News Aggregator

Kubernetes Debugging Recipe: Practical Steps to Diagnose Pods Like a Pro

Aggregated on: 2025-10-24 19:25:53

Automation isn’t optional at enterprise scale. It’s resilient by design. Kubernetes provides remarkable scalability and resilience , but when pods crash, even seasoned engineers struggle to translate complex and cryptic logs and events. This guide walks you through the spectrum of AI-powered root cause analysis and manual debugging, combining command-line reproducibility and predictive observability approaches.

From Distributed Monolith to Composable Architecture on AWS

Aggregated on: 2025-10-24 18:25:53

You adopted microservices for independence and agility. Instead, every deployment requires coordinating multiple teams and testing the entire system. What you built is a distributed monolith, complexity spread across systems, but still bound by monolithic coupling. The shift from technical boundaries to business-driven boundaries is the only path to true agility. Many organizations discover too late that microservices alone do not guarantee independence. Domain-Driven Composable Architecture (DDCA) provides a methodology to escape this rigidity. This article is a practical playbook for decomposing services into Packaged Business Capabilities (PBCs) aligned with business domains and mapped to AWS patterns such as EventBridge, Step Functions, and DynamoDB Streams. It explains when DDCA fits and when it does not, and covers security, anti-patterns, and operational realities, so you can adopt composability with a clear view of the investment required.

Unhandled Promise Rejections: The Tiny Mistake That Crashed Our Node.js App

Aggregated on: 2025-10-24 17:25:53

Imagine deploying a Node.js backend service that works flawlessly in development, only to have it mysteriously crash in production. Everything ran fine on your laptop, but on the live server, the process keeps shutting down unexpectedly. In our case, the culprit was a single unhandled promise rejection — one missing .catch() in our code caused Node to exit abruptly whenever an error occurred. That one “tiny” mistake made the difference between a stable service and frequent downtime. In this article, we’ll explore how a misconfigured error handling in a Node/Express API can bring down an application, and how to diagnose and fix it to prevent future crashes.

Performance Testing 101: A Beginner's Guide to Building Robust Applications

Aggregated on: 2025-10-24 16:25:53

Welcome! This guide is for anyone who has built an application and wants to ensure it doesn't fall over when real people start using it. We'll walk through the essentials of performance testing without the complicated jargon, focusing on practical steps you can take to make your app robust and reliable. You may find this article too abstract, but be sure the next time we will go over the real example of making a performance test with Java, Gatling, and Docker Compose.

Strategic Domain-Driven Design: The Forgotten Foundation of Great Software

Aggregated on: 2025-10-24 15:25:53

When teams talk about domain-driven design (DDD), the conversation often jumps straight to code — entities, value objects, and aggregates. Yet, this is where most projects begin to lose direction. The essence of DDD is not in the tactical implementation, but in its strategic foundation — the part that defines why and where we apply the patterns in the first place. The strategic aspect of DDD is often overlooked because many people do not recognize its importance. This is a significant mistake when applying DDD. Strategic design provides context for the model, establishes clear boundaries, and fosters a shared understanding between business and technology. Without this foundation, developers may focus on modeling data rather than behavior, create isolated microservices that do not represent the domain accurately, or implement design patterns without a clear purpose.

Build a Dynamic Web Form Using Camunda BPMN and DMN

Aggregated on: 2025-10-24 14:25:53

Business Process Model and Notation (BPMN) is the universal standard for visually modeling and automating business processes. It is used to design and automate workflows, defining the sequence of tasks, approvals, and user interactions. Whereas Decision Model and Notation (DMN) models the complex decision logic that can be embedded within those processes to automate business rules in a structured, reusable way. Camunda's process orchestration platform provides a collaborative environment for Business and IT developers via an intuitive visual Modeler that adheres to BPMN and DMN standards. Modeling with Camunda reduces the time it takes to develop and maintain real-world business processes through automation. Beyond automation, combining BPMN and DMN allows us to create dynamic web forms where the fields, validations, and even flow of the form adapt real-time business rules, instead of being hardcoded. This makes applications more flexible, easier to maintain, and business-driven.

Cloud Agnostic MLOps: How to Build and Deploy AI Models Across Azure, AWS, and Open Source

Aggregated on: 2025-10-24 13:25:53

Artificial intelligence has become the centerpiece of every digital strategy. What began as isolated proof-of-concepts running on data scientists’ laptops is now expected to scale across clouds, business units, and continents. Enterprises quickly discover that the challenge is not building AI models. It’s operationalizing them sustainably.

Diagnosing and Fixing a Page Fault Performance Issue With Arm64 Atomics

Aggregated on: 2025-10-24 12:25:53

While running a synthetic benchmark that pre-warmed the cache, we noticed an abnormal performance impact on Ampere CPUs. Digging deeper, we found that there were many more page faults happening with Ampere CPUs when compared to x86 CPUs. We isolated the issue to the use of certain atomic instructions like ldadd, which load a register, add a value to it, and store data in a register in a single instruction. This triggered two “page faults” under certain conditions, even though this is logically an all-or-nothing operation, which is guaranteed to be completed in one step. In this article, we will summarize how to qualify this kind of problem, how memory management in Linux works in general, explain how an atomic Arm64 instruction can generate multiple page faults, and show how to avoid performance slowdowns related to this behavior.

I Built a Full Stack App Using Only Vibe Coding Prompts: Here’s What Happened

Aggregated on: 2025-10-24 11:25:53

You know that moment when you stare at your screen and think, “What if I just let the vibes lead?” That’s exactly what I did. I decided to build a full-stack app, not with a strict roadmap, not with a pre-decided stack, and not even with a design in Figma, but by coding my way through it by vibe. I used AI tools, intuition, and years of muscle memory to go with the flow. No formal planning, no architecture diagrams, no syntax lookups, just prompts, patterns, and pure gut feel.

Evolving Golden Paths: Upgrades Without Disruption

Aggregated on: 2025-10-23 19:10:53

The platform team had done it again — a new version of the golden path was ready. Cleaner templates, better guardrails, smoother CI/CD. But as soon as it rolled out, messages started flooding in: “My pipeline broke!”, “The new module isn’t compatible with our setup!” Sound familiar? Every platform engineer knows that delicate balance — driving innovation while ensuring developer stability. Golden paths promise simplicity and speed, but without careful version management, they can easily turn from enablers into disruptors.

AI Won't Replace Front-End Developers, It'll Replace the Boring Parts

Aggregated on: 2025-10-23 18:10:53

I’ve been working as a front-end engineer at a service-based company. When I joined as an intern last year, I came across the term "pixel-perfect" for the first time. During my internship, I was assigned a feature where I had to display pages on the screen, with the content coming from an MDX file. I built the entire page from scratch, and when it was time for review, the reviewer turned out to be our Chief Design Officer (CDO).

Data Quality at Write Time: Engineering Reliability With Delta Expectations

Aggregated on: 2025-10-23 17:10:53

Data quality failures don't announce themselves. They compound silently — a malformed timestamp here, a negative revenue figure there — until a quarterly board deck shows impossible numbers or an ML model degrades into uselessness. A 2023 Gartner study pegged the cost at $12.9 million annually per organization, but that figure misses the hidden expense: engineering time spent firefighting data incidents instead of building features. The traditional approach treats validation as a post-processing step. You write data to storage, then run Great Expectations or Deequ checks, discover failures, and either fix the pipeline or quarantine bad records. This pattern creates a fundamental gap: the window between data landing and validation completion. In high-throughput lakehouses processing terabytes daily, that window can represent millions of corrupted records propagating downstream before anyone notices.

LangGraph Beginner to Advanced: Part 2 — Hello World Graph in LangGraph

Aggregated on: 2025-10-23 16:10:53

Awesome. Now this is quite exciting. We’re actually about to start coding in LangGraph for the very first time. Now that we’ve covered all the theory, admittedly the boring section, we’re now going to actually code up some graphs, and we’re about to code up our very first graph in this subsection. But in this section, we’re not going to be building any AI agents. Why? Since we haven’t really learned how to code in LangGraph yet, or how to combine all these LLM APIs and tools, I thought it would get pretty messy to build one right now.

Series: Toward a Shared Language Between Humans and Machines

Aggregated on: 2025-10-23 15:10:53

Large language models (LLMs) today produce fluent and coherent texts, to the point of giving the illusion of a real conversation. But behind this apparent mastery arises an interesting question: do machines really understand us, or are they only predicting words? In a four-part series, we will explore one of the deepest challenges of artificial intelligence: building a true common ground between human meaning and machine logic. From cognitive limits to quantum horizons, and including the strategic role of humans, each article will shed light on one facet of these questions.

Ranking Full-Text Search Results in PostgreSQL Using ts_rank and ts_rank_cd With Hibernate 6 and posjsonhelper

Aggregated on: 2025-10-23 14:10:53

In a previous article, we explored how to implement full-text search in PostgreSQL using Hibernate 6 and the posjsonhelper library. We built queries with to_tsvector, to_tsquery, and their simpler wrappers for the plainto_tsquery, phraseto_tsquery, and websearch_to_tsquery functions. This time, we’ll extend that foundation and explore how to rank search results based on their relevance using PostgreSQL’s built-in ranking functions like ts_rank and ts_rank_cd.

Applying Domain-Driven Design With Enterprise Java: A Behavior-Driven Approach

Aggregated on: 2025-10-23 13:10:53

When it comes to software development, one of the biggest mistakes is delivering precisely what the client wants. While this may sound cliché, the problem persists even after decades in the industry. A more effective approach is to begin testing with a focus on business needs. Behavior-driven development (BDD) is a software development methodology that emphasizes behavior and domain terminology, also known as ubiquitous language. It uses a shared, natural language to define and test software behaviors from the user's perspective. BDD builds on test-driven development (TDD) by concentrating on scenarios that are relevant to the business. These scenarios are written as plain-language specifications that can be automated into tests, which also serve as living documentation.

Fundamentals of Logic Hallucinations in AI-Generated Code

Aggregated on: 2025-10-23 12:10:53

Tools like GitHub Copilot, ChatGPT, Cursor, and other AI coding assistants can generate boilerplate, suggest algorithms, and even create full test suites within seconds. This accelerates development cycles and reduces repetitive coding work. Hallucinations, however, are a common problem of AI-generated code. There are several types of hallucinations, and in this article, I will focus on some basic logical hallucinations.

How to Build an MCP Server and Client With Spring AI MCP

Aggregated on: 2025-10-23 11:10:53

If it’s spring, it’s usually conference time in Bucharest, Romania. This year was, as always, full of great speakers and talks. Nevertheless, Stephan Janssen’s one, where the audience met the Devoxx Genie IntelliJ plugin he has been developing, was by far my favorite. The reason I mention it is that during his presentation, I heard about Anthropic’s Model Context Protocol (MCP) for the first time. Quite late though, considering it was released last year in November. Anyway, to me, the intent of standardizing how additional context could be brought into AI applications to enrich and enhance their accuracy was basically what’s been missing from the picture. With this aspect in mind, I have been motivated enough to start studying about MCP and to experiment with how its capabilities can improve AI applications. In this direction, from high-level concepts to its practical use when integrated in AI applications, MCP has really caught my attention. The result: a series of articles.

Mastering Audio Transcription With Gemini APIs: A Developer's Guide

Aggregated on: 2025-10-22 19:25:52

Understanding Audio Transcription via Gemini APIs Gemini models are multimodal large language models. They can process and generate various types of data, including text, code, images, audio, and video. Gemini models also offer powerful audio transcription capabilities, enabling developers to convert spoken content into text. This can help in building a transcription service, creating subtitles for videos, and developing voice-enabled applications. If you are looking to convert speech to text using Gemini's powerful AI models, this comprehensive guide will show you how to implement audio transcription using different Gemini APIs. We will go from basic implementation to advanced real-time streaming. Gemini supports the following audio formats as input: WAV, MP3, AIFF, AAC, OGG, and FLAC. We will look at generateContent, streamGenerateContent, and BidiGenerateContent(LiveAPI) APIs. You can find all supported APIs at https://ai.google.dev/api. generateContent is a standard REST endpoint, which processes the request and returns a single response. streamGenerateContent uses SSE (server-sent-events) to send partial responses as they are generated. This API is a better choice for applications like chatbots, which need a faster and more interactive experience.

The Dark Side of Apache Iceberg’s Data Time Travel Feature

Aggregated on: 2025-10-22 18:25:52

Overview Apache Iceberg is a high-performance open table format for large analytic tables that supports expressive SQL, full schema evolution, hidden partitioning, time travel and rollback, data compaction, and interoperability through the Iceberg REST catalog. With its robust features, Iceberg is becoming popular in the Data Lake and Lakehouse industries. In this article, we are going to discuss the pros and cons of the most fascinating feature, “Time Travel Query.” We will also discuss the precautions while adopting time travel features.

Automating Excel Workflows in Box Using Python, Box SDK, and OpenPyXL

Aggregated on: 2025-10-22 17:25:52

In many organizations, MS Excel remains the go-to tool for storing and sharing structured data, whether it’s tracking project progress, managing audit logs, or maintaining employee or resource details. Yet, a surprisingly common challenge persists: data is still being copied and updated manually. Teams across different functions, especially management and DevOps, often find themselves entering or syncing data from one source into Excel spreadsheets manually and repeatedly. This not only consumes time but also introduces room for errors and inconsistencies.

From Platform Cowboys to Governance Marshals: Taming the AI Wild West

Aggregated on: 2025-10-22 16:25:52

The rapid ascent of artificial intelligence has ushered in an unprecedented era, often likened to a modern-day gold rush. This "AI gold rush," while brimming with potential, also bears a striking resemblance to the chaotic and lawless frontier of the American Wild West. We are witnessing an explosion of AI initiatives — from unmonitored chatbots running rampant to independent teams deploying large language models (LLMs) without oversight — all contributing to skyrocketing budgets and an increasingly unpredictable technological landscape. This unbridled enthusiasm, though undeniably promising for innovation, concurrently harbors significant and often underestimated dangers. The current trajectory of AI development has indeed forged a new kind of "lawless land." Pervasive "shadow deployments" of AI systems, unsecured AI endpoints, and unchecked API calls are running wild, creating a critical lack of visibility into who is developing what, and how. Much like the historical gold rush, this is a full-throttle race to exploit a new resource, with alarmingly little consideration given to inherent risks, essential security protocols, or spiraling costs. The industry is already rife with cautionary tales: the rogue AI agent that inadvertently leaked highly sensitive corporate data, or the autonomous agent that, in a mere five minutes, initiated a thousand unauthorized API calls. These "oops moments" are not isolated incidents; they are becoming distressingly common occurrences in this new, unregulated frontier.

Exploring Best Practices and Modern Trends in CI/CD

Aggregated on: 2025-10-22 15:25:52

Let’s start with statistics: continuous integration, deployment, and delivery are among the top IT investment priorities in 2024 and 2025. To be exact, according to GitLab’s 2024 Global DevSecOps report, it is in 8th place (and security is the top priority!). However, it shouldn’t be surprising, as CI/CD practice brings a lot of benefits to IT teams — it helps to accelerate software delivery and detect vulnerabilities and bugs earlier. In this blog post, we focus on CI/CD best practices and modern trends. But first, let's remember what continuous integration and continuous delivery are.

What Is End-to-End Testing?

Aggregated on: 2025-10-22 14:25:52

Being a part of the software team, you would have heard about end-to-end or E2E testing. The testing team ideally prefers to have a round of end-to-end testing to ensure the functional working of the application. Every software application should undergo end-to-end testing to ensure it functions as specified. This testing approach builds confidence in the system and helps development teams determine whether the software is ready for production deployment.

GraphQL vs REST API: Which Is Better for Your Project in 2025?

Aggregated on: 2025-10-22 13:25:52

Key Takeaways REST APIs excel in simplicity, caching, and microservices architecture, with widespread adoption and a mature tooling ecosystem GraphQL provides precise data fetching, reduces over-fetching, and offers superior flexibility for complex data relationships Performance varies by use case: REST wins for simple CRUD operations and caching scenarios, while GraphQL shines in mobile apps and complex queries API Gateway integration is crucial for managing both approaches effectively, providing unified security, monitoring, and transformation capabilities No universal winner: The choice depends on project requirements, team expertise, and specific technical constraints rather than inherent superiority Understanding REST APIs and GraphQL: The Foundation of Modern API Architecture When evaluating modern API architectures, developers frequently encounter the question: "What is a RESTful API, and how does it compare to GraphQL?" According to recent industry data, over 61% of organizations are now using GraphQL, while REST continues to dominate enterprise environments. Understanding both approaches is essential for making informed architectural decisions. What Is a RESTful API? A RESTful API (Representational State Transfer) is an architectural style that leverages HTTP protocols to create scalable web services. REST and RESTful services follow six key principles: statelessness, client-server architecture, cacheability, layered system, uniform interface, and code on demand (optional). Unlike the traditional SOAP protocol and REST debate, where SOAP and REST discussions centered on protocol complexity, RESTful APIs embrace simplicity and web-native patterns.

AI/ML-Based Storage Optimization: Training a Model to Predict Costs and Recommend Configurations

Aggregated on: 2025-10-22 12:25:52

Abstract As cloud storage grows in size and complexity, the challenge of keeping costs under control becomes more urgent. Traditional storage management relies on static rules and manual analysis, but these approaches struggle to keep up with today’s dynamic, data-driven environments. AI and machine learning (ML) are now being used to analyze how data is accessed, predict future costs, and recommend the most cost-effective storage tiers and configurations. This article walks through the process of building a simple machine learning model in Python to predict S3 storage costs and suggest optimal storage classes. Along the way, you’ll see what’s required to get started, the practical value of ML in cloud storage, and lessons learned from real-world deployments.

Building Scalable CRM Systems: Architecture Patterns and Data Modeling Strategies

Aggregated on: 2025-10-22 11:25:52

Customer relationship management (CRM) systems represent one of the most complex software engineering challenges in enterprise development. Beyond their apparent simplicity lies a sophisticated ecosystem requiring careful architectural decisions, robust data modeling, and scalable system design. As organizations grow from hundreds to millions of customer records, the technical decisions made during initial development determine whether the system becomes a competitive advantage or a performance bottleneck. This article examines the core engineering challenges of building modern CRM systems, focusing on architecture patterns, data modeling strategies, and performance optimization techniques that enable systems to scale effectively while maintaining data integrity and user experience quality.

Scaling Boldly, Securing Relentlessly: A Tailored Approach to a Startup’s Cloud Security

Aggregated on: 2025-10-21 19:10:52

Launching a SaaS startup is like riding a rocket. At first, you’re just trying not to burn up in the atmosphere — delivering features, delighting users, hustling for feedback. But, as you start to scale, you realize: security isn’t just a cost center — it’s an accelerant for growth, trust, and resilience. For SaaS startups racing from MVP to unicorn, robust security isn’t just about compliance; it fuels innovation, safeguards reputation, and unlocks enterprise sales. But faced with fierce market demands and thin resources, how can founders, engineers, and security leads scale infrastructure and build trust — all without slowing the agile hustle?

Is My Application's Authentication and Authorization Secure and Scalable?

Aggregated on: 2025-10-21 18:10:52

Nowadays, most application requires authentication and authorization due to increased threat levels, and not only do they need to be secured, but also scalable due to increased traffic volume. It's not that the application doesn't have authentication and authorization in place, but the point is, does it provide security, scalability, and more features around this area? Authentication and authorization are a domain in themselves, and most developers/architects start by using a homegrown mechanism, which is not only less secure most of the time because of a lack of domain expertise, but also lots of time spent in non-core activity, and because of that, the product road-map gets a hit, and value addition in the product becomes slow. This blog will talk in detail about the common mistakes made in this area and how we can avoid or overcome them if we are already stuck.

Next-Gen DevOps: Rule-Based AI Auto-Fixes for PMD, Veracode, and Test Failures

Aggregated on: 2025-10-21 17:10:52

Let Your Pipeline Fix the Small Stuff: A Practical Guide to Self-Healing DevOps Think back to your last deployment. Everything looked great — until a small test failed or a static analysis tool threw a warning. Suddenly, the whole pipeline froze. Someone had to stop what they were doing, dig through logs, make a tiny fix, and restart the process. It’s not a disaster, but it’s death by a thousand cuts. Over the past decade, our CI/CD pipelines have gotten incredibly sophisticated. We run static analysis tools like PMD or SonarQube, security scanners such as Snyk or Veracode, and layer in unit and integration tests to catch regressions early. These gates keep our code safe and compliant, but they also introduce a familiar bottleneck: one small failure can stall the entire flow.

A 5-Minute Fix to Our CI/CD Pipeline That Saved Us 5 Hours a Day

Aggregated on: 2025-10-21 16:10:52

If you’ve ever worked at a startup, you know the rhythm: you’re in constant motion, juggling priorities, shipping fast, putting out small fires, and trying not to light new ones. It’s a bit like running a restaurant during dinner rush: the orders keep coming, and you’re too busy cooking to notice that the stove needs cleaning. After months of sprinting to ship new features, first full-stack session recordings, then our MCP server, then support for annotated recording screens, and most recently, a browser extension and VS Code extension… We finally took a breath and looked at something we hadn’t touched in a while: our CI/CD pipelines.

The Rise of Passkeys

Aggregated on: 2025-10-21 15:10:52

What Are Passkeys? You know how annoying it is to remember all those different passwords for every single website? And how terrifying it is when you hear about a company getting hacked, and suddenly, your password for that site might be out there? Well, imagine logging into PayPal without a password, and even if PayPal's systems got totally breached, your login wouldn't be compromised. That's pretty much what passkeys are all about, and lets deep dive into the secret sauce behind them. If you're like billions of others with a smartphone, you might have already used passkeys on your phone. Companies like Apple, Microsoft, and Google have been rolling out passkeys widely recently. So what is it? PassKeys are a new, more secure way to sign in to websites and apps. They are a replacement for traditional passwords and are designed to be resistant to phishing, easier to use, and secure.

Automating Storage Tiering and Lifecycle Policies in AWS S3 Using Python (Boto3)

Aggregated on: 2025-10-21 14:10:52

Managing cloud storage efficiently is essential for organizations of all sizes. Amazon Web Services (AWS) provides features like S3 Intelligent-Tiering and lifecycle policies to help automatically move files to the most cost-effective storage locations based on how often they are accessed. This article explains, in simple terms and with step-by-step code, how you can use Python and the Boto3 library to automate these processes — making sure your data is always stored in the right place at the right price. References to AWS documentation and helpful resources are provided throughout.

The Dangerous Middle: Agile Roles That AI Will Erode First

Aggregated on: 2025-10-21 13:10:52

TL; DR: Dangerous Middle and the Future of Scrum Masters and Agile Coaches AI is changing product and tech work. Peter Yang, a renowned product leader, argues that AI will split product roles into two groups: Generalists who can prototype end-to-end with AI, and specialists in the top 5% of their fields. Everyone else in the dangerous middle risks being squeezed. How does this apply to agile practitioners: Scrum Masters, Product Owners, Agile Coaches, and transformation leads? It does, with important nuances.

JavaScript Internals: Understanding Runtime Optimization and How to Write Performant Code

Aggregated on: 2025-10-21 12:10:52

From a straightforward browser scripting language, JavaScript has morphed into an ultra-flexible and versatile technology that powers everything from dynamic front-end UIs and back-end services (Node.js) to automation scripts and IoT devices (with libraries like Johnny-Five). Yet, that flexibility introduces a lot of complexity in writing efficient, performant code. Fortunately, JavaScript engines working to execute your code employ a number of optimization strategies during runtime to improve performance. For these optimizations to be most effective, though, you as the developer must understand how the engines practically work and adopt coding practices that kowtow to their internal processes.

A Practical Guide to Deploying Microservices on Kubernetes

Aggregated on: 2025-10-21 11:10:52

So, you're thinking about using Kubernetes to run microservices? You're not the only one. As of 2024, more than 60% of businesses use Kubernetes, and by 2027, that percentage is predicted to climb to more than 90%. That's pretty crazy, huh? But here's the deal. Microservices provide us a lot of freedom and the opportunity to grow, but they can be hard to manage. Do you know what it means? There are dozens or perhaps hundreds of services floating around, and it can be hard to keep track of everything. That's where Kubernetes comes in. It's like having a really clever helper who takes care of all the scalability, deployment, and management for you.

Coarse Parallel Processing of Work Queues in Kubernetes: Advancing Optimization for Batch Processing

Aggregated on: 2025-10-20 19:10:51

In today’s modern distributed systems landscape, many tasks consist of batch processing, and parallel processing divides the jobs into smaller portions and units that can be executed in parallel. Kubernetes, also known for its container orchestration, provides an object called Job to handle such cases. Not all batch jobs are similar. Depending on the details and nature of the task, some coordination is required between them; we might need different patterns for distributing such workloads. That is where the coarse parallel processing model fits in.

AI-Driven Enhancements to Project Risk Management in the PMO

Aggregated on: 2025-10-20 18:10:51

Introduction I began writing this article as a study of how AI can strengthen delivery frameworks. But very quickly I realized: even if I focus solely on its impact on risk management, the topic is still too vast for a single article. That’s why I've tried to strike a balance – to go deeper than general concepts while keeping the text concise enough to be practical and accessible.

MultiCloudJ: Building Cloud-Agnostic Applications in Java

Aggregated on: 2025-10-20 17:10:51

According to a 2024 Gartner report, more than 92% of large enterprises now operate in multi-cloud environments. This reflects strategic priorities such as geographic scalability, high availability, regulatory compliance, and cost optimization. But with these benefits comes significant complexity. Each provider — AWS, GCP, Alibaba Cloud, and others — exposes its own APIs, semantics, and SDKs. As a result, development teams must reconcile divergent models for storage, databases, identity, and more. The outcome is often fragmented codebases filled with conditional logic, code forking, duplicated workflows, and costly rewrites when onboarding new providers. For large organizations, this slows delivery, increases operational risk, and erodes the developer experience.

Adobe Service Runtime: Keep Calm and Shift Down!

Aggregated on: 2025-10-20 16:10:51

Microservices at Adobe Adobe’s transformation from desktop applications to cloud offerings triggered an explosion of microservices. Be it Acrobat, Photoshop, or Adobe Experience Cloud, they are all powered by suites of microservices mainly written in Java. With so many microservices created, every developer had to go through the same painful processes, i.e., security, compliance, scalability, resiliency, etc., to create a production-grade microservice. That was the genesis of Adobe Service Runtime.

Django Architecture vs FastAPI: A Learning Path

Aggregated on: 2025-10-20 15:10:51

When choosing a backend for your next Python project, the comparison between Django and FastAPI often comes up. Many developers who have spent years in Django’s “batteries-included” world eventually experiment with FastAPI for its modern, async-first approach. A Reddit thread titled “Django Architecture versus FastAPI” captures this exact debate: a long-term Django user moving to FastAPI for asynchronous advantages. I am a contributor to FastOpp, an open-source FastAPI starter package for AI web applications. It uses pre-built admin components to give FastAPI functionality comparable to Django for AI-first applications.

AIOps for Predictive Incident Management: A Novel Approach to Proactive DevOps

Aggregated on: 2025-10-20 14:10:51

With the new developments in artificial intelligence, incident management is slowly moving away from the reactive approach in today’s IT landscape. Most of the businesses today operate on the reactive model, where actions are taken only after an incident has disrupted the functioning of the systems. However, with the help of AIOps, which uses artificial intelligence and machine learning, organizations can adopt a proactive approach and take actions to avoid a possible failure from occurring in the first place. This change in the incident management approach will lead to reduced system outages and better utilization of resources to strengthen the systems in place across organizations. Predictive incident management will soon become a core element of IT services for business continuity and operational effectiveness.

Should You Use Azure Data Factory?

Aggregated on: 2025-10-20 13:10:51

So you've been thrown into the world of Azure Data Factory (ADF) and you're wondering what the heck you've gotten yourself into? Been there. Let me break it down for you in terms that actually make sense. What Is This Thing Anyway? Think of Azure Data Factory as the ultimate data moving service. It's like having a really smart conveyor belt system that can grab data from basically anywhere, do stuff to it, and dump it somewhere else. Need to pull customer data from your SQL database, clean it up, and shove it into a data lake? ADF's got you covered.

Creating AI Agents Using the Model Context Protocol: A Comprehensive Guide to Implementation with C#

Aggregated on: 2025-10-20 12:10:51

The fast development of Large Language Models (LLMs) has permanently changed artificial intelligence applications because it provides capabilities for natural language understanding and generation along with reasoning abilities. The remarkable achievements of LLMs remain limited because these models cannot access real-time data sources nor utilize external computational tools [2]. The isolation prevents these models from delivering up-to-date information while restricting their capacity to work with evolving systems and to execute tasks beyond text generation. The current methods for resolving this problem require developers to build application-specific connections for each data source or tool, leading to complex systems that are difficult to manage, scale, and standardize across AI applications. Implementing communication protocols, authentication mechanisms, and data transformation logic requires custom work for each integration project, resulting in high development costs and system incompatibility across systems [3].

3 Dangerous Paradoxes of AI in Software Development

Aggregated on: 2025-10-20 11:10:51

Welcome to crunch time. Your leadership team is on your back about the company’s AI strategy. The board is demanding new productivity innovations. Agentic AI is being sold to you as the next industrial-age miracle. But cut through the bravado and fluff, and there’s a very different and more complex story unfolding for the developers and engineers who have to actually make all of this work.

Event-Driven Chaos Engineering: From Failure to Resilience in Kubernetes

Aggregated on: 2025-10-17 19:10:50

Imagine a ship sailing through unpredictable seas. Traditional chaos engineering is like scheduling fire drills on calm days — useful practice, but not always reflective of real storms. Kubernetes often faces turbulence in the moment: pods fail, nodes crash, or workloads spike without warning. Event-driven chaos engineering is like training the crew with surprise drills triggered by real conditions. Instead of waiting for disaster, it turns every unexpected wave into a chance to strengthen resilience.

AI-Powered Cybersecurity: Inside Google’s Gemini and Microsoft’s Security Copilot

Aggregated on: 2025-10-17 18:10:50

The digital sphere keeps changing at a rapid rate, and cyber threats become more and more refined. With the growth of artificial intelligence (AI), tech giants are now embracing this revolutionary innovation to fortify their digital defences. In this area, leaders including Google and Microsoft have rolled out sophisticated AI-based cybersecurity systems: Google’s Gemini and Microsoft’s Security Copilot . Not only do these platforms improve threat detection and response, but they also drastically change the model for managing security in consumer and enterprise landscapes. This blog examines the recent developments in these tools and their implication for the future of cybersecurity. Rise of AI in Cybersecurity AI has become a game-changer in cybersecurity because it is capable of doing threat detection more quickly and accurately, automating repetitive functions, and allowing predictive analysis. Established security measures tend to be foundationally signature-based or require manual handling of incidents, slow to keep up with the fast advancement of modern threats. AI, on the contrary, is capable of analyzing enormous amounts of data in real time, detecting patterns, and adjusting to new forms of attack. Google and Microsoft have integrated AI into their systems to form potent security solutions for both the individual and the organization.

Taming the Storm: How Chaotic Exploration Shapes Big Picture Event Storming

Aggregated on: 2025-10-17 17:10:50

Having delved into the intricacies of event storming, its various types, and effective workshop facilitation, it’s now time to embark on our first practical example. Initial requirements Our customer requested that we implement a platform to support their training center. Although the list of requirements they provided was somewhat limited, it gave us a starting point for our work:

Beyond Keywords: Modernizing Enterprise Search with Vector Databases

Aggregated on: 2025-10-17 16:10:50

In the modern enterprise, data is everywhere — in emails, documents, customer records, support tickets, code repositories, and more. But as the volume and complexity of data grow exponentially, so does the challenge of finding the right information when you need it. Traditional search engines, based on keyword matching, are often frustrating and inefficient, returning irrelevant or incomplete results. Fortunately, a new approach is transforming enterprise search: semantic search powered by vector databases. By understanding meaning rather than just matching words, this technology enables organizations to unlock deeper insights and dramatically improve efficiency.

Level Up Your Engineering Workflow with Copilot Templates

Aggregated on: 2025-10-17 15:10:50

Software engineers often find themselves writing the same patterns of code again and again. Unit tests, API endpoints, error handling wrappers, and configuration files — these are essential parts of building robust applications, but they are also repetitive and time-consuming. Enter GitHub Copilot. Many engineers already use Copilot as a “pair programmer” to autocomplete functions and suggest code snippets. But Copilot becomes far more powerful when you combine it with structured templates. Templates allow you to guide Copilot, enforce best practices, and generate high-quality, standardized code quickly.