News Aggregator

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.

PostgreSQL Full-Text Search vs. Pattern Matching: A Performance Comparison

Aggregated on: 2025-10-17 14:10:50

A previous article explains why PostgreSQL full-text search (FTS) is not a good candidate for implementing a general find functionality, where the user is expected to provide a pattern to be looked up and matched against the fields of one or multiple entities. Considering the previously explained technical challenges, it is clear that FTS is great for semantic and language-aware search, although it cannot cover raw searches in detail for various use cases. In the field of software development, it isn’t uncommon for us to need to accept trade-offs. Actually, almost every decision that we make when architecting and designing a product is a compromise. The balance is tilted depending on the purpose, requirements, and specifics of the developed product so that the solution and the value delivery are ensured, and the customers are helped to accomplish their needs.

Anypoint Mulesoft Masking Sensitive Data With DataWeave Custom Function in Logging

Aggregated on: 2025-10-17 13:10:49

When working with Mulesoft integration applications, especially when exchanging data between services, APIs, or third-party applications. It's important to mask sensitive data while logging. Masking specific fields in payloads refers to the process of obfuscating sensitive information (like personal identifiers, financial data, or authentication tokens) before it's logged.

We Tested Context7 With ZK Documentation: Here's What We Learned

Aggregated on: 2025-10-17 12:10:49

AI coding assistants have become increasingly capable in understanding not only code but also project-specific documentation. In this experiment, we tested Context7, a new MCP server, to see how well it could work with a large-scale, real-world documentation set — ZK Framework’s developer documentation. For readers unfamiliar with ZK, it’s a Java-based web framework that allows developers to build rich web applications with minimal JavaScript by providing server-side components. Because ZK documentation covers both UI and server-side concepts, it serves as a good benchmark for evaluating how AI models handle complex, domain-specific technical text.

DevEx Ambient Agent With Advanced Knowledge Graph

Aggregated on: 2025-10-17 11:10:49

As developers, we all face friction points throughout the day — from repetitive tasks to sifting through documentation or getting stuck on a challenging bug. The Agentic DevEx Assistant was built to help with these challenges by acting as a developer’s co-pilot. Instead of just generating code, this tool works alongside you, proactively offering contextual assistance, automating common tasks, and helping you navigate your team’s collective knowledge. My aim is to reduce that friction, speed up feedback loops, and create a more efficient and enjoyable development environment.

From Ticking Time Bomb to Trustworthy AI: A Cohesive Blueprint for AI Safety

Aggregated on: 2025-10-16 19:10:49

The emergence of AI agents has created a "security ticking time bomb." Unlike earlier models that primarily generated content, these agents interact directly with user environments, giving them freedom to act. This creates a large and dynamic attack surface, making them vulnerable to sophisticated manipulation from a myriad of sources, including website texts, comments, images, emails, and downloaded files. The potential consequences are severe, ranging from tricking the agent into executing malicious scripts and downloading malware to falling for simple scams and enabling full account takeovers. This new reality of interactive agents renders traditional safety evaluations insufficient and demands a more comprehensive blueprint — one that connects foundational strategy to practical defense and scales through industry-wide collaboration.

Centralized Job Execution Strategy in Cloud Data Warehouses

Aggregated on: 2025-10-16 18:10:49

Control Diagram The Architecture: Core Components Query vault table Controller procedures Trigger point (can be an external or internal trigger) Details of Core Components 1. Query Vault Table This table serves as the heart of this strategy. We can securely store all the queries that load data into the cloud data warehouse in this Vault table. This table contains the following fields:

The Ethics of AI Exploits: Are We Creating Our Own Cyber Doomsday?

Aggregated on: 2025-10-16 17:10:49

As artificial intelligence advances at rates never previously encountered, its impact upon society is taking hold ever more profoundly and extensively. From autonomous vehicles and personalized medicine to generative media and intelligent infrastructure, AI is changing every area it touches. But lurking in the background of these revolutionary promises is a chilly, black fear: Are we also building the tools of our own digital demise? The ethics of AI exploits, although intentional or emergent, raise profoundly disturbing questions about the cybersecurity, anonymity, and even global security of the future.

Deep Linking in Enterprise Android Apps: A Real-World, Scalable Approach

Aggregated on: 2025-10-16 16:10:49

In modern enterprise Android development, navigating users through massive apps with multiple modules, features, and roles can quickly become complex. This is where deep linking steps in as a game-changer. It enables direct routing to specific parts of your app — skipping redundant screens and delivering a seamless, contextual experience. In this article, we’ll walk through a real-world example and a modern architecture that makes deep linking not only functional but also secure, maintainable, and testable. We’ll use Jetpack components like ViewModel, dependency injection with Hilt, clean business logic through UseCases, and URI validation to keep things safe and predictable.

Crypto Agility for Developers: Build Agile Encryption Now

Aggregated on: 2025-10-16 15:10:49

In 2025, software development is evolving rapidly with the rise of Vibe Coding and Agentic AI, but so is the cryptographic landscape that underpins these systems. As quantum computing moves closer to practical applicability and encryption standards become outdated, one imperative is becoming unavoidable: crypto agility. Crypto agility, the ability of systems to switch between cryptographic algorithms and protocols quickly without requiring massive re-engineering, is no longer just a security or compliance concern. It’s becoming a core requirement of resilient, future-ready software. For developers building applications that require trustworthiness and security, now is the time to understand and embrace crypto agility.

Maximize Your AI Value With Small Language Models

Aggregated on: 2025-10-16 14:10:49

Just about every developer I know has the same story about their first generative AI project. They spin up a proof of concept using GPT-4 or Claude, get amazing results, and then watch their AWS bill explode when they try to scale. The promise of AI inevitably meets the reality of infrastructure costs, and suddenly that revolutionary feature becomes a budget line item nobody wants to defend. For many engineering teams, there’s an alternative. Instead of defaulting to the biggest, most powerful models available, more engineering teams are discovering that small language models (SLMs) can deliver 90% of the value at 10% of the cost. The math is compelling, but the implementation story is arguably even better. Here’s what to know about shrinking your model to maximize your results.

Build AI Agents with Phidata: YouTube Summarizer Agent

Aggregated on: 2025-10-16 13:10:49

This is the first article in a two-part series on building AI agents from the ground up. In this article, we will explore the value of AI agents, introduce popular agentic AI platforms, and walk through a hands-on tutorial for building a simple AI Agent. The second part of the series will dive deeper with a hands-on tutorial, where we’ll build agents that can automate tasks and interact with external tools and APIs.

A Technical Practitioner's Guide to Integrating AI Tools into Real Development Workflows

Aggregated on: 2025-10-16 12:10:49

While leadership debates AI strategy, there's a growing divide in development teams: junior developers are shipping features faster with Cursor and GitHub Copilot, while senior engineers question whether AI-assisted code is maintainable at scale and can often be found criticizing the junior devs for their use of AI If you're a tech lead, architect, or senior developer, navigating this transition is not easy. The Technical Reality: AI Tools in Production Codebases Let's address the elephant in the room: AI coding tools are not magic, but they're not gimmicks either:

Porting From Perl to Go: Simplifying for Platform Engineering

Aggregated on: 2025-10-16 11:10:49

The Problem With the brew upgrade Command By default, the brew upgrade command updates every formula (terminal utility or library). It also updates every cask (GUI application) it manages. All are upgraded to the latest version — major, minor, and patch. That’s convenient when you want the newest features, but disruptive when you only want quiet patch-level fixes. Last week, I solved this in Perl with brew-patch-upgrade.pl, a script that parsed brew upgrade’s JSON output, compared semantic versions, and upgraded only when the patch number changed. It worked, but it also reminded me how much Perl leans on implicit structures and runtime flexibility.

Centralized Configuration Management With Consul

Aggregated on: 2025-10-15 19:25:49

What Is Centralized Configuration? In modern microservice architectures, multiple applications often share common configuration data (e.g., database settings). These might be multiple instances of the same service or entirely different services. Regardless of the service behavior, instead of maintaining configuration at the service level, we can centralize it in one place and distribute it across all services. HashiCorp Consul provides a solution for this. Consul config distribution

Building a Fault-Tolerant Microservices Architecture With Kubernetes, gRPC, and Circuit Breakers

Aggregated on: 2025-10-15 18:25:48

Over the last decade, microservice architectures have become commonplace when designing scalable, maintainable, and independently deployable applications. Breaking down a system into multiple, domain-focused services, development squads can quickly develop, have varying technology stacks per service, and independently scale an application's constituent pieces. But this flexibility has its cost: operational complexity and failure propagation. Unlike monoliths, whose failures may be localized to one runtime, microservices communicate over networks. Each service-to-service invocation creates a possibility of latency, partial failure, or total unavailability. In a critical dependency, when this occurs, it causes cascading failures — where one service's downtime propagates through the system, ruining the user experience or even causing complete outages.

Why Domain-Driven Design Is Still Essential in Modern Software Development

Aggregated on: 2025-10-15 17:25:48

There’s no doubt that software has become the invisible infrastructure of our modern world. Over a decade ago, Forbes published a prophetic article titled “Now Every Company Is a Software Company.” At the time, that sounded bold — today, it feels like common sense. Whether in banking, healthcare, logistics, or agriculture, software has moved from the background to the core of business strategy. This means that every company, regardless of industry, is now a software-driven organization. Success no longer depends only on market reach or physical infrastructure but also on how effectively a business translates its goals into code. In other words, the quality of your software often determines the quality of your company’s decisions, processes, and customer experience.

Python Development With Asynchronous SQLite and PostgreSQL

Aggregated on: 2025-10-15 16:25:48

After years of working from the comfort of Python and Django, I moved to the wild asynchronous world of FastAPI to improve latency in web-based AI applications. I started with FastAPI and built an open-source stack called FastOpp, which adds command-line and web tools similar to Django. Initially, things went smoothly using SQLite and aiosqlite to add AsyncIO to SQLite. I used SQLAlchemy as my Object Relational Mapper (ORM) and Alembic as the database migration tool. Everything seemed to work easily, so I added a Python script to make things similar to Django’s migrate.py.

Distributed Locking in Cloud-Native Applications: Ensuring Consistency Across Multiple Instances

Aggregated on: 2025-10-15 15:25:48

Overview I am sure that most of us may have used some kind of locking during development, or may have faced issues of incorrect results in some states that are difficult to reproduce. Things are not that complex when we need to manage them within the process or even multiple processes, but on the same machine. It is also very common these days that most of us are involved in making cloud-native applications/services, where there are multiple instances of the service[s], either due to high availability/load balancing. In case of multiple instances of service[s], things become trickier when you face a situation where you need to make sure that certain operations must be performed in a synchronized manner, and it's not about multiple threads/processes but multiple pods/nodes in a native environment.

The Era of AI-First Backends: What Happens When APIs Become Contextualized Through LLMs?

Aggregated on: 2025-10-15 14:25:48

Introduction: What Happens When APIs Start Thinking? Wondered what your backend might "think" about? Up until now, we have viewed LLMs (e.g., OpenAI's GPT series) as a code assistant or a chatbot. However, behind the scenes of those experiences is something that can take things to a much more impactful level: an AI-first backend experience. In this type of environment, APIs do not simply follow the pre-packaged flow, http status codes, or utility functions of a backend. Instead, they think, adapt, and develop logic dynamically at runtime based on LLMs. Imagine the API you are building does not adhere to the rigid flow of a flowchart or the meticulously precise steps of an HTTP post or get setup within your functionality. Rather, it responds and adapts logic based on the tone of the user, the prosody of the interaction, or state of the world (trends and behaviors at the time). Sounds like science fiction? Not anymore. Let's unpack how this works, why it will change the way you think about your applications, and how you can "test" it out today.

Types of Web 3 APIs

Aggregated on: 2025-10-15 13:25:48

An API (Application package interface) is a software tool that enables researchers and developers to access some third-party data and functionality within a main software. Usually, it’s a collection of software commands that act as an interface to an external database. Web 3 APIs act as translators, enabling applications to interact with features like smart contracts and on-chain data, empowering you to harness the power of Web3 without diving deep into technical complexities. Various API categories —REST, SOAP, RPC, and WebSocket— offer unique strengths tailored to different use cases:

Indexing Across Data Models: From Tables to Documents to Text

Aggregated on: 2025-10-15 12:25:48

Every modern software application relies on a database to persist and manage its data. The choice of database technology is largely influenced by the application’s data model and its read and write throughput. For large datasets, query efficiency is critical. An inefficient query that works on a small dataset can quickly turn into a performance bottleneck when scaled to hundreds of thousands or millions of data points. While query optimization helps, it alone cannot guarantee high throughput. Factors such as data modeling, normalization, partitioning strategies, indexing, and even hardware resources all play a role in determining how quickly a system can serve reads and process writes.

Beyond Secrets Manager: Designing Zero-Retention Secrets in AWS With Ephemeral Access Patterns

Aggregated on: 2025-10-15 11:25:48

Secrets management in AWS has traditionally relied on long-lived secrets stored in Secrets Manager or Parameter Store. But as attack surfaces grow and threat actors become faster at exploiting exposed credentials, even rotated secrets begin to look like liabilities. The future of security in AWS leans toward ephemeral access, where credentials are generated just-in-time, scoped to the minimum needed permission, and vanish as soon as they are no longer needed. This article explores how to build a zero-retention secrets architecture in AWS, one that minimizes persistent secrets and instead leverages IAM roles, STS, session policies, and Lambda-based brokers. No Vault, no standing tokens, just-in-time, context-aware access.

Senior Developers, What to Read Next?

Aggregated on: 2025-10-14 19:10:48

Recently, one of my best friends, who is, in the meantime, one of the smartest developers I have the luck to know, asked me what book he should read next to further develop his skills. It took me some time to gather my thoughts, and it might be useful for others, too. Spoiler alert: I could not find a single book that I would say is the one to read as a senior developer. Instead, I summarized the books that I found good for one reason or another. As the summary also declared, this is a subjective list; feel free to agree or disagree with my choices, as well as feel free to leave a comment or contact me in any other way to share your thoughts.

Agentic AI: Why Your Copilot Is About to Become Your Coworker

Aggregated on: 2025-10-14 18:10:48

You've spent the last two years playing with ChatGPT, GitHub Copilot, and various AI assistants. You ask questions, they answer. You request code, they generate it. But here's what's changing in 2025: AI is about to stop waiting for your instructions and start completing entire workflows autonomously. Welcome to the age of agentic AI — and it's going to fundamentally change how software gets built, deployed, and maintained.

Where Stale Data Hides Inside Your Architecture (and How to Spot It)

Aggregated on: 2025-10-14 17:10:48

Every system collects stale data over time — that part is obvious. What’s less obvious is how much of it your platform will accumulate and, more importantly, whether it builds up in places it never should. That’s no longer just an operational issue but an architectural one. In my experience, I’ve often found stale data hiding in corners nobody thinks about. On the surface, they look harmless, but over time, they start shaping system behavior in ways that are hard to ignore. And it’s not just a rare edge case: studies show that, on average, more than half of all organizational data ends up stale. That means the risks are not occasional but systemic, quietly spreading across critical parts of the platform.

CNCF Triggers a Platform Parity Breakthrough for Arm64 and x86

Aggregated on: 2025-10-14 16:10:48

The Challenge Developing open-source software for deployment on Arm64 architecture requires a robust continuous integration and continuous deployment (CI/CD) environment. Yet, there has historically been a disparity between the levels of support for Arm64 and traditional x86 processor architectures, with Arm64 usually at a disadvantage. Developers of infrastructure components for multiple architectures have certain expectations of their work environments: Consistency of the tools and methods they use across platforms, so they don’t have to adopt different development procedures just to adopt a less prevalent platform. Performance from their platforms and support mechanisms, so their deployment schemes don’t suffer from speed deficiency when they choose to support multiple platforms. Testing coverage so the very same tests for efficiency, compliance, and security apply to all platforms simultaneously and without substantial differentiation. Maintainability, enabling developers to automate their integration and redevelopment processes so they apply to all platforms without alteration. Product managers for these same components have these same requirements, plus at least two more:

Advanced Snowflake SQL for Data Engineering Analytics

Aggregated on: 2025-10-14 16:10:48

Snowflake is a cloud-native data platform known for its scalability, security, and excellent SQL engine, making it ideal for modern analytics workloads. Here in this article I made an attempt to deep dive into advanced SQL queries for online retail analytics, using Snowflake’s capabilities to have insights for trend analysis, customer segmentation, and user journey mapping with seven practical queries, each with a query flow, BI visualization, a system architecture diagram, and sample inputs/outputs based on a sample online retail dataset.

Our Path to Better Certificate Management With Vault and FreeIPA

Aggregated on: 2025-10-14 15:10:48

Managing public key infrastructure (PKI) is challenging, especially in dynamic, cloud-native environments. In the “good old days,” you could create a virtual machine, place a certificate on it, and forget about it for a couple of years (or at least until the certificate expired). But as modern infrastructure has evolved, a more automated and scalable approach is needed. In this article, we’ll explore how to configure HashiCorp Vault as a subordinate Certificate Authority (CA) under FreeIPA, how to request certificates, and build a certificate chain trusted by any host in your infrastructure.

Inside Microsoft Fabric: How Data Agents, Copilot Studio, and Real-Time Intelligence Power the AI-Driven Enterprise

Aggregated on: 2025-10-14 14:10:48

Microsoft Fabric has been everywhere since its preview in 2023. From the rapid growth of features to rapid adoption, what began as a unified data platform is now a full-stack ecosystem. For an experienced Power BI user, Fabric will be both familiar and upgraded, even complex at that. The learning curve is steep but justified by the payoff. BI teams are enabled to move beyond dashboards to orchestration, governance, and scalability of analytics. Business intelligence in Fabric is not just about mastering a single tool anymore, it is about mastering a suite of interconnected tools and technologies. From Delta Lake architecture and OneLake semantics to streaming pipelines, SQL endpoints, and choosing between DirectLake/Import modes, the landscape demands fluency across the entire platform. In this article, we are going to explore how Fabric’s AI agents ecosystem works in practice by primarily focusing on the following topics: