News Aggregator

UX Research in Agile Product Development: Making AI Workflows Work for People

Aggregated on: 2026-01-12 20:15:03

During my eight years working in agile product development, I have watched sprints move quickly while real understanding of user problems lagged. Backlogs fill with paraphrased feedback. Interview notes sit in shared folders collecting dust. Teams make decisions based on partial memories of what users actually said. Even when the code is clean, those habits slow delivery and make it harder to build software that genuinely helps people. AI is becoming part of the everyday toolkit for developers and UX researchers alike. As stated in an analysis by McKinsey, UX research with AI can improve both speed (by 57%) and quality (by 79%) when teams redesign their product development lifecycles around it, unlocking more user value.

Kotlin Code Style: Best Practices for Former Java Developers

Aggregated on: 2026-01-12 19:15:03

Many Kotlin codebases are written by developers with a Java background. The syntax is Kotlin, but the mindset is often still Java, resulting in what can be called "Java with a Kotlin accent." This style compiles and runs, but it misses the core advantages of Kotlin: conciseness, expressiveness, and safety. Common symptoms include:

Apache Spark 4.0: What’s New for Data Engineers and ML Developers

Aggregated on: 2026-01-12 18:15:03

Undoubtedly one of the most anticipated updates in the world of big-data engines, the release of Apache Spark 4.0 is a big step in the right direction. According to the release notes, this shift involved closing more than 5,100 sprint tickets, facilitated by the negligence of over 390 active contributors. Machine learning and data engineering professionals, the new features of SQL, additional capabilities for Python, management of streaming states, and the newly introduced Spark Connect framework in Spark 4.0 will further reinforce the trend of high-performance, easy-to-use, scalable data analytics.

The Night We Split the Brain: A Telling of Control & Data Planes for Cloud Microservices

Aggregated on: 2026-01-12 17:15:03

You know those pages you receive in the middle of the night? Not a full-blown fire, mind you, but rather a slow-burning panic? Let me tell you one of those stories that changed the way my team built software forever. It was 2 a.m., and the graphs looked bad. Not dead, mind you, but sick. Our microservices were still talking, but P95 latencies were rising high in the sky, like a lazy balloon. And retries were starting to cascade. The whole system felt like it was in a swamp. So what was the problem? A “safe” configuration change to our API gateway, a new rate limit, and slight change of routing. It turned out that this change and a previous deploy of an unrelated service that occurred at least an hour earlier had collided in some silent serpentine handshake. The result was a slow, luscious, and irresistible drain on performance.

Leveraging AI-Based Authentication Factors in Modern Identity and Access Management Solutions

Aggregated on: 2026-01-12 16:15:03

It is not an understatement that identity is the new perimeter. With cyberattacks on the rise across industries, from finance and governments to healthcare, the protection of user identities has become more crucial than ever before. Taking a look at some of the traditional authentication methods — passwords, PINs, security tokens, and basic biometrics, there is a need to innovate within this sphere. Since their inception, all these methods have formed the robust backbone of an effective Identity and Access Management solution. However, it is increasingly important to revamp these methods as cyberattacks become more widespread and increasingly sophisticated.

Data Lakehouse vs. Data Mesh: Rethinking Scalable Data Architectures in 2026

Aggregated on: 2026-01-12 15:15:03

Introduction Over the last decade, the data ecosystem has changed immensely. Data warehouses, the core of analytics, faced issues with unstructured data and scaling. Meanwhile, early data lakes offered some level of flexibility, but poorly governed data and schema drift led to numerous problems. Now, there are two new contenders to the data paradigm: the Data Lakehouse and the Data Mesh. Both are futuristic scalable data architectures, but each has a different approach to the core problem. In 2026, enterprises will continue to face the question of whether to modernize with a centralized Lakehouse or a decentralized Mesh.

Why PostgreSQL Vacuum Matters More Than You Think

Aggregated on: 2026-01-12 14:15:03

Why PostgreSQL Vacuum Matters More Than You Think Keeping PostgreSQL fast and stable is not just about good schema design or indexing. One of the most overlooked pillars of database health is the Vacuum process. It is easy to ignore because it operates quietly in the background, yet it is crucial for long-term performance, storage efficiency, and even preventing database outages. In this article, I will walk through why Vacuum exists, what happens when it is neglected, and when it makes sense to tune or run it manually.

Pragmatic Paths to On-Device AI on Android with ML Kit

Aggregated on: 2026-01-12 13:15:03

There isn’t a single canonical way to add on-device AI to Android apps. Your ideal path depends on latency, privacy, UX, and maintainability. Google’s ML Kit gives you interchangeable building blocks — text recognition, barcode scanning, object/pose detection, translation, and more — that you can compose to fit your constraints. This guide lays out a pragmatic architecture, drop-in code, and a performance checklist you can ship in a sprint. The theme is intentional minimalism: pick one capability, wrap it behind a tiny interface, wire it to CameraX if needed, and iterate with metrics instead of speculative complexity. When ML Kit Is the Smart Choice On-device by default: You get low latency, offline reliability, and strong privacy because images and text don’t need to leave the device for common tasks. This dramatically reduces legal/compliance risk and eliminates network tail latency that can frustrate users during capture flows. Production-hardened models: The bundled models handle rotation, noise, motion blur, and imperfect lighting better than most “roll-your-own” attempts. You benefit from years of tuning without owning a training pipeline. Modular adoption: Add exactly one capability at a time; you don’t need a model server, autoscaling, or a feature-flagged rollout of custom models. That simplicity keeps your blast radius small. Great Android ergonomics: ML Kit works cleanly with CameraX, coroutines, and lifecycle components. That means less boilerplate and fewer foot-guns when you integrate with the camera stack, orientation changes, or backgrounding/foregrounding transitions. Common wins:

Serverless Spark Isn't Always the Answer: A Case Study

Aggregated on: 2026-01-12 12:15:03

Processing billions of records with strict latency requirements isn't a "pick your favorite database" problem. It's an architectural decision that will define system scalability, team velocity, and operational budgets for years to come. The challenge involves multiple competing constraints:

Why Encryption Alone Is Not Enough in Cloud Security

Aggregated on: 2026-01-09 20:30:02

It is often assumed that encryption is the gold standard method for securing assets in the cloud. Cloud providers give assurances that all their services are “encrypted by default.” Several regulatory and cloud compliance policies mandate that organizations encrypt data at rest, in use, and in transit. All of this should make cloud environments secure, right? However, the reality is slightly more nuanced. Many breaches occur not because encryption algorithms are weak or because attackers can crack them. They occur because attackers never need to. Instead, attackers exploit other weaknesses. Access may be over-permissive, key governance may be poorly managed, configurations may be exposed, and there may be an overall lack of visibility into how data is actually being used.

The Rise of Diskless Kafka: Rethinking Brokers, Storage, and the Kafka Protocol

Aggregated on: 2026-01-09 19:30:01

Apache Kafka has come a long way from being just a scalable data ingestion layer for data lakes. Today, it is the backbone of real-time transactional applications. In many organizations, Kafka serves as the central nervous system connecting both operational and analytical workloads. Over time, its architecture has shifted significantly — from brokers managing all storage, to Tiered Storage, and now toward a new paradigm: Diskless Kafka. Diskless Kafka refers to a Kafka architecture in which brokers use no local disk storage. Instead, all event data is stored directly in cloud object storage such as Amazon S3, Google Cloud Storage, or Azure Blob Storage.

Beyond Extensions: Architectural Deep-Dives into File Upload Security

Aggregated on: 2026-01-09 17:30:02

Allowing users to upload files is a staple of modern web applications, from profile pictures to enterprise document management. However, for a security engineer or backend developer, an upload field is essentially an open invitation for an attacker to place an arbitrary binary on your filesystem. When validation fails, the consequences range from localized data theft to a total Remote Code Execution (RCE) scenario, where an attacker gains a web shell and full control over the host. This article explores why standard defenses often fail and how modern architectural patterns — and their flaws — impact the security posture of your application.

Mastering Fluent Bit: Developer Guide to Telemetry Pipeline Routing (Part 12)

Aggregated on: 2026-01-09 16:30:02

This series is a general-purpose getting-started guide for those who want 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 it is worth exploring, where to get started, and how to get hands-on with learning about the topic as it relates to the Fluent Bit project.

How to Build and Deploy an AI Agent on Kubernetes With AWS Bedrock, FastAPI and Helm

Aggregated on: 2026-01-09 15:30:01

The capabilities offered by AI are no longer limited to large, centralized platforms. Today, engineering teams are increasingly embracing lightweight, specialized AI agents that can be managed, scaled, and deployed just like microservices in a cloud-native environment — whether for summarizing large documents, translation, classification, or other analytical tasks. In this tutorial, you will create, deploy, and run an AI model that provides REST APIs for summarization and translation using AWS Bedrock, FastAPI, Docker, and deployment on Amazon EKS via Helm. This provides a reusable process for integrating AI into operations: one agent, one task, clear boundaries, and full Kubernetes-native visibility and control.

Multi-Region Apache Kafka using Synchronous Replication for Disaster Recovery With Zero Data Loss (RPO=0)

Aggregated on: 2026-01-09 14:30:01

Apache Kafka is the backbone of modern event-driven systems. It powers real-time use cases across industries. But deploying Kafka is not a one-size-fits-all decision. The right strategy depends on performance, compliance, and operational needs. From self-managed clusters to fully managed services and Bring-Your-Own-Cloud (BYOC) models, each approach offers different levels of control, simplicity, and scalability. Selecting the right deployment model is a strategic decision that affects cost, agility, and risk.

Essential Techniques for Production Vector Search Systems Part 2 - Binary Quantization

Aggregated on: 2026-01-09 14:30:01

After implementing vector search systems at multiple companies, I wanted to document efficient techniques that can be very helpful for successful production deployments of vector search systems. I want to present these techniques by showcasing when to apply each one, how they complement each other, and the trade-offs they introduce. This will be a multi-part series that introduces all of the techniques one by one in each article. I have also included code snippets to quickly test each technique.

From Code to Runtime: How AI Is Bridging the SAST–DAST Gap

Aggregated on: 2026-01-09 13:30:01

Let’s start with two pillars that modern application security teams rely on: Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST). SAST is a method in which source code is analyzed early in the application development lifecycle to identify potential vulnerabilities. On the other hand, DAST is used to test running applications to uncover hidden flaws — specifically from an attacker’s perspective. Both approaches are equally valuable. However, they are often not used together. Security teams juggle multiple point solutions and, on top of that, are overwhelmed by false positives. As a result, they struggle to answer a simple question: Which vulnerabilities are actually exploitable in production?

How AI Is Rewriting DevOps: Practical Patterns for Faster, Safer Releases

Aggregated on: 2026-01-09 12:30:02

DevOps has always sought to deliver software faster without breaking things — a balancing act between velocity and stability. Now, artificial intelligence is dramatically shifting that balance. AI-powered tools and practices are weaving into every stage of the delivery pipeline, helping teams ship code at lightning speed with greater safety. Analysts predict that by 2027, over 50% of enterprise teams will have AI agents in their pipelines to boost speed, quality, and governance. Early adopters are already seeing significant gains; one study found that embedding AI into development led to 20% to 30% faster delivery with 40% fewer defects in releases. These improvements aren’t about traditional automation alone — they’re driven by intelligent systems that learn and adapt. In this article, we’ll explore how AI is rewriting DevOps from an engineer’s perspective. We’ll examine real-world tools and examples, from coding assistants like GitHub Copilot to AIOps platforms, and highlight practical AI-driven patterns that enable faster, safer software releases. This is not just hype or theory; it’s a trend analysis grounded in emerging best practices that advanced engineering teams are adopting today. We’ll look at how AI assists in coding, testing, deployments, and operations, all while keeping quality and security in focus. Let’s dive into the key areas where AI is transforming DevOps and the patterns you can leverage for speed and reliability.

Speak Their Language: How Communication Profiling Prevents Agile Delivery Breakdowns

Aggregated on: 2026-01-08 20:15:01

Agile delivery failures are usually explained with comfortable excuses. The backlog was unclear. The scope changed. The estimates were wrong. The architecture was fragile. The process wasn’t followed closely enough. In real delivery environments, especially complex or hybrid ones, those explanations rarely hold up for long.

When Services Think for Themselves: Traditional Orchestration vs. Agentic AI Microservices

Aggregated on: 2026-01-08 19:15:01

Understanding How Traditional Orchestration Manages Microservices From Netflix to Spotify and Walmart, industry stalwarts across the globe leverage microservices at scale to deliver rapid innovation across their services. Microservices architecture has brought a fundamental shift in the way modern cloud computing drives applications to scale, evolve, and deploy independently. The foundational pillars upon which this innovation rests include orchestration platforms such as Kubernetes and Docker Swarm. These platforms automate the deployment and management of containerized services. As the scope of these services expands, the reliance on human-defined policies, configurations, and operational thresholds has increased. However, such scale must be accompanied by streamlined automation to avoid scaling bottlenecks. As a result, this begets the question: Can a sufficiently autonomous thinking agent take over the operational heavy lifting and make microservices truly self-managing?

Essential Techniques for Production Vector Search Systems Part 1 - Hybrid Search

Aggregated on: 2026-01-08 18:15:01

After implementing vector search systems at multiple companies, I wanted to document efficient techniques that could be very helpful for successful production deployments of vector search systems. I want to present these techniques, showcasing when to apply each of them, how they complement each other, and the trade-offs they introduce. This will be a multi-part series that introduces all of the techniques one by one in each article. I have also included code snippets to quickly test each of the techniques.

Telemetry-Driven AI Architecture: Closing the Loop from UX to Models

Aggregated on: 2026-01-08 17:15:01

Most Android AI features die quietly after launch. You ship a smart recommendation, a ranking model, or an LLM-powered assistant. It works great on your test data, metrics look decent, and then… real users behave differently. Edge cases appear, traffic shifts, product changes. The model slowly drifts out of sync with reality.

Enterprise Kubernetes Failures: 20 Critical Misconfigurations Guardon Catches Before Outages

Aggregated on: 2026-01-08 16:15:01

Kubernetes incidents in large organizations don’t come from exotic zero-days — they come from basic YAML mistakes made thousands of times a year by developers under pressure. While we commonly talk about 15–20 misconfigurations that appear in every enterprise, the truth is much deeper: Kubernetes is an ecosystem of complexity, and prevention requires more than static checks. Guardon, a lightweight, developer-first Kubernetes guardrail extension, helps organizations detect these issues early — but it also does far more. It acts as a standardization layer, a cost-optimization tool, a security enforcer, and a compliance assistant, all directly inside GitHub, GitLab, or Bitbucket, long before code reaches CI/CD.

Platform Engineering Golden Paths: Stop Building Developer Portals, Start Shipping Code

Aggregated on: 2026-01-08 15:15:01

Here’s the uncomfortable truth: if your platform team is spending 80% of its time building portals and only 20% paving paths, you’re doing platform engineering backward. The revolution isn’t about prettier UIs — it’s about invisible automation that makes the right thing the easiest thing. The Portal Problem Nobody Talks About Platform teams are solving the wrong problem. They’re building museums of infrastructure when developers need highways to production. I’ve seen this pattern repeat at companies ranging from scrappy Series A startups to multinational corporations: hire a platform team, mandate Backstage or Humanitec, spend six months integrating everything, launch with fanfare — and then watch adoption plateau at 30% while developers continue cowboy-coding in production.

Building a Containerized Quarkus API and a CI/CD Pipeline on AWS EKS/Fargate with CDK

Aggregated on: 2026-01-08 14:15:01

In a recent post, I have demonstrated the benefits of using AWS ECS (Elastic Container Service), with Quarkus and the CDK (Cloud Development Kit), in order to implement an API for the customer management. In the continuity of this previous post, the current one will try to go a bit further and replace ECS by EKS (Elastic Kubernetes Service) as the environment for running containerized workloads. Additionally, an automated CI/CD pipeline, using AWS CodePipeline and AWS CodeBuild, is provided.

Secure Log Tokenization Using Aho–Corasick and Spring

Aggregated on: 2026-01-08 13:15:01

Modern microservices, payment engines, and event-driven systems are generating massive volumes of logs every second. These logs are critical for debugging, monitoring, observability, and compliance audits. But there is an increasing and hazardous problem: Sensitive data — things like credit card numbers, email addresses, phone numbers, SSNs, API keys, and session tokens — often accidentally appear in logs. Once it's stored in log aggregators such as ELK, Splunk, CloudWatch, Datadog, or S3, this sensitive data becomes a high-risk liability.

Managing Changing Hardware/Peripherals in a Robust Point of Sale System

Aggregated on: 2026-01-08 12:15:01

Retail point-of-sale systems today offer a wide range of options for peripherals and hardware. Their technical specifications play a major role in selection, and big retailers often choose multiple vendors to reduce a single point of failure. This gives them an advantage to negotiate price or support as well. Technically, these peripherals also require updating with new models and may have new feature sets. This necessitates the redevelopment of point-of-sale applications, increasing development costs. Another problem with managing hardware interactions is that rapid scanning would generate a burst of requests, and we need a mechanism to handle them all. Failure to do so would result in lost messages, eventually causing poor customer experience or loss to retailers as they would sell items not scanned properly.

Handling Logging After Migrating UiPath to Automation Cloud

Aggregated on: 2026-01-07 20:15:00

Migrating UiPath Orchestrator from an on-premises deployment to Automation Cloud simplifies infrastructure management, but it also changes how execution logs can be accessed and consumed. Teams migrating existing Splunk-based observability pipelines often discover that familiar on-prem logging patterns no longer apply once workloads move to the cloud. In on-prem environments, Orchestrator and robot logs are typically available as files on the server filesystem, making them easy to ingest into centralized monitoring platforms using standard forwarders. Automation Cloud removes direct access to the underlying infrastructure, forcing teams to rethink how logging should be handled after migration.

AWS Bedrock vs Azure OpenAI vs Gemini API: A Practical Comparison

Aggregated on: 2026-01-07 19:15:00

Choosing a cloud AI platform isn't just about which has the "best" model — it's about integration, pricing, compliance, and how well it fits your existing infrastructure. After building production systems on all three platforms, here's my engineering-focused breakdown to help you make the right choice.

Implementing Idempotency in Distributed Spring Boot Applications Using MySQL

Aggregated on: 2026-01-07 18:15:00

Why Idempotency Breaks in Real Systems Modern distributed systems expose APIs that trigger state-changing operations such as payments, orders, the account acquisition process, or account updates. In such environments, the chance of duplicate transactions being initiated is quite high and unavoidable due to network retries, a Kafka rebalancer issuing multiple requests, load balancers, and other factors. Without proper safeguards, these duplicate transactions/requests can lead to data inconsistency, financial discrepancies, and variations in business invariants. Idempotency is a well-established technique used to ensure that repeated executions of the same request produce a single, consistent outcome. While idempotency can be enforced at the application level using in-memory caches or request deduplication logic, these approaches would fail for a horizontally scaled microservice architecture, where multiple application instances may process requests concurrently and across numerous different regions.

How to Send .NET Crash Dumps to Slack From an ECS Fargate Task

Aggregated on: 2026-01-07 17:30:00

Sometimes .NET applications crash in production, and nobody knows why, because logs and metrics are ok. It's quite bothersome and makes debugging very unpleasant. In such cases, memory dumps might simplify debugging and reduce troubleshooting time from days to minutes. This article explains how to configure dumps for .NET applications deployed to AWS ECS Fargate and then forward them to the development team in the most convenient and secure way.

Automated Deployment Using a CI/CD Pipeline (Mule 4 | CloudHub 2.0)

Aggregated on: 2026-01-07 16:30:00

The purpose of this article is to depict and demonstrate how to automate the build and deployment process using a CI/CD pipeline with CloudHub 2.0 (Mule 4). Prerequisites Anypoint CloudHub account (CloudHub 2.0) app.runtime – 4.9.0 mule.maven.plugin.version – 4.3.0 Anypoint Studio – Version 7.21.0 OpenJDK – 11.0

The Hidden Security Risks in ETL/ELT Pipelines for LLM-Enabled Organizations

Aggregated on: 2026-01-07 15:30:00

As organizations integrate large language models (LLMs) into analytics, automation, and internal tools, a subtle yet serious shift is occurring within their data platforms. ETL and ELT pipelines that were originally designed for reporting and aggregation are now feeding models with logs, tickets, emails, documents, and other free-text inputs. These pipelines were never built with adversarial AI behavior in mind.

Why Small Language Models Are Transforming AI Adoption for Everyone

Aggregated on: 2026-01-07 14:30:00

You’ve probably seen it yourself over the last couple of years: whenever people talk about artificial intelligence (AI), the spotlight almost always lands on large language models (LLMs). Tools like ChatGPT, Claude, and Gemini have practically become the poster children for modern AI — and it’s not hard to understand why. These systems have been remarkable in pushing natural language processing forward, and they continue to capture headlines and imagination across industries, including IT and software, marketing, manufacturing, and e-commerce.

RPA Validation in Life Sciences: 5 Pitfalls and How to Avoid Them

Aggregated on: 2026-01-07 13:30:00

The issue with RPA was discovered during an FDA audit at a Global Biotech company. There was a lack of validation documentation, requirement traceability, and testing, and missing evidence. That’s when it was noted that a successful automation project is indeed a regulatory finding. This is not an unusual event in life sciences; bots aren’t just scripts, they are regulated systems. The development should include compliance, risk management, and audit readiness, as with any other GxP systems.

Orchestrating Retail-Scale Data on Google Cloud

Aggregated on: 2026-01-07 12:30:00

Digital retail never sleeps. Carts open and close at 2 a.m., promotions spike traffic without warning, and supply signals move from warehouses to web in minutes. In that environment, data pipelines are not just utilities — they are the nervous system that keeps analytics current, inventory visible, and decisions grounded in fact. The challenge is designing pipelines that stay elastic under peak load, deliver trustworthy data consistently, and keep costs predictable. Google Cloud’s modular services — Pub/Sub for event ingestion, Dataflow for processing, BigQuery for analytics, and Cloud Composer for orchestration — provide the foundation. What matters is how they fit together into patterns that remain reliable when traffic doubles or triples overnight.

Solving the Cold Start Problem in Edge AI: A Guide to Data-Saving Learning

Aggregated on: 2026-01-06 20:15:00

We have all seen the demo: a computer vision model achieves 99% accuracy on a test dataset. Then, we deploy it to an edge device — a drone, a security camera, or an industrial robot — and performance crashes. The problem is domain shift. The lighting is different, the camera angle is skewed, or the background noise has changed. In traditional MLOps, the solution is to collect thousands of new images from the edge device, label them manually, and retrain the model from scratch.

Developer Tools That Actually Matter in 2026

Aggregated on: 2026-01-06 19:15:00

Though I am an architect, I am a developer at heart. I explore developer tools every year and publish my favorites here. As developers, we’ve all been through the hype cycles. Every year brings a new wave of tools that promise to change everything. Most fade away within months. But 2026 is different — and not because of buzzwords. The tools that are making real differences now are the ones solving actual problems we face every single day. I’ve spent the last few months testing what’s actually working in production environments, talking to teams across different tech stacks, and trying tools that claim to make our lives easier. Here’s what I found: the best tools aren’t the flashiest ones. They’re the ones that disappear into your workflow and just work.

Metadata, Not Data Volume, Is the Real Bottleneck in Modern Data Lakes

Aggregated on: 2026-01-06 18:15:00

For more than a decade, data engineering best practices have revolved around a single assumption: data volume is the primary scalability challenge. We optimized Parquet sizes, tuned partitioning strategies, compressed aggressively, and scaled compute to handle terabytes and petabytes of data. As long as queries scanned fewer files and clusters had enough memory, performance generally improved.

Why Data Engineers Need to Think Like Product Managers

Aggregated on: 2026-01-06 17:15:00

Introduction Today, the work of a data engineer is more complex than simply building pipelines and platforms. Data engineers are no longer just builders; they are now vital parts of value creation in a data driven organization. However, many engineers continue to evaluate success using the number of completed jobs and created tables, rather than their actual worth to the business. This is where a Product Manager (PM) mindset comes in handy. Adopting a product manager’s way of thinking is not about managing Jira boards and marketing roadmaps. It is about thinking of data assets as products complete with customers, lifecycle, and tangible results.

BYOLM with Spring AI & MCP: Secure, Swappable AI Everywhere

Aggregated on: 2026-01-06 16:15:00

Introduction Artificial intelligence has rapidly moved from research labs into everyday tools. Yet, most users remain locked into vendor‑controlled ecosystems, where the choice of language model (LM) is dictated by the provider. This creates friction for developers, educators, and organizations who want flexibility, privacy, and control. The Bring Your Own Language Model (BYOLM) paradigm challenges this status quo. By designing a configurable middleware layer, extensions for Chrome, Word, and other applications can seamlessly integrate with swappable LLMs. Combined with Spring AI and Model Context Protocol (MCP), this architecture empowers users to safeguard sensitive data, authenticate access securely, and orchestrate reproducible AI labs. This article may be referred to as a sequel to this article on DZone, and readers are encouraged to read it. Motivation The motivation behind BYOLM is simple yet powerful: freedom of choice. Traditional AI assistants often operate as black boxes, offering little transparency into how data is processed or stored. For developers and mentors, this lack of control is unacceptable. BYOLM allows individuals and organizations to:

Understanding Parquet Scans: How Readers Skip Work and Stay Fast

Aggregated on: 2026-01-06 15:15:00

Parquet is a columnar file format designed for efficient data storage and retrieval. On disk, it is organized around row groups, column chunks, and pages. Along with that, each file also has a footer that describes how everything fits together. A Parquet reader that understands this layout can avoid a lot of work during the scan, such as skipping entire row groups, column chunks, and pages, and decoding only the values that matter. This article uses a single sample Parquet file to explain exactly what happens during column reads and some common optimization techniques.

6 Software Development and DevOps Trends Shaping 2026

Aggregated on: 2026-01-06 14:15:00

In 2025, many teams tried new things in software development and DevOps — AI copilots, new platforms, more automation, and more security checks. Some of it worked great, some of it created new mess (tool sprawl, unclear ownership, higher cloud bills, and “we ship faster but break more”). Heading into 2026, the focus is shifting from experimentation to ensuring reliability and repeatability. Leaders and practitioners are asking the same questions: How do we move fast without losing quality? How do we keep systems secure without slowing teams down? How do we reduce toil, control costs, and still deliver features that matter?

A Practical Guide to Semantic Caching With Redis LangCache

Aggregated on: 2026-01-06 13:15:00

Semantic cache is an advanced caching mechanism that differs from traditional caching, which relies on exact keyword matching; it stores and retrieves data based on semantic similarity. Redis LangCache is a fully hosted semantic caching service that helps cache LLM prompts and responses semantically, thereby reducing LLM usage costs. In this tutorial, let's learn how to quickly create a simple application and use LangCache for caching LLM queries. Also, see if we can combine fuzzy logic to improve the responses.

Effectively Managing AI Agents for Testing

Aggregated on: 2026-01-06 12:15:00

Large language models and AI agents have already transformed many fields and are changing our lives in fundamental ways. In the testing domain, AI agents have a clear path for making immediate improvements in process and quality, and ultimately for producing reliable, performant, secure, and compliant software. Check out Demystifying Agentic Test Automation: What It Means for QA Teams. But it’s not obvious how to take advantage of these capabilities. While AI agents are not fully predictable, they can be managed reliably via robust control mechanisms. Let's see how. What Does It Mean to Manage AI Agents in QA? There are several important aspects to managing AI agents, both in general and specifically in the testing domain.

Unlocking Hidden Value in Dirty Data: A Practical NLP Pattern for Legacy Records

Aggregated on: 2026-01-05 20:29:59

In the era of Digital Transformation (DX), we are often told that "data is the new oil." However, for many enterprises, that oil is crude, unrefined, and full of sludge. Consider the automotive, manufacturing, or healthcare industries. For decades, technicians and operators have been typing notes into free-text fields. These millions of records contain critical information about asset health, maintenance history, and compliance. But because they are unstructured, full of typos, and riddled with domain-specific slang, they remain invisible to standard analytics tools.

Securing Verifiable Credentials With DPoP: A Spring Boot Implementation

Aggregated on: 2026-01-05 19:29:59

In my previous article, I demonstrated how to implement OIDC4VCI (credential issuance) and OIDC4VP (credential presentation) using Spring Boot and an Android wallet. This follow-up focuses on a critical security enhancement now mandated by EUDI standards: DPoP (Demonstrating Proof-of-Possession). The Problem With Bearer Tokens Traditional Bearer tokens have an inherent weakness: anyone who obtains the token can use it. If an attacker intercepts or steals a Bearer token, they can impersonate the legitimate client until the token expires (or is revoked).

Beyond the Spec Sheet: Performance Tuning a Massive DWH Migration on AWS

Aggregated on: 2026-01-05 18:29:59

Moving large-scale financial systems to the public cloud is a high-stakes game. In regulated industries, you often cannot perform casual test-in-production experiments. You have to design correctly and then verify quickly. When migrating a massive data warehouse (DWH) from on-premises hardware to AWS EC2, the natural instinct is to match specifications: "If I had 16 cores and 128 GB RAM on-prem, I need an x1e.xlarge on AWS."

Beyond Fuzzy Matching: Engineering a Multi-Signal ML Pipeline for CRM Deduplication

Aggregated on: 2026-01-05 17:29:59

The CRM Problem We All Face Almost every CRM platform struggles with duplicate customer records because the data is a messy mix flowing in from too many places — web forms, call centers, imports from ERP, partner feeds, and even legacy systems from multiple business units. The exact same company can pop up under slightly different names, with spelling variations, inconsistent addresses, or missing suite numbers. A simple single-rule fuzzy match isn't enough to connect these dots. To truly fix this, we need something smarter: a multi-signal pipeline. This means using a blocking layer to speed up things, checking fuzzy name and address similarity, using geographic distance as a reality check, and implementing a small machine learning classifier to weigh all the evidence and decide if two records should be merged.

Raw Agent Systems Explained: From Single Agents to Multi-Agent Networks

Aggregated on: 2026-01-05 16:29:59

Raw agent technology is at the forefront of the next phase of artificial intelligence (AI) development. According to a survey conducted by IBM involving 2,900 executives, the implementation of AI-enabled workflows is expected to increase from 3% to 25% by 2025, with 70% of respondents expressing confidence in the organizational impact of agentic AI. This form of AI allows language models to be directed through natural language, facilitating decision-making and uncertainty management. Gartner forecasts that by 2026, 75% of large companies will adopt multi-agent systems, while BCG anticipates a rise in revenue from $5.7 billion in 2024 to $53 billion by 2030. This study examines raw agent systems, ranging from single-agent frameworks to multi-agent networks that promote AI collaboration, and discusses LangGraph implementations and their significant challenges.