News Aggregator

Federated API Management: Deploying APIs From WSO2 to AWS API Gateway

Aggregated on: 2025-11-04 19:10:59

As enterprises evolve and grow, organizations are increasingly operating APIs across multiple environments, such as cloud, on-premises, and edge. In such setups, federated API gateway deployments have emerged as a powerful architectural pattern. This approach separates the control plane from the runtime gateways, enabling centralized API governance while allowing APIs to run closer to users or services for improved performance and resilience. At the core of this architecture is the idea of:

This Compiler Bottleneck Took 16 Hours Off Our Training Time

Aggregated on: 2025-11-04 18:10:59

A 60-hour training job had become the new normal. GPUs were saturated, data pipelines looked healthy, and infra monitoring didn’t flag any issues. But something was off. The model wasn't large, nor was the data complex enough to justify that duration. What we eventually discovered wasn't in the Python code or the model definition. It was buried deep in the compiler stack. Identifying the Invisible Bottleneck

Series (4/4): Toward a Shared Language Between Humans and Machines — Humans as Co-Creators: Ethics, Strategy, and the Future of a Shared Language

Aggregated on: 2025-11-04 17:10:59

AI inspires both fascination and fear: are machines capable of replacing us, or are they merely assistants? The real question is not substitution, but co-creation. How can we preserve the uniqueness of human intelligence while harnessing the power of models? This article explores the ethical, economic, and political challenges of a future where humans and machines will have to invent a common language together. In areas such as code translation or transcompilation, neural models can outperform traditional methods and speed up processes. But their role is not to replace human expertise; it is to extend and enhance it. In fields such as medicine, architecture, or education, AI can help simulate, plan, and generate alternatives, but in the end, it is the human who must decide, interpret, and give meaning.

Agentic AI using Apache Kafka as Event Broker with the Agent2Agent Protocol (A2A) and MCP

Aggregated on: 2025-11-04 16:25:59

Agentic AI is gaining traction as a design pattern for building more intelligent, autonomous, and collaborative systems. Unlike traditional task-based automation, agentic AI involves intelligent agents that operate independently, make contextual decisions, and collaborate with other agents or systems—across domains, departments, and even enterprises. In the enterprise world, agentic AI is more than just a technical concept; it is a transformative force. It represents a shift in how systems interact, learn, and evolve. But unlocking its full potential requires more than AI models and point-to-point APIs—it demands the right integration backbone.

From Noise to Outcome-Driven Observability: An SLO-First Strategy to Deliver Business Value Through Telemetry

Aggregated on: 2025-11-04 15:25:59

Editor’s Note: The following is an article written for and published in DZone’s 2025 Trend Report, Intelligent Observability: Building a Foundation for Reliability at Scale. Outcome-driven observability, anchored by a strategy that puts service-level objectives (SLOs) first, is a shift in modern software engineering practices. It moves the conversation beyond a reactive, tool-centric approach to a proactive, discipline-driven methodology.

Advanced Patterns in Salesforce LWC: Reusable Components and Performance Optimization

Aggregated on: 2025-11-04 14:25:59

If you’ve built Lightning Web Components (LWC) at scale, you’ve probably hit the same walls I did: duplicated logic, bloated bundles, rerenders that come out of nowhere, and components that were never meant to talk to each other but somehow ended up coupled. When I first transitioned from Aura and Visualforce to LWC, the basics felt easy: reactive properties, lifecycle hooks, and clean templates. But as our team started building enterprise-grade Salesforce apps dozens of screens, hundreds of components the cracks started showing. Performance dipped. Reusability turned into a myth. New devs struggled to onboard without breaking something.

Top Takeaways From Devoxx Belgium 2025

Aggregated on: 2025-11-04 13:25:59

In October 2025, I visited Devoxx Belgium, and again it was an awesome event! I learned a lot and received quite a lot of information, which I do not want to withhold from you. In this blog, you can find my takeaways of Devoxx Belgium 2025! Introduction Devoxx Belgium is the largest Java conference in Europe. This year, it was already the 22nd edition. As always, Devoxx is being held in the fantastic theatres of Kinepolis Antwerp. Each year, there is a rush on the tickets. Tickets are released in several batches, so if you could not get a ticket during the first batch, you will get another chance.

AIOps to Agentic AIOps: Building Trustworthy Symbiotic Workflows With Human-in-the-Loop LLMs

Aggregated on: 2025-11-04 12:25:59

Editor’s Note: The following is an article written for and published in DZone’s 2025 Trend Report, Intelligent Observability: Building a Foundation for Reliability at Scale. Imagine a world where the 3:00 AM PagerDuty alert doesn’t lead to a frantic scramble, but rather to a concise summary of the problem, a vetted solution, and a one-click button to approve the fix. This transformative capability represents the next frontier of AIOps (artificial intelligence for IT operations), powered by agentic AI systems that are designed to perceive, reason, act, and learn. This shift promises a significant reduction in mean time to resolution (MTTR) but critically relies on human-in-the-loop (HITL) safeguards to ensure accountability and prevent issues like AI hallucinations.

Detecting Supply Chain Attacks in NPM, PyPI, and Docker: Real-World Techniques That Work

Aggregated on: 2025-11-03 20:25:59

The digital ecosystem breathes through trust. Every npm install, every pip install, every docker pull represents a leap of faith — a developer placing confidence in code written by strangers, maintained by volunteers, distributed through systems they've never seen. This trust, however, has become the Achilles' heel of modern software development. Supply chain attacks don't knock on your front door. They slip through the dependencies you invited in yourself.

Building a Resilient Observability Stack in 2025: Practical Steps to Reduce Tool Sprawl With OpenTelemetry, Unified Platforms, and AI-Ready Monitoring

Aggregated on: 2025-11-03 19:25:59

Editor’s Note: The following is an article written for and published in DZone’s 2025 Trend Report, Intelligent Observability: Building a Foundation for Reliability at Scale. Platform consolidation is an important topic in 2025 as tool sprawl and platform fragmentation are costing engineering teams time, money, and focus. Some surveys of observability practitioners show that 80% of teams are working on reducing vendor count and consolidating their observability and monitoring tools.

5 Critical Databricks Performance Hacks That Most Engineers Miss (100x Faster Queries)

Aggregated on: 2025-11-03 18:25:59

Databricks performance tuning is not guesswork; it needs a deep understanding of internals. In this guide, I will explore six practical optimization techniques every data engineer should apply to achieve faster, cost-efficient production.

What Is Agent Observability? Key Lessons Learned

Aggregated on: 2025-11-03 17:25:59

Agents are proliferating like wildfire, yet there is a ton of confusion surrounding foundational concepts such as agent observability. Is it the same as AI observability? What problem does it solve, and how does it work? Fear not, we'll dive into these questions and more. Along the way, we will cite specific user examples as well as our own experience in pushing a customer-facing AI agent into production.

Understanding Bigfile Tablespace Defaults in Oracle Database 23ai: Impact and Benefits

Aggregated on: 2025-11-03 16:25:59

Oracle Database 23ai introduces a significant but subtle shift in its default storage architecture, the adoption of bigfile tablespaces as the default for most tablespaces, including core ones like SYSTEM and SYSAUX. This change reflects Oracle’s continued evolution towards simplifying storage management and scaling capabilities. As database professionals with years of experience managing complex environments, understanding this new default and its impact on performance and administration is critical. Background: What Are Bigfile Tablespaces? Bigfile tablespaces were originally introduced in Oracle 10g as a means to address scalability challenges in very large databases. Unlike traditional small-file tablespaces, which consist of multiple datafiles each limited in size (typically up to 32 GB or 128 GB, depending on block size and OS), a bigfile tablespace consists of a single large datafile that can grow up to 128 terabytes (TB) or more, depending on the platform and block size.

Navigating the Cyber Frontier: AI and ML's Role in Shaping Tomorrow's Threat Defense

Aggregated on: 2025-11-03 15:25:59

Abstract This article explores the transformative role of artificial intelligence (AI) and machine learning (ML) in cybersecurity. It delves into innovative strategies such as adaptive cyber deception and predictive behavioral analysis, which are reshaping defense mechanisms against cyber threats. The integration of AI in zero-trust architectures, quantum cryptography, and automation within cybersecurity frameworks highlights a shift towards more dynamic and proactive security measures. Furthermore, the challenges of the "black box" problem in AI decision-making and the potential for AI to automate routine cybersecurity tasks are discussed. The narrative underscores the importance of complementing technology with human insight for effective digital defenses. Introduction: A Personal Encounter With Cyber Evolution Let me rewind a few years back — a time when I was knee-deep in implementing a creditworthiness model at my previous role at Sar Tech LLC/Capital One. It was around the same time I encountered the formidable intersection of artificial intelligence (AI) and cybersecurity. While tuning machine learning (ML) algorithms to reduce loan approval risks, I witnessed firsthand how AI could pivot an organization's security posture in ways I hadn’t quite imagined before. This realization didn't stem from an academic paper or industry panel — it came from the challenge of protecting sensitive data while simultaneously fine-tuning predictive models. It was an "aha" moment, one which highlighted the potential of AI and ML in a broader, more dynamic context of cybersecurity.

8 Business Continuity Lessons Learned from the CrowdStrike Outage

Aggregated on: 2025-11-03 14:25:58

The July 2024 CrowdStrike outage sent shockwaves across global enterprises, paralyzing operations and forcing services offline. A faulty update to a widely used endpoint detection and response (EDR) solution caused the event to cascade into a full-blown operational crisis. The outage was so disruptive that it has since become a defining case study on preventing the impact of an EDR outage. Many organizations saw it as a wake-up call, making it a critical reminder that business continuity planning (BCP) must evolve alongside the growing adoption of third-party cybersecurity services. Companies now ensure uptime with the following lessons learned from Crowdstrike.

Delta Lake 4.0 and Delta Kernel: What's New in the Future of Data Lakehouses

Aggregated on: 2025-11-03 13:25:58

In data storage, the idea of a data lakehouse has transformed data storage and analysis in organizations. Data lakehouses combine the low-cost and scalability storage ability of data lakes and data warehouse’s reliability and performance. In this space, some players have emerged, such as Delta Lake, as strong open-source frameworks for implementing robust ACID-compliant data Lakes. Now, with the introduction of Delta Lake 4.0 and the development of Delta Kernel, the future of the lakehouse architecture is in a revolutionary transition. Brimming with features driving performance, scaling, and interoperability, these updates are to keep up with the increasing dynamics of data workloads in 2025 and beyond.

Human-AI Readiness

Aggregated on: 2025-11-03 12:25:58

The expectations are cosmic. The investments are colossal. Amazon, Google, Meta, and Microsoft collectively spent over $251 billion on infrastructure investment to support AI in 2024, up 62% from 2023's $155 billion, and they plan to spend more than $300 billion in 2025. The prize for those who can provide "superior intelligence on tap," as some are now touting, is infinite. The AI ecosystem is exploding, with new startups and innovative offerings pouring out of global tech hubs. The technology isn’t just evolving; it’s erupting. The theory of AI adoption is also evolving. While everyone acknowledges that risk remains high and vigilance is necessary, concerns are shifting from Terminator-style apocalyptic fantasies to the practical realities of global social disruption anticipated, as AI’s impact cascades through, well, everything. As is becoming clear, we’ll be living next to and collaborating with AI interfaces in every form, from phones and smart glasses to robots and drones.

A Framework for Securing Open-Source Observability at the Edge

Aggregated on: 2025-10-31 19:25:57

The Edge Observability Security Challenge Deploying an open-source observability solution to distributed retail edge locations creates a fundamental security challenge. With thousands of locations processing sensitive data like payments and customers' personally identifiable information (PII), every telemetry component running on the edge becomes a potential entry point for attackers. Edge environments operate in spaces where there is limited physical security, bandwidth constraints shared with business-critical application traffic, and no technical staff on-site for incident response. Therefore, traditional centralized monitoring security models do not fit in these conditions because they require abundant resources, dedicated security teams, and controlled physical environments. None of them exists on the edge.

Series (3/4): Toward a Shared Language Between Humans and Machines —Quantum Language and the Limits of Simulation

Aggregated on: 2025-10-31 18:25:57

Imagine compressing thousands of dimensions of meaning into a few qubits capable of processing all that information in parallel. That is the promise of Quantum Natural Language Processing. But can we truly translate the richness of human language into the abstract logic of quantum mechanics, without any grounding in reality? This article explores that frontier where science fiction and fundamental research meet. Research in this field experiments with translations by leveraging quantum parallelism and entanglement. In other words, it uses the unique properties of qubits to process multiple meanings simultaneously and to establish connections between them.

Streamlining Real-Time Ad Tech Systems: Techniques to Prevent Performance Bottlenecks

Aggregated on: 2025-10-31 17:25:57

Ad tech platforms operate at the bleeding edge of scale. Every millisecond counts as billions of requests flow through auctions, targeting services, and delivery pipelines. With net margins slim—most revenue is passed on to publishers — every CPU cycle saved can translate directly into profit. Optimizing these systems is not simply about shaving latency; it is about building architectures that remain efficient as workloads multiply and business pressures intensify. Minor inefficiencies, invisible in isolation, compound into significant overhead when magnified across thousands of servers. In a domain where revenue depends on speed and precision, the difference between sustainable growth and runaway costs often comes down to how carefully systems are engineered at the micro-level.

A Developer’s Experience of Onboarding to a Platform

Aggregated on: 2025-10-31 16:25:57

After years of managing cloud services in a traditional setting — manually provisioning clusters, setting up networks, managing credentials, and navigating deployment scripts — I thought I had mastered the rhythm of delivery. Dashboards, support tickets, and carefully planned change windows were the most important things in my life. It was safe, predictable, and well-organized, but it was also slow, tiring, and full of dependencies that only worked after a lot of planning and careful changes. Then came the shift.

Implementing Zero-Downtime Upgrades in an Enterprise SaaS Application

Aggregated on: 2025-10-31 15:25:57

Ensuring your enterprise SaaS application remains always available is more than just a technical objective; it’s a fundamental business requirement. Even short periods of downtime, such as those during routine software updates, can disrupt customers’ operations, erode their trust, and lead to contractual penalties if service-level agreements aren’t met. SaaS applications serve users across multiple time zones. Scheduling downtime that accommodates all users is impractical, making zero-downtime upgrades essential for global businesses. Zero-downtime processes allow for quicker deployment of features, bug fixes, and security patches, supporting agile development and reducing time-to-market. Mastering zero-downtime upgrade procedures, in tandem with robust multi-cloud and multi-region architectures, is essential in maintaining service availability. Recent high-profile outages on major cloud platforms have underscored the disruptive impact of unexpected downtime, affecting organizations worldwide across industries. By distributing workloads across multiple cloud providers and hosting applications in multiple geographic regions, organizations can reduce single points of failure and improve resilience against large-scale outages. If one cloud provider or region experiences issues, traffic can be quickly rerouted to healthy environments, minimizing the impact on end users.

Lessons Learned From Running Disaster Recovery Drills

Aggregated on: 2025-10-31 14:25:57

Disaster recovery (DR) is not just about backing up data — it’s also about ensuring that when the unexpected issue strikes, systems, people, and processes can recover quickly and efficiently. While planning and documentation are essential, the true test of a DR strategy comes from running drills. Through multiple exercises across organizations, here are the critical learnings that can significantly improve the effectiveness of DR initiatives.

Deployable Architecture: The Cornerstone of Scalable Platform Engineering

Aggregated on: 2025-10-31 13:25:57

As architects, you’ve likely seen the same story unfold across growing organizations: teams move fast, each solving problems in their own way — building pipelines, wiring infrastructure, and embedding security into their services from scratch. Initially, it works. But as the organization scales, the cracks begin to show. Environments drift. Deployments become brittle. Governance becomes reactive. And suddenly, the well-intentioned architecture you’ve crafted becomes challenging to replicate, secure, or evolve consistently across teams.

How Modern Developers Use AI-Assisted Coding to Validate Products Faster

Aggregated on: 2025-10-31 12:25:57

Software development has changed a lot in the past two years. I've been working with AI coding assistants since they first appeared. The most interesting part? It's not just about writing code faster. AI has changed how we validate our products. My co-founder and I noticed something strange on our latest project. Our team was shipping features super fast. But we also had more edge cases and security issues. This is the new reality. You move faster, but things get more complex. Most teams using AI tools face this.

An Open-Source ChatGPT App Generator

Aggregated on: 2025-10-31 11:25:57

OpenAI released ChatGPT apps just a couple of days ago. Such apps are incredibly interesting from a UX perspective, because sometimes a chat user interface simply won't cut it. Sometimes, you simply need a graphical user interface. For such cases, there are "ChatGPT apps." So, what is a ChatGPT app? Well, it's a fully functional user interface with buttons, dropdown lists, checkboxes, and everything you can create on the web. It can be as complex as Google Maps or as simple as a collect email form. It is basically "an app" hosted inside your AI chatbot. You can try a simple such app by clicking here.

When Coalesce Is Slower Than Repartition: A Spark Performance Paradox

Aggregated on: 2025-10-30 19:10:56

If you've worked with Apache Spark, you've probably heard the conventional wisdom: "Use coalesce() instead of repartition() when reducing partitions — it's faster because it avoids a shuffle." This advice appears in documentation, blog posts, and is repeated across Stack Overflow threads. But what if I told you this isn't always true? In a recent production workload, I discovered that using repartition() instead of coalesce() resulted in a 33% performance improvement (16 minutes vs. 23 minutes) when writing data to fewer partitions. This counterintuitive result reveals an important lesson about Spark's Catalyst optimizer that every Spark developer should understand.

SQL Ledger in SQL Server 2022: Tamper-Evident Audit Trails and Immutable Ledger Tables

Aggregated on: 2025-10-30 18:10:56

SQL Server 2022 introduced the Ledger feature to meet the growing need for tamper-evident audit trails in regulated and audit-heavy industries such as finance, healthcare, and supply chains. One of the most notable implementations of this feature is the append-only ledger table, which ensures that sensitive data is immutable once added, providing stronger guarantees of integrity and compliance. Below, we incorporate and expand on the example and details from Microsoft's official article on creating and using append-only ledger tables, showcasing its capabilities to preserve data integrity and support audit scenarios.

A Comprehensive Analysis of Async Communication in Microservice Architecture

Aggregated on: 2025-10-30 17:10:57

Microservice architecture has become a standard practice for companies, small and large. One of the challenges is communication between different services. I’ve worked with microservices for a decade now, and I’ve seen a lot of people struggle to understand how to implement a proper communication protocol. In this series of articles, I’ll share my knowledge and expertise on async communication in microservices.

From Model to Microservice: A Practical Guide to Deploying ML Models as APIs

Aggregated on: 2025-10-30 16:10:56

You’ve done it. You’ve spent weeks cleaning data, feature engineering, and hyperparameter tuning. You have a Jupyter Notebook showing a beautiful .fit() and a .predict() that works perfectly. The model accuracy is 99%. Victory! But now comes the hard part. Your stakeholder asks, "That's great, but how do we get this into the new mobile app?" Suddenly, the reality hits: a model in a notebook delivers zero business value. To be truly useful, your machine learning model needs to be integrated into applications, and the most robust, scalable way to do so is to deploy it as a Microservice API.

Keyword vs Semantic Search With AI

Aggregated on: 2025-10-30 15:10:56

When building a search for an application, you typically face two broad approaches: Traditional keyword-based search — match words exactly or with simple variants. Semantic (or vector) search — match meaning or context using AI embeddings. There’s also a hybrid approach, but I will leave that for a future article. Instead, in this post, I’ll walk you through how the two broad approaches work in Python using MariaDB and an AI embedding model, highlight where they differ, and show code that you can adapt.

Building Reactive Microservices With Spring WebFlux on Kubernetes

Aggregated on: 2025-10-30 14:10:57

Migrating from a monolithic Java 8 system to a reactive microservice architecture on Kubernetes allowed us to dramatically improve performance and maintainability. In this article, I’ll share our journey, key Spring Cloud Kubernetes features we adopted, the challenges we faced during development, and the lessons we learned along the way. Business Logic We have a data processing logic that streams information into S3 storage using Kafka, Spark Streaming, and Iceberg. Initially, we encountered multiple challenges, such as file optimization issues and Spark’s unpredictable memory behavior. After addressing these issues, we achieved significant cost savings. Once the insert service was completed, we needed to select an appropriate search engine service. We chose Trino as it fulfilled the needs of our data science department. We also serve customers who perform operations on our S3 data, which can result in high system load. Before this modernization, our platform ran on an old monolithic architecture built with Java 8, which created several performance and maintenance challenges.

Improving Developer Productivity With End-to-End GenAI Enablement

Aggregated on: 2025-10-30 13:10:56

This is a very common scenario that every developer can relate to — I am focused on a feature, and suddenly my project buddy requests a PR review or asks for help when a test case is failing. Now, I need to context-switch to help my buddy, or the code review will be delayed. Every engineering team faces the same bottlenecks — context switching, boilerplate work, delayed code reviews, and slow onboarding. The goal is to improve developer enablement and boost productivity through automation. Generative AI amplifies that goal. From writing user stories to generating test cases, GenAI can automate repetitive tasks and provide real-time guidance. But the challenge is to connect all those capabilities cohesively rather than treat them as isolated tools.

How to Get a Frequency Table of a Categorical Variable as a Data Frame

Aggregated on: 2025-10-30 12:10:57

Categorical data is data with a predefined set of values. Using “Child,” “Adult,” or “Senior” instead of a person's age as a number is one example of age categorization. However, before using categorical data, one must know about various forms of categorical data First of all, categorical data may or may not be defined in an order. To say that the size of a box is small, medium, or large means that there is an order described as small < medium < large. The same does not hold for, say, sports equipment, which could also be categorial data, but differentiated by names like dumbbell, grippers, or gloves; that is, you can order the items on any basis. Those that can be ordered are known as “ordinal” while those where there is no such ordering are “nominal” in nature.

Building a New Testing Mindset for AI-Powered Web Apps

Aggregated on: 2025-10-30 11:10:56

The technology landscape is undergoing a profound transformation. For decades, businesses have relied on traditional web-based software to enhance user experiences and streamline operations. Today, a new wave of innovation is redefining how applications are built, powered by the rise of AI-driven development. However, as leaders adopt AI, a key challenge has emerged: ensuring its quality, trust, and reliability. Unlike traditional systems with clear requirements and predictable outputs, AI introduces complexity and unpredictability, making quality assurance (QA) both more challenging and more critical. Business decision-makers must now rethink their QA strategy and investments to safeguard reputation, reduce risk, and unlock the full potential of intelligent solutions.

From Autocomplete to Co-Creation: How AI Changes Developing/Debugging Workflows in Engineering

Aggregated on: 2025-10-29 19:25:56

The Shift to Co-Creation We are in the middle of a new era of software engineering, where AI coding assistants are no longer just autocomplete helpers but valuable collaborators in the development and debugging process. These tools can speed up the creation of scripts, help navigate unfamiliar languages, and reduce the time spent on repetitive tasks. Yet, the engineer’s role remains central: applying expertise, understanding the problem space, and ensuring solutions are accurate, secure, and effective. AI acts as a helping hand that makes the process of creation faster. In this article, I will share several real-time examples to show how AI assistants are changing development and debugging workflows, from scripting with unfamiliar languages to working with complex APIs and debugging.

Emerging Patterns in Large-Scale Event-Driven AI Systems

Aggregated on: 2025-10-29 18:25:56

Modern distributed systems are increasingly being transformed by event-driven architectures (EDA) and the integration of artificial intelligence (AI). Organizations across FinTech, e-commerce, and IoT domains are moving from static request–response models to asynchronous, event-driven systems capable of processing billions of transactions in near real time. The traditional AI pipeline, train → deploy → infer, is designed for batch use and is effective when insights can wait. However, there are domains in which decisive action must occur immediately, for example, fraud detection, IoT telemetry, or autonomous navigation. Waiting would be an unacceptable risk.

ZEISS Demonstrates the Power of Scalable Workflows With Ampere® Altra® and SpinKube

Aggregated on: 2025-10-29 17:25:56

The Challenge The cost of maintaining a system capable of processing tens of thousands of near-simultaneous requests, but which spends greater than 90 percent of its time in an idle state, cannot be justified. Containerization promised the ability to scale workloads on demand, which includes scaling down when demand is low. Maintaining many pods among a plurality of clusters just so the system doesn’t waste time in the upscaling process contradicts the point of workload containerization. The Solution Fermyon produces a platform called SpinKube that leverages WebAssembly (WASM), originally created to execute small elements of bytecode in untrusted web browser environments, as a means of executing small workloads in large quantities in Kubernetes server environments. Because WASM workloads are smaller and easier to maintain, pods can be spun up just-in-time as network demand rises without consuming extensive time in the process. And because WASM consists of pre-compiled bytecode. It can be executed on server platforms powered by Ampere® Altra® without all the multithreading and microcode overhead that other CPUs typically bring to their environments — overhead that would, in less compute-intensive circumstances such as these, be unnecessary anyway.

Make Static Sites Feel Dynamic With APIs Only (No Backend Needed)

Aggregated on: 2025-10-29 16:25:56

A static site does not have to feel frozen. With a bit of JavaScript, a static page can request data from an API and update the page on the fly. That is the whole idea behind an API-only approach: HTML, CSS, and JavaScript live on a CDN, the browser calls APIs for content, and the page updates itself. Why should teams care? It is fast, cheap, and simple. Static files load from a CDN, deploys are trivial, and scale happens without heavy servers. It also works for real sites, like a blog fed by a headless CMS API, a product grid powered by a commerce API, or a contact form that posts to a forms service.

End of Static Knowledge Bases? How MCP Enables Live RAG

Aggregated on: 2025-10-29 15:10:56

There's a secret about production RAG systems that nobody talks about: the hardest part isn't building them — it's keeping them updated. Companies spend weeks curating documents, tuning embeddings, and perfecting their retrieval pipelines. Everything works beautifully at launch. Then reality hits. Prices change. Policies update. Products get renamed. Within weeks, the knowledge base is serving confidently wrong answers based on outdated information.

Series (2/4): Toward a Shared Language Between Humans and Machines — From Multimodality to World Models: Teaching Machines to Experience

Aggregated on: 2025-10-29 14:10:56

What if the key to a shared language lay in experience itself? Researchers are now exploring approaches that connect text with images, sounds, and interactions within a three-dimensional world. Sensorimotor grounding, multimodal perception, and world models, all these paths aim to give machines the kind of anchoring they still so painfully lack.

A Developer's Practical Guide to Support Vector Machines (SVM) in Python

Aggregated on: 2025-10-29 13:10:56

Support Vector Machines (SVMs) are one of the most powerful and versatile supervised machine learning algorithms. Initially famous for their high-performance "out of the box," they are capable of performing both linear and non-linear classification, regression, and outlier detection. For classification tasks, the core idea behind SVM is to find the optimal hyperplane that best separates the different classes in the feature space.

Debugging a Spark Driver Out of Memory (OOM) Issue With Large JSON Data Processing

Aggregated on: 2025-10-29 12:10:56

As a data engineer, I recently encountered a challenging scenario that highlighted the complexities of Apache Spark memory management and Spark internal processing. Despite working with what seemed like a moderate dataset (25 GB), I experienced a driver Out of Memory (OOM) error that halted my data replication job. In this article, I will discuss Spark's internal processing complexity and memory management that can help us build a resilient data replication solution.

HSTS Beyond the Basics: Securing AI Infrastructure and Modern Attack Vectors

Aggregated on: 2025-10-29 11:10:56

It all started while I was working with a colleague on web security. I heard that their team is enabling HSTS as part of their Black Friday security upgrades to their website. The first question that popped into my mind is, why do you require HSTS if there is HTTP/2 and HTTP/3? You can read my article on Hackernoon to understand the basics of HSTS. For starters, HTTP Strict Transport Security (HSTS) is a web security policy mechanism that helps protect websites against protocol downgrade attacks and cookie hijacking. Introduced in 2012 as RFC 6797, HSTS has become a critical component of modern web security infrastructure, ensuring that browsers communicate with web servers exclusively over secure HTTPS connections. But as AI systems grow and move to production in enterprises, HSTS would become critical for protecting machine learning pipelines, API endpoints, and model deployments. Let's explore advanced use cases and how HSTS principles apply to AI security.

Building Secure Software: Integrating Risk, Compliance, and Trust

Aggregated on: 2025-10-28 19:25:55

This paper outlines a practical approach to secure software engineering that brings together: Static and Dynamic Application Security Testing (SAST & DAST) Information Security Risk Assessment (ISRA) Software Composition Analysis (SCA) Continuous Vulnerability Management Measuring Security Confidence (MSC) framework OWASP Top 10 secure coding standards It also examines how regulations like the General Data Protection Regulation (GDPR) and the upcoming EU Cyber Resilience Act (CRA) are changing expectations around secure-by-design software and lifecycle accountability.

Building Cloud Ecosystems With Autonomous AI Agents: The Future of Scalable Data Solutions

Aggregated on: 2025-10-28 18:25:55

AI agents are a reality now and are one of the key research goals for AI companies and research labs. These agents automate monotonous and complicated workflows within cloud environments. They are able to enhance human functionalities in code generation and debugging. They improve productivity by reducing manual efforts for creative and higher-level thinking, while the AI agents do what they do best. With this, AI agents are evolving cloud and data systems. Scalability is maximized and efficiency is realized with their implementation because humans are finally getting the time to revolutionize, while AI is doing the tedious work, optimizing resources, predicting problems, and tailoring solutions. They can even detect errors quickly and make decisions based on data.

Unlocking Scalable Data Lakes: Building With Apache Iceberg, AWS Glue, and S3

Aggregated on: 2025-10-28 17:25:55

Introduction: The Pain of Traditional Data Lakes Over the last decade, cloud object storage (Amazon S3, Azure Blob, Google Cloud Storage) has become the de facto substrate for data lakes. The promise was alluring: cheap, durable, infinitely scalable storage with a “store first, model later” mindset. But in practice, traditional data lakes quickly turned into “data swamps.” Engineers face recurring issues:

Optimizing Search: A Patent-Backed Approach to Perceived Speed

Aggregated on: 2025-10-28 16:25:55

So, imagine it’s a Friday night after a long week. The kids are finally asleep, and you’re ready to unwind with the new season of Stranger Things. You open the Netflix app, select that banner on the home page, and press play! And then you see that dreaded loading circle that just wouldn’t go away. Why?

Production-Ready Multi-Agent Systems: From Theory to Enterprise Deployment

Aggregated on: 2025-10-28 15:25:55

Your single AI agent is about to become obsolete. While you're debugging prompt chains, your competitors are deploying agent teams that coordinate like human organizations — achieving 40% cost reductions and 3x faster execution. This guide reveals the production patterns that separate the 20% of successful multi-agent deployments from the 80% that fail. You'll learn why the supervisor/worker pattern dominates, how evaluator agents prevent million-dollar mistakes, and what Uber, LinkedIn, and Klarna learned the hard way. The $5.4 Billion Reality Check Something fundamental shifted in 2024. The AI agent market exploded to $5.4 billion, with the majority of enterprises deploying multi-agent systems. But here's the uncomfortable truth: while everyone talks about agents, most implementations are elaborate prompt chains pretending to be intelligent systems.

Amazon Bedrock Guardrails for GenAI Applications

Aggregated on: 2025-10-28 14:25:55

Amazon Bedrock Guardrails enable you to implement safeguards and enforce responsible AI policies for your generative AI applications, tailored to specific use cases. With Guardrails, you create multiple tailored configurations and apply them across different foundation models, ensuring a consistent user experience and standardized safety controls across all your generative AI applications. Guardrails allow you to configure denied topics to prevent undesirable subjects from being discussed and content filters to block harmful content in both input prompts and model responses. Guardrails can be used with text-only foundation models.