News Aggregator

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.

Agentic AI Design Patterns and Principles: Building Autonomous, Collaborative Systems

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

Autonomous, goal-driven software entities are replacing conventional rule-based workflows in modern system design thanks to agentic AI. Systems that are context-aware, resilient, and capable of coordinated decision-making are made possible by these agents' capacity for reasoning, cooperation, and constant adaptation. This article explores the core design patterns and principles that empower these intelligent, collaborative systems. Core Design Principles for Building Agentic Systems Agentic systems operate beyond static logic — reasoning, deciding, and adapting in dynamic environments. Designing such systems requires principles that balance autonomy, safety, and coordination. Below are the foundational pillars every architect should consider:

Post-AGI Architecture: From the Monolithic Myth to the Paradigm of Augmented Collective Intelligence

Aggregated on: 2025-12-17 13:29:49

The Structural Limits of the Current Approach The industry is currently seeing a clear decoupling between the commercial roadmaps of vendors and the reality of engineering. The pressure to deploy Artificial General Intelligence (AGI) rests partly on a hypothesis of linearity. The idea is that increasing computing power will mechanically suffice to spark the emergence of human-level intelligence. But is this realistic? Gartner (1) predicts that AGI will not materialize for at least a decade. The analyst highlights that simply scaling current technologies will not suffice without several fundamental breakthroughs. Even by 2035, they consider it unlikely that AGI will truly be fully achieved.

The Importance of Critical Thinking in Software Testing

Aggregated on: 2025-12-17 12:29:49

When we start questioning the purpose of the application, its design, or the acceptance criteria in the user stories, we’re already engaged in critical thinking. It provides more value by testing an application by evolving and uncovering various functional, non-functional, and domain-specific scenarios. As we analyze the requirements and apply critical thinking to them, we encounter multiple permutations and combinations to test and verify that we are building the product right. It enables the identification of the hidden scenarios, edge cases, and potential risks an end user might face while using the application.

What Apple’s Native Containers Mean for Docker Users

Aggregated on: 2025-12-16 20:14:49

Did you know you can now run containers natively on macOS? At WWDC 2025, Apple announced Containerization and Container CLI — in other words, native Linux container support. Historically, running containers on macOS required launching a full Linux VM, typically via HyperKit or QEMU, to host the Docker Engine. That’s no longer necessary. This is a major shift because Apple’s containerization framework means developers may no longer need third-party tools like Docker for local container execution. Using Apple's new Virtualization and Containerization frameworks, each container runs natively on macOS inside its own lightweight Linux VM. These VMs boot in under a second, isolate workloads cleanly, and are tightly optimized for Apple silicon. Effectively, Apple gives each container a minimal kernel environment without the overhead of managing a full VM runtime.

Event-Driven Architecture's Dark Secret: Why 80% of Event Streams Are Wasted Resources

Aggregated on: 2025-12-16 19:14:49

Event-driven architecture has become the darling of modern software engineering. Walk into any tech conference, and you'll hear evangelists preaching about decoupling, scalability, and real-time processing. What they don't tell you is the dirty secret hiding behind all those beautiful architecture diagrams: most of what we're streaming is waste. After analyzing production deployments across 15 different applications over the past 18 months, I've uncovered a pattern that should make every architect nervous. Research shows that approximately 80% of event streams represent wasted computational resources, storage costs, and engineering effort. But before you dismiss this as hyperbole, let me show you exactly what's happening under the hood of your "cutting-edge" event infrastructure.

Understanding Multimodal Applications: When AI Models Work Together

Aggregated on: 2025-12-16 18:14:49

You snap a photo of a hotel lobby and ask your AI assistant, "Find me places with this vibe." Seconds later, you get recommendations. No keywords, no descriptions — just an image and a question. This is multimodal AI in action. For years, AI models operated in silos. Computer vision models processed images. Natural language models handled text. Audio models transcribed speech. Each was powerful alone, but they couldn't talk to each other. If you wanted to analyze a video, you'd need separate pipelines for visual frames, audio tracks, and any text overlays, then somehow stitch the results together. Not anymore. What Is Multimodal AI?Multimodal AI systems process and understand multiple data types simultaneously — text, images, video, audio — and crucially, they understand the relationships between them. The core modalities: Text: Natural language, code, structured data Images: Photos, diagrams, screenshots, medical imagery Video: Sequential visual data with audio and temporal context Audio: Speech, environmental sounds, music GIFs: Animated sequences (underrated for UI tutorials and reactions How Multimodal Systems Actually WorkThink of it like a two-person team: One person describes what they see ("There's a red Tesla at a modern glass building, overcast sky, three people in business attire heading inside"), while the other interprets the context ("Likely a corporate HQ. The luxury EV and professional setting suggest a high-level business meeting"). Modern multimodal models work similarly — specialized components handle different inputs, then share information to build unified understanding. The breakthrough isn't just processing multiple formats; it's learning the connections between them. In this guide, we'll build practical multimodal applications — from video content analyzers to accessibility tools — using current frameworks and APIs. Let's start with the fundamentals. How Multimodal AI Works Behind the ScenesLet's walk through what actually happens when you upload a photo and ask, "What's in this image?" The Three Core Components1. Encoders: Translating to a Common Language Think of encoders as translators. Your photo and question arrive in completely different formats —pixels and text. The system can't compare them directly. Vision Encoder: Takes your image (a grid of RGB pixels) and converts it into a numerical vector —an embedding. This might look like [0.23, -0.41, 0.89, 0.12, ...] with hundreds or thousands of dimensions. Text Encoder: Takes your question "What's in this image?" and converts it into its own embedding vector in the same dimensional space. The key: These encoders are trained so that related concepts end up close together. A photo of a cat and the word "cat" produce similar embeddings — they're neighbors in this high-dimensional space. 2. Embeddings: The Universal Format An embedding is just a list of numbers that captures meaning. But here's what makes them powerful: Similar concepts have similar embeddings (measurable by cosine similarity) They preserve relationships (king - man + woman ≈ queen) Different modalities can share the same embedding space When your image and question are both converted to embeddings, the model can finally "see" how they relate. 3. Adapters: Connecting Specialized Models Here's where it gets practical. Many multimodal systems don't build everything from scratch — they connect existing, powerful models using adapters. What's an adapter? A lightweight neural network layer that bridges two pre-trained models. Think of it as a translator between two experts who speak different languages. Common pattern: Pre-trained vision model (like CLIP's image encoder) → Adapter layer → Pre-trained language model (like GPT) The adapter learns to transform image embeddings into a format the language model understands This is how systems like LLaVA work — they don't retrain GPT from scratch. They train a small adapter that "teaches" GPT to understand visual inputs. Walking Through: Photo + QuestionLet's trace exactly what happens when you ask, "How many people are in this photo?" Step 1: Image Processing Your photo → Vision Encoder → Image embedding [768 dimensions]The vision encoder (often a Vision Transformer or ViT) processes the image in patches, like looking at a grid of tiles, and outputs a rich numerical representation. Step 2: Question Processing "How many people are in this photo?" → Text Encoder → Text embedding [768 dimensions]Step 3: Adapter Alignment Image embedding → Adapter layer → "Visual tokens"The adapter transforms the image embedding into "visual tokens" — fake words that the language model can process as if they were text. You can think of these as the image "speaking" in the language model's native tongue. Step 4: Fusion in the Language Model The language model now receives: [Visual tokens representing the image] + [Text tokens from your question]It processes this combined input using cross-attention — essentially asking: "Which parts of the image are relevant to the question about counting people?" Step 5: Response Generation Language model → "There are three people in this photo." Why This Architecture MattersModularity: You can swap out components. Better vision model released? Just retrain the adapter. Efficiency: Training an adapter (maybe 10M parameters) is far cheaper than training a full multimodal model from scratch (billions of parameters). Leverage existing strengths: GPT-4 is already great at language. CLIP is already great at vision. Adapters let them collaborate without losing their individual expertise. The Interaction Flow Real-World Applications That Actually MatterUnderstanding the architecture is one thing. Seeing it solve real problems is another. Healthcare: Beyond Single-Modality DiagnosticsMedical diagnosis has traditionally relied on specialists examining individual data types —radiologists read X-rays, pathologists analyze tissue samples, and physicians review patient histories. Multimodal AI is changing this paradigm. Microsoft's MedImageInsight Premium demonstrates the power of integrated analysis, achieving 7-15% higher diagnostic accuracy across X-rays, MRIs, dermatology, and pathology compared to single-modality approaches. The system doesn't just look at an X-ray in isolation — it understands how imaging findings relate to patient history, lab results, and clinical notes simultaneously. Oxford University's TrustedMDT agents take this further, integrating directly with clinical workflows to summarize patient charts, determine cancer staging, and draft treatment plans. These systems will pilot at Oxford University Hospitals NHS Foundation Trust in early 2026, representing a significant step toward production deployment in critical healthcare environments. The implications extend beyond accuracy improvements. Multimodal systems can identify patterns that span multiple data types, potentially catching early disease indicators that single-modality analysis would miss. E-commerce: Understanding Intent Across ModalitiesThe retail sector is experiencing a fundamental transformation through multimodal AI that understands customer intent expressed through images, text, voice, and behavioral patterns simultaneously. Consider a customer uploading a photo of a dress they saw at a wedding and asking, "Find me something similar but in blue and under $200." Traditional search requires precise keywords and filters. Multimodal AI understands the visual style, color transformation request, and budget constraint in a single query. Tech executives predict AI assistants will handle up to 20% of e-commerce tasks by the end of 2025, from product recommendations to customer service. Meta's Llama 4 Scout, with its 10 million token context window, can maintain a sophisticated understanding of customer interactions across multiple touchpoints, remembering preferences and providing genuinely personalized experiences. Content Moderation: Evaluating Context, Not Just ContentContent moderation has evolved from simple keyword filtering to sophisticated context-aware systems that evaluate whether content violates policies based on the interplay between text, images, and audio. OpenAI's omni-moderation-latest model demonstrates this evolution, evaluating images in conjunction with accompanying text to determine if content contains harmful material. The system shows a 42% improvement in multilingual evaluation, with particularly impressive gains in low-resource languages such as Telugu (6.4x) and Bengali (5.6x). Companies like Grammarly and ElevenLabs have integrated these capabilities into their safety infrastructure, ensuring that AI-generated content across multiple modalities meets safety standards. The key advancement isn't just detecting problematic content but also understanding when context makes seemingly innocuous content harmful, or when potentially sensitive content is actually acceptable within a proper context. Accessibility: Breaking Down Digital BarriersMultimodal AI is revolutionizing accessibility by creating systems that can process text, images, audio, and video simultaneously to identify and remediate accessibility issues in real-time. New vision-language models can generate alt text that describes not just what's in an image, but the relationships, contexts, and implicit meanings that make images comprehensible to users who can't see them. Advanced personalization engines can automatically adjust contrast for users with low vision in the evening, simplify language complexity for users who need it, or predict when someone might need additional navigation support. Practical implementations already exist: OrCam wearable devices for people who are blind instantly read text, recognize faces, and identify products using multimodal AI. WordQ and SpeakQ help people with dyslexia or ADHD by combining text analysis with speech synthesis to suggest words and read text aloud. By 2026 to 2027, AI-powered accessibility scans are projected to detect approximately 70% of WCAG success criteria with 98% accuracy, dramatically reducing the manual effort required to make digital content accessible. What Actually Goes Wrong at ScaleThe technical literature often glosses over practical difficulties that trip up real implementations: Data alignment is deceptively difficult. Synchronizing dialogue with facial expressions in video or mapping sensor data to visual information in robotics requires precision that can fundamentally corrupt your model's understanding if done incorrectly. A 100-millisecond audio-video desynchronization might seem trivial, but it can teach your model that people's lips move after they speak. Computational demands are substantial. Multimodal fine-tuning requires 4-8x more GPU resources than text-only models. Recent benchmarking shows that optimized systems can achieve 30% faster processing through better GPU utilization, but you're still looking at significant infrastructure investment. Google increased its AI spending from $85 billion to $93 billion in 2025 largely due to multimodal computational requirements. Cross-modal bias amplification represents an insidious challenge. When biased inputs interact across modalities, effects compound unpredictably. A dataset with demographic imbalances in images combined with biased language patterns can create systems that appear more intelligent but are actually more discriminatory. The research gap is substantial — Google Scholar returns only 33,400 citations for multimodal fairness research, compared with 538,000 for language model fairness. Legacy infrastructure struggles. Traditional data stacks excel at SQL queries and batch analytics but struggle with real-time semantic processing across unstructured text, images, and video. Organizations often must rebuild entire data pipelines to support multimodal AI effectively. What's Coming: Trends Worth Watching Several emerging developments are reshaping the landscape: Extended context windows of up to 2 million tokens reduce reliance on retrieval systems, enabling more sophisticated reasoning over large amounts of multimodal content. This changes architectural decisions—instead of chunking content and using vector databases, you can process entire documents, videos, or conversation histories in a single pass. Bidirectional streaming enables real-time, two-way communication where both human and AI can speak, listen, and respond simultaneously. Response times have dropped to 0.32 seconds on average for voice interactions, making the experience feel genuinely natural rather than transactional. Test-time compute has emerged as a game-changer. Frontier models like OpenAI's o3 achieve remarkable results by giving models more time to reason during inference rather than simply scaling parameters. This represents a fundamental shift from training-time optimization to inference-time enhancement. Privacy-preserving techniques are maturing rapidly. On-device processing and federated learning approaches enable sophisticated multimodal analysis while keeping sensitive data local, addressing the growing concern that multimodal systems create detailed personal profiles by combining multiple data types. The Strategic Reality By 2030, Gartner predicts that 80% of enterprise software will be multimodal. This isn't a gradual evolution — it's a fundamental restructuring of how AI systems perceive and interact with information. However, Deloitte survey data reveals a sobering implementation gap: while companies actively experiment with multimodal AI, most expect fewer than 30% of current experiments to reach full scale in the next six months. The difference between recognizing potential business value and successfully delivering it in production remains substantial. Success requires more than technical capability. Organizations must address computational requirements, specialized talent acquisition (finding professionals who understand computer vision, NLP, and audio processing simultaneously is challenging), and ethical frameworks that account for cross-modal risks rather than isolated data flaws. The promise of multimodal AI is substantial, but it demands responsible exploration with higher standards of data integration, fairness, and security. As these systems mature toward more natural, efficient, and capable interactions that mirror human perception and cognition, they will become the foundation for a new generation of AI applications. The transformation is already underway. The developers and organizations that begin building multimodal capabilities now — while proactively addressing the associated challenges — will be best positioned to capitalize on this fundamental shift in artificial intelligence capabilities. The era of AI systems that truly understand the world, rather than just processing isolated data streams, has arrived. It's time to build accordingly

How We Predict Dataflow Job Duration Using ML and Observability Data

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

Efficiently managing large-scale data pipelines requires not only monitoring job performance but also anticipating how long jobs will run before they begin. This paper presents a practical, telemetry-driven approach for predicting the execution time of Google Cloud Dataflow jobs using machine learning. By combining Apache Airflow for workflow coordination, OpenTelemetry for collecting traces and resource metrics, and BigQuery ML for scalable model training, we develop an end-to-end system capable of generating reliable runtime estimates. The solution continuously ingests real-time observability data, performs feature engineering, updates predictive models, and surfaces insights that support capacity planning, scheduling, and early anomaly detection. Experimental results across multiple regression techniques show that observability-rich signals significantly improve prediction accuracy. This work demonstrates how integrating modern observability frameworks with machine learning can help teams reduce costs, avoid operational bottlenecks, and operate cloud-based data processing systems more efficiently.

Building Your Tech Career Like Code: A Systematic AI Approach

Aggregated on: 2025-12-16 16:14:48

The traditional “climb the ladder” approach to tech careers has transformed to “climb the lattice.” A data analyst pivots to cloud architecture, a back-end developer transitions to DevSecOps, or a project manager evolves into a technical product owner. As AI accelerates technological change, it requires faster learning and adaptation than any previous transition. Most developers approach career planning like they're coding without requirements: hoping for the best while crossing their fingers. But what if we applied the same systematic thinking we use to architect solutions to engineer our careers?

Parallel Paths and Possibilities to Gen AI for Developers: The Saga of Two Stacks Unfolded via Building a RAG Application in Tandem

Aggregated on: 2025-12-16 15:14:48

Generative AI (GenAI) is rapidly transforming the landscape of intelligent applications, driving innovation across industries. Python has emerged as the language of choice for GenAI development, thanks to its simplicity, agility in prototyping, and a rich ecosystem of machine learning libraries like TensorFlow, PyTorch, and LangChain. However, Java — long favored for enterprise-scale systems — is actively evolving to stay relevant in this new paradigm. With the rise of Spring AI, Java developers now have a growing toolkit to integrate GenAI capabilities without abandoning their existing infrastructure. While switching from Java to Python is technically feasible, it often involves a shift in development culture and tooling preferences. The convergence of these two ecosystems — Python for experimentation and Java for scalability — offers a compelling narrative for hybrid GenAI architectures.

How Synthetic Data Generation Accelerates the Software Development Lifecycle in the Enterprise

Aggregated on: 2025-12-16 14:14:48

Today’s enterprises operate under a fundamental tension between time-to-market and regulatory compliance. Fierce competition keeps them on their toes to develop faster, while concerns about data protection compel them to comply with regulations. Data privacy regulations such as GDPR, CPRA, and HIPAA may have enhanced data protection, but they have also slowed innovation cycles.

Building Cost-Efficient ETL with Apache Spark Structured Streaming

Aggregated on: 2025-12-16 13:14:48

Businesses want fraud detection within seconds, personalized recommendations while customers are still browsing, and instant updates for IoT dashboards. Real-time data has gone from a luxury to a necessity. Apache Spark Structured Streaming has become one of the most popular engines for building these pipelines. But here’s the catch: streaming ETL can be expensive if not designed with cost in mind.

Chaos Engineering for Architects: Designing Systems That Embrace Failure

Aggregated on: 2025-12-16 12:14:48

The Architect's Dilemma: When Perfect Designs Meet Reality Our beautifully designed architecture diagrams are lies. Not intentional ones, but lies nonetheless. They show clean boxes with arrows between them, depicting a world where services always respond, networks never partition, and databases never lock up.

AI Data Storage: Challenges, Capabilities, and Comparative Analysis

Aggregated on: 2025-12-15 20:14:48

The explosion in the popularity of ChatGPT has once again ignited a surge of excitement in the AI world. Over the past five years, AI has advanced rapidly and has found applications in a wide range of industries. As a storage company, we’ve had a front-row seat to this expansion, watching more and more AI startups and established players emerge across fields like autonomous driving, protein structure prediction, and quantitative investment. AI scenarios have introduced new challenges to the field of data storage. Existing storage solutions are often inadequate to fully meet these demands. In this article, we’ll deep dive into the storage challenges in AI scenarios, critical storage capabilities, and comparative analysis of storage products. I hope this post will help you make informed choices in AI and data storage.

Streaming vs In-Memory DataWeave: Designing for 1M+ Records Without Crashing

Aggregated on: 2025-12-15 19:14:48

The Real Problem With Scaling DataWeave MuleSoft is built to handle enterprise integrations — but most developers test with small payloads. Everything looks fine in dev, until one day a real file with 1 million records hits your flow. Suddenly, your worker crashes with an OutOfMemoryError, and the job fails halfway through. The truth is, DataWeave by default works in-memory. That’s acceptable for small datasets, but in production, we often deal with:

Escaping the "Excel Trap": Building an AI-Assisted ETL Pipeline Without a Data Team

Aggregated on: 2025-12-15 18:14:48

Business data often lives in hundreds of disconnected Excel files, making it invisible to decision-makers. Here is a pattern for Citizen Data Engineering using Python, GitHub Copilot, and Qlik Sense to unify data silos without writing a single line of manual code. In the enterprise world, the most common database isn't Oracle or PostgreSQL — it’s Excel.

DZone's 2025 Community Survey

Aggregated on: 2025-12-15 16:44:48

Another year passed right under our noses, and software development trends moved along with it. The steady rise of AI, the introduction of vibe coding — these are just among the many impactful shifts, and you've helped us understand them better. Now, as we move on to another exciting year, we would like to continue to learn more about you as software developers, your tech habits and preferences, and the topics you wish to know more about. With that comes our annual community survey — a great opportunity for you to give us more insights into your interests and priorities. We ask this because we want DZone to work for you.

From Metrics to Action: Adding AI Recommendations to Your SaaS App

Aggregated on: 2025-12-15 16:14:48

You log into your DevOps portal, pinched to think about 300 different metrics: CPU, latency, errors, all lighting up red on your dashboard. But what to prioritize? It’s what an AI-based recommendation tool could resolve. Every SaaS platform managing cloud operations records an incredible amount of telemetry data. Most products, however, simply provide visualization: interesting graphics, yet no actionable information. What if your product could provide automated suggestions for config, scaling, or alerts based on tenant behavior?

2026 IaC Predictions: The Year Infrastructure Finally Grows Up

Aggregated on: 2025-12-15 15:44:48

The industry spent the last decade racing to automate the cloud, and 2026 will be the year we find out what happens when automation actually wins. AI is writing Terraform and OpenTofu faster than teams can review it. Cloud providers are shipping higher-level services every month. Business units want new environments on demand. The IaC footprint inside large enterprises is exploding.

Beyond Containers: Docker-First Mobile Build Pipelines (Android and iOS) — End-to-End from Code to Artifact

Aggregated on: 2025-12-15 15:14:48

Introduction In many mobile app shops, builds are still done locally (on dev laptops) or through fragile CI scripts. This leads to inconsistent builds, wasted hours onboarding developers, or debugging “but it worked on my machine” issues. Using Docker — already popular for backend and microservices — mobile teams can also build a reproducible, scalable, and version-controlled pipeline for both Android and iOS (to the extent possible), which speeds up development, reduces “works on my machine” issues, and enables hybrid mobile/web-backend synergy.

The Agent Trap: Why AI's Autonomous Future Might Be Its Biggest Liability

Aggregated on: 2025-12-15 14:14:48

I've been covering enterprise AI deployments since Watson was still pretending to revolutionize healthcare, and I've learned to distinguish genuine paradigm shifts from rebranded hype cycles. What's happening with agentic AI in 2025 feels uncomfortably like both. The pitch is seductive: autonomous software agents that plan, reason, and execute complex tasks without constant human supervision. Instead of asking a chatbot for information, you delegate an entire workflow — "book my travel to the conference in Austin, find a hotel near the venue, block my calendar, and brief me on attendees I should meet." The agent figures out the rest.

Ambient Agentic Systems – A New Era Begins

Aggregated on: 2025-12-15 13:14:48

In recent years, the field of generative artificial intelligence (gen AI) has transformed sectors like healthcare, manufacturing, automobiles & finance. GPT-4, Claude, and Gemini have demonstrated remarkable capabilities in language understanding, content creation, and reasoning. However, these significant strides have brought forth their fair share of challenges, like maintaining performance, efficiency, and adaptability as they scale. Finetuning and deploying sophisticated gen AI models require significant computational power, which can be costly and infrastructure-intensive. This has meant that only large organizations with deep pockets could leverage gen AI at scale.

Virtual Threads in JDK 21: Revolutionizing Java Multithreading

Aggregated on: 2025-12-15 12:14:48

What is Virtual Thread Multi-threading is a widely used feature across the industry for developing Java-based applications. It allows us to run operations in parallel, enabling faster task execution. The number of threads created by any Java application is limited by the number of parallel operations the OS can handle; in other words, the number of threads in a Java application is equal to the number of OS threads. Until now, this limitation has created a bottleneck on further scaling any application, considering the current fast-paced ecosystem. To overcome this limitation, Java has introduced the concept of Virtual Thread in JDK21. A Java application creates a Virtual Thread and is not associated with any OS thread. It means every Virtual Thread does not need to be dependent on a Platform Thread (aka OS thread). Virtual Thread will work on any task independently and will acquire a Platform Thread only when it needs to perform any I/O operation.

Zero Trust in CI/CD Pipelines: A Practical DevSecOps Implementation Guide

Aggregated on: 2025-12-12 20:26:21

Securing modern CI/CD pipelines has become significantly more challenging as teams adopt cloud-native architectures and accelerate their release cycles. Attackers now target build systems, deployment workflows, and the open-source components organizations rely on every day. This tutorial provides a practical look at how Zero Trust principles can strengthen the entire software delivery process. It walks through real steps you can apply immediately using identity-based authentication, automated scanning, policy checks, and hardened Kubernetes deployments. The goal is simple: make sure that only trusted code, moving through a trusted pipeline, reaches production. As organizations continue transitioning to cloud-native applications and distributed systems, the CI/CD pipeline has become a critical part of the software supply chain. Unfortunately, this also makes it an increasingly attractive target for attackers. Compromising a build system or deployment workflow can lead to unauthorized code changes, credential theft, or even the silent insertion of malicious workloads into production.

ITBench, Part 3: IT Compliance Automation with GenAI CISO Assessment Agent

Aggregated on: 2025-12-12 19:26:21

Developed as part of IBM's ITBench framework, which we introduced in ITBench, Part 1: Next-Gen Benchmarking for IT Automation Evaluation, the Chief Information Security Officer (CISO) Compliance Assessment Agent (CAA) represents a pioneering methodology for automating cybersecurity compliance processes in modern IT environments. This AI-powered agent addresses the critical challenge of scaling security compliance operations in complex, rapidly evolving IT environments and technologies. Traditional compliance approaches that rely on dedicated security teams to manually identify weaknesses and assess compliance posture are no longer viable for modern organizations operating at scale.

Secrets in Code: Understanding Secret Detection and Its Blind Spots

Aggregated on: 2025-12-12 18:41:21

In a world where attackers routinely scan public repositories for leaked credentials, secrets in source code represent a high-value target. But even with the growth of secret detection tools, many valid secrets still go unnoticed. It’s not because the secrets are hidden, but because the detection rules are too narrow or overcorrect in an attempt to avoid false positives. This creates a trade-off between wasting development time investigating false signals and risking a compromised account. This article highlights research that uncovered hundreds of valid secrets from various third-party services publicly leaked on GitHub. Responsible disclosure of the specific findings is important, but the broader learnings include which types of secrets are common, the patterns in their formatting that cause them to be missed, and how scanners work so that their failure points can be improved.

Synergizing Intelligence and Orchestration: Transforming Cloud Deployments with AI and Kubernetes

Aggregated on: 2025-12-12 17:26:21

Artificial Intelligence Artificial Intelligence (AI) is reshaping the way today's cloud infrastructure is operated and deployed natively with Kubernetes. AI has become a major driver in helping global businesses streamline resources, scale workloads, and automate several activities. By incorporating AI with Kubernetes, cloud management advances to an entirely new level, enabling smarter decision making, automation, and complete optimization of resources. In this article, we describe how AI can support cloud platforms — especially those powered by Kubernetes — outlining the barriers to adoption and the concrete results achieved when these technologies are applied. As cloud computing matures, the demand for more efficient, scalable and automated cloud deployment continues to grow, pushing organizations to redefine their cloud environments. Kubernetes, the open-source container orchestration platform, has become fundamental for managing container-based applications in the cloud. AI is transforming how cloud resources are utilized, and Kubernetes provides an advanced platform for deploying containerized applications automatically. Together, they form a strong foundation for an ecosystem that fosters innovation, scalability and cost-effectiveness. This article discusses how the combination of AI and Kubernetes is streamlining cloud operations and enabling unprecedented levels of efficiency and creativity.

Blockchain Use Cases in Test Automation You’ll See Everywhere in 2026

Aggregated on: 2025-12-12 16:26:21

The rapid evolution of digital ecosystems has placed test automation at the center of quality assurance for modern software. But as systems grow increasingly distributed, data-sensitive, and security-driven, traditional automation approaches struggle to maintain transparency, consistency, and trust. This is why blockchain technology — once associated primarily with cryptocurrencies — is now becoming a fundamental part of enterprise testing processes. By 2026, blockchain-backed test automation frameworks are no longer conceptual — they are mainstream. Leading enterprises, development teams, and innovative test automation companies are leveraging blockchain to improve traceability, ensure integrity, and create tamper-proof testing ecosystems. Blockchain’s inherent strengths — immutability, decentralization, transparency, and cryptographic security — make it an ideal solution to strengthen test automation pipelines.

The Observability Gap: Why Your Monitoring Strategy Isn't Ready for What's Coming Next

Aggregated on: 2025-12-12 15:26:21

Anyone who’s been to London knows the announcements at the Tube to “Mind the gap,” but what about the gap that’s developing in our monitoring and observability strategies? I’ve been through this ordeal before, and have run a distributed system that was humming along perfectly. My alerts were manageable, my dashboards made sense, and when things broke, I could usually track down the issue in a reasonable amount of time. Fast forward 3–5 years, and things have changed. We added Kubernetes, embraced microservices, and maybe even sprinkled in some AI-powered features these days. Suddenly, you're drowning in telemetry data, your alert fatigue is real, and correlating issues across your distributed architecture feels stressful.

How to Test POST Requests With REST Assured Java for API Testing: Part II

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

In the previous article, we learnt the basics, setup, and configuration of the REST Assured framework for API test automation. We also learnt to test a POST request with REST Assured by sending the request body as: String JSON Array/ JSON Object Using Java Collections Using POJO In this tutorial article, we will learn the following:

Modern Blueprint for Privacy-First AI/ML Systems

Aggregated on: 2025-12-12 13:26:21

The era of identifier-driven machine learning is over. The next decade belongs to privacy-preserving architectures where systems learn from patterns, not people. Here’s what that means in practice Process and anonymize data on the device, not in the cloud. Design and run experiments that do not require specific user identifiers. Train global models through federated learning. Treat data as perishable by design, not as a policy checkbox. If you’re building ML or analytics infrastructure today, privacy isn’t an add-on. You need to treat it as a core architectural constraint and a trust multiplier.

The Tinker and the Tool: Lessons Learned for Using AI in Daily Development

Aggregated on: 2025-12-12 12:11:21

AI tools have swept through the development landscape like a storm. From co-pilots integrated directly into IDEs (such as GitHub Copilot and Amazon CodeWhisperer) to large language models (LLMs) used for conceptual design (such as Claude and custom agents), AI can write code faster than any engineer. It can review pull requests, write unit tests, and even analyze project structure. The value is undeniable: AI can support massive productivity gains. Yet, beyond the market hype, there is a fundamental lesson to be learned: AI is a powerful tool, but it is not a replacement for human intellect.

Taming Gen AI Video: An Architectural Approach to Addressing Identity Drift and Hallucination

Aggregated on: 2025-12-11 19:11:21

If you've spent any time experimenting with generative AI video tools like Runway or Google's Veo, you've seen the magic. You've also, almost certainly, hit the architectural roadblocks. A character's face subtly morphs from one scene to the next until they’re unrecognizable by the tenth clip. Objects you never prompted mysteriously pop up in the background. These aren't just minor bugs; they are critical consistency failures that can derail any serious AI video project.