News Aggregator

Caching Mechanisms Using Spring Boot With Redis or AWS ElastiCache

Aggregated on: 2025-09-30 18:21:17

To decrease latency, improve responsiveness, and lessen database loads, caching has become crucial for today's performance-demanding applications. Successful caching strategies can be implemented by developers using Redis or AWS ElastiCache in conjunction with Spring Boot's elegant caching abstraction. Low-latency responses, high throughput, cost-effectiveness, and scalability are all becoming more and more important for modern applications. Effective caching can improve responsiveness to requests for recently requested data by 10–100 times, while reducing the database load by 70–90%.

Creating Real-Time Dashboards Using AWS OpenSearch, EventBridge, and WebSockets

Aggregated on: 2025-09-30 17:21:17

If you've attempted to build a dashboard, then you're familiar with the hassle of polling. You hit your API every couple of seconds, grab updates, and pray your data doesn't feel stale. However, if we're being honest, polling is inefficient, wasteful, and antiquated. In the modern era, users expect supplies to be dynamic and flowing. We, as developers, should meet that expectation without melting our servers. In this post, I will walk you through a serverless, event-driven architecture that I've leveraged to build real-time dashboards using AWS. This architecture will tie together EventBridge, OpenSearch, and API Gateway WebSockets with a hint of Lambda and DynamoDB. By the end, you'll have some understanding of how all the pieces are tied together to create a live dashboard data pipeline that can scale, can be cost-friendly, and actually feels fast for the end-user.

Building GitOps Pipelines With Helm on OpenShift: Lessons From the Trenches

Aggregated on: 2025-09-30 16:21:17

After spending the last two years knee-deep in Kubernetes deployments and watching too many "quick fixes" turn into production incidents, I've become a true believer in GitOps. Not because it's the latest buzzword, but because it actually works when you need to sleep at night. Last month, our team finally finished migrating our entire microservices platform to a GitOps workflow using Helm and OpenShift. It wasn't pretty, and we definitely learned some things the hard way. But now that the dust has settled, I wanted to share what we've discovered about making this stack work in the real world.

Experts Say This Is the Best LLM for Front-End Tasks

Aggregated on: 2025-09-30 15:21:17

Front-end development is seeing a new wave of automation thanks to large language models (LLMs). From generating UI code to reviewing pull requests, these AI models promise to speed up workflows. But which LLMs truly shine for front-end tasks? We found three experts who had shared their opinions on this topic. In this article, we will analyze their findings and opinions and try to understand which models deliver the most value when integrated into modern front-end workflows.

Scoped Filtering: A Practical Bridge to RBAC

Aggregated on: 2025-09-30 14:21:17

You’re a startup fresh out of your development-focused cycle, starting to gain traction and demo your product to potential clients. As someone working at a freshly minted Series A company, I understand the priority: get the product working. In our case, that meant demonstrating our data insights solution worked — before implementing sophisticated (but necessary) controls like role-based access control (RBAC). But now, it’s time. Clients are onboarding, and you need to ensure that only the right people can access the right customer data.

The Secret to Fast-Tracking Legacy System Modernization With GenAI

Aggregated on: 2025-09-30 13:21:17

“Generative AI is shifting from coding assistants to enterprise transformation, enabling organizations to analyze and modernize complex legacy systems.” — Gartner, Generative AI for Enterprise Transformation, 2024 Generative AI (GenAI) is often framed as a tool for accelerating developer productivity, with most discussions centering on code generation. Although that narrative captures attention, it fails to address a deeper, high-value opportunity: transforming and modernizing legacy systems. Enterprises grappling with decades-old applications can leverage GenAI not just to write code faster, but to analyze, refactor, and modernize legacy applications intelligently.

Master Advanced Error-Handling to Make PySpark Pipelines Production-Ready

Aggregated on: 2025-09-30 12:21:17

In PySpark, processing massive datasets across distributed clusters is powerful but comes with challenges. A single bad record, missing file, or network glitch can crash an entire job, wasting compute resources and leaving you with stack traces that have many lines. Spark’s lazy evaluation, where transformations don’t execute until an action is triggered, makes errors harder to catch early, and debugging them can feel like very, very difficult.

AI Risks in Product

Aggregated on: 2025-09-30 11:21:17

TL; DR: AI Risks — It’s A Trap! AI is tremendously helpful in the hands of a skilled operator. It can accelerate research, generate insights, and support better decision-making. But here’s what the AI evangelists won’t tell you: it can be equally damaging when fundamental AI risks are ignored. The main risk is a gradual transfer of product strategy from business leaders to technical systems — often without anyone deciding this should happen. Teams add “AI” and often report more output, not more learning. That pattern is consistent with long-standing human-factors findings: under time pressure, people over-trust automated cues and under-practice independent verification, which proves especially dangerous when the automation is probabilistic rather than deterministic (Parasuraman & Riley, 1997; see all sources listed below). That’s not a model failure first; it’s a system and decision-making failure that AI accelerates.

5 Manual Testing Techniques Every Tester Should Know

Aggregated on: 2025-09-29 19:06:17

Despite rapid advancements in test automation and the use of AI in software testing, manual testing is still a fundamental part of software Quality Assurance in 2025. Recent data from multiple industry reports confirm the ongoing value of manual testing in comparison to test automation. For example, only about 5% of companies perform fully automated testing, meaning all test cases are automated without manual intervention. Approximately 2/3rds of companies use a mixed approach, trying to balance both manual and automated testing efforts. Manual testing remains inevitable for the areas that require human insight, judgment, and flexibility. According to this, we may confidently say that you must have the main manual testing techniques to succeed in ensuring quality assurance on your project. So, let's walk through 5 key manual testing techniques:

How to Integrate AI APIs Into Your Projects

Aggregated on: 2025-09-29 18:06:17

Artificial intelligence isn’t just a buzzword anymore; it’s the new electricity of software development. Every other app now wants to “predict,” “recommend,” or “chat back.” But here’s the catch: integrating AI APIs can feel like wrestling an octopus. You start with excitement, then suddenly you’re buried under API keys, weird JSON outputs, and cryptic error messages. Don’t worry! You’re not alone. In this blog, we’ll break down how to integrate AI APIs into your projects without losing your sanity. We’ll cover the prep work, the integration process, best practices, and a few survival tips straight from the trenches.

A Guide to Using Browser Network Calls for Data Processing

Aggregated on: 2025-09-29 17:06:17

It was a good sunny day in Seattle, and my wife wanted to have the famous viral Dubai Chocolate Pistachio Shake. With excitement, we decided to visit the nearest Shake Shack, and to our surprise, it was sold out, and we were told to call them before visiting. There is no guarantee that it will be available the next day as well because of limited supply. Two days later, I went there again to see if there would be any, and again I was faced with disappointment. I didn't like the way, I either have to call them to check for an item or go to their store to check if it's available.

Phantom Liquidity: Why Microsecond Trades Break the Dev Simulator

Aggregated on: 2025-09-29 16:06:17

In the simulator, everything clears. The matching engine hums, the order book is balanced, and every test trader goes home happy. Then you ship it to production, and phantom liquidity vanishes faster than coffee on a trading floor. Orders that should have executed simply do not exist. The illusion is perfect until reality disagrees. I have spent enough time watching green checkmarks in dev turn into red faces in prod to know one thing: simulators lie, especially at the microsecond scale. They give you the polite version of the story — one without jitter, clock drift, or packets arriving a hair out of order. If you are lucky, you catch it in testing. If not, the market catches you.

Error Budgets 2.0 Agentic AI for SLO-Apprehensive Deployments

Aggregated on: 2025-09-29 15:06:17

Service level objectives (SLOs) and error budgets are key in site reliability engineering (SRE). They help teams balance reliability with innovation, ensuring users get a stable service while developers can safely deliver new features But in practice, administering error budgets inside CI/CD channels is hard:

Why CI and CD Should Be Treated as Separate Disciplines (Not CI/CD)

Aggregated on: 2025-09-29 14:06:17

For years, teams have bundled continuous integration (CI) and continuous delivery (CD) into a single concept: CI/CD. This shorthand suggests a seamless pipeline, but in practice, it creates confusion and hides the fact that CI and CD solve very different problems. CI is like the quality control process in a factory, meticulously inspecting and testing every component to ensure it's safe and meets standards before it's ever installed. CD, on the other hand, is the logistics company, using a deliberate strategy to deliver the finished product to the customer, monitoring its journey, and having a plan for a safe return if something goes wrong. Treating them as one often creates unoptimized workflows, blurs the separation of responsibilities, and causes confusion about what is needed when.

Build a Face-Matching ID Scanner With ReactJS and AI

Aggregated on: 2025-09-29 13:06:16

Picture this: you’re building a web app that can verify someone’s identity by having them snap a selfie with their webcam and upload a photo of their ID. It’s like something out of a sci-fi movie, but you can make it happen with ReactJS and face-api.js (a super cool library built on TensorFlow.js). This setup lets you create a working prototype in a few hours, all running right in the browser — no fancy servers required. In this guide, I’ll walk you through building a React component that compares a live webcam feed to an ID photo to confirm a match. We’ll talk about why this is awesome for quick prototyping and toss in some ideas for where you could use it.

The Serverless WebSocket: Building Real-Time Applications With Cloudflare, Hono, and Durable Objects

Aggregated on: 2025-09-29 12:06:17

The demand for real-time applications has exploded, from collaborative documents and live data dashboards to multiplayer games and instant messaging. WebSockets, with their persistent, bi-directional communication protocol, have become the de facto standard for building these experiences. However, the traditional approach — running a dedicated server to manage thousands of long-lived connections — introduces significant complexities in scalability, cost, and operational overhead. This paradigm is being fundamentally challenged by the rise of serverless computing. But can the stateless, ephemeral nature of typical serverless functions truly support a stateful, persistent protocol like WebSockets?

Better Data Beats Better Models: The Case for Data Quality in ML

Aggregated on: 2025-09-29 11:06:16

The phrase “Garbage in, Garbage out” is not a new one, and nowhere is this phrase more applicable than in machine learning. The most sophisticated and complex model architecture will crumble under the weight of poor data quality. Conversely, high-quality and reliable data can power even simple models to drive significant business impact. In this post, we will deep dive into why data quality is critical, what dimensions matter most, the problems poor data creates, and how organizations can actively monitor and improve data quality. We will also examine a practical example of credit score and close with the case for treating data quality as a first-class citizen in ML workflows.

Federated Learning: Training Models Without Sharing Raw Data

Aggregated on: 2025-09-26 19:06:15

As machine learning programs require ever-larger sets of data to train and improve, traditional central training routines creak under the burden of privacy requirements, inefficiencies in operations, and growing consumer skepticism. Liability information, such as medical records or payment history, can't easily be collected together in a place due to ethical and legal restrictions. Federated learning (FL) has a different answer. Rather than forwarding data to a model, it forwards the model to the data. Institutions and devices locally train models on their own data and forward only learned updates, not data.

Networking’s Open Source Era Is Just Getting Started

Aggregated on: 2025-09-26 18:06:15

For most of its history, networking has been a standards-first, protocol-governed domain. From the OSI model to the TCP/IP stack, progress was measured in working groups and RFCs, not GitHub commits. But that is changing fast. Projects like eBPF and Cilium, along with the architectural demands of Kubernetes, are moving networking from a specification-bound world into a software-driven, open source ecosystem. What happened to servers, developer tooling, and CI/CD pipelines is now happening to the network layer. The open source future has arrived, and it is finally catching up to the packet path.

LLM-First Vibe Coding

Aggregated on: 2025-09-26 17:06:15

For many years now, software engineers have used the Integrated Development Environment (IDE) as their main place to write and debug code. Your IDE should become a partner that helps you by predicting what you need to do, correcting mistakes automatically, and making complex code from simple prompts. "Vibe coding" is changing the field of software engineering rapidly. Its main idea is LLM-first development. Andrej Karpathy, who was Tesla's AI Director at the time, came up with the idea of vibe coding. He came up with this way of working that lets developers participate in LLM code generation [1]. The developer now needs designers to act as high-level architects who use natural language prompts to guide AI systems while they work on developing the vision for the product. Karpathy says that he builds projects and web apps by looking at them visually, giving them verbal commands, running the system, and copying code, which all lead to functional results [1]. With the traditional IDE-first development method, developers have to write every line of code. With vibe coding, on the other hand, this is not the case. Vibe coding changes software development at its core because it lets developers use AI tools to make a development environment that is completely interactive. The article shows that this trend is more than just a passing fad because it changes how software is made and kept up-to-date. Why LLM-First Development? The quick rise of LLM-first development is due to big productivity gains for developers and a complete change in the cognitive requirements of software engineering. The best thing about vibe coding is that it makes development work easier, so engineers can focus on more creative and strategic tasks.

Why One-Week Sprints Make Vibe Coding Work Better

Aggregated on: 2025-09-26 16:06:15

One-week sprint cycles in Scrum can significantly improve project outcomes through vibe coding approaches. Research shows that Agile techniques increase the success rate of projects by 21% compared to traditional methods (Ogirri & Idugie, 2024). Developers using AI assistance produce 26% more and finish 55% faster. Vibe coding maximizes the developer's flow state and focused attention, which works well with shorter cycle iterations. Teams that use one-week sprints can leverage knowing how to deliver working functionality more often. It aligns with Agile's purpose of delivering continuous value to stakeholders. Moreover, shorter sprints lower prompt drift and allow quicker verification of features developed by AI. Product managers and entrepreneurs who utilize Lean practices may see their 'build, measure, learn' loop accelerated by incorporating vibe coding into Agile development. Cross-functional teams use this potent combination to develop functional prototypes, as detailed coding experience or significant investment is no longer required.

Complex Data Tasks Are Now One-Liners With AI in Databricks SQL

Aggregated on: 2025-09-26 15:06:15

As data engineers, we’ve all encountered those recurring requests from business stakeholders: “Can you summarize all this text into something executives can read quickly?”, “Can we translate customer reviews into English so everyone can analyze them?”, or “Can we measure customer sentiment at scale without building a new pipeline?”. Traditionally, delivering these capabilities required a lot of heavy lifting. You’d have to export raw data from the warehouse into a Python notebook, clean and preprocess it, connect to an external NLP API or host your own machine learning model, handle retries, manage costs, and then write another job to push the results back into a Delta table. The process was brittle, required multiple moving parts, and — most importantly — took the analysis out of the governed environment, creating compliance and reproducibility risks. With the introduction of AI functions in Databricks SQL, that complexity is abstracted away. Summarization, translation, sentiment detection, document parsing, masking, and even semantic search can now be expressed in one-line SQL functions, running directly against governed data. There’s no need for additional infrastructure, no external services to maintain, and no custom ML deployments to babysit. Just SQL, governed and scalable, inside the Lakehouse.

Basic Security Setup for Startups

Aggregated on: 2025-09-26 14:06:15

Preamble I recently had a conversation with my friend about starting a new company. We discussed the various stages a company should go through to become mature and secure enough to operate in the modern market. This article will outline those stages. The suggested approach is based on the following principles: Security by default Security by design Identification, authentication, and authorization Segregation of responsibilities You can follow this flow assuming that you're starting a product from scratch without any existing VNETs, IDPs, or parent companies' networks. However, if you have any of these things, you must adjust the flow accordingly.

Implementing a Multi-Agent KYC System

Aggregated on: 2025-09-26 13:06:15

Every engineer who implemented KYC systems has dealt with a frustrating reality. You build rule-based engines that break every time regulations change. Document processing takes days because everything goes through manual review queues. API integrations become brittle nightmares when you're trying to coordinate identity verification, OCR services, and watchlist screening. The numbers tell the story: most KYC systems process documents in 2–3 days with false positive rates hitting 15-20%. That means one in five legitimate customers gets flagged for manual review. Meanwhile, compliance teams burn out reviewing thousands of documents daily, and customer support fields endless calls about delayed approvals.

Building a Real-Time Data Mesh With Apache Iceberg and Flink

Aggregated on: 2025-09-26 12:06:15

If you’ve ever tried to scale your organization’s data infrastructure beyond a few teams, you know how fast a carefully planned “data lake” can degenerate into an unruly “data swamp.” Pipelines are pushing files nonstop, tables sprout like mushrooms after a rainy day, and no one is quite sure who owns which dataset. Meanwhile, your real-time consumers are impatient for fresh data, your batch pipelines crumble on every schema change, and governance is an afterthought at best. At that point, someone in a meeting inevitably utters the magic word: data mesh. Decentralized data ownership, domain-oriented pipelines, and self-service access all sound perfect on paper. But in practice, it can feel like you’re trying to build an interstate highway system while traffic is already barreling down dirt roads at full speed.

AI Transformation Déjà Vu

Aggregated on: 2025-09-26 11:06:15

TL;DR: AI Transformation Failures Organizations seem to fail their AI transformation using the same patterns that killed their Agile transformations: Performing demos instead of solving problems, buying tools before identifying needs, celebrating pilots that can’t scale, and measuring activity instead of outcomes. These aren’t technology failures; they are organizational patterns of performing change instead of actually changing. Your advantage isn’t AI expertise; it’s pattern recognition from surviving Agile. Use it to spot theater, demand real problems before tools, insist on integration from day one, and measure actual value delivered.

Implementing Vector Search in Databricks

Aggregated on: 2025-09-25 19:22:30

Search has always been at the heart of analytics. Whether you’re tracking down the right transaction, filtering a customer record, or pulling a specific review, the default approach has traditionally been keyword search. Keyword search is simple and effective when you know exactly what you’re looking for, but it quickly falls apart when the language is messy, ambiguous, or when meaning matters more than exact words. That’s where vector search changes the game. Instead of matching literal keywords, vector search relies on embeddings — high-dimensional numeric representations of text, images, or other unstructured content — that capture semantic meaning.

The GPT-5 Impact

Aggregated on: 2025-09-25 18:22:30

ChatGPT happened. A host of models happened. Improvements continue to come out at an accelerated pace. The focus of this small article is to see if we can keep pace with our designs and remain both efficient and relevant to the latest and greatest. I don't have a host of Elo benchmarks and ratings to evaluate these models. All I have is a small design for solving Math and Science problems that has generally kept me honest and grounded, whether it was using Cursor or Windsurf, or lately, GitHub CoPilot to write code, or in the choice of models (GPT-4o was clearly my favorite up until today!).

Boosting Developer Productivity in Kubernetes-Driven Workflows: A Practical Checklist

Aggregated on: 2025-09-25 17:22:30

Editor's Note: The following is an article written for and published in DZone's 2025 Trend Report, Kubernetes in the Enterprise: Optimizing the Scale, Speed, and Intelligence of Cloud Operations. Kubernetes has become the backbone of application deployment. Its flexibility and scalability are long-time proven, but its adoption by developers can still be a challenge. The misuse of Kubernetes configuration, through the thousands of options, can make applications less performant or less resilient in that they would be a single old-school server. To fully take advantage of Kubernetes, organizations must prioritize the developer experience by embracing platform engineering practices that abstract complexity and provide self-service capabilities, enabling teams to deploy applications with confidence.

AI-Powered Triathlon Coaching: Building a Modern Training Assistant With Claude and Garmin

Aggregated on: 2025-09-25 16:22:30

The Triathlon Training Challenge Triathlon is arguably one of the most complex sports to train for. Unlike single-discipline sports, triathletes must master three distinct activities — swimming, cycling, and running — while managing the intricate balance between them. The challenge isn’t just about getting better at each sport; it’s about understanding how training in one affects the others, managing fatigue across disciplines, and walking a razor-thin line between optimal training and injury. The modern triathlete faces an overwhelming array of variables. How many hours per week should you train? What distribution across sports? When do you push hard, and how much recovery do you need between sessions? Add in technique refinement, physiological monitoring, equipment optimization, nutrition periodization, and injury prevention, and you have a sport where the “art of training” has evolved into a complex science requiring constant analysis and adjustment.

The Design System Team: Goals, Pains, and Successes

Aggregated on: 2025-09-25 15:22:30

A design system is a collection of reusable components, guidelines, patterns, and best practices (including accessibility and responsiveness) that help a company build consistent and efficient user interfaces. It provides the building blocks to create a cohesive user experience across your product or products and platforms. Multiple disciplines are involved: design, front-end engineering, product management, and more. A design system team is a group of people who cover the disciplines mentioned and who are responsible for the design system.

AWS Glue Crawlers: Common Pitfalls, Schema Challenges, and Best Practices

Aggregated on: 2025-09-25 14:22:30

AWS Glue is a powerful serverless data integration that simplifies data discovery, preparation, and transformation. However, as with any tool, real-world application reveals quirks and corner cases that are not clearly identified in documentation. In this article, let's talk about some key challenges observed from my hands-on experience while building data pipelines using Glue crawlers when dealing with CSV files, schema evolution, partitioning, and crawler update settings.

Digital Experience Monitoring and Endpoint Posture Checks Usage in SASE

Aggregated on: 2025-09-25 13:22:30

In this article, I will go through the concepts of digital experience monitoring (DEM) and Endpoint Posture Checks and discuss how these essential capabilities are integrated into the SASE framework to enforce the zero trust principle. Together, these capabilities empower enterprises’ security and IT teams to maintain optimal performance, a strong security posture, and trust, regardless of where users connect. Digital Experience Monitoring Digital experience monitoring (DEM) helps to monitor and provide observability across the entire path. It delivers granular, real-time telemetry across endpoints, network paths, and application services, regardless of user location. In the past, enterprises that adopted cloud resources had to deploy various tools to monitor problems within cloud applications, network infrastructure, or on-premises devices, to provide a consistent user experience for hybrid and remote workforces.

Is Anyone There? Listening to Your Users Through Conversational AI Observability

Aggregated on: 2025-09-25 12:22:30

You’ve done it. After months of development, your team has launched a state-of-the-art conversational AI assistant. It’s powered by the latest LLM, the interface is slick, and the potential is enormous. Then the first piece of user feedback lands in your inbox. It just says: "The bot is confusing."

Lessons Learned From Building Production-Scale Data Conversion Pipelines

Aggregated on: 2025-09-25 11:22:30

Building production-scale data pipelines usually involves wrangling outputs from multiple legacy systems. Whether you’re trying to build out business intelligence use cases, handle a system migration, or lay the foundations for a new data warehouse, chances are high that you’ll have to normalize and integrate the outputs of multiple systems that were never designed to talk to one another. Recently, we built a production-scale data pipeline converting one data set from one enterprise system (Health Information Exchanges) to be used as an input into another (a claims-powered risk stratification algorithm). Although these two formats fundamentally represented the same underlying event (clinical encounters), the two systems spoke completely different “languages” — different coding standards, field definitions, and expectations about what was required. The goal was not a one-off ETL script, but a reusable, production-ready pipeline that downstream applications could rely on.

Death by a Thousand YAMLs: Surviving Kubernetes Tool Sprawl

Aggregated on: 2025-09-24 18:22:30

Editor's Note: The following is an article written for and published in DZone's 2025 Trend Report, Kubernetes in the Enterprise: Optimizing the Scale, Speed, and Intelligence of Cloud Operations. Kubernetes is eating the world.

The New API Economy With LLMs

Aggregated on: 2025-09-24 17:22:30

Large language models (LLMs) are becoming more advanced in understanding context in natural language. With this, a new paradigm is emerging — using LLMs as APIs. Traditionally, an API call would be GET /users/123/orders and you would receive a JSON in return, which would return the orders for the user 123. APIs facilitate the interaction between different software systems.

Key Principles of API-First Development for SaaS

Aggregated on: 2025-09-24 16:22:30

Having worked in software development for over 8 years, I have repeatedly watched developers struggle to integrate APIs into platforms as an afterthought. The situation is common. Someone builds a beautiful web app, then the business team asks for mobile support, third-party integrations, and suddenly you're reverse-engineering your own application to expose endpoints that make sense. Luckily, this is changing. With API-first development, we can design the architecture with the API as part of it from day one. This is especially beneficial for SaaS products as they rely on third-party integrations and ecosystem support.

Using TanStack Query for Scalable React Applications

Aggregated on: 2025-09-24 15:07:30

When building React applications, data fetching often starts with the native fetch API or tools like Axios. While this approach works for small projects, larger applications require features such as caching, retries, synchronization, and request cancellation, and it is here that TanStack Query, formerly React Query, excels. It provides a battle-tested abstraction for CRUD operations with powerful state management built in. In this article, we’ll walk through fetching data with useQuery, performing mutations with useMutation, and highlighting some features that make TanStack Query a helpful tool for scaling React apps.

Resilient Data Pipelines in GCP: Handling Failures and Latency in Distributed Systems

Aggregated on: 2025-09-24 14:07:30

I have spent years designing and operating data pipelines in Google Cloud, and one thing has not changed: resilience is not optional. It does not matter how nice your design diagrams look or how scalable the architecture is. In practice, nodes die, quotas are exhausted, regions are shaded, schemas alter unannounced, and message queues are clogged up at the most unpredictable moments. The main distinction between a functional pipeline and a resilient pipeline lies in the fact that the former can withstand failures and still meet latency requirements. The article explains my philosophy on resilience in distributed data pipelines on GCP, based not only on the experience of running these systems, but also more broadly on systems research and Google operational experience.

Why I Ditched Redis for Cloudflare Durable Objects in My Rate Limiter

Aggregated on: 2025-09-24 13:07:30

Have you ever watched your serverless application crumble under unexpected traffic? Last month, our AI-powered image generator went viral on social media, and within hours, we were drowning in requests. Our traditional rate-limiting setup couldn't keep up with the distributed load across Cloudflare's edge network. This experience taught me that rate limiting in serverless environments requires a fundamentally different approach. Here's how I built a production-ready rate limiter using Cloudflare Durable Objects that handles thousands of concurrent requests while running at the edge.

Shipping Responsible AI Without Slowing Down

Aggregated on: 2025-09-24 12:07:30

In software engineering, launch day rarely fails because a unit test was missing; in machine learning (ML), that’s not the case. Inputs far from training data, adversarial prompts, proxies that drift away from human goals, or an upstream artefact that isn’t what it claims to be can all sink a release. The question is not “can every failure be prevented?” but “can failures be bounded, detected quickly, and recovered from predictably?” Two research threads shape this approach. The first maps where ML goes wrong in production: robustness gaps, weak runtime monitoring, misalignment with real human objectives, and systemic issues across the stack (supply chain, access, blast radius). The second focuses on how teams make decisions that stand up to scrutiny: a deliberative loop that’s open, informed, multi-vocal, and responsive. Put together, the operating model feels like standard software engineering — just opinionated for ML.

Top 7 Mistakes When Testing JavaFX Applications

Aggregated on: 2025-09-24 11:07:30

JavaFX is a versatile tool for creating rich enterprise-grade GUI applications. Testing these applications is an integral part of the development lifecycle. However, Internet sources are very scarce when it comes to defining best practices and guidelines for testing JavaFX apps. Therefore, developers must rely on commercial offerings for JavaFX testing services or write their test suites following trial-and-error approaches. This article summarises the seven most common mistakes programmers make when testing JavaFX applications and ways to avoid them.

LLMs at the Edge: Decentralized Power and Control

Aggregated on: 2025-09-23 19:07:29

Most of the large language models' applications have been implemented in centralized cloud environments, raising concerns about latency, privacy, and energy consumption. This chapter examines the potential application of LLMs in decentralized edge computing, where computing tasks are distributed across interconnected devices rather than centralized hosts. Therefore, by applying approaches like quantization, model compression, distributed inference, and federated learning, LLMs solve the problems of limited computational and memory resources on edge devices, making them suitable for practical use in real-world settings. Several advantages of decentralization are outlined in the chapter, such as increased privacy, user control, and enhanced system robustness. Additionally, it focuses on the potential of employing energy-efficient methods and dynamic power modes to enhance edge systems. The conclusion re-emphasizes that edge AI is the way forward as a responsible and performant solution for the future of decentralized AI technologies, which would be privacy-centric, high-performing, and put the user first.

Running AI/ML on Kubernetes: From Prototype to Production — Use MLflow, KServe, and vLLM on Kubernetes to Ship Models With Confidence

Aggregated on: 2025-09-23 18:07:29

Editor's Note: The following is an article written for and published in DZone's 2025 Trend Report, Kubernetes in the Enterprise: Optimizing the Scale, Speed, and Intelligence of Cloud Operations. After training a machine learning model, the inference phase must be fast, reliable, and cost efficient in production. Serving inference at scale, however, brings difficult problems: GPU/resource management, latency and batching, model/version rollout, observability, and orchestration of ancillary services (preprocessors, feature stores, and vector databases). Running artificial intelligence and machine learning (AI/ML) on Kubernetes gives us a scalable, portable platform for training and serving models. Kubernetes schedules GPUs and other resources so that we can pack workloads efficiently and autoscale to match traffic for both batch jobs and real-time inference. It also coordinates multi-component stacks — like model servers, preprocessors, vector DBs, and feature stores — so that complex pipelines and low-latency endpoints run reliably.

From Requirements to Results: Anchoring Agile With Traceability

Aggregated on: 2025-09-23 17:07:29

Agile is one of the most widely adopted project management methodologies in the field of software development because it enables teams to deliver incrementally, adapt quickly to changes, and prioritize collaboration over rigid processes. However, Agile’s fast-changing nature can also expose one of its weaknesses, which is traceability. Traditional project management approaches, such as Waterfall, make sure that requirements are tied to design documents, test cases, and acceptance metrics. This pipeline ensures that every feature can be traced back to its origin. On the other hand, Agile prioritizes lightweight artifacts and fast iteration, which pose challenges to tracking how individual backlog items map to higher-level business objectives. As a project manager, I’ve seen this gap firsthand. Teams often run into questions like: Are we building the features that align with stakeholder needs? Do the tests validate the requirements? Did we guarantee full coverage across multiple sprints? Without a clear system of traceability, the results are often uncertain.

AI Readiness: Why Cloud Infrastructure Will Decide Who Wins the Next Wave

Aggregated on: 2025-09-23 16:52:29

Everywhere I go, cloud and DevOps teams are asking the same question: “Are we ready for AI?”

Model Evaluation Metrics Explained

Aggregated on: 2025-09-23 16:07:29

Measuring the true performance of machine learning models goes far beyond headline accuracy. The metrics you choose shape not only how you tweak your algorithms, but how your models impact users, businesses, and critical systems. In this article, we break down the most practical and widely used evaluation metrics: Accuracy, Precision, Recall, F1 Score, and ROC-AUC. Alongside technical definitions, we'll discuss their strategic importance-how these numbers map to real-world outcomes and business objectives. Whether you're shipping a product or publishing research, knowing how to evaluate model success is foundational to effective machine learning. We'll also look at common metric pitfalls-and how to avoid them.

Mastering Fluent Bit: Top 3 Telemetry Pipeline Output Plugins for Developers (Part 7)

Aggregated on: 2025-09-23 15:07:29

This series is a general-purpose getting-started guide for those of us wanting to learn about the Cloud Native Computing Foundation (CNCF) project Fluent Bit. Each article in this series addresses a single topic by providing insights into what the topic is, why we are interested in exploring that topic, where to get started with the topic, and how to get hands-on with learning about the topic as it relates to the Fluent Bit project.

Testing Automation Antipatterns: When Good Practices Become Your Worst Enemy

Aggregated on: 2025-09-23 14:07:29

Note: This article is a summary of a talk I gave at VLCTesting in 2023. Here's the recording (Spanish). Test automation is a fundamental tool for gaining confidence in what we build in a fast and efficient way. However, we often encounter practices that, while seemingly beneficial in the short term, generate significant problems in the long term: antipatterns.