News Aggregator

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.

Stop React Native Crashes: A Production-Ready Error Handling Guide

Aggregated on: 2025-10-10 15:10:46

You are demonstrating your next mobile application idea to investors, and suddenly the application crashes. Now you are clueless whether it crashed due to bad WIFI at the venue, your service is down, or your application itself hit a snag. There are no logs or alerts to tell you what just happened. Mere imagination of this scenario can send any founder or developer into panic and nervousness. Therefore, having robust error handling is critical to modern-day mobile applications. React Native has become an industry standard in building cross-platform native mobile applications. React Native boasts a rich ecosystem of error handling at various levels of architecture. However, often projects lack clear guidelines on how to handle various error scenarios. This results in either overdoing or underdoing error handling in otherwise robust and feature-rich applications. In this article, you will learn how to adopt a three-layer error handling mechanism, which is gaining widespread popularity with major enterprise-level applications built at leading companies. The article will give you a practical guide instead of pure theory, so you are ready to adapt these concepts immediately.

Fixing a Test Involves Much More Than Simply Making It Pass

Aggregated on: 2025-10-10 14:10:46

Software tests are essential mechanisms in the development lifecycle designed to verify that code behaves as expected. They catch bugs, ensuring functionality aligns with requirements, and maintain overall system reliability through automated assertions on inputs, outputs, and states. However, fixing a failing test extends far beyond merely adjusting it to pass, as this superficial approach can mask deeper issues in the underlying code or architecture. True resolution demands a thorough investigation into the root cause. It could be a logic error, an environmental mismatch, a dependency failure, or an outdated assumption in the test itself, followed by targeted corrections that preserve the test's intent as a safeguard against regressions. Blindly modifying assertions to match unexpected outcomes, such as changing an expected value from false to true without validating why the discrepancy occurred, risks introducing false positives, eroding trust in the test suite.

AI-Assisted Kubernetes Diagnostics: A Practical Implementation

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

Kubernetes troubleshooting follows a repetitive pattern: identify unhealthy pods, examine descriptions, review logs, analyze events, and correlate information to find root causes. For common issues like CrashLoopBackOff, ImagePullBackOff, or OOMKilled pods, engineers repeat the same diagnostic steps daily, sometimes dozens of times per week in busy production environments. The traditional workflow requires running multiple kubectl commands in sequence, mentally correlating outputs from pod descriptions, container logs, event streams, and resource configurations. An engineer investigating a single failing pod might execute 5–10 commands, read through hundreds of lines of output, and spend 10-30 minutes connecting the dots between symptoms and root causes. For straightforward issues like memory limits or missing images, this time investment yields solutions that follow predictable patterns.

Building Realistic Test Data in Java: A Hands-On Guide for Developers

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

There’s something that every backend or API developer faces sooner or later: the need for good fake data. Whether you’re testing a new API, populating a database for demos, or simply trying to make your unit tests less “boring”, fake data is part of your daily routine. The problem? Most fake data feels… fake. You end up with “John Doe” and “123 Main Street” repeated over and over, which doesn’t look great when showing a prototype to your team or client.

Securing HTTPS From the Inside Out: Preventing Client-Side Interception Attacks

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

Overview HTTPS is the most common mechanism used to protect client-server communication on the internet. Most teams focus on SSL/TLS and server-side hardening — and for good reason — but security is layered: the system is only as strong as its weakest link. This article focuses on a specific class of HTTPS man-in-the-middle (MITM) attacks that is not widely discussed. The aim is to harden one of the weaker links in many deployments. If you believe it’s perfectly safe to use a banking site over HTTPS on a device that isn’t yours (a friend’s laptop, an office machine), please read on.

Popular DevOps Automation Tools

Aggregated on: 2025-10-09 19:10:45

DevOps automation tools streamline the path from idea to deployment by turning repetitive, error-prone steps into reliable, repeatable workflows that reduce toil and shorten mean time to recovery (MTTR). This article walks through five categories of DevOps automation tools. Expanding your tool stack thoughtfully is less about quantity and more about reducing change failure rate and increasing deployment frequency in a safe way.

Using LLMs to Automate Root Cause Analysis in Incident Response

Aggregated on: 2025-10-09 18:25:45

Executive Summary In today’s complex cloud and microservices-based systems, it’s no surprise that things break. While we’ve made huge strides in detecting issues quickly with modern observability tools, getting to the actual root of a problem — what really caused the incident — is still a tough, manual, and time-consuming task. That’s where large language models (LLMs) step in. These AI models are trained to understand logs, alerts, documentation, and natural language — all of which are crucial during incidents. By tapping into the power of LLMs, teams can significantly speed up root cause analysis (RCA), reduce downtime, and even lay the foundation for self-healing systems.

Evaluating LLM-Powered Voice Assistants: A Guide Beyond Traditional Metrics

Aggregated on: 2025-10-09 17:10:45

Voice assistants have evolved from being simple, rule-based systems to advanced conversational agents driven by large language models (LLMs). Early versions of voice assistants could only handle specific tasks with pre-defined commands. In contrast, modern LLM-powered assistants can now engage in long and open-ended conversations, follow complex instructions, and perform multi-step reasoning. These improved capabilities bring new evaluation challenges. Traditional metrics like intent classification accuracy, slot-filling accuracy/recall, and goal completion rates can no longer capture the overall quality of a voice assistant. Assistant responses can sound fluent and plausible, even when they contain factual errors or unsafe content. For example, an LLM assistant might correctly identify a user’s request to “find Italian restaurants” (intent) and extract the location “downtown” (slot), but then respond with a restaurant name that doesn’t even exist. Traditional benchmarks would mark the intent/slot task as successful, without accounting for the factual error. Therefore, new metrics and techniques are needed to assess factuality, safety, reasoning ability, instruction following, and user experience.

Building a Supply Chain Digital Twin Technology

Aggregated on: 2025-10-09 16:10:45

About Supply Chain Supply chain means that the entire system is involved, from the producer to the customer. This process involves producing and delivering a product or service to the customer. It includes all the methods, systems, organizations, people, activities, information, and resources. The following are some of the steps involved in the supply chain:

How GenAI Can Eliminate SME Bottlenecks in Enterprise Systems

Aggregated on: 2025-10-09 15:25:45

In large-scale enterprises, tribal knowledge often stalls developer productivity and slows operational agility. Subject matter experts (SMEs) become bottlenecks when critical business logic and configuration decisions live only in legacy codebases or in the minds of a few veterans. This article presents an architecture that leverages GraphCodeBERT embeddings, FAISS vector search, and large language models (LLMs) like Sonnet or Claude to transform complex enterprise logic into conversational explanations. Drawing on real-world use cases in asset lifecycle processing and internal platform operations, it outlines a scalable, GenAI-driven approach to overcoming SME bottlenecks and accelerating engineering velocity.

Low Code, High Impact: A Solution Architect’s Guide to Building Scalable Community Platforms With AI and Low-Code Tools

Aggregated on: 2025-10-09 14:10:45

As a solution architect, I’ve spent most of my career building massive enterprise systems — cloud-native platforms, scalable APIs, and robust infrastructure. So when I took it upon myself to build a digital hub for our Chhath Puja community in Southern California, it was a refreshing shift from the corporate grind. This time, there was no project team, no IT budget, and no stakeholder meetings. Just me — and the need to launch something meaningful, quickly.

Virtualization Meets Acceleration: Powering AI Workloads

Aggregated on: 2025-10-09 13:25:45

Artificial Intelligence has quickly moved from buzzword to business driver. From chatbots and fraud detection to medical imaging and predictive analytics, AI has found a home in nearly every industry. But as AI evolves, so do the infrastructure demands that support it. Training large models or running real-time inference pipelines isn’t trivial — it takes serious compute, bandwidth, and orchestration. Here’s the good news: most enterprises already have the core building blocks needed for an AI-ready high-performance computing (HPC) environment sitting quietly in their data centers. We're talking about Cisco UCS servers, Cisco network switches, and VMware virtualization — technologies that are already deeply embedded in IT ecosystems across industries.

Efficiently Reading Large Excel Files (Over 1 Million Rows) Using the Open-Source Sjxlsx Java API

Aggregated on: 2025-10-09 12:10:45

If you are a developer, regardless of the technologies you use, at some point, you will face the challenge of handling large Excel files. Most commonly, you will see the "out of memory" error when handling these files.

Theia Deep Dive, Part 2: Mastering Customization

Aggregated on: 2025-10-09 11:25:45

In the first part, we set up the basics: Theia runs in the browser, plugins work, themes and icons load, and we even added a splash screen. That gives us a functional IDE, but it’s still pretty close to stock. This part is about shaping it into our own product. We’ll start by stripping out what we don’t need, then adjust the UI and wire up contributions so the editor feels focused and intentional.

The Top 20 Problems With Batch Processing (and How to Fix Them With Data Streaming)

Aggregated on: 2025-10-08 19:25:45

Batch processing has long been the default approach for moving and transforming data in enterprise systems. It works on fixed schedules, processes data in large chunks, and often relies on complex chains of jobs that run overnight. While this was acceptable in the past, today’s digital businesses operate in real time — and can’t afford to wait hours for fresh insights. Delays, errors, and inconsistencies caused by batch workflows lead to poor decisions, missed opportunities, and growing operational costs. In this post, we’ll look at common issues with batch processing and show why data streaming is the modern alternative for fast, reliable, and scalable data infrastructure.

Strapi v5: Customization Nuances

Aggregated on: 2025-10-08 18:25:45

Strapi is an open-source headless CMS. The library allows integration with external databases, the implementation of custom controllers, and customization of the UI to match a project's branding. According to GitHub, around 30,000 developers use Strapi CMS in their projects. This article is primarily aimed at developers who work with or plan to integrate Strapi CMS into their applications.

Theia Deep Dive, Part 1: From Zero to Your Own IDE

Aggregated on: 2025-10-08 17:25:45

Intro: Why Build Your Own IDE? There are countless reasons to create your own IDE. Perhaps you are creating a sandbox similar to CodeSandbox, but adapted to your own framework. Or an Arduino-style environment where a strictly controlled user interface is required for flashing firmware and interacting with hardware. Maybe your SaaS product needs a built-in cloud editor. Maybe you're creating an educational platform where users should only see part of the code. Or you're releasing your own language or framework, and want the developer tools not to look like an add-on plugin. Or maybe you've decided to create your own Cursor with an AI assistant. Whatever your goal, creating your own IDE is more than possible. In this guide, I'll show you how to do it with Eclipse Theia: a fully modular, open-source platform designed to adapt to your needs.

Converting ActiveMQ to Jakarta (Part III: Final)

Aggregated on: 2025-10-08 16:25:45

Advanced Technical Approach Some Java frameworks have taken on the complexity of supporting both javax and jakarta package namespaces simultaneously. This approach makes sense for frameworks and platform services, such as Jetty and ActiveMQ, where the core development team needs to move the code base forward to support newer JDKs, while also providing a way for application developers to adopt Jakarta EE gradually. This simplifies the support for open-source frameworks, as there are fewer releases to manage, and in the event of a security bug, being able to release one mainline branch vs having to go back and backport across past versions. However, supporting both javax and jakarta namespaces simultaneously in a single application is complicated and time-consuming. Additionally, it opens additional scenarios that may lead to errors and security gaps for enterprise applications. This limits the ability to set up verification checks and source code scanning to block pre-Jakarta libraries from being used or accidentally pulled in through transitive dependencies. It creates a lot of ambiguity and reduces the effectiveness of DevOps teams in providing pre-approved SDKs to be used by enterprise developers. With the pitfalls outweighing the benefits, enterprise projects should not need to support both javax and jakarta namespaces simultaneously in most scenarios.

Transforming Your Node.js REST API into an AI-Ready MCP Server

Aggregated on: 2025-10-08 15:25:45

The evolution of large language models (LLMs) and agentic AI requires a fundamental shift in how applications expose their capabilities. Traditional REST APIs are designed for software-to-software communication, requiring developers to read documentation and write custom integration code. The Model Context Protocol (MCP) is an open standard designed to solve this by creating a unified, machine-readable interface that AI agents can dynamically discover and interact with. This article provides a comprehensive guide on converting an existing Node.js REST API into an MCP server using the official TypeScript SDK, focusing on the architectural changes and crucial use cases that this conversion unlocks.

When Offline Still Matters: Why Some Mobile Apps Work Without Wi-Fi — and Others Don’t

Aggregated on: 2025-10-08 14:25:45

Ever been stranded with no Wi-Fi and suddenly discover that your go-to app is useless? You're sitting on a delayed flight, stuck in the middle of a subway tunnel commute, or out camping in some serene spot (but signal-less), and you fire up that one app you use every day, only to be greeted by an error message. "No internet connection." Brilliant.

Golden Paths in IDPs: From Developer Chaos to Clarity

Aggregated on: 2025-10-08 13:25:45

A new team spins up a service. They wire together a CI pipeline, stitch in some Terraform for infrastructure, and maybe — remember to add monitoring. Another team does the same, but with different tools, different conventions, and different assumptions. Fast forward a few months, and what was once a fast-moving engineering org is now struggling with undocumented know-how, snowflake pipelines, and production incidents caused by inconsistent practices. Developers spend more time debugging environments than delivering features. Platform teams are stuck firefighting, trying to enforce standards that retroactively were never embedded in the first place.

8 Challenges in Multimodal Training Data Creation

Aggregated on: 2025-10-08 12:25:45

Multimodal AI processes multiple forms of data, like images, sounds, and words, all at once, to empower your applications to not just listen to our voice or read text but also pick up facial expressions and the details around us. This technology is rapidly making our daily interactions easier and natural, and when using applications with which you can communicate, it feels almost as if you are chatting with your friends. The first multimodal large language model that handled both text and images effectively was GPT-4 in 2023. The most recent multimodal model, GPT-4o Vision, is equipped to create interactions that are incredibly lifelike.

The Agile AI Manifesto

Aggregated on: 2025-10-08 11:25:45

TL;DR: The Agile Manifesto Predicted AI The Agile world is splitting into two camps: Those convinced AI will automate practitioners out of existence, and those dismissing it as another crypto-level fad. Both are wrong. The evidence reveals something far more interesting and urgent: Principles written in 2001, before anyone imagined GPT-Whatever, align remarkably well with the most transformative technology of recent years. This is not a coincidence. I believe it is proof that human-centric values transcend technological disruption; it is the Agile AI Manifesto. The Broken Debate Walk into any Agile community event today, and you will encounter two opposing camps, each equally confident and equally wrong:

Pattern Searching and PostgreSQL Full-Text Search: Understanding the Mismatch

Aggregated on: 2025-10-07 19:25:45

A while ago, a request emerged to implement a global find functionality in one of our products. Briefly, the purpose was to be able to search for multiple (different) entities by a pattern that is looked up and matched against several of their attributes. Not very complicated at first glance, such a feature comes with a lot of concerns and trade-offs that need to be analyzed, in addition to the implementation itself, in order to ensure at least a pleasant user experience. As the underlying database was PostgreSQL and I was aware of its powerful full-text search (FTS) capability, I considered it a potentially good candidate, so I started researching it, trying things out, aiming to compile the solution around it. Unfortunately for the requirement, but fortunately for me and for the interesting FTS capabilities I became aware of during this study, a few hours later, I understood why it cannot be used for pattern searching.

Building Cost-Effective Internet Scale Applications

Aggregated on: 2025-10-07 18:25:44

As developers, it is important that when we build an application, we design keeping both its targeted scale and the cost of running it in mind. If we are building an internet-scale application that could start from zero traffic on day one and gradually achieve a scale of millions of daily users, we have to choose a design and set of tools that can scale in a similar fashion. This is easier said than done. Developers usually over-provision in anticipation of higher traffic and end up paying larger costs than they could have managed without. Following an appropriate design and the right choice of technologies can make a huge difference. In this article we will take a look at various serverless technologies offered by cloud providers and focus on AWS as a reference to discuss how we can build a fairly common kind of application which involve front end, backend and a database that could be blazingly fast, while if designed well could cost you zero in your monthly bill to start with and eventually scale as your user base scales.