News Aggregator

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:

Making AI Better: A Deep Dive Across Users, Developers, and Businesses

Aggregated on: 2025-10-14 13:10:48

Introduction - Making AI Better. In my previous article, I discussed why making AI faster, better, and cheaper is a critical need today. And I introduced my aim to draw from real-world experiences to discuss doing so I also shared a deep dive into the main challenges and strategies to make AI Faster, while bringing out three key perspectives: End Users, AI Developers, and Businesses. In this article, I will focus on and take a deep dive into the second pillar — Making AI Better.

A Fresh Look at Optimizing Apache Spark Programs

Aggregated on: 2025-10-14 12:10:48

I have spent countless hours debugging slow Spark jobs, and it almost always comes down to a handful of common pitfalls. Apache Spark is a powerful distributed processing engine, but getting top performance requires more than just running your code on a cluster. Even with Spark’s built-in Catalyst optimizer and Tungsten execution engine, a poorly written or configured Spark job can run slowly or inefficiently. In my years as a software engineer, I have learned that getting top performance from Spark requires moving beyond the defaults and treating performance tuning as a core part of the development process. In this article, I will share the practical lessons I use to optimize Spark programs for speed and resource efficiency.

How Developers Use Synthetic Data to Stress-Test Models in Noisy Markets

Aggregated on: 2025-10-14 11:10:48

Every quant knows the ritual: collect historical prices, engineer features, and run a backtest. Yet when those same backtests are applied to thinly traded equities or frontier markets, results collapse. Missing data points, illiquidity, regulatory shifts, and outright distortions creep in. The backtest looks elegant on paper, but fails instantly in production. The issue is not strategy alone — it is the dataset itself. Markets like India, Southeast Asia, or even small-cap pockets in developed economies simply do not provide the clean, high-frequency datasets that models built on U.S. equities assume. That fragility pushes developers toward a new approach: synthetic data generation. By constructing engineered datasets that mimic volatility, liquidity droughts, and regime shifts, quants can rehearse reality in controlled environments.

Operationalizing Responsible AI: Turning Ethics Into Engineering

Aggregated on: 2025-10-13 19:10:48

Lately, it feels like everyone is talking about responsible AI — but what does it actually mean when you are the engineer pushing a model to production? You already check for latency, accuracy, and monitoring before a release — but do you ever check off "ethical AI"? When your model delivers a prediction or recommendation and a user asks, "Why these results and not the other ?", do you have a clear explanation or just a shrug and "the algorithm suggested it"? This is the uncomfortable gap between AI capability and AI accountability.

Automating REST Interface Generation

Aggregated on: 2025-10-13 18:10:48

Abstract In the context of financial services application development, adherence to architectural guidelines is essential. A team of IT architects experimented with Large Language Models (LLMs) to automate the generation of REST interfaces, reducing development time by up to 30%. However, the implementation revealed limitations in managing complexity and ensuring rule consistency. This article explores the challenges faced, the solutions adopted, and the prospects for further improving the process. The starting point I have been working for more than 5 years as an IT architect, supporting application development teams in financial services.

Apache Iceberg REST Catalog: The Key to Vendor-Agnostic Data Interoperability

Aggregated on: 2025-10-13 17:10:48

Apache Iceberg Open Table Format was released in the community with many features, but became popular due to interoperability. This interoperability makes Iceberg vendor-agnostic and SQL engine-agnostic. Iceberg REST Catalog (IRC) made interoperability smooth and simple. IRC can solve integration-related challenges in big data analytics ecosystems. This becomes so important, especially in the Data Mesh framework, where you have multiple data publishers and consumers connected through a central governance platform. Using IRC, companies can save costs on the data redundancies, compute, and operational overhead.

What Is API Testing?

Aggregated on: 2025-10-13 16:10:48

APIs are the buzzword of the software industry these days. They power most modern applications and allow seamless communication between different systems, services, and platforms. From booking a cab to making online payments, APIs silently work in the background to connect everything. They not only save development time but also enhance scalability, flexibility, and innovation for businesses. In short, APIs act as the backbone of the digital ecosystem we rely on every day.

AI Infrastructure: Compute, Storage, Observability, Security, and More

Aggregated on: 2025-10-13 15:10:47

In this third article of the AI infrastructure series, you will learn about AI infrastructure compute, storage, observability, performance, optimization (deep dive), and security. This is the final part in my three-part AI infrastructure series. It's recommended to read the previous two articles published on DZone: AI Infrastructure for Agents and LLMs: Options, Tools, and Optimization AI Infrastructure Guide: Tools, Frameworks, and Architecture Flows Compute Layer Architecture The Compute Layer provides the raw processing power needed for AI workloads, with specialized considerations for GPU management, resource allocation, and workload scheduling. This layer must handle the unique characteristics of AI workloads: high memory requirements, long-running processes, and dynamic resource needs.

AI-Driven Developer Tools: Transforming the Future of Software Development

Aggregated on: 2025-10-13 14:10:47

Artificial intelligence is no longer such a far-fetched example of technology in software development; it is already a strong catalyst for change in software development. Machine learning requires less time, offers more intelligent decision-making, and streamlines repetitive tasks by using I-based developer tools. Rather than developers losing time to debugging, boilerplate code, or testing every possible scenario, AI tools will assist with many of these implementations. It is streamlining the efforts of software teams so that they can deliver on projects more quickly, with less code debt, and more time to spend on creative problem-solving and innovation.

Write Once, Enforce Everywhere: Reusing Rego Policies Across Build and Runtime

Aggregated on: 2025-10-13 13:10:47

In most organizations, security and compliance are enforced twice — once during build-time checks and again at runtime through admission controllers and monitoring systems. Often, the policies written at build-time are not reused at runtime, leading to drift, redundancy, and gaps in enforcement. With the rise of Open Policy Agent (OPA) and Rego, teams now have the opportunity to unify policy logic and reuse it seamlessly across both phases. This article discusses the principles, design patterns, and practical techniques for reusing Rego policies at build-time and runtime, helping teams reduce duplication, improve compliance confidence, and accelerate software delivery.

Beyond Traditional Load Balancers: The Role of Inference Routers in AI Systems

Aggregated on: 2025-10-13 12:10:47

Inference routing is the process of routing AI inference requests to the most suitable model based on cost, latency, quality, etc. Unlike simple round robin-based routing found in traditional load balancers, factors such as request complexity, cost constraints, and GPU resource availability are considered in the decision-making layer. It acts as a layer that ensures requests are served by the optimal model for the given request, improving efficiency and performance in multi-model environments. A few examples of inference routers are vLLM router, Azure Inference router, OpenRouter, etc. Selecting the Correct Model for the Current Use Case Selecting the correct model for a use case involves benchmarking and evaluating models against well-defined criteria, as illustrated in Azure AI Foundry’s model benchmarks approach. This process starts by identifying the request type, such as text generation, summarization, or reasoning, and then comparing candidate models on metrics like accuracy, latency, throughput, and cost. Benchmarks provide standardized tests that simulate real-world use cases, enabling developers to assess trade-offs between performance and efficiency.

Why Enterprise AI Needs Agentic Messaging Platforms

Aggregated on: 2025-10-13 11:10:48

Enterprise AI initiatives often follow a predictable pattern. They launch with ambitious goals: "We need AI agents that can automate workflows, integrate with our systems, and execute complex business logic." The demonstrations are compelling. The potential is clear. But then the implementation reality sets in.

Infusing AI into Your Java Applications

Aggregated on: 2025-10-10 19:10:46

Artificial intelligence (AI) is becoming increasingly pervasive. As an Enterprise Java developer, you might be wondering what value AI can add to your business applications, what tools Java provides to easily do that, and what skills and knowledge you might need to learn. In this article, we equip you with the basic knowledge and skills that you need to start exploring the capabilities of AI to build intelligent and responsive Enterprise Java applications. When we talk about AI in this article, we mean getting responses from a large language model (LLM) based on a request that the Java application sends to the LLM. In our article’s example, we create a simple chatbot that customers can ask for planetary tourist destination recommendations, and then use to book a spaceship to visit them. We demonstrate using Java frameworks like LangChain4j with Quarkus to efficiently interact with LLMs and create satisfying applications for end-users.

Diving into JNI: My Messy Adventures With C++ in Android

Aggregated on: 2025-10-10 18:10:46

So, I've been deep in the trenches with JNI lately (yeah, that Java Native Interface stuff) while working on a project where we had to plug a C++ AI assistant into our Android app. At first, it felt like stepping into a weird twilight zone — half Java, half C++, and all these random edge cases you never think about until you hit them. I remember staring at the stack trace for what felt like hours, realizing that one tiny missed DeleteLocalRef was enough to crash the whole app. Thought I'd share what actually tripped me up, what worked, and some ways to make life a little less miserable if you ever have to do this. What the Hell Is JNI Anyway? JNI is basically the bridge that lets Java (or Kotlin) talk to C/C++ code and vice versa. On Android, it’s the only real way to get heavy lifting done efficiently or access low-level APIs that Java/Kotlin just can't reach. Honestly, the first time I tried to wrap my head around it, I felt like I was learning a new language on top of Java and C++ at the same time.

Long-Running Durable Agents With Spring AI and Dapr Workflows

Aggregated on: 2025-10-10 17:10:46

Over the last year, we have seen a rise in various patterns and usages that combine popular frameworks, such as Spring AI and LLM interactions. In January this year, Christian from the Spring AI team published Building Effective Agents with Spring AI, covering common agentic patterns described in the Anthropic paper titled Building Effective Agents. I strongly recommend both of these blog posts to gain a good understanding of how these concepts are shaping up and the tools needed to implement the patterns suggested in these two articles.

Introduction to Spring Data Elasticsearch 5.5

Aggregated on: 2025-10-10 16:10:46

It's been a while since my first article dedicated to Spring Data Elasticsearch usage as a NoSQL database was published. A couple of articles with configuration changes or hints followed the first article. Therefore, the main goal of this article is to define a new baseline for the full Elasticsearch setup. Note: All previous articles are listed at the end.