News Aggregator

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.

Why the Principle of Least Privilege Is Critical for Non-Human Identities

Aggregated on: 2025-09-23 13:07:29

Attackers only really care about two aspects of a leaked secret: does it still work, and what privileges it grants once they are in. One of the takeaways from GitGuardian’s 2025 State of Secrets Sprawl Report was that the majority of GitLab and GitHub API keys leaked in public had been granted full read and write access to the associated repositories. Once an attacker controls access to a repository, they can do all sorts of nasty business. Both platforms allow for fine-grained access controls, enabling developers to tightly restrict what every token can and can't do. The question is then, why are teams not following the principle of least privilege for their projects? And what can be done to better secure the enterprise against overpermissioned NHIs?

Scaling ML Experiments: The High-Throughput Playbook

Aggregated on: 2025-09-23 12:07:29

From Guesswork to Growth: Why A/B Testing Is Non-Negotiable Every product decision is a bet under uncertainty. A/B testing turns those bets into measurable, causal learning. By randomly assigning users to control versus treatment, you create two groups that are — on average — identical. Any difference in conversion, retention, revenue, or latency can be attributed to the change, not to seasonality, campaigns, or shifting user mix. Randomization gives you a credible counterfactual.

Top 5 RAD Platforms for Developers

Aggregated on: 2025-09-23 11:07:29

Rapid Application Development platforms are more in demand as companies aim to deliver secure, scalable systems faster while adhering to a developer-first approach. This article reviews five popular RADs that can meet the needs of professional developers. This blog post reviews five popular Rapid Application Development (RAD) platforms: WaveMaker, OpenXava, OutSystems, Oracle APEX, and Jmix. I will break down each platform's team fit, productivity, security, support, lock-in, licensing, exploring the advantages of each, and how easy it is to get started.

AI Infrastructure for Agents and LLMs: Options, Tools, and Optimization

Aggregated on: 2025-09-22 19:22:29

,Infrastructure, whether on cloud, on-premise, or in a hybrid cloud, plays a critical role in implementing the AI architecture. This article is part of a series of articles that explores the diverse infrastructure options available for deploying and optimizing AI agents and large language models (LLMs). It delves into the crucial role infrastructure plays in realizing AI architectures, particularly for inference. We'll examine various tools, including open-source solutions, and illustrate the inference flow with diagrams, highlighting key considerations for efficient and scalable AI deployments. Modern AI applications demand sophisticated infrastructure that can handle the computational intensity of large language models, the complexity of multi-agent systems, and the real-time requirements of interactive applications. The challenge lies not just in selecting the right tools, but in understanding how they integrate across the entire technology stack to deliver reliable, scalable, and cost-effective solutions.

Isolation Level for MongoDB Multi-Document Transactions (Strong Consistency)

Aggregated on: 2025-09-22 18:22:29

Many outdated or imprecise claims about transaction isolation levels in MongoDB persist. These claims are outdated because they may be based on an old version where multi-document transactions were introduced, MongoDB 4.0, such as the old Jepsen report, and issues have been fixed since then. They are also imprecise because people attempt to map MongoDB's transaction isolation to SQL isolation levels, which is inappropriate, as the SQL Standard definitions ignore Multi-Version Concurrency Control (MVCC), utilized by most databases, including MongoDB. Martin Kleppmann has discussed this issue and provided tests to assess transaction isolation and potential anomalies. I will conduct these tests on MongoDB to explain how multi-document transactions work and avoid anomalies.

How to Build Secure Knowledge Base Integrations for AI Agents

Aggregated on: 2025-09-22 17:22:29

Done well, knowledge base integrations enable AI agents to deliver specific, context-rich answers without forcing employees to dig through endless folders. Done poorly, they introduce security gaps and permissioning mistakes that erode trust. The challenge for software developers building these integrations is that no two knowledge bases handle permissions the same way. One might gate content at the space level, another at the page level, and a third at the attachment level.

Integrating AI Into Test Automation Frameworks With the ChatGPT API

Aggregated on: 2025-09-22 16:07:29

When I first tried to implement AI in a test automation framework, I expected it to be helpful only for a few basic use cases. A few experiments later, I noticed several areas where the ChatGPT API actually saved me time and gave the test automation framework more power: producing realistic test data, analyzing logs in white-box tests, and handling flaky tests in CI/CD. Getting Started With the ChatGPT API ChatGPT API is a programming interface by OpenAI that operates on top of the HTTP(s) protocol. It allows sending requests and retrieving outputs from a pre-selected model as raw text, JSON, XML, or any other format you prefer to work with.

Spring REST API Client Flavors: From RestTemplate to RestClient

Aggregated on: 2025-09-22 15:07:29

Just as humans have always preferred co-existing and communicating ideas, looking for and providing pieces of advice from and to their fellow humans, applications nowadays find themselves in the same situation, where they need to exchange data in order to collaborate and fulfill their purposes. At a very high level, applications’ interactions are carried out either conversationally (the case of REST APIs), where the information is exchanged synchronously by asking and responding, or asynchronously via notifications (the case of event-driven APIs), where data is sent by producers and picked up by consumers as it becomes available and they are ready.

Stop Reactive Network Troubleshooting: Monitor These 5 Metrics to Prevent Downtime

Aggregated on: 2025-09-22 14:07:29

Downtime in sectors like manufacturing and healthcare isn’t just inconvenient — it’s potentially catastrophic. I’ve overseen ecosystems for years and realized that preventing such bottom-line disasters requires a watchful eye and a constant finger on the network pulse. This is possible with real monitoring across pinpointed variables: knowing which handful of key metrics predict problems in your specific environment, understanding the difference between normal fluctuations and actual performance issues, and translating technical problems into business impact before executives start asking uncomfortable questions about IT spending.

Azure IOT Cloud-to-Device Communication Methods

Aggregated on: 2025-09-22 13:22:29

Today, managing communication between the cloud and millions of smart devices is challenging. Suppose you are managing a huge number of devices out there and you need to push some critical device state update to them all, but many of them are offline or may have spotty network issues; how do you make sure this message gets through? The Azure IoT Hub provides three major cloud-to-device communication mechanisms: C2D messages, direct methods, and desired properties in the device twin. These are each designed for different use cases. This article presents how to effectively select these methods to build reliable, scalable, and effective IoT solutions. Knowing the details when to use each one for what scenarios will help to build robust and reliable IOT solutions.

Benchmarking Instance Types for Amazon OpenSearch Workloads

Aggregated on: 2025-09-22 12:22:28

Choosing the optimal instance type for Amazon OpenSearch clusters is crucial for balancing performance and cost. With AWS offering both the OpenSearch-specialized OM2 instances and the newer general-purpose M7g instances, organizations face an important decision. While OM2 instances are tailored for OpenSearch with high memory-to-vCPU ratios, M7g instances bring the latest technology, promising enhanced overall performance. The best choice depends on your specific workload characteristics and requirements.

Think in Graphs, Not Just Chains: JGraphlet for TaskPipelines

Aggregated on: 2025-09-22 11:22:28

JGraphlet is a tiny, zero-dependency library for building task pipelines in Java. Its power comes not from a long list of features, but from a small set of core design principles that work together in harmony. At the heart of JGraphlet is simplicity, backed by a Graph. Add Tasks to a pipeline and connect them to create your graph. Each Task has an input and output. A TaskPipeline builds and executes a pipeline while managing the I/O for each Task.

Your SDLC Has an Evil Twin — and AI Built It

Aggregated on: 2025-09-19 19:22:27

You think you know your SDLC like the back of your carpal-tunnel-riddled hand: You've got your gates, your reviews, your carefully orchestrated dance of code commits and deployment pipelines. But here's a plot twist straight out of your auntie's favorite daytime soap: there's an evil twin lurking in your organization (cue the dramatic organ music).

Tiny Deltas, Big Wins: Schema-Less Thrift Patching at Planet Scale

Aggregated on: 2025-09-19 18:22:27

Introduction: The Power of Tiny Deltas Imagine this common scenario: you have a binary Thrift blob, perhaps holding crucial transaction data or image metadata, stored in a distributed cache. Suddenly, a single field within that blob needs an update — maybe a transaction status change, or an image is flagged as sensitive. The catch? You don't have the Thrift IDL (Interface Definition Language) schema readily available on the serving layer, and redeploying the data producers is simply not an option due to the sheer scale and complexity of your operations. This is where the fbthrift library's parseObject/serializeObject API shines, offering a remarkably elegant solution. It enables you to deserialize, mutate, and re-emit a Thrift frame using only numeric field IDs, bypassing the need for code generation or schema uploads. This capability is invaluable for scenarios like hot-patches, rapid feature-flag flips, or compliance-driven data redactions, all without the overhead of re-sending or re-processing an entire message.

Distributed Cloud-Based Dynamic Configuration Management

Aggregated on: 2025-09-19 17:22:27

It is not uncommon for back-end software to have a configuration file to start up with. These are generally YAML or JSON files, which are loaded by the system while starting up, and are then used to set up initial configuration for a system. Values included here may affect business logic or infrastructure. Let us create a new service called DumplingSale (because I love dumplings, or as we call them, momos). This service is used for managing the sales of dumplings.

Deep Dive into Distributed File System Permission Management: Linux Security Integration

Aggregated on: 2025-09-19 16:22:27

In multi-user environments with high-security requirements, robust permission controls are fundamental for resource isolation. Linux's file permission model provides a flexible access control mechanism, ensuring system security through user/group permission settings. For distributed file systems supporting Linux, compliance with this model is critical for consistent security. This article explores key Linux permission mechanisms and their implementation in a FUSE-based distributed file system.

A Backend-First Approach to Production-Scale LLM Applications

Aggregated on: 2025-09-19 15:07:27

A few months ago, I launched the first version of my platform, which operated without AI functionality. It worked well for its initial purpose, but I knew it could do more. A few weeks ago, I rolled out version two, this time with large language models (LLMs) as its core component. It was designed to operate through a structured workflow in which the frontend sends requests to the backend, where the platform applies business logic before accessing OpenAI's API to generate responses. All operations performed as expected during controlled testing sessions. As more people started using the platform, new problems appeared. These were mostly caused by user actions and factors such as slow internet, accidental browser refreshes, and other interruptions that affected the user experience. Users will always do unexpected things in production, and not all of it is their fault. I had to accept that and find a way for the platform to handle these hiccups smoothly. The solution was to add safeguards, a safety net to catch problems and keep the system running gracefully. I redesigned the platform, putting the backend at the center of all large language model operations.

VS Code Agent Mode: An Architect's Perspective for the .NET Ecosystem

Aggregated on: 2025-09-19 14:07:27

GitHub Copilot agent mode had several enhancements in VS Code as part of its July 2025 release, further bolstering its capabilities. The supported LLMs are getting better iteratively; however, both personal experience and academic research remain divided on future capabilities and gaps. I've had my own learnings exploring agent mode for the last few months, ever since it was released, and had the best possible outcomes with Claude Sonnet Models. After 18 years of building enterprise systems — ranging from integrating siloed COTS to making clouds talk, architecting IoT telemetry data ingestions and eCommerce platforms — I've seen plenty of "revolutionary" tools come and go. I've watched us transition from monoliths to microservices, from on-premises to cloud, from waterfall to agile. I've learned Java 1.4, .NET 9, and multiple flavors of JavaScript. Each transition revealed fundamental flaws in how we think about software construction.

7 API Integration Patterns: REST, gRPC, SSE, WS, and Queues

Aggregated on: 2025-09-19 13:07:27

There are multiple API integration patterns. I have already mentioned and described some of the differences in different articles: gRPC vs REST, WebSockets vs SSE. This text is a kind of One Ring article — one to rule them all. I want you to have a single place where you can find a comparison of all the API integration patterns done in a clear and consistent manner. Thus, I have put here all the previous comparisons, and added some more into this text.

Exploring Text-to-Cypher: Integrating Ollama, MCP, and Spring AI

Aggregated on: 2025-09-19 12:07:27

When text-to-query approaches (specifically, text2cypher) first entered the scene, I was a bit uncertain how it was useful, especially when existing models were hit-or-miss on result accuracy. It would be hard to justify the benefits over a human expert in the domain and query language. However, as technologies have evolved over the last couple of years, I've started to see how a text-to-query approach adds flexibility to rigid applications that could previously only answer a set of pre-defined questions with limited parameters.

Spring Boot WebSocket: Building a Multichannel Chat in Java

Aggregated on: 2025-09-19 11:07:27

As you may have already guessed from the title, the topic for today will be Spring Boot WebSockets. Some time ago, I provided an example of WebSocket chat based on Akka toolkit libraries. However, this chat will have somewhat more features, and a quite different design. I will skip some parts so as not to duplicate too much content from the previous article. Here you can find a more in-depth intro to WebSockets. Please note that all the code that’s used in this article is also available in the GitHub repository.