News Aggregator

Why Tailwind CSS Can Be Used Instead of Bootstrap CSS

Aggregated on: 2025-07-10 13:11:46

While Bootstrap has been a component-based approach for quick UI development, Tailwind CSS has emerged as a more zero-runtime, flexible, and utility-based approach, helping us to give more freedom for website development. Tailwind CSS vs. Bootstrap Comparison Feature

How My AI Agents Learned to Talk to Each Other With A2A

Aggregated on: 2025-07-10 12:11:46

Alright, welcome to the final post in this three-part series. Let's do a quick recap of the journey so far: In Part 1, I laid out the problem with monolithic AI "brains" and designed the architecture for a specialist team of agents to power my "InstaVibe Ally" feature. In Part 2, we did a deep dive into the Model Context Protocol (MCP), and I showed you exactly how I connected my Platform Interaction Agent to my application's existing REST APIs, turning them into reusable tools. But my agents are still living on isolated islands. My Social Profiling Agent has no way to give its insights to the Event Planner. My platform integrator can create post and event. I've built a team of specialists, but I haven't given them a way to collaborate. They're a team that can't talk.

Top 5 Trends in Big Data Quality and Governance in 2025

Aggregated on: 2025-07-10 11:11:46

Big data isn’t just about collecting more information. It’s about making sure the data you rely on is trustworthy. As we head into 2025, the pressure on developers and data teams to deliver clean, reliable, and compliant data is stronger than ever. With AI tools getting smarter, pipelines becoming more distributed, and privacy regulations continuing to evolve, we’re entering a new phase where quality isn’t a bonus. It’s a requirement. For developers and data engineers, this shift means being responsible not just for how data flows, but also for how it’s validated, documented, and governed. A bad dataset today can ripple downstream into broken dashboards, faulty ML models, and costly compliance issues.

Breaking Free from ZooKeeper: Why Kafka’s KRaft Mode Matters

Aggregated on: 2025-07-09 20:26:46

Any modern distributed system which requires high throughput, scaling, high availability etc., utilizes Kafka as one of its component. Thus, making Kafka a popular platform which need no introduction for itself. However even though being an integral part of Kafka, Apache ZooKeeper is neither explored nor understood as much it should have. In this article we would briefly touch upon these aspects, and understand the next generation Kafka via KRaft mode and the benefits it bring over ZooKeeper.

Exploring Data Redaction Enhancements in Oracle Database 23ai

Aggregated on: 2025-07-09 19:26:46

Data redaction, a feature introduced in Oracle Database 12c as part of the Advanced Security Option (ASO), continues to evolve in Oracle Database 23ai (23.6). Oracle has significantly enhanced the DBMS_REDACT package in this release, offering improved flexibility and SQL compatibility for redacted columns. These enhancements enable redaction policies to integrate more smoothly with modern SQL constructs, removing the errors and limitations that previously constrained their use. This article provides an in-depth walkthrough of these new capabilities using practical SQL examples. It is essential to understand that redaction under the DBMS_REDACT package is a chargeable option unless you are on Oracle Autonomous Database, where it’s available at no extra cost.

Run Scalable Python Workloads With Modal

Aggregated on: 2025-07-09 18:26:46

Nowadays, most projects that utilize Artificial Intelligence (AI) models demand significant computational resources. Almost each time a new model comes out, and outperforms previous ones, it seems to require more computational resources to run efficiently. A lot of people will say that there are exceptions, such as the DeepSeek model, but that is not actually true. Models like DeepSeek are competitive with larger models but are not better than them. At least at this point, size seems to be directly correlated with the power of a model. Traditionally, deploying AI at scale meant managing a very complex infrastructure, from provisioning servers or clusters to writing deployment scripts and even managing cloud-specific services. However, this overhead has not only become a major pain point for a lot of ML teams but has also become a limiting factor, stopping them from trying out new models and constraining their creativity. To avoid these limiting factors we need to adapt our approach, and this is exactly what Modal enables us to do as a unified cloud platform for running code for data and AI tasks.

Advanced Insight Generation: Revolutionizing Data Ingestion for AI-Powered Search

Aggregated on: 2025-07-09 17:26:46

Effectively using unstructured information is crucial for businesses aiming to stay competitive. Traditional data ingestion methods often struggle to maintain data quality and relevance, particularly when preparing massive datasets for AI-driven chat applications. Standard text parsers treat documents as simple text, ignoring complex structures like tables, figures, and hierarchical sections. This leads to significant context loss and misinterpretations, ultimately hindering the performance of Retrieval-Augmented Generation (RAG) systems. Our advanced insight generation approach offers a powerful solution by improving data ingestion and indexing through state-of-the-art AI, dynamic chunking, vector embedding, and intelligent indexing. Preserving Structure and Context: Intelligent OCR and Document Intelligence A key innovation in this pipeline is the integration of intelligent Optical Character Recognition (OCR) with Azure Document Intelligence. Unlike traditional OCR, our intelligent OCR recognizes complex document layouts, including tables, charts, and multi-column formats. These AI-powered capabilities preserve the original structure and hierarchy of the content, ensuring that crucial contextual information is retained. Document Intelligence further enhances this process by:

Testing Java Applications With WireMock and Spring Boot

Aggregated on: 2025-07-09 16:26:46

Your application has an integration with another system. In your unit integration tests, you want to mock the other system's behaviour. WireMock is a testing library that helps you with mocking the APIs you depend on. In this blog, you will explore WireMock for testing a Spring Boot application. Enjoy! Introduction Almost every application has an integration with another system. This integration needs to be tested, of course. Testcontainers are a good choice for writing unit integration tests. This way, your application will talk to a real system in your tests.

The AWS Playbook for Building Future-Ready Data Systems

Aggregated on: 2025-07-09 15:11:46

Data infrastructure isn’t just about storage or speed—it’s about trust, scalability, and delivering actionable insights at the speed of business.Whether you're modernizing legacy systems or starting from scratch, this series will provide the clarity and confidence to build robust, future-ready data infrastructure. Why Modernize Data Infrastructure? Traditionally, data infrastructure was seen as a back-office function. Teams poured data into massive warehouses and hoped insights would emerge. However, the landscape has fundamentally changed:

The Agile Paradox

Aggregated on: 2025-07-09 14:11:46

TL; DR: The Agile Paradox Many companies adopt Agile practices like Scrum but fail to achieve true transformation. This “Agile Paradox” occurs because they implement tactical processes without changing their underlying command-and-control structure, culture, and leadership style. True agility requires profound systemic changes to organizational design, leadership, and technical practices, not just performing rituals. Without this fundamental shift from “doing” to “being” agile, transformations stall, and the promised benefits remain unrealized.

The Battle of the Frameworks: Choosing the Right Tech Stack

Aggregated on: 2025-07-09 13:11:46

When Twitter (now X) was launched in 2006, it was built using Ruby on Rails, a framework known for its rapid development capabilities. At the time, this choice allowed Twitter to quickly scale and expand during its early days. However, as the platform's user base grew exponentially, its initial tech stack began to show limitations. By 2008, Twitter's architecture was struggling to keep pace with the increasing volume of users, tweets, and data. The result? Frequent outages and performance issues that hindered user experience and stifled growth.

Stop Prompt Hacking: How I Connected My AI Agent to Any API With MCP

Aggregated on: 2025-07-09 12:11:46

In Part 1 of this series, I laid out the high-level architecture for my "InstaVibe Ally" and made the case for building a team of specialist AI agents instead of a single, monolithic brain. I sketched out a system where an Orchestrator delegates tasks to a Social Profiler, a Planner, and a Platform Interaction Agent. Now, I'm going to zoom in on one of the most critical, practical challenges you’ll face: How do you actually let your agent use your application's APIs?

Continuous Quality Engineering: The Convergence of CI/CD, Chaos Testing, and AI-Powered Test Orchestration

Aggregated on: 2025-07-09 11:11:46

Software development requires more than minimal improvements since software engineers must reform their methods toward quality development, speedy development, and resilient systems. A groundbreaking approach to system development in uncertain situations arises when CI/CD pipeline chaos testing combines AI-driven orchestration tactics. Software delivery systems achieve superior results when antifragility features are integrated into their blueprint development stage through design. The financial costs stemming from software failures in 2022 resulted in $2.41 trillion of losses for U.S. companies because of subpar software quality.

The Rise of the Intelligent AI Agent: Revolutionizing Database Management With Agentic DBA

Aggregated on: 2025-07-08 20:11:45

I have spent more than a decade working closely with data teams and have seen firsthand how the role of the database administrator (DBA) has evolved, and at times, struggled to keep up with the demands of our increasingly data hungry world. I remember my early days of tuning queries manually at 4 am or 5 am chasing intermittent performance issues that seemed to vanish just when logs got interesting. Back then, being a DBA I used to feel like being a firefighter who's constantly responding to alarms, outages and bottlenecks. Fast forward to today, the landscape looks very different. Thanks to rapid advances in artificial intelligence and machine learning, we're stepping into a new era as one where database management is no longer reactive, but proactive and even autonomous. Welcome to the age of the AI-powered DBA.

Micro Frontends to Microservices: Orchestrating a Truly End-to-End Architecture

Aggregated on: 2025-07-08 19:26:45

Modular architecture is everywhere in modern app development, but it is rarely done systematically. You may have created micro frontends or microservices, but the process of integrating them might feel alien or disjointed. This article aims to bridge that gap by seamlessly connecting React shells down to service meshes utilizing proven patterns.

Multiple Stakeholder Management in Software Engineering

Aggregated on: 2025-07-08 18:26:45

One of the key challenges faced by the engineering leaders is to manage multiple stakeholders. These stakeholders often have different project priorities. This article suggests strategies to deal with the challenges encountered by engineering leaders in managing multiple stakeholders with often conflicting priorities. Background Managing a mid size engineering team of size ~20 engineers presents multiple challenges, specifically when it comes to stakeholder management and project execution. Engineering leaders in this position frequently find themselves juggling the needs and expectations of multiple stakeholders across multiple projects. This results in the below challenges:

The Unofficial Rules of Not Screwing Up Your Software

Aggregated on: 2025-07-08 17:26:45

These are not just technical tips, but principles that shaped how I learned and think about engineering. Take what’s useful, ignore what isn’t. But remember, being a great developer isn’t only about code. 1. “The Most Elegant Solution Is the One You Never Have to Maintain.” The most effective code is often the code you never have to write. This might sound counter-intuitive, but one of the most profound lessons I've learned is that sometimes, the most effective solution is to avoid writing new code altogether.

10 Predictions Shaping the Future of Web Data Extraction Services

Aggregated on: 2025-07-08 16:11:45

AI is reshaping how businesses collect and process online information, bringing a fundamental change to data extraction. Traditional scraping methods are becoming smarter and more adaptable to complex website structures through AI and machine learning technologies. Web data extraction tools have grown beyond basic data collectors. Modern solutions now include advanced features like immediate monitoring, automated pattern recognition, and enhanced processing capabilities. These improvements help businesses find the quickest way to learn about the big amount of online data.

Understanding N-Gram Language Models and Perplexity

Aggregated on: 2025-07-08 15:11:45

Language models are designed to predict what words are likely to come next in a sequence, assigning probabilities to each possible continuation. In this article, we explore how an N-gram language model works, how it assigns probabilities to sentences and sequences of words, and then examine how well this model performs. By understanding and evaluating this approach, we gain insight into how language models handle the complexity of human language. Applications Augmentative communication Machine translation Spelling correction Speech recognition Language Models Definition A language model consists of a finite set V and a function f(x1, x2, ..., xn) such that:

Evaluating Accuracy in RAG Applications: A Guide to Automated Evaluation

Aggregated on: 2025-07-08 14:11:45

Generative AI applications are rapidly gaining momentum across industries, providing intelligent responses to a wide range of queries. Whether for customer support, content generation, or knowledge management, the ability of AI to deliver contextually relevant and accurate answers is critical. One powerful approach that has emerged is Retrieval-Augmented Generation (RAG), which integrates large language models (LLMs) with external knowledge sources to enhance the quality of AI-generated responses. The retrieval component draws on external databases to provide real-world information, while the generation component refines the content into human-like responses. RAG’s usefulness lies in its ability to extend the capabilities of traditional language models, making them more context-aware and accurate. Applications of RAG span across multiple industries: for example, in customer support, RAG models dynamically access knowledge bases to deliver real-time, accurate answers to inquiries, reducing the need for manual intervention. In corporate environments, RAG helps automate information retrieval from large document repositories, improving response accuracy in knowledge-sharing platforms. It also plays a crucial role in fields like healthcare and education, where it enhances decision-making by fetching relevant research papers or educational materials, which LLMs can summarize for easy understanding.

How to Embed SAP Analytics Cloud (SAC) Stories Into Fiori Launchpad for Real-Time Insights

Aggregated on: 2025-07-08 13:11:45

As enterprise decision-making becomes more data-driven, users increasingly expect real-time insights at their fingertips, without switching systems. SAP Analytics Cloud (SAC) is the go-to platform for reporting and dashboarding in modern SAP landscapes, while SAP Fiori Launchpad (FLP) remains the user interface hub for business operations. Embedding SAC stories directly into the Fiori Launchpad offers a seamless, role-based analytics experience that empowers users with actionable insights where they work. Whether you are enabling call center agents to monitor customer KPIs or helping sales representatives review order analytics within their business applications, embedded SAC delivers the best of both worlds: real-time data from S/4HANA or IS-U via live CDS views, and rich visualizations presented natively in the Fiori experience.

Advancing DSLs in the Age of GenAI

Aggregated on: 2025-07-08 12:26:45

In the dynamic realm of software development, we often find ourselves reaching for the familiar comfort of general-purpose programming languages (GPLs) like Java, Python, C#, or JavaScript. However, when dealing with highly specialized tasks within specific domains these languages do not offer the same flexibility. Imagine attempting to assemble a complex piece of machinery using only a hammer and a screwdriver - while possible, the process would be inefficient and error prone. This is precisely where Domain-Specific Languages (DSLs) step in, offering a more refined and targeted approach. A DSL, at its core, is a programming language or specification meticulously designed and optimized for a particular domain or problem area. Unlike GPLs that aim to be all things to all developers, DSLs are laser-focused on expressing solutions within a specific context, using terminology, concepts, and abstractions that are inherently familiar to experts in that field. Think of a DSL as a suit tailored to solve a specific problem, rather than an off-the-rack garment that requires extensive alterations. By embracing the principles of specialization, DSLs empower developers and domain experts to collaborate more effectively, build more maintainable systems, and ultimately deliver greater value.

Zero-Trace Paradigm: Emerging Technologies in Personal Data Anonymization

Aggregated on: 2025-07-08 11:11:45

Emerging technologies like homomorphic encryption and zero-knowledge proofs can definitely help organizations approach zero-trace personal data anonymization. These and similar techniques can bring datasets to a near-zero-trace status, even achieving it in limited cases. There’s a major force that’s acting against efforts at implementing the zero-trace paradigm, though, and it’s difficult to discuss this paradigm without delving into what undermines it. Put simply, personal data has to come from somewhere before anonymization can even be considered. For very large datasets of personal information covering broad swaths of the population, the data broker industry remains the most practical source for many applications. Unfortunately, data brokers have proven themselves incapable of securing the almost exclusively plaintext datasets they hold.

How Developers Are Driving Supply Chain Innovation With Modern Tech

Aggregated on: 2025-07-07 20:11:45

Modern supply chains are under increasing pressure. The old models cannot keep up, from disrupted logistics during global crises to rising consumer expectations for speed and transparency. As a developer who has worked with logistics systems and enterprise software, I have seen firsthand where the cracks are and, more importantly, how to build smarter, leaner, and more responsive supply chains using technology. This blog is not a broad overview as it is a technical take on how developers and engineers can inject innovation directly into supply chain systems. API-First Architecture to Break Silos Many supply chains still run on fragmented systems: separate platforms for procurement, inventory, logistics, and fulfillment. These silos slow everything down and make real-time decision-making nearly impossible. An API-first strategy is key to integrating these systems and enabling better data flows across the board.

How to Build Your First Generative AI App With Langflow: A Step-by-Step Guide

Aggregated on: 2025-07-07 19:11:45

As you all know, Generative AI is reshaping how we build certain applications — but diving into LangChain code or orchestrating complex pipelines can be intimidating for newcomers. That’s where I feel Langflow comes in very handy for first-timers trying to explore and build such applications. Langflow is a low-code, visual interface that lets you prototype and deploy LLM-powered applications without writing a single line of backend code. Whether you're building a chatbot, a document summarizer, or a custom retrieval-augmented generation (RAG) app — Langflow lets you do it visually and quickly.

Understanding k-NN Search in Elasticsearch

Aggregated on: 2025-07-07 18:11:45

Businesses are increasingly relying on intelligent search capabilities to enhance customer experience, automate insights, and unlock the potential of unstructured information. Elasticsearch, a leading distributed search and analytics engine, is at the heart of many such systems. One of its powerful and lesser-known capabilities is support for k-nearest neighbors (k-NN) search, a method particularly useful for similarity-based retrieval in domains such as semantic search, recommendation engines, and image recognition. This article delves into what Elasticsearch and k-NN search are, how the two are integrated, and how to configure and optimize k-NN in Elasticsearch for real-world applications.

A Software Engineer’s Guide to Thrive in Gen AI Era: Master It or Fade Out

Aggregated on: 2025-07-07 17:26:45

Gen AI is everywhere, seriously shaking things up in our world, and having expertise in this technology has become a critical skill for landing job interviews. According to this Gartner report, global spending on Gen AI is expected to reach $644 billion in 2025 (76.4% increase from 2024). It also notes that despite high failure rate for Gen AI PoCs (proof-of-concept), foundation model providers continue to pour billions of dollars annually to enhance model’s size, performance and reliability, signaling strong confidence in future. During the COVID-19 pandemic, software engineers were in high demand, commanding top salaries as companies scrambled to hire talent capable of tackling complex challenges. However, there was a market reset by 2022 where the industry pivoted toward efficiency, and the arrival of ChatGPT marked a transformative shift, redefining roles and accelerating AI integration across the tech sector.

Tracing Stratoshark’s Roots: From Packet Capture to System Call Analysis

Aggregated on: 2025-07-07 16:26:45

The journey that led to the creation of open-source Sysdig and Falco traces its roots to packet capture—a domain where many of the original developers had honed their expertise over the years. This deep connection to network traffic analysis, combined with inspiration from technologies like BPF, libpcap, tcpdump, and Snort, laid the groundwork for innovations that extended packet capture principles into the evolving worlds of containers and cloud security. Wireshark is a well-known network analysis tool that uses libpcap, a user-level library for capturing and filtering packets. At its core, Wireshark relies on libpcap—a versatile library that manages live packet capture, filtering, and file handling. Libpcap became a foundation for countless tools, providing a generic interface that could be extended to fit various needs. Snort, for instance, built a rule engine for network intrusion detection directly on top of libpcap, leveraging its packet capture capabilities to detect and prevent suspicious network activity.

The QA Paradox: To Save Artificial Intelligence, We Must Stop Blindly Trusting Data—And Start Trusting Human Judgment

Aggregated on: 2025-07-07 15:26:45

Artificial Intelligence is undoubtedly driving a generational shift in our current society. However, excessive reliance on data can threaten its credibility and introduce risks. Generative AI models produce convincingly erroneous information (Farid, 2024; NewsGuard, 2025), while biased algorithms perpetuate and amplify societal inequalities (AIMultiple, 2024; UN Women, 2025). This reliance on data—AI’s greatest strength—becomes its critical vulnerability. A flawed, incomplete, or unrepresentative dataset reflecting our diverse world adds further complexity to AI. We need a fundamental shift in quality assurance (QA) approaches to extract AI's transformative potential while mitigating its inherent risks. Implicit trust in data-driven outputs is no longer tenable. Human QA professionals’ nuanced, contextual, and ethical judgment must be elevated as an essential corrective. This article advocates for rebalancing the equation: augmenting data-driven insights with irreplaceable human judgment to ensure AI serves humanity equitably and responsibly.

Top Load Balancing Algorithms: Choosing the Right Strategy

Aggregated on: 2025-07-07 14:11:45

Modern software applications need load balancing as an essential component when they expand across distributed systems and containerized environments and microservices architecture. The application system provides consistent user service through load balancing even when traffic increases or servers become unavailable. Different load balancing strategies demonstrate varying levels of quality. The selection of a suitable load balancing strategy depends on understanding system behavior during high loads and determining acceptable tradeoffs. This analysis examines the primary load balancing algorithms which divide into static and dynamic categories and examines the advantages and disadvantages of each approach together with recommendations for their appropriate deployment scenarios.

Deploy Serverless Lambdas Confidently Using Canary

Aggregated on: 2025-07-07 13:11:45

Releasing software quickly and safely is tough, but it’s becoming a basic expectation. The right setup can help teams deliver faster without losing reliability. AWS Lambda, a popular serverless compute service, combined with continuous deployment practices and canary release strategies, allows teams to deploy changes frequently while minimizing risk. This article explores the importance of continuous deployment, examines rolling vs. canary deployment strategies, and provides guidance on implementing canary releases for Lambda functions with best practices and pitfalls to avoid. The Importance of Continuous Deployment Continuous deployment is the practice of releasing software updates in an automated, frequent manner. For businesses, this means new features and fixes get to users faster, enabling quicker feedback and adaptation to market needs. Frequent, small releases also reduce the risk associated with each deployment compared to large infrequent launches.

Stop Building Monolithic AI Brains, Build a Specialist Team Instead

Aggregated on: 2025-07-07 12:11:45

You’ve been there. You’ve got a killer app idea, and you want to sprinkle in some AI magic. The first instinct? Build a single, massive AI model—a "genius brain" that can handle anything a user throws at it. But let's be real, as soon as things get even a little complex, that approach starts to fall apart. Your "genius" model becomes a jack-of-all-trades and a master of none. It gets confused, it becomes a massive bottleneck when traffic spikes, and trying to update one part of its knowledge is a complete nightmare. Sound familiar?

Orchestrating Edge Computing with Kubernetes: Architectures, Challenges, and Emerging Solutions

Aggregated on: 2025-07-07 11:11:45

Edge computing has emerged as a transformative approach to handle data processing closer to the data source rather than relying on centralized cloud infrastructures. This is particularly important for real-time applications that demand low latency, higher bandwidth efficiency, and more autonomy in operations. Kubernetes, an open-source container orchestration platform, has revolutionized how applications are deployed and managed across distributed systems. Its powerful orchestration capabilities make it an ideal solution for managing workloads in edge computing environments, where resources are often constrained, and the system architecture is highly decentralized.

WebAssembly (Wasm) and AI at the Edge: The New Frontier for Real-Time Applications

Aggregated on: 2025-07-04 20:26:43

In the fast-moving digital world, users demand instant responses and smarter technology on every device they use. This demand is pushing for advancement to faster, secure, and efficient computing techniques. Two technologies are coming to the fore as powerful solutions — WebAssembly (Wasm) and artificial intelligence at the edge (Edge AI) — redefining the ways real-time applications are built and deployed. WebAssembly and Edge AI together form a powerful duo that bridges performance and intelligence at the device level. By enabling high-speed execution and local decision-making, they reduce latency, enhance privacy, and deliver seamless user experience, marking a major shift in how real-time applications are developed for today’s diverse and demanding digital environments.

Modernizing Apache Spark Applications With GenAI: Migrating From Java to Scala

Aggregated on: 2025-07-04 19:26:43

If you're working on big data projects using Spark, you've likely come across discussions within your team about Java vs. Scala vs. Python, along with comparisons in terms of implementation, API support, and feasibility. These technologies are typically chosen on a case-by-case basis depending on the specific use case. For example, data engineering teams often prefer to use Scala over Java because of:

Are Traditional Data Warehouses Being Devoured by Agentic AI?

Aggregated on: 2025-07-04 18:26:43

From a technical architecture perspective, I believe this wave of AI will profoundly reshape the entire software ecosystem. DSS systems are designed around the logic of human decision-making as the ultimate consumer. However, with the advent of the Agentic AI era, the final "consumer" is more likely to be an agent. This will lead to a complete redesign—or even elimination—of traditional data warehouses and complex ETL pipelines. Conventional data warehouses emphasize structure and query patterns, but they will be replaced by Agentic Data Stack architectures that focus on semantics and response patterns. Introduction: The Signal Behind Snowflake’s CEO Change In the spring of 2024, Snowflake, a star in the cloud data warehouse space, announced a change in leadership: Sridhar Ramaswamy, former head of Google’s advertising business, succeeded the legendary CEO Frank Slootman, who had helped Snowflake reach a $60 billion valuation.

Event Storming Workshops: A Closer Look at Different Approaches

Aggregated on: 2025-07-04 17:26:43

Event Storming serves as a strategic approach to gain a comprehensive understanding of the business domain, aiming to unveil as many uncertainties and complexities as possible prior to initiating design and implementation. Each phase of this process demands varying degrees of detail and breadth of information. At times, a broad overview suffices, while certain scenarios necessitate an in-depth exploration of the intricate aspects of specific requirements. Preparing for Your Event Storming Workshop Who to Invite? A successful workshop hinges on three participant groups:

The Shift to Open Industrial IoT Architectures With Data Streaming

Aggregated on: 2025-07-04 16:26:43

Operational Technology (OT) has traditionally relied on legacy middleware to connect industrial systems, manage data flows, and integrate with enterprise IT. However, these monolithic, proprietary, and expensive middleware solutions struggle to keep up with real-time, scalable, and cloud-native architectures. Just as mainframe offloading modernized enterprise IT, offloading and replacing legacy OT middleware is the next wave of digital transformation. Companies are shifting from vendor-locked, heavyweight OT middleware to real-time, event-driven architectures using Apache Kafka and Apache Flink, enabling cost efficiency, agility, and seamless edge-to-cloud integration.

Leveraging AI: A Path to Senior Engineering Positions

Aggregated on: 2025-07-04 15:26:43

As I sit here, reflecting on my past experiences as a software engineer, I am reminded of the countless hours spent trying to streamline the development processes and improve productivity for the team. It's an issue that plagues many of us, but one that I believe can be addressed with the help of AI agents. I clearly remember when I first encountered AI agents - I was skeptical, to say the least. But as I delved into the world of machine learning and automation together, I began to see the possibilities for these tools to transform the business. After months of experimenting with a variety of AI-powered tools, I am excited to share my findings with the community.

How Predictive Analytics Became a Key Enabler for the Future of QA

Aggregated on: 2025-07-04 14:26:43

In recent years, software development has experienced significant growth and increased emphasis on quality. For instance, conventional software testing has primarily been mechanical, where defects are detected and corrected during testing. Although this method has been used for some time and has been somewhat helpful, it has been considered unsuitable in the current context of derivation, integration, and frequent updates. This is where predictive analytics comes in—a new approach that turns the model from one of post facto testing and design to one of pre-testing. Real-time analytics is a process that identifies potential defects and failures by analyzing historical data, leveraging machine learning, and applying statistical models to predict future occurrences. This enables development teams to take preventive measures to prevent system failures, reducing downtimes and enhancing software quality. This is not just a change in the tools employed in developing these software systems; it is a change mandated to enable software development in a new way.

The Evolution of Software Integration: How MCP Is Reshaping AI Development Beyond Traditional APIs

Aggregated on: 2025-07-04 13:26:43

As software engineers, we've spent years mastering the art of API integration. We've wrestled with REST endpoints, debugged authentication flows, and built countless adapters to make disparate systems talk to each other. But as artificial intelligence transforms from experimental technology to production necessity, we're witnessing a fundamental shift in how software systems need to communicate. The API Foundation: A Double-Edged Success Story We need to acknowledge what APIs have helped us accomplish; they helped revolutionize software development by providing standardized ways for systems to interact. For example, the Stripe payment API enabled developers across the world to add complex financial transactions through simple HTTP calls, and GitHub's REST API enabled an entire ecosystem of development tools. These successes shaped how we think about system integration.

How to Troubleshoot Common Linux VPS Issues: CPU, Memory, Disk Usage

Aggregated on: 2025-07-04 12:26:43

Linux, for its reputation of stability, security, and efficiency, several of the internet is hosted on Linux-based cloud servers, web hosts, and enterprise applications. This reliability positions it ideal for Virtual Private Servers (VPS), where even during high-demand workloads, it remains operational. Linux has native tools that are very powerful for diagnosing and solving your issues in no time. The commands such as top, htop, vmstat, and sar are used to monitor the system resources in real-time so that the performance bottleneck can be identified and corrective action can be taken before your VPS starts to freeze.

One Checkbox to Cloud: Migrating from Tosca DEX Agents to E2G

Aggregated on: 2025-07-04 11:26:43

If you read my recent article on the Elastic Execution Grid (E2G), you’ll know I’ve been exploring some of the newer additions to the Tosca ecosystem. It’s been an interesting learning curve and raised a few thoughts about how we approach execution infrastructure. That said, I realize many teams are still working smoothly with their existing DEX testing setups, and for good reason. If your workflows are stable and reliable, it can be hard to justify changing what already works. The effort to switch can feel like more hassle than it’s worth.

How to Use AI to Understand Gaps in Your Resume and Job Descriptions

Aggregated on: 2025-07-03 20:11:43

At the beginning of my career, when I applied for jobs, I felt there was always a gap between what my resume had and what the job description said. I used to spend many hours staring at my resume, second-guessing whether it reflected my skills correctly or aligned with the Job Description and what recruiters were looking for. Not knowing if my resume would surpass the Applicant Tracking Systems (ATS) drove me to understand what might work in today's job search game. That's when I realized that there might be many of those who are struggling with such issues. However, people who are constantly up-leveling their skills in software engineering and have gained might find it easier to at least get through the ATS filtering with their experience, specific job requirements, or domain knowledge needed to crack the job. But for someone who's new to software engineering or who knows how to use AI but needs a head start with building an AI (LLM-based) tool for themselves, this solution might help.

Self-Supervised Learning Techniques

Aggregated on: 2025-07-03 19:26:43

Visual tracking systems are essential for applications ranging from surveillance to autonomous navigation. However, these systems have a significant Achilles' heel: they rely heavily on large, labeled datasets for training. This reliance makes it challenging to deploy them in real-world situations where labeled data is scarce or expensive to obtain. In this article, we will learn about self-supervised learning (SSL) and how it leverages unlabeled data to train models. What Is the Problem? Visual tracking involves identifying and following an object across frames in a video. Traditional methods depend on vast amounts of labeled data to learn how to recognize and track objects accurately. This dependence poses several problems:

Real-Time Market Data Processing: Designing Systems for Low Latency and High Throughput

Aggregated on: 2025-07-03 18:26:43

In financial markets, real-time data processing is critical for trading, risk management, and decision-making. Market data systems must ingest and process millions of updates per second while ensuring ultra-low latency. During my time at Bloomberg and Two Sigma, I worked on optimizing such systems for speed and reliability. By the means of this article, I’d like to explore key challenges in real-time market data processing, design strategies, and optimizations—with code snippets where applicable. I’ll try my best to keep things succinct without going into too much details!

The OWASP Top 10 for LLM Applications: An Overview of AI Security Risks

Aggregated on: 2025-07-03 17:26:43

The world of AI, especially with Large Language Models (LLMs) and Generative AI, is changing the game. It's like we've unlocked a superpower for creating content, automating tasks, and solving tricky problems. But, as with any new superpower, there are new ways things can go wrong. Open Worldwide Application Security Project (OWASP) experts have put together a list of the top 10 security risks specifically for these new AI applications in 2025. Think of it as a field guide to help everyone from developers to CISO’s, spot and fix these new kinds of digital vulnerabilities. Let's break down these top 10 AI security risks, with simple explanations and everyday examples:

Designing Microservices Architecture With a Custom Spring Boot Starter and Auto-Configuration Framework

Aggregated on: 2025-07-03 16:11:43

Spring Boot has Java development with its embedded servers, auto-configuration, and convention-over-configuration philosophy. Among its many features, one of the most powerful — but often underused — is the ability to create custom Spring Boot starters. These components enable development teams to package and reuse common configuration and boilerplate logic, making microservices more modular, maintainable, and consistent across a large-scale enterprise platform. This article explores how to build and use custom Spring Boot starters with auto-configuration to centralize concerns such as database access, authentication, and WebSocket communication, especially valuable in environments like loan servicing and trading platforms that operate with numerous microservices.

Seata the Deal: No More Distributed Transaction Nightmares Across (Spring Boot) Microservices

Aggregated on: 2025-07-03 15:11:43

In a time when nostalgia may have gone a little too far, the software development world has been swept by a heated debate: has the massive adoption of the microservices pattern truly delivered the expected benefits, or is its balance sheet more uncertain? Many teams are starting to wonder whether it’s time for a “homecoming” to the good old, reliable monolith — or if the answer lies somewhere in between, in the form of the modular monolith. A distracted architect distributed transactions

Building V1 Gen-AI Products at Scale: Technical Product Patterns That Work

Aggregated on: 2025-07-03 14:11:43

Shipping the first version of the Gen-AI product is not only a technical problem but a systems-level event. Coordinating the product, infra, security, design, and executive layers when doing it in the enterprise or a consumer-grade setting is necessary. This is especially true when the product interacts with real users and serves in a business-critical environment. This is not a testbed prompt with an open-source LLM. This is a real-world deployment; at this scale, every minute of latency and every hallucination incurred ceases to be some model parameter and becomes a business liability.