to find ways to get my bills sorted and identify specific product items. Whether shopping at Whole Foods, QFC, or Costco, I wanted to access my information in an accessible way. To solve this problem, I probably need an app that will read my bills and categorize accordingly. There must be an existing some app for that, or we can now use AI to automate most of it.

Shift-Left Strategies for Cloud-Native and Serverless Architectures

Aggregated on: 2025-12-26 18:14:54

The growth observed in modern-day cloud applications is staggering to say the least. Applications are being built faster and deployed at a faster pace. However, there can be several obstacles on this journey toward proactive security, as security and compliance often lag behind rapid development cycles. Traditional, end-of-cycle security checks simply haven’t kept up. Shift-left security has become a true game-changer in this regard. The whole architectural framework of shift-left security depends on moving critical security practices earlier in the development lifecycle. Incorporating security in the development lifecycle should not be an afterthought. Within this context, teams are empowered to identify and eliminate risks at design time, build time, and during CI/CD — not after. These modern workloads are highly dynamic and interconnected, and a single mishap can trickle down across the entire environment. And as cloud-native and serverless architectures grow more prominent by the day, it becomes imperative to adopt this proactive approach. In this article, we will take a look at some of the ways in which shift left security strategy can be incorporated into cloud native and serverless architecture from day one.

Custom Attributes in Relational Databases

Aggregated on: 2025-12-26 17:14:54

Learn five proven approaches to handle custom attributes in relational databases (RDBMS) — from EAV and JSON columns to dynamic schema automation. Understand their pros, cons, and when to use each. Every modern product needs flexibility — users want to add their own fields, labels, or attributes without waiting for a database migration. The problem? Relational databases weren’t built for change. This article explores five ways to bring flexibility to RDBMS, from JSON and metadata-driven models to fully automated schema evolution, so you can support dynamic attributes without sacrificing performance.

The Architect's Guide to Logging

Aggregated on: 2025-12-26 14:14:54

Every developer and architect thinks they understand logging until they’re staring at a production issue at 3:00 a.m. Realizing that their logs lack context, have no defined structure, and they’re sifting through a wall of text, desperately looking for that needle in a haystack. If this sounds familiar, it’s time to upgrade your logging strategy. Good logging is the black box recorder of your system. Here are the best tips to ensure your logs are an asset, not an obstacle.

Decoupling Azure Releases With GitHub Actions

Aggregated on: 2025-12-25 20:14:54

Cloud deployments often fail because environment configurations are hardcoded into the build process. Here is a pattern to decouple your Build Artifacts from your Deployment Logic using GitHub Actions and a flexible JSON Configuration map. In the world of Kubernetes, we are used to the separation of concerns: Docker builds the image, and Helm/Kustomize handles the environment configuration. However, when working with serverless (Azure Functions) or PaaS (App Service), developers often fall into the trap of monolithic pipelines. They build a package that only works in DEV, and then rebuild it for PROD.

A Principled Framework for Scalable Experimentation and Reliable A/B Testing

Aggregated on: 2025-12-25 18:14:53

If you’ve ever shipped a feature and thought, “Did we actually make things better?”, you’re not alone. A/B testing is supposed to be our scientific answer to that question, but running good experiments takes more than sprinkling some feature flags and plotting a graph. In practice, many teams learn experimentation the hard way. They launch tests with unclear hypotheses, biased assignments, or underpowered sample sizes, only to discover weeks later that their results are inconclusive or misleading. This means going back to the drawing board, restarting experiments, and losing valuable time, a hit to both product velocity and team morale.

Extracting Clean Excel Tables From PDFs Using Python + Docling

Aggregated on: 2025-12-25 17:14:53

PDFs remain the most widely used format for distributing structured reports — financial statements, regulatory filings, research documents, fund fact sheets, and more. Yet despite their structured appearance, PDFs are not machine-readable. Extracting tables reliably is famously error-prone and often requires hours of manual cleanup. This is especially true in finance and enterprise environments where analysts rely on Excel for modeling and reporting.

AI-Driven Schema Evolution and Adaptive Pipelines

Aggregated on: 2025-12-25 16:14:53

The Modern Challenge of Schema Drift In dynamic enterprise ecosystems, schemas rarely stay static. APIs evolve, microservices introduce new fields, IoT devices emit data in new formats, and third-party providers alter payloads overnight. The result: schema drift — the silent killer of data reliability. A single schema mismatch can cascade through hundreds of tables, breaking joins, corrupting aggregates, and halting analytics pipelines. Traditional ETL frameworks rely on static definitions or manual version control to handle schema changes. But at today’s scale — millions of daily events, hundreds of interconnected datasets, dozens of teams — manual remediation simply doesn’t scale.

Autonomous Pipelines: Transforming CI/CD With Full Automation

Aggregated on: 2025-12-25 14:14:53

As software development practices have advanced over time, so too have the methodologies for managing code and changes. The autonomous pipeline, as it relates to continuous integration or continuous delivery (CI/CD) technology, embodies the next step in sophistication, where the pipeline can function almost entirely independently with no or very little human interaction. In an autonomous pipeline, the entire code integration and delivery process is managed automatically, producing fewer opportunities for human mistakes and allowing for faster release cycles. As organizations continue to seek more reliable and efficient software delivery practices, the desire for autonomous capabilities has become a trend to further reduce the need for human involvement in the CI/CD workflow process. This represents a fundamental shift in CI/CD practices that allows for self-governed decision-making and execution to be performed entirely independently of human input.

Unlocking the Power of Reflection in Mobile Development

Aggregated on: 2025-12-24 20:14:53

This article explores the concept of reflection in software development, with a particular focus on mobile platforms like iOS (Swift) and Android (Kotlin/Java). Reflection allows code to inspect and interact with objects, types, and properties at runtime, offering a way to make applications more dynamic and adaptable. Reflection in Mobile Development In software development, reflection gives us the ability to inspect and interact with objects, types, and members at runtime, without knowing their specifics at compile time. While it’s often overlooked, reflection can be a powerful tool when used correctly, offering flexibility and adaptability in situations where static code might otherwise be limiting.

Penetration Testing Strategy: How to Make Your Tests Practical, Repeatable, and Risk-Reducing

Aggregated on: 2025-12-24 18:14:53

Penetration testing — “pentesting” — still surprises teams. Some treat it as a checkbox before launch; others expect it to magically find every vulnerability. The truth sits in the middle: a well-planned penetration testing strategy turns a point-in-time assessment into a practical tool that reduces business risk, informs engineering priorities, and improves resilience over time. This article walks through how to build a penetration testing strategy that’s repeatable, cost-effective, and aligned with your business goals. It’s written for security leaders, engineering managers, and CISOs who want tests that do more than produce reports — they change behavior and reduce real risk.

Data Modeling: From ERwin to the Cloud

Aggregated on: 2025-12-24 17:14:53

Data modeling has transformed beyond recognition. We have moved from a simple entity-relationship diagram to sophisticated cloud architectures, and honestly, it is not just about shinier technology — it is a complete rethink of how we handle data. I learned the basics of ERwin back when it ruled the enterprise world. All industries used it, including banks, hospitals, and government agencies. The tool did wonders to standardize and document, which made CFOs and compliance officers happy [1]. The vast database designs across massive organizations could be counted on. Though I will admit, the licensing costs were brutal — especially for smaller teams who just needed basic modeling capabilities.

JavaScript Data Grid Comparison: 8 Popular Options Reviewed

Aggregated on: 2025-12-24 16:14:53

Why does choosing the right JavaScript Data Grid still matter in 2026? Data grids remain a cornerstone of web applications: dashboards, admin panels, CRMs, analytics, and enterprise systems all rely on them. The choice of the right grid still defines performance, customization flexibility, accessibility, and cost. To find which grid fits your needs, I reviewed eight top JavaScript data grids and compared them by performance, customization, accessibility, cost, integration, and devX.

Expose Any MCP Server as a Web API

Aggregated on: 2025-12-24 14:14:53

Transform your MCP server into an HTTP API that anyone can access from anywhere The Goal You have an MCP server running locally. You want others to use it via HTTP calls.

Implementing Automated Validation and Anomaly Detection

Aggregated on: 2025-12-23 20:29:52

Ensuring data quality has become much harder because contemporary systems generate data at high volume, high velocity, and high variety. Ensuring data consistency, completeness, and accuracy is harder as large-scale data pipelines often pull data from different sources in different formats. Traditional manual review processes simply can't keep up as the datasets are constantly being expanded and updated. Manual checks not only cause delays but also rely heavily on human judgment, and when the workload is either too big or too fast, the checks are usually no longer applicable. This situation in large-scale environments results in a lack of anomaly identification, inconsistent validation, and increased operational risk.

Atomic Writes in NoSQL: A Multi-Cloud Deep Dive

Aggregated on: 2025-12-23 18:29:52

Atomic writes used to be one of the important reasons we stuck with relational databases. The rule was simple: either all your updates succeed, or they all fail. But as we moved to NoSQL databases in distributed systems, we often traded that safety for scale. Now, the pendulum is swinging back. Developers building microservices and server-less apps are realizing that writing manual undo logic (compensating transactions) is a nightmare, and they want their NoSQL databases to handle that heavy lifting again. However, atomicity isn't standard across the cloud. AWS, Azure, GCP, and Alibaba all offer transaction capabilities, but they have wildly different rules regarding locking, limits, idempotency, and consistency guarantees.

Blockchain + AI Integration: The Architecture Nobody's Talking About

Aggregated on: 2025-12-23 17:29:52

Walk into any tech conference today, and you'll hear buzzwords flying: AI this, blockchain that. But ask anyone about the actual architecture required to integrate these technologies, and you'll mostly get hand-waving. That's because while everyone talks about the potential of combining blockchain's trustless verification with AI's decision-making capabilities, very few teams have solved the architectural nightmares that come with it. Here's the uncomfortable truth: these technologies weren't designed to work together. Blockchain prioritizes transparency, immutability, and deterministic execution. AI thrives on opacity, continuous learning, and probabilistic outputs. Forcing them into the same system is like trying to merge a public ledger with a black box — and expecting both to play nice.

Bridging the Gap Between Data Lakes and Warehouses

Aggregated on: 2025-12-23 16:29:52

In the current analytics landscape, companies rely heavily on data lakes and data warehouses as their primary sources for data storage and analysis. On the one hand, data lakes allow easy storage of a variety of raw and non-processed data, and on the other hand, data warehouses support formatting, storage, and processing of data in a manner that suits reporting and analytics. Figure 1: Evolution from data lakes and warehouses to the lakehouse model

A Practical Guide to Blocking Cyber Threats

Aggregated on: 2025-12-23 14:29:52

As cyberthreats dominate the news headlines day after day, it is important for large multinational organizations and nonprofits to take immediate notice of such events. Nonprofits often work under stark resource constraints, such as minimal IT staff and limited access control methods — yet the critical information they carry, from donor to staff information, must always be protected. As cyberattacks on nonprofits are rising faster than ever, the limitations that nonprofits have often put in place make them an ideal target for phishing, account takeover, and insider misuse. One of the critical and initial methods nonprofits can implement to protect their assets is the Principle of Least Privilege. The principle is based on the simple idea that bare minimum access to the appropriate resource should be provided to the subject, and no more than what is required for them to do their job. In general, there are basically no blanket permissions and no “admin for convenience.” It is a highly practical and actionable approach to fortify their defenses — without requiring a major personnel or technical overhaul. The principle — when implemented correctly — reduces the attack surface area for nonprofits and prevents such attacks from happening in the first place.

Phantom APIs: The Security Nightmare Hiding in Your AI-Generated Code

Aggregated on: 2025-12-22 20:14:52

The call came at 2:47 AM on a Tuesday in October 2024. I'd been following API security incidents for fifteen years, but this one made my coffee go cold as the CISO walked me through what happened. Their fintech had discovered attackers extracting customer financial data through /api/v2/admin/debug-metrics — an endpoint that shouldn't exist. No developer remembered building it. Their OpenAPI specs contained zero references to it. Yet there it was, quietly serving PII to anyone who stumbled across the URL.

LLMOps Explained: How It Works, Key Benefits, and Best Practices

Aggregated on: 2025-12-22 18:14:52

Let us say you are interacting with a bank’s AI assistant regarding a payment issue. All of a sudden, it reflects the incorrect answer and freezes your account. Disgusting, yeah? Now, can you imagine this situation happening with numerous customers simultaneously? Such failures can destroy trust and attack the company’s expenditure if the systems are misplaced. It is exactly where LLMOps marks its entry. It is the backdoor process ensuring large language models operate seamlessly, reliably, and safely. In other terms, LLMOps helps to keep the AI agents beneficial instead of harmful. Still figuring out the answer to - “what is LLMOps”? Let us explore in depth through this blog. What Is LLMOps? Large language model operations (LLMOps) refer to the practices and workflows involved in the management of large language models (LLMs). It is done through their development, deployment, monitoring, and maintenance. This refers to a type of model, such as OpenAI's GPT series or Google's BERT. It represents the latest advancement in AI technology, which is trained on large datasets to handle tasks, including but not limited to text generation, translation, question answering, etc.

Recent Linux Kernel Features Relevant to System Design

Aggregated on: 2025-12-22 17:14:52

Every new version of the Linux kernel provides changes that have an immediate influence on hardware interaction, memory efficiency, system speed, and security. These developments are very relevant not just to huge business servers but also to embedded systems, multimedia platforms, and real-time gadgets. The need to support increasingly complex hardware, provide more secure abstractions, and lower overhead drives kernel development. A technical overview of a number of new features that meet these needs is given in this document: SOF Dynamic Pipeline Support, io_uring, DAMON, Landlock LSM,memfd_secret, and IOMMU FD API. Each is described along with its purpose, relevant system design scenarios, and possible real-world applications.

Pandera: The Open-Source Framework for Data Validation

Aggregated on: 2025-12-22 16:14:52

Pandera is an open-source library for validating DataFrame-like objects (such as pandas, Polars, and Dask) by defining schemas that specify column names, data types, and statistical properties. At runtime, Pandera checks these schemas to ensure data quality. Its syntax is similar to Pydantic, providing a class-based API for defining DataFrameModels with column definitions and constraints. Pandera also integrates seamlessly with data processing pipelines by offering decorators that automatically validate function inputs and outputs. Pandera is a lightweight and expressive data validation library for Python that helps ensure the reliability and robustness of data processing pipelines. It provides an API for defining "schemas" that describe the expected properties of dataframe-like objects, including column names, data types, and statistical properties. By explicitly defining and enforcing these assumptions at runtime, Pandera catches data quality issues early, preventing silent data corruption from propagating downstream to analytical models or production applications. It offers two main ways to define schemas: an object-based API for simpler validation and a class-based, Pydantic-style API that uses Python type hints for more complex cases.

Defect Report in Software Testing: Best Practices for QA and Developers

Aggregated on: 2025-12-22 14:14:52

Defects are an unavoidable part of software development. But when they slip into production and reach your customers, the consequences go beyond poor user experience — they can damage your brand’s credibility. That’s why every defect must be logged and addressed promptly before it drives users away. The key to a timely resolution lies in effective defect reporting.

Cloud Migration of Microservices: Strategy, Risks, and Best Practices

Aggregated on: 2025-12-19 20:14:50

The article discusses the principles of microservice architecture and its comparison with monolithic systems. It also covers migration strategies based on the 6R/7R model, as well as analysis of key risks, including budget overruns and security breaches. Special attention is given to migration planning, monitoring, and automation methods, such as the use of Kubernetes, CI/CD (continuous integration/continuous delivery), DevSecOps (development, security, operations), and the Twelve-Factor App principles. Empirical statistics and comparative analysis of different strategies are also presented in tables. In conclusion, directions for further research are identified, such as using artificial intelligence (AI), multi-cloud approaches, and unifying evaluation methods.

Rethinking QA: From DevOps to Platform Engineering and SRE

Aggregated on: 2025-12-19 19:14:50

The Wake-Up Call The software development landscape is undergoing a significant transformation, challenging traditional roles and requiring new skills. While DevOps has been a key element for over a decade — promoting collaboration and continuous delivery — the specific role of a "DevOps engineer" is changing. Recent market analyses indicate a shift: while some reports suggest a stabilization or slight decrease in dedicated DevOps job postings, platform engineering and site reliability engineering (SRE) roles are experiencing a rise. This change does not mean the end of DevOps principles, but rather their deeper integration into specialized functions. This article argues that the core principles of DevOps are being integrated into new, vital roles, necessitating a significant adjustment for Quality Assurance (QA) professionals. Failing to adapt risks QA teams becoming bottlenecks in modern engineering workflows. On the other hand, embracing this change provides QA with a unique chance to expand its influence and become an essential quality facilitator.

Cracking the Innovation Code: Design Thinking Explained

Aggregated on: 2025-12-19 18:14:50

In both personal and professional life, we regularly face problems that are new, ambiguous, or unusually complex. Quick fixes sometimes work, but arriving at a simple, creative, and robust solution usually requires a different approach. Traditional problem solving often relies on familiar patterns and past experience, which can constrain options and stifle innovation when the context has changed or the problem is not yet fully understood.

Fortifying Cloud Security Operations with AI-Driven Threat Detection

Aggregated on: 2025-12-19 17:14:50

With the rapid adoption of cloud technologies, organizations are rushing to migrate their workloads and data to the cloud — often at a breakneck pace. Cyber hackers are not far behind in this race. On-premises systems are no longer the primary targets. Cloud environments, including both hybrid and multi-cloud setups, have become enticing targets for malicious attackers. Adding to this challenge, attacks are becoming more sophisticated day by day. Relying solely on traditional security methods such as static rules and signature-based detection — often requiring human intervention — is no longer sustainable. Artificial intelligence (AI) is a game changer in this regard. By enabling proactive threat detection and automated workflows, AI has the potential to drive a paradigm shift in cloud security operations. Understanding Cloud Threats Cloud environments are becoming increasingly complex. This creates a double-edged sword, as the same complexity that enables scalability also makes these environments vulnerable to novel threats. Let’s explore some common cloud threats:

Zero Trust Model for Nonprofits: Protecting Mission in the Digital Age

Aggregated on: 2025-12-19 16:14:50

In an increasingly globally connected world, nonprofit organizations are as much at risk and vulnerable to cyber threats as large multinational corporations, if not more so. To keep cyber threats at bay, traditional security models have often relied on devices such as firewalls, virtual private networks (VPNs), and similar tools, often based on the underlying assumption that anyone inside the network is trusted by default. Zero Trust Architecture (ZTA) is based on the concept that nothing is trusted by default, whether it is an internal or external stakeholder. The model offers a fundamentally different approach: never trust, always verify. This approach is particularly critical, as nonprofits often handle sensitive donor information, volunteer and beneficiary data, and other confidential information that must always remain secure. Why Nonprofits Are Attractive Targets Even though nonprofits might have limited budgets, they are still attractive targets for cybercriminals, often because they hold a wealth of sensitive and valuable information. High-value assets that can be exploited include donor databases, payment records, and personally identifiable information (PII) of beneficiaries. Additionally, nonprofits that rely on volunteers, contractors, or third-party partners can also be at risk if their access controls are weak. These high-value assets can be exploited for financial gain, identity theft, or ransomware attacks. Once a cyberattack occurs, there can be an erosion of donor trust, with regulatory penalties potentially applied if data breaches occur.

Technology Evolution From Traditional Automation to AI-Driven MCP Servers

Aggregated on: 2025-12-19 15:14:50

Technology has always been about solving problems faster, smarter, and more efficiently. Over the past few decades, we’ve witnessed a remarkable transformation in how businesses automate processes — from rigid, rule-based scripts to intelligent, adaptive systems powered by artificial intelligence (AI). One of the most fascinating journeys in this evolution is the shift from traditional automation frameworks to AI-driven Multi-Cloud Platform (MCP) servers. In this article, we'll explore that journey, why it matters, and what the future holds for organizations embracing this paradigm shift — now with supporting code examples you can adapt to your stack.

Macroscopic Quantum Tunneling: Unlocking the Quantum Secret Inside an Electrical Circuit

Aggregated on: 2025-12-19 14:14:50

This prize recognizes the three researchers "for the discovery of macroscopic quantum tunneling and the quantization of energy in an electrical circuit." What are the applications for the development of quantum computing? Bridging the Divide: The Microscopic and Macroscopic Worlds Indeed, this prize is all the more important as quantum mechanics now constitutes the heart of the most advanced digital technologies underpinning research in the field of quantum computing.

Kubernetes 101: Understanding the Foundation and Getting Started

Aggregated on: 2025-12-19 13:14:50

About This Series This is part 1 of a 5-part series on Kubernetes implementation. These posts cut through the hype to focus on practical decisions: what Kubernetes actually does, when you need it, and how to implement it effectively. Series Outline Kubernetes 101 – What it is, when you need it, and hands-on setup (this post) Networking – Service discovery, ingress, and how pods actually talk Deployment strategies – Rolling updates, blue-green, canary releases Storage – Persistent volumes and running stateful workloads Production operations – Monitoring, logging, scaling, and troubleshooting What Kubernetes Actually Does Kubernetes manages containerized workloads across a cluster of machines. You define the desired state in YAML; Kubernetes reconciles the actual state to match. Container crashes? Kubernetes restarts it. A node dies? Kubernetes reschedules pods elsewhere. Need to scale? Update the replica count.

End-to-End Test Automation With Playwright, GitHub Page, and Allure Reports

Aggregated on: 2025-12-19 12:14:50

Automated testing is essential for delivering high-quality web applications. Modern CI/CD pipelines require reliable, scalable, and transparent test automation to ensure confidence in every release. In this article, we explore how Playwright, GitHub Actions, and Allure Reports work together to create a powerful end-to-end testing automation framework. You will learn how to set up Playwright tests, integrate them into a GitHub Actions CI pipeline, and generate professional Allure reports that provide clear insights into test execution.

Looking at the Evolving Landscape of ITSM Through the Lens of AI

Aggregated on: 2025-12-18 20:14:50

As today’s businesses march forward alongside rapid developments in artificial intelligence (AI), progress has reached nearly every major functional area of technology. One such area on the cusp of transformational change is Information Technology Service Management (ITSM). For the last decades, traditional ITSM systems have relied heavily on manual workflows and unstructured processes. One can argue that while these systems introduced order and consistency, they also created bottlenecks, long resolution times, and reactive operations. With the advent of large language models (LLMs) and agentic AI, this technology paradigm is undergoing a rapid shift. As we speak, AI is reshaping how services are delivered, managed, and optimized. With the power of AI, traditional support channels across service desks are gradually evolving into proactive, self-healing ecosystems where issues are anticipated before they disrupt business, and routine manual tasks resolve themselves automatically. In this article, we explore the key ways AI has become the driving force behind the ITSM revolution.

Building AI Agents Using Docker cagent and GitHub Models

Aggregated on: 2025-12-18 19:14:50

The landscape of AI development is rapidly evolving, and one of the most exciting developments in 2025 from Docker is the release of Docker cagent. cagent is Docker’s open-source multi-agent runtime that orchestrates AI agents through declarative YAML configuration. Rather than managing Python environments, SDK versions, and orchestration logic, developers define agent behavior in a single configuration file and execute it with cagent run. In this article, we’ll explore how cagent’s integration with GitHub Models delivers true vendor independence, demonstrate building a real-world podcast generation agent that leverages multiple specialized sub-agents, and show you how to package and distribute your AI agents through Docker Hub. By the end, you’ll understand how to break free from vendor lock-in and build AI agent systems that remain flexible, cost-effective, and production-ready throughout their entire lifecycle.

When DNS Breaks The Internet: Lessons From The Amazon Outage

Aggregated on: 2025-12-18 18:14:50

Have you ever had an “Oh boy” moment when your favorite application does not load and you assume there is a fault with your Internet connection? In October 2025, this occurred on a global scale — but in point of fact, it was not your Internet connection that failed; it was Amazon’s. A slight misconfiguration of DNS on behalf of Amazon Web Services (AWS) caused a nationwide catastrophe on the Internet, taking with it such corporate behemoths as Fortnite, Alexa, and, not forgetting, the mobile ordering facility at McDonald’s.

Vision Language Action (VLA) Models Powering Robotics of Tomorrow

Aggregated on: 2025-12-18 17:14:50

The robotics industry is undergoing a fundamental transformation. For decades, robots have been confined to narrow, pre-programmed tasks in controlled environments — assembly lines, warehouses, and labs where predictability reigns. Vision-language-action (VLA) models represent a critical breakthrough in this evolution by combining visual perception, language understanding, action generation, and the potential for generalization. VLA models are poised to redefine what machines can do in the physical world. We will go over different VLA models in the industry today that you can leverage in your work. What Are Vision-Language-Action (VLA) Models Vision-language-action (VLA) models combine visual perception and natural language understanding to generate contextually appropriate actions. Traditional computer vision models are designed to recognize objects, whereas VLA models interpret scenes, reason about them, and guide physical actions in real-world environments.

We Taught AI to Talk — Now It's Learning to Talk to Itself: A Deep Dive

Aggregated on: 2025-12-18 16:14:50

A Master Blueprint for the Next Era of Human-AI Interaction In the rapidly evolving world of artificial intelligence, prompt engineering has become a crucial component of effective human-AI interaction. However, as large language models (LLMs) become increasingly complex, the traditional human-focused approach to prompting is reaching a critical point. What was once a delicate skill of crafting precise instructions is now becoming a bottleneck, causing inefficiencies and subpar results. This article explores the concept of AI-generated intent, arguing that the future of human-AI collaboration hinges not on humans becoming more proficient at crafting prompts, but on AI's learning to generate and refine their prompts and those of their peers. I. The Breaking Point: Why Human Prompting is Failing The inherent limitations of human language and cognitive biases often restrict the full potential of advanced AI models. While early LLMs responded well to carefully crafted human prompts, the growing sophistication of these models, particularly in multi-step reasoning tasks, has exposed the limitations of this approach. The issue isn’t a lack of human ingenuity, but rather the fundamental mismatch between human communication styles and the optimal operational logic of AI.

Why Your UEBA Isn't Working (and How to Fix It)

Aggregated on: 2025-12-18 15:14:50

User Entity Behavior Analysis (UEBA) is a security layer that uses machine learning and analytics to detect threats by analyzing patterns in user and entity behavior. Here’s an oversimplified example of UEBA: suppose you live in Chicago. You’ve lived there for several years and rarely travel. But suddenly there’s a charge to your credit card from a restaurant in Italy. Someone is using your card to pay for their lasagna! Luckily, your credit card company recognizes the behavior as suspicious, flags the transaction, and stops it from settling. This is easy for your credit card company to flag: they have plenty of historical information on your habits and have created a set of logical rules and analytics for when to flag your transactions.

Agile Manifesto: The Reformation That Became the Church

Aggregated on: 2025-12-18 14:14:50

TL, DR: The Reformation That Became the Church The Agile Manifesto followed Luther’s Reformation arc: radical simplicity hardened into scaling frameworks, transformation programs, and debates about what counts as “real Agile.” Learn to recognize when you’re inside the orthodoxy and how to practice the principles without the apparatus. How Every Disruptive Movement Hardens Into the Orthodoxy It Opposed In 1517, Martin Luther nailed his 95 theses to a church door to protest the sale of salvation. The Catholic Church had turned faith into a transaction: Pay for indulgences, reduce your time in purgatory. Luther's message was plain: You could be saved through faith alone, you didn't need the church to interpret scripture for you, and every believer could approach God directly.

Infrastructure as Code: How Automation Evolved to Power AI Workloads

Aggregated on: 2025-12-18 13:14:50

If you read my articles published on DZone this year, you would have sensed that I love automation and that Infrastructure as Code (IaC) is my buddy for automating infrastructure provisioning. Recently, I started exploring and learning about the major shifts happening in the IaC landscape. As part of my weekend readings in the last couple of months, I came across several exciting announcements from HashiConf 2025, Pulumi's new AI capabilities, and a revolutionary platform called Formae. In this article, let's learn about how IaC progressed in 2025 and how it helped automation, particularly for provisioning AI infrastructure.

Agentic AI in Cloud-Native Systems: Security and Architecture Patterns

Aggregated on: 2025-12-18 12:14:50

AI has long progressed past statistical models that generate forecasts or probabilities. The next generation of AI systems is agents, autonomous cloud-native systems capable of acting and intervening in an environment without human intervention or approval. Agents can provision infrastructure, reroute workloads, or optimize costs. They can also remediate incidents or apply other autonomous transformations at scale in cloud-native systems. Autonomy is particularly powerful in cloud-native ecosystems: think of self-tuning Kubernetes clusters, self-adapting CI/CD pipelines that dynamically route riskier code to human gatekeepers, or self-orchestrating serverless functions that maintain performance SLAs under previously unseen load spikes. But with autonomy comes a great responsibility: giving an AI agent the power to act in the cloud-native environment changes the nature of the threat surface in a fundamental way.

Momento Migrates Object Cache as a Service to Ampere® Altra®

Aggregated on: 2025-12-17 20:44:49

Organization Momento caching infrastructure for cloud applications is complex and time-consuming. Traditional caching solutions require significant effort in replication, failover management, backups, restoration, and lifecycle management for upgrades and deployments. This operational burden diverts resources from core business activities and feature development. Solution Momento provides a serverless cache solution, utilizing Ampere-based Google Tau T2A instances, that automates resource management and optimization, allowing developers to integrate a fast and reliable cache without worrying about the underlying infrastructure. Based on the Apache Pelikan open-source project, Momento’s serverless cache eliminates the need for manual provisioning and operational tasks, offering a reliable API for seamless results.

Why Your AI Transformation is Broken

Aggregated on: 2025-12-17 19:14:49

C-suite executives are rushing to implement their AI transformation strategies. Visions of cost savings, streamlined workforces, and exploding productivity are making them foam at the mouth. Despite this AI feeding frenzy, however, many of the same execs are becoming disillusioned by the whole AI transformation boondoggle.

CMDB vs. IT Asset Management: Why Confusing Them Can Break Your IT Operations

Aggregated on: 2025-12-17 18:14:49

Today, organizations are investing in technology more than ever before. However, many of them stumble — not because they lack resources, but because they confuse seemingly similar elements of technology implementation. A common example is the misunderstanding between two essential tools: Configuration Management Databases (CMDBs) and IT Asset Management (ITAM) systems. At a cursory glance, both appear to track IT resources, but once you peel back a few layers, the difference is much more significant. Imagine CMDBs as a city map that shows how different IT components interact and how business processes flow together. ITAM, on the other hand, is more like a ledger. It tracks ownership, costs, and the entire lifecycle of hardware and software assets. Technically speaking, CMDBs focus on mapping relationships between configuration items, while ITAM manages asset tracking. When these two technologies are mixed up, IT teams can face serious challenges in making informed business decisions.

Engineering Smart Prefetch: Search With Foresight

Aggregated on: 2025-12-17 17:14:49

Do you remember the hype for that final season of Game of Thrones? This was the only time I streamed a new episode within minutes of it being released. Millions of others did the same after waiting months to learn the fate of Westeros. Now imagine how amazing the viewing experience would have been if, the moment you selected the episode, it played instantly in the highest quality. How, though?

How AI Search Solves the Problem of Working With Unstructured Data

Aggregated on: 2025-12-17 16:14:49

Are you struggling with unstructured data, like support tickets, employee feedback, and documents? Many businesses face this challenge, leading to wasted time and missed insights. Unstructured datasets make up up to 90% of all enterprise-generated data, yet most systems are optimized for structured, field-based records. AI-powered search can interpret intent and context, find conceptually similar content, and improve results over time based on user behavior. Today, we’ll explore how AI search can transform the way you interact with data. What’s Unstructured Data and Why It’s Hard to Work With? Unstructured data refers to any information that doesn’t have a predefined format and does not conform to fixed schemas of databases. Common examples in enterprise environment include:

Model Context Protocol: The Missing Layer in Agentic AI

Aggregated on: 2025-12-17 15:14:49

AI agents are growing at a breakneck pace and are becoming highly efficient at automating routine tasks. However, amid all the exciting innovation across different use cases, even the most advanced models fall short due to a fundamental limitation: real-world applicability. They can think autonomously, yet they struggle to act reliably in real-world environments. For all their reasoning power, large language models (LLMs) often remain isolated. To unlock their full usability, they must be connected to the right tools, data sources, and systems. This is where the Model Context Protocol (MCP) is rewriting the rules of the AI landscape. One could say that MCP is the missing layer in the current Agentic AI stack. It is a unifying protocol that provides models with a predictable way to integrate with external environments. Its power lies in being cleanly designed, extensible, and capable of working across a broad array of platforms and runtimes. While MCP is still in its early stages, its rapidly growing use cases already allow developers and enterprises to build automation and agent workflows with far greater confidence. In this sense, MCP is doing for AI what HTTP did for the web: laying the foundational bricks for an ecosystem of intelligent, interoperable, and highly capable systems.