News Aggregator

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.

Advanced gRPC in Microservices: Hard-Won Insights and Best Practices

Aggregated on: 2025-07-03 13:11:43

Building microservices at scale often means pushing beyond the basics of gRPC. Many teams adopt gRPC for its high performance and cross-language support, only to discover subtle complexities when running it in production. In this article, we delve into advanced gRPC concepts — streaming, deadlines, interceptors, load balancing — and share practical “dos and don’ts” learned from real-world systems. We’ll also examine how industry leaders like Netflix have leveraged gRPC to boost productivity and solve tough issues in their microservice architectures. The goal is a leadership-level view of gRPC: not just how to write a service, but how to build and scale a gRPC-based microservice ecosystem effectively. Let’s explore the key concepts and hard-won lessons for making the most of gRPC in production.

How We Broke the Monolith (and Kept Our Sanity): Lessons From Moving to Microservices

Aggregated on: 2025-07-03 12:11:43

If you’ve ever been nervous about deploying code on a Friday, trust me — you’re not alone. A few years ago, I was leading a team at a major e-commerce company, wrangling a monolithic beast that could break in a hundred creative ways. The idea of microservices was everywhere, but nobody really tells you about the messy parts. Here’s what we learned the hard way — warts and all — while moving from monolith to microservices.

Why Traditional CI/CD Falls Short for Cloud Infrastructure

Aggregated on: 2025-07-03 11:56:43

For years, CI/CD pipelines have been the gold standard for software delivery—fast, repeatable, and reliable. But when it comes to cloud infrastructure, the model breaks down. It’s not that CI/CD is broken. It’s that infrastructure isn’t software. It has different constraints, different risks, and very different failure modes. And treating it like software introduces risk, drift, friction, and operational overhead—right when teams need speed and stability most. The more your cloud estate grows, the more these problems compound—until visibility, control, and velocity start to erode.

Top Tools for Front-End Developers

Aggregated on: 2025-07-03 11:11:42

My Favorite Tools for Writing High-Quality Code With Maximum Fun and Minimal Effort As a developer, having the right tools can transform your workflow from tedious to enjoyable, while ensuring your code is top-notch. Over time, I’ve discovered a set of tools that strike the perfect balance between power, ease of use, and even a bit of fun! Whether you’re a seasoned coder or just diving in, these tools can make your development life easier and more productive. Quick List IDE WebStorm API tools Mockoon Postman Debuging Chrome Developer Tools React Developer Tools Redux DevTools Accessibility Chrome Accessibility Inspector Lighthouse ANDI (Accessible Name & Description Inspector) WAVE (Web Accessibility Evaluation Tool) 1. WebStorm: The All-in-One IDE WebStorm is an amazing Integrated Development Environment (IDE) that now comes with free — yes, you heard that right!

Stabilizing ETL Pipelines With Airflow, Presto, and Metadata Contracts

Aggregated on: 2025-07-02 20:26:42

Wednesday. 10:04 AM. The dashboard says conversions dropped 18%. Product’s panicking. Marketing’s quiet-slacking you. But nothing’s failed—Airflow’s green, Hive tables are updating, and your pipeline logs look squeaky clean. That’s when it hits you: this isn’t a failure. It’s something worse. It’s silent data drift.

Parallel Data Conflict Resolution in Enterprise Workflows: Pessimistic vs. Optimistic Locking at Scale

Aggregated on: 2025-07-02 19:11:42

Data conflict resolution is not a simple backend detail in modern enterprise systems, particularly when supporting complex concurrent operations. It is a full-stack architectural matter that affects consistency, UX, observability, and trust in the system. We experienced this firsthand while building a government system that handled claims adjudication. Multiple case workers accessed and edited the shared records in parallel, and the premise that all system parts could be operated separately started to break down. Locking was no longer a database concern; it became a product at that scale.

Deploying LLMs Across Hybrid Cloud-Fog Topologies Using Progressive Model Pruning

Aggregated on: 2025-07-02 18:26:42

Large Language Models (LLMs) have become backbone for conversational AI, code generation, summarization, and many more scenarios. However, their deployment poses significant challenges in environments where compute resources are limited mostly in hybrid cloud-fog architectures, where real-time inference may need to run closer to the edge. In these instances, progressive model pruning plays a pivotal role offering solution to reduce model size and computation cost without impacting accuracy. In this article, we will discuss how to efficiently deploy LLMs across cloud-fog topologies using layer-aware, resource-adaptive pruning techniques.

Top NoSQL Databases and Use Cases

Aggregated on: 2025-07-02 17:26:42

Traditional relational databases (SQL), while robust and reliable, are not always the ideal solution. The demand for high-performance, scalable, and schema-flexible data storage systems has driven the adoption of NoSQL databases, which offer alternatives to the rigid structure of relational systems. NoSQL (short for “Not Only SQL”) databases are designed to handle large volumes of unstructured, semi-structured, or structured data, with a focus on flexibility, horizontal scalability, and real-time performance. They are increasingly used in modern architectures, especially where speed, flexibility, and high throughput are essential, such as mobile applications, real-time analytics, and IoT systems.

MCP and The Spin-Off CoT Pattern: How AI Agents Really Use Tools

Aggregated on: 2025-07-02 16:11:42

Why MCP Is the Missing Piece in the AI Tool Integration Puzzle I’ve read many articles explaining what MCP is, but none explore how AI actually handles these server interactions under the hood. Here’s my take, using what I call the ‘Spin-off CoT’ concept. Picture this: You’re having a conversation with an AI assistant about the weather in San Francisco. Behind the scenes, something fascinating happens. The AI doesn’t just “know” the current temperature — it spawns what I call a “spin-off Chain of Thought” (CoT) to handle tool interactions. This pattern, which emerged naturally from our exploration of the Model Context Protocol (MCP), reveals something profound about how AI systems should integrate with external tools.

Why API-First Frontends Make Better Apps

Aggregated on: 2025-07-02 15:11:42

In today’s software landscape, building a frontend that simply looks good isn’t enough. The real power lies in how well it interacts with APIs because that’s where the data lives, the business logic resides, and the real-time user experience gets delivered. If your frontend is still structured as a UI-first project where data integration is a postscript, it’s time to flip that approach. This guide walks through what it means to design API-centric frontends from the ground up, how to avoid common pitfalls, and how to build interfaces that are both dynamic and maintainable.

Vibe Coding: Conversational Software Development — Part 1 Introduction

Aggregated on: 2025-07-02 14:11:42

Since I started coding, I have seen developer communities strive to make programming more human-readable, almost like writing in English or a preferred language. Many modern languages introduced syntactic sugar to make code more intuitive and conversational. These efforts have made significant advancements, but now, we are witnessing something far more transformative. Natural language can now be translated directly into functional software. The concept is trending and is widely referred to as vibe coding. It is an AI-first approach for rapid software development. Let me try to explain the idea with the help of a step-by-step diagram that I have added below. As the picture shows, you put down your thoughts or overall idea as a prompt. You direct what step you want to achieve or what is your end goal. The chat-based AI works on your prompt and comes up with a generated code. You preview the output of the code and can fine-tune it further. Once you are happy, you put that code into your server.

Microservice Madness: Debunking Myths and Exposing Pitfalls

Aggregated on: 2025-07-02 13:11:42

Microservice is the false belief that adding a message broker to your app will somehow magically make it faster and more scalable. Ignoring the fact that this is, in itself, an oxymoron—and that your app quite literally becomes two billion times slower—the absolute dumbest argument I've ever heard in favor of microservices is: