News Aggregator

Taming the JVM Latency Monster

Aggregated on: 2026-03-26 20:23:11

An Architect's Guide to 100GB+ Heaps in the Era of Agency In the "Chat Phase" of AI, we could afford a few seconds of lag while a model hallucinated a response. But as we transition into the Integration Renaissance — an era defined by autonomous agents that must Plan -> Execute -> Reflect — latency is no longer just a performance metric; it is a governance failure. When your autonomous agent mesh is responsible for settling a €5M intercompany invoice or triggering a supply chain move, a multi-second "Stop-the-World" (STW) garbage collection (GC) pause doesn't just slow down the application; it breaks the deterministic orchestration required for enterprise trust. For an integrator operating on modern Java virtual machines (JVMs), the challenge is clear: how do we manage mountains of data without the latency spikes that torpedo agentic workflows? The answer lies in the current triumvirate of advanced OpenJDK garbage collectors: G1, Shenandoah, and ZGC.

Automating Maven Dependency Upgrades Using AI

Aggregated on: 2026-03-26 19:23:11

Enterprise Java applications do not often break due to business logic. The reason they break is that dependency ecosystems evolve all the time. Manual maintenance in most large systems consists of hundreds of third-party libraries, and small upgrades occur regularly as a result of security patches, code corrections, or vendor advice. The problem is not recognizing outdated libraries. Tools such as OWASP Dependency-Check, Snyk, and Black Duck already do it well. The problem is a wastage of the developer's time in repetitive actions: checking Maven Central for the latest versions, validating whether the upgrade is safe, reading release notes, guessing what test cases should be executed, and raising a pull request with meaningful documentation.

MinIO AIStor and Ampere® Computing Reference Architecture for High-Performance AI Inference

Aggregated on: 2026-03-26 18:38:11

MinIO AIStor is a highly scalable, high-performance object storage solution tailored for AI workloads, especially in distributed or cloud-native environments. Designing a cluster for AI inference requires high-performance storage and efficient data retrieval. Thus, careful consideration must be given to storage architecture, compute resources, networking, and scalability. MinIO, Ampere®, Supermicro®, and Micron Technology Inc have partnered to deliver and validate performance through comprehensive testing on an Ampere® Altra® 128 core-powered storage cluster consisting of eight nodes to allow for: Scalability: Horizontal scaling of storage and I/O performance. Redundancy: Built-in erasure coding ensures data durability even if nodes or drives fail. High throughput: Parallel access and distributed storage enable fast read/write operations, suitable for AI or big data analytics. Kubernetes and bare-metal friendly: Can be deployed both on Kubernetes or as standalone Bare-metal nodes. The Ampere® Altra® family of processors provides predictable, consistent high performance under maximum load conditions. This is achieved through single-threaded compute cores, consistent operating frequency, and high core counts per socket. As a result, customers benefit from exceptional performance per rack, per watt, and per dollar.

Microsoft Responsible AI Principles Explained for Engineers

Aggregated on: 2026-03-26 18:23:11

How to Turn Responsible AI Principles into Real, Enforceable Systems Industry leaders in the tech industry are moving forward with artificial intelligence in all areas. Relatively, AI systems started to influence healthcare, insurance claims, hiring, credit scoring, fraud detection, and customer interactions by making decisions in respective areas. These are all the domains where decisions made by the AI system are very critical, though if mistakes happen, it will not be considered only as technical bugs, but it can lead to real-world harm, regulatory violations, and loss of trust in the system. Microsoft defines a set of responsible AI principles to guide the development and deployment of AI systems. These responsible AI principles help to reduce the mistakes made by the AI system. These principles provide a strong ethical and governance foundation. However, many engineering teams struggle with a critical gap.

Isolation Boundaries in Multi-Tenant AI Systems: Architecture Is the Only Real Guardrail

Aggregated on: 2026-03-26 17:23:11

Multi-tenant AI systems operate and fail differently from single-tenant traditional software. These systems don’t usually fail because of bypassed authentication; they usually fail because the system quietly allowed tenants to share something they shouldn’t have, such as execution paths, configuration state, retry pressure, or storage namespaces. In most single-tenant software, a single mistake usually affects only one customer, whereas in multi-tenant AI platforms, that same mistake can propagate sideways before any member of the development or operations team notices. The impact radius is no longer contained by default, unlike in single-tenant software.

Stateful AI: Streaming Long-Term Agent Memory With Amazon Kinesis

Aggregated on: 2026-03-26 16:38:10

As autonomous agents evolve from simple chatbots into complex workflow orchestrators, the “context window” has become the most significant bottleneck in AI engineering. While models like GPT-4o or Claude 3.5 Sonnet offer massive context windows, relying solely on short-term memory is computationally expensive and architecturally fragile. To build truly intelligent systems, we must decouple memory from the model, creating a persistent, streaming state layer. This article explores the architecture of streaming long-term memory (SLTM) using Amazon Kinesis. We will dive deep into how to transform transient agent interactions into a permanent, queryable knowledge base using real-time streaming, vector embeddings, and serverless processing.

When Kubernetes Says "All Green" But Your System Is Already Failing

Aggregated on: 2026-03-26 16:23:11

It's not a theoretical scenario. The cluster health checks all come back "green." Node status shows Ready across the board. Your monitoring stack reports nominal CPU and memory utilization. And somewhere in a utilities namespace, a container has restarted 24,069 times over the past 68 days — every five minutes, quietly, without triggering a single critical alert. That number — 24,069 restarts — came from a real non-production cluster scan run last week, an open-source Kubernetes scanner that operates with read-only permissions — it can see the state of the cluster, but it cannot and did not change a single thing. The failures we found were entirely of the cluster's own making. The namespace it lived in showed green in every dashboard the team monitored. No alert had fired. No ticket had been created. The workload had essentially been broken for over two months, and the cluster's observability layer had communicated exactly nothing about it.

Building Centralized Master Data Hub: Architecture, APIs, and Governance

Aggregated on: 2026-03-26 15:38:11

Many enterprises operating with a large legacy application landscape struggle with fragmented master data. Core entities such as country, location, product, broker, or security are often duplicated across multiple application databases. Over time, this results in data inconsistencies, redundant implementations, and high maintenance costs. This article outlines Master Data Hub (MDH) architecture, inspired by real-world enterprise transformation programs, and explains how to centralize master data using canonical schemas, API-first access, and strong governance.

From 30s to 200ms: Optimizing Multidimensional Time Series Analysis at Scale

Aggregated on: 2026-03-26 15:23:11

Monitoring production systems in real-time is crucial for reliability. Multidimensional anomaly detection is a very helpful tool in this regard. However, it does require time-series analysis to be blazing fast. This follow-up blog shows how to speed them up by using different strategies like indexing, filtering, bucketing, etc., to achieve a consistent performance in the 100s of ms range. Recap Most teams learn the hard way that global all-green dashboards can hide real incidents in a single cohort. In Part 1: A Guide to Multidimensional Anomaly Detection, we covered the why and the solution blueprint.

MCP vs Skills vs Agents With Scripts: Which One Should You Pick?

Aggregated on: 2026-03-26 14:38:10

I have been writing and building in the AI space for a while now. From writing about MCP when Anthropic first announced it in late 2024 to publishing a three-part series on AI infrastructure for agents and LLMs on DZone, one question keeps coming up in comments, DMs, and community calls: What is the right tool for the job when building with AI? For a long time, the answer felt obvious. You pick an agent framework, write some Python, and ship it. But the ecosystem has moved fast. We now have MCP servers connecting AI to the real world, Skills encoding domain know-how as simple markdown files, and agent scripts that can orchestrate entire workflows end to end. The options are better than ever. The confusion around them is too.

Document Generation API: How to Automate Personalized Document Creation at Scale

Aggregated on: 2026-03-26 14:38:10

Every company has the same hidden bottleneck: someone, somewhere, is manually building documents. They pull a client’s name from the CRM, paste it into a Word template, double-check the date, adjust the logo placement, and export to PDF. On a good day, that’s an intern handling a manageable workload. On a bad day, it’s an engineer who wired the entire layout into iText or PDFKit, and now Marketing needs the font changed across every document type. Both approaches share the same problem: they don’t scale. They’re manual workarounds dressed up as processes, and they collapse the moment volume jumps from a few hundred records to 50,000 invoices that need to ship overnight. Legacy Mail Merge tools hit the same wall.

Why RAG Alone Isn’t Enough: How MCP Completes the Agentforce Intelligence Stack?

Aggregated on: 2026-03-26 14:23:10

Retrieval-augmented generation (RAG) has emerged as one of the key building blocks for AI-based systems in recent years. RAG takes a language model and mixes it with external knowledge access. In short, it permits a system to extract useful information from big data sources and provide context-aware responses. On the surface, that may seem fantastic for smart agents, AI assistants, and question-answering systems. RAG can produce relevant information at scale and without needing to retrain the underlying model, generalizing across many domains. But in actual enterprise applications, constraints begin to appear. RAG is strong at fetching documents or data snippets and incorporating them into generated responses, but it has weaknesses in structured reasoning, long-horizon planning, and tool use. For machines that are required to access multiple systems, carry out stepwise operations, or undertake complex workflows, RAG alone is not enough. Models can hallucinate steps, misunderstand instructions, or fail to recognize dependencies between tools.

Bringing AI Agents to Cloud Engineering: How Autonomous Operations Are Changing Reliability at Scale

Aggregated on: 2026-03-26 13:23:10

Modern cloud systems are getting harder to manage. That is not a new observation, but the gap between system complexity and human response is growing faster than most teams expect. Microservices run across regions, deployments happen constantly, and workloads change without warning. Even well-staffed operations teams struggle to keep up. Traditional automation helps, but only to a point. Scripts, alerts, and scheduled jobs work when failure patterns are known in advance. They break down when incidents are unclear, cross multiple services, or do not match existing rules. In practice, many incidents still rely on human judgment, context switching, and experience under pressure.

Data Driven API Testing in Java With REST Assured and TestNG: Part 4

Aggregated on: 2026-03-26 12:23:10

APIs are at the heart of almost every application, and even small issues can have a big impact. Data-driven API testing with JSON files using REST Assured and TestNG makes it easier to validate multiple scenarios without rewriting the same tests again and again. By separating test logic from test data, we can build cleaner, flexible, and more scalable automation suites. In this article, we’ll walk through a practical, beginner-friendly approach to writing API automation tests with REST Assured and TestNG using JSON files as the data provider.

Stop Writing Slow Pandas Code: Vectorization and Modern Alternatives Explained

Aggregated on: 2026-03-25 20:08:10

Pandas performance problems rarely look catastrophic. They appear as pipelines that take four hours instead of twenty minutes, jobs that time out on datasets they handled comfortably six months ago, and transformation steps that become the silent bottleneck in an otherwise reasonable architecture. The code looks correct. It is just slow. The cause is almost always the same: Python-level row iteration where vectorized column operations belong, or datasets that have grown large enough that single-threaded execution is the real constraint. Both are fixable. This article covers the specific patterns that cause most Pandas slowdowns, with benchmark numbers and the modern alternatives, Polars and DuckDB, for when Pandas itself is not the right tool.

Production Database Migration or Modernization: A Comprehensive Planning Guide [Part 1]

Aggregated on: 2026-03-25 18:08:10

Migrating a production database that supports critical backend API services is one of the most challenging undertakings in software engineering. Whether you're modernizing from a legacy relational database to a NoSQL database like MongoDB, moving to a cloud-native solution like Azure Cosmos DB or AWS DynamoDB, or simply upgrading your database to a newer version, the stakes are high. A poorly executed migration can result in data loss, extended downtime, revenue impact, and erosion of customer trust — not to mention frustration among internal stakeholders! Commonly, migration timelines extend 4–6x longer than originally anticipated due to poor preparation, planning, and internal coordination. This extension drives up not only costs but also uncertainty and risk for other projects impacted by the migration.

Beyond “Lift-and-Shift”: How AI and GenAI Are Automating Complex Logic Conversion

Aggregated on: 2026-03-25 17:23:10

Image Source: Houston SEO Directory on Unsplash (For Illustrative purposes only) For the past decade, the promise of the cloud has been a siren song for enterprises trapped by the gravity of their legacy data warehouses. The initial, tempting path was “lift-and-shift”: move the applications and data, as-is, to a cloud VM. The industry has since learned a hard lesson.

AI Agents vs LLMs: Choosing the Right Tool for AI Tasks

Aggregated on: 2026-03-25 16:23:10

Large language models have changed how software teams think about automation, reasoning, and intelligence. Almost overnight, tasks that once required brittle rules or custom ML pipelines became promptable. But as adoption has grown, so has confusion. Teams now ask a new question that did not exist a few years ago: should we use a large language model directly, or should we build an AI agent around it? This distinction matters more than it seems. I have seen teams over-engineer agentic systems for problems that only needed a single LLM call. I have also seen teams struggle with fragile prompt chains when what they really needed was planning, memory, and tool orchestration.

Tokens and Transactions With AI

Aggregated on: 2026-03-25 15:53:10

Based on NVIDIA CEO Jesen Huang’s commentary on the Role of Databases for the Agentic Era in his GTC 2026 keynote. The diagram below is a readable version of Jensen's "Best Slide"; the content is created using LLM from the talk's transcript and then edited. Summary of the Talk [wrt Databases] For a database audience, the keynote underscores a fundamental shift: Data is no longer just stored and queried — it is continuously activated to power agentic systems. The talk highlights that the center of gravity is moving from traditional transactional and analytical databases toward AI-driven data platforms that unify structured, unstructured, and real-time data streams into a single operational fabric. Massive growth in AI infrastructure — driven by data center expansion and trillion-dollar-scale compute demand — signals that data systems must scale not just for queries, but for continuous inference and agent workflows.

Privacy-Conscious AI Development: How to Ship Faster Without Leaking Your Crown Jewels

Aggregated on: 2026-03-25 15:23:10

AI-assisted development is accelerating software delivery — but it also amplifies a question many teams still ignore: what happens to your sensitive data when you use AI tools? API keys, customer PII, internal business logic, production logs — once shared with third-party AI services, you may lose control over where that data is stored, who can access it, and how it’s used. Even with reputable providers, data may be logged or cached outside your visibility; support teams may access snippets; and content may be used to improve models unless you explicitly opt out. The result is elevated compliance risk (e.g., GDPR/CCPA) and potential competitive exposure if proprietary logic becomes training data.

Data-Driven API Testing in Java With REST Assured and TestNG: Part 3

Aggregated on: 2026-03-25 14:53:10

Data-driven testing enables testers to execute the same test logic with multiple sets of input data, improving coverage and reliability with minimal effort. By combining CSV files with TestNG’s @DataProvider annotation, test data can be easily separated from the test logic. This approach enables maintainability and makes test automation more scalable and flexible. This article explains how to implement data-driven testing with CSV files and TestNG in a clear, practical, and easy-to-follow manner.

Retries Will Bankrupt You Before Any Attacker Gets the Chance

Aggregated on: 2026-03-25 14:23:10

I've watched a $40,000 AWS bill materialize in a weekend. No breach, no botnet, no disgruntled ex-employee with root access. Just a misconfigured retry policy on a Lambda-backed payment processor that hit a flaky downstream vendor API during a Saturday night deployment. Every timeout spawned three children. Each child could time out too. That’s the thing nobody tells you when they hand you the Polly documentation and say, “Add resilience.” Resilience, implemented carelessly, is just a different failure mode with a credit card attached.

Operationalizing Agentic AI in Enterprises: A Problem-Constraints-Tradeoffs Case

Aggregated on: 2026-03-25 13:38:10

Editor’s Note: The following is an article written for and published in DZone’s 2026 Trend Report, Generative AI: From Prototypes to Production, Operationalizing AI at Scale. Our problem did not show up as a lack of intelligence. It appeared as instability.

Mastering Serverless Architecture: Event-Driven Design with Azure Functions and Cosmos DB

Aggregated on: 2026-03-25 13:23:10

The landscape of modern software engineering has shifted dramatically from monolithic, stateful applications toward decoupled, event-driven architectures. At the forefront of this evolution is the combination of Azure Functions and Azure Cosmos DB. This powerful duo enables developers to build systems that are massively scalable, cost-effective, and resilient. In this article, we take a deep dive into the technical intricacies of building end-to-end event-driven systems. We explore the mechanics of the Cosmos DB Change Feed, architectural design patterns such as CQRS and Materialized Views, and practical implementation strategies for production-grade serverless applications.

Swift: Master of Decoding Messy JSON

Aggregated on: 2026-03-25 12:23:10

I recently came across an interesting challenge involving JSON decoding in Swift. Like many developers, when faced with a large, complex JSON response, my first instinct was to reach for “quick fix” tools. I wanted to see how online resources, various JSON-to-Swift converters, and even modern AI models would handle a messy, repetitive data structure. To be honest, I was completely underwhelmed.

Agent-of-Agents Pattern: Enhancing Software Testing

Aggregated on: 2026-03-24 20:08:10

The Pre-Production Bottleneck A pull request (PR) gets merged, code review is complete, unit tests are green, and the feature looks good. But then comes the familiar question: Is this actually ready for production? Most engineering teams have a checklist: regression tests, security scans, performance validation, and integration checks. The problem is that executing all of this takes significant time. A full regression suite might take one to two hours. For a feature that touched a few files, running everything feels wasteful. But manually picking tests? That's how bugs slip into production.

Building Scalable Agentic Assistants: A Graph-Based Approach

Aggregated on: 2026-03-24 19:08:10

About a year ago, we were drawn into what appeared to be a straightforward problem: building an interface assistant that could answer questions about payments, disputes, refunds, transactions, and a few other sub-domains and provide insights. The reality turned out far more complex. Many teams already had multiple apis, data sources, internal tools, and domain experts collaborating. What we didn't have was a way to wire all this together into something that felt coherent, reliable, and scalable. Early experiments with single-agent chatbots worked for demos, but they collapsed under real organizational complexity. We needed to stop thinking in terms of agentic systems and start treating it as a coordinated system of agents, each with a narrow responsibility.

Robust Network Layer in Swift via Clean Architecture Approach

Aggregated on: 2026-03-24 18:08:10

Networking is the backbone of almost every modern iOS application. However, as projects grow in complexity, the network layer often becomes a “junk drawer” for URL construction, messy completion handlers, and scattered error logic. This tight coupling makes unit testing difficult and maintenance a nightmare. In this article, we are going to build a reusable, testable, and type-safe Network Layer from scratch. By leveraging Clean Architecture principles, the power of Swift Generics, and the modern elegance of Async/Await, we will create a solution that separates concerns and scales with your app.

Data-Driven API Testing in Java With REST Assured and TestNG: Part 2

Aggregated on: 2026-03-24 17:08:09

In the previous article, we explored how to implement data-driven testing using Object arrays and TestNG’s @DataProvider annotation. While this approach works well for small to medium-sized datasets, it is not ideal for handling large volumes of data. To address this limitation, TestNG also supports the use of Iterators, which provide a more efficient way to manage large and dynamic datasets. This article focuses on how to perform data-driven API automation testing using an Iterator with a DataProvider annotation of TestNG.

MariaDB Doesn't Depend on MySQL

Aggregated on: 2026-03-24 16:08:09

When MariaDB was first announced in 2009 by Michael “Monty” Widenius, it was positioned as a “fork of MySQL”. I think that was a Bad Idea™. Okay, maybe it wasn’t a bad idea as such. After all, MariaDB indeed is a fork of MySQL. But what is a fork in the software sense, and how is this reflected in MariaDB? A fork is a software project that takes the source code of another project and continues development independently from the original. Forks often start by maintaining compatibility with their parent project, but they can evolve to become detached from their own features, architecture, bug tracker, mailing list, development philosophy, and community. This is the case of MariaDB, with the addition that it continues to be highly compatible with old MySQL versions and with its current ecosystem at large.

The Phantom Write Problem: Why Your Idempotency Implementation Is Silently Losing Data

Aggregated on: 2026-03-24 15:08:09

Idempotency implementations commonly pass unit tests yet silently corrupt data in production due to four failure modes — including failure modes that manifest as "phantom writes" — a pattern previously undocumented as a unified class of idempotency failures. This article identifies these patterns based on debugging 12 production incidents and introduces the Idempotency Barrier pattern, a unified approach combining transactional state machines, atomic claiming, and boundary-aware key propagation. After deployment across three financial platforms, the pattern eliminated 99.98% of duplicate payment incidents and reduced monthly reconciliation costs by over $220,000. Disclosure: This research stems from debugging production incidents across multiple high-scale payment and order fulfillment platforms between 2023 and 2025. Company-specific details have been anonymized.

Understanding SHORTUSR/USRFIELDS in AUTHINFO to Meet 12-Character Identity Limits for MQ on Windows

Aggregated on: 2026-03-24 14:08:10

Introduction: Modern Directories Meet Legacy Constraints As organisations strengthen security and centralise identity management, IBM MQ administrators increasingly integrate with enterprise LDAP directories such as Microsoft Active Directory or OpenLDAP. This enables authentication using corporate credentials and authorisation through LDAP users or their group membership, instead of relying on local OS users. However, on Windows platforms, IBM MQ still enforces a long‑standing 12‑character limit on the user ID used for authorisation. This limitation does not come from LDAP; it originates from how MQ maps authenticated identities to Windows principals for Object Authority Manager (OAM) checks. IBM MQ’s Object Authority Manager was designed to work uniformly across Windows, UNIX (AIX/Linux), and z/OS, where OS usernames traditionally max out at 12 characters.

Imprisoning the Panic

Aggregated on: 2026-03-24 13:08:09

This single line of code recently made a significant portion of the Internet unavailable throughout the world. Rust let (feature_values, _) = features.append_with_names(&self.config.feature_names).unwrap();

Building an Agentic AIOps Pipeline With IBM Storage Insights, n8n, and Elastic

Aggregated on: 2026-03-24 12:08:10

IBM Storage Insights is a cloud-based storage monitoring and analytics platform designed to provide visibility across enterprise storage environments. It continuously collects telemetry from storage systems, analyzes capacity and performance trends, detects risks, and generates alerts when thresholds or anomalies are detected. These alerts can range from capacity and performance issues to configuration, security, and hardware health notifications. In large environments, Storage Insights becomes a critical early-warning system — but it can also generate a high volume of alerts that require triage, investigation, and remediation. Some are critical and demand immediate attention, many are informational, and a surprising number are duplicates of issues that have already been investigated and resolved. Over time, this creates a familiar problem for operations teams: alert fatigue. Engineers spend more time triaging notifications than solving real problems, and valuable context is scattered across dashboards, chat threads, and ticketing systems.

Beyond Reactive HPA: Designing a Predictive Autoscaler with KEDA and Time-Series Forecasting

Aggregated on: 2026-03-23 20:23:09

Kubernetes scaling relies predominantly on the Horizontal Pod Autoscaler (HPA), a robust feedback loop that adjusts capacity based on observed metric saturation. While reliable for steady-state traffic, HPA is inherently reactive, it mitigates resource exhaustion only after it has begun. For workloads with steep, predictable traffic ramps (such as morning log-in spikes or scheduled synchronization jobs), this reactive lag guarantees a period of transient performance degradation. To achieve strict Service Level Objectives (SLOs) during these ramps, infrastructure must shift from reacting to current load to anticipating future demand. This article details a feed-forward architecture using time-series forecasting (Prophet) and Kubernetes Event-Driven Autoscaling (KEDA) to provision capacity before the demand arrives.

Why Supply Chain Planning Still Breaks Even with Advanced Forecasting Tools

Aggregated on: 2026-03-23 19:23:09

A supply chain planning team reviews its dashboard on a Monday morning. Forecast accuracy looks strong. Demand models are updated. Inventory targets are aligned with the plan. Yet by midweek, urgent expediting requests appear, stockouts emerge in critical locations, and excess inventory quietly piles up elsewhere. This is not a failure of forecasting software. It is a failure of how planning actually works in the real world.

From 13,000 to 20,000+ Endpoints: Architecting Forensics for the Remote Workforce

Aggregated on: 2026-03-23 18:23:09

Traditional forensic processes fail when dealing with a large number of devices (over 20,000), such as in Fortune 500–level organizations. At that scale, the idea of taking a full disk image of a 512 GB laptop over a VPN is virtually impossible before the device shuts down, the user restarts it, or the legal window expires. To overcome the physics bottleneck (bandwidth), we need to reverse how we think about remote data collection. Instead of bringing all the data to the tool, we need to send the tool to the data.

Why AI Agents Are the New Backbone of Software Quality

Aggregated on: 2026-03-23 17:23:09

The story of software testing is one of constant reinvention. From the early days of manual testing to the widespread adoption of test automation, each stage has served to keep pace with the growing complexity of digital products. Now, QA is entering its most transformative chapter. Systems can explore apps using behavior-driven heuristics and probabilistic user flows, simulating multiple interaction patterns more quickly, identifying signals that indicate a higher likelihood of defects before they surface in production, accelerating QA cycles, and refining testing strategies. Statistics show a clear shift in the adoption curve. The World Quality Report 2025–26 reveals that 88% of interviewees already apply AI in QA workflows, while the latest McKinsey survey states that AI implementation can boost software quality outcomes by up to 31–45%.

Shrink a Bloated Git Repository and Optimize Pack Files

Aggregated on: 2026-03-23 16:23:09

Executive Summary Large Git repositories slow down developers, CI/CD, and release processes. The main culprits are big binary blobs, long-lived histories of rarely used files, and repeated commits of generated artifacts. This guide provides a comprehensive, step-by-step approach to: Measure where the bloat is and surgically remove it by rewriting history with safe, modern tools, Aggressively repack objects for performance, and put guardrails in place — such as Git LFS, CI size policies, and partial clone — to keep your repo lean over time. By the end, you will know how to identify the largest objects hidden in your commit DAG, remove historical binaries without breaking your trunk, safely coordinate a force-push for the team, reduce pack files by orders of magnitude, and adopt practices that prevent bloat from coming back.

Securing the Software Supply Chain in the Age of AI Agent Swarms

Aggregated on: 2026-03-23 15:23:09

If your team is using AI agents to write code, pick dependencies, or trigger builds, your delivery model is evolving fast, and your trust model needs to evolve with it. Agents are already improving quickly and can be a major force multiplier for engineering teams. But even with better models, trust decisions still need explicit controls: dependency validation, pipeline integrity checks, and artifact verification before runtime. At machine speed, those checks have to be automated, not optional. I started paying attention to this after watching a team roll out an LLM-based coding assistant. Within weeks, their build logs had packages that were not in any approved registry. Nobody noticed because the builds were green. That experience convinced me that supply chain security is no longer something you bolt on later. It is part of how you ship software now.

Getting Started With Qwen Code for Coding Tasks

Aggregated on: 2026-03-23 14:53:09

Nowadays, there are quite a lot of AI coding assistants. In this blog, you will take a closer look at Qwen Code, a terminal-based AI coding assistant. Qwen Code is optimized for Qwen3-Coder, so when you are using this AI model, it is definitely worth looking at. Enjoy! Introduction There are many AI models and also many AI coding assistants. Which one to choose is a hard question. It also depends on whether you run the models locally or in the cloud. When running locally, Qwen3-Coder is a very good AI model to be used for programming tasks. In previous posts, DevoxxGenie, a JetBrains IDE plugin, was often used as an AI coding assistant. DevoxxGenie is nicely integrated within the JetBrains IDEs. But it is also a good thing to take a look at other AI coding assistants. And when you are using Qwen3-Coder, Qwen Code is an obvious choice.

Building an LLM-Based Agent: Step 0

Aggregated on: 2026-03-23 14:23:09

Newcomers to the LLM world often start by "chatting for fun," but very quickly they run into a bigger question about how to make it not only answer, but actually do work. From that moment, they begin to touch on the idea of an agent, meaning a system that can accept a goal, break down tasks, use tools, and self-check results to get real work done. This first post serves as the foundation for the entire series. We will not go far yet and focus on one concrete thing: building a minimal yet correct chat framework that we can later upgrade into an agent without rewriting everything from scratch. Once this framework is stable, we will see that two factors determine quality from the very beginning, namely how we package context using messages and how we write prompts in a clear and disciplined way.

Rate Limiting Strategies With Redis: Fixed Window, Sliding Window, and Token Bucket

Aggregated on: 2026-03-23 13:08:09

The Rate Limiting Problem API rate limiting protects services from overload, prevents abuse, and ensures fair resource distribution across clients. Without rate limiting, a single high-volume client can degrade performance for all users, whether through malicious attacks or unintentional bugs causing request loops. Traditional approaches, like in-memory counters, fail in distributed systems. When multiple API servers handle requests, each maintains separate counts, making it impossible to enforce consistent limits. Redis solves this by providing a centralized, fast key-value store that all servers can query atomically.

AI Can Help With Migration; It Cannot Own It

Aggregated on: 2026-03-23 12:08:09

There’s a growing assumption that as AI agents become more capable, system migrations will eventually become fully autonomous. If an agent can refactor code, translate SQL dialects, infer schemas, and generate tests, then perhaps it should be able to re-platform an organization’s data stack end to end. That assumption makes sense if migration is understood primarily as translation. And sometimes it is. But in many real organizations, migration is not just about moving workloads. It is about using that moment to rethink what the system should be. And that changes the nature of the problem in ways that are less visible from the outside.

Vibe Coding Is Great for Demo; It’s Not a Strategy for GenAI Value in the SDLC

Aggregated on: 2026-03-20 20:08:08

If your IDE can whip up a feature in minutes, why do releases still get delayed, incidents keep popping up, and PR queues stay backed up? That's the big question driving vibe coding. The idea sounds great: just say what you want in plain language, have an LLM generate the code, copy it over, run it, and then you're ready to go. It feels like software has finally turned into something that puts English first.

10 Strategies for Scaling Synthetic Data in LLM Training

Aggregated on: 2026-03-20 19:08:08

With businesses desperately searching for ways to reduce data bottlenecks associated with LLMs, synthetic data is now emerging as a leading solution. For those encountering difficulties in accessing, purchasing, and utilizing high-quality datasets due to scarcity, legalities, or costs, synthetic data provides a way out. You can also generate "long-tail" data that is difficult to find and use at scale. Large language model (LLM) training teams are experiencing challenges in sourcing sufficient quality data for training purposes. Although data may exist, the data often has contractual restrictions or other limitations on its usage. Even if there are no contractual restrictions, cleaning, validating, and standardizing such data so that it produces consistent results during training is an extremely costly process. Due to this, synthetic data has emerged as a critical element in the training strategies of numerous LLM training teams.

Modern Best Practices for Web Security Using AI and Automation

Aggregated on: 2026-03-20 18:08:08

Beyond being a cliché, "artificial intelligence" and its associated automation technologies have driven major developments in security, signifying that important changes have occurred in this field. In the field of cybersecurity, artificial intelligence refers to systems that acquire data, trace patterns, and forecast trends. Typically, this application is performed using machine learning, neural networks, or other high-performance data processing algorithms. There are limited domains in which an AI-driven system is more effective than humans or conventional security systems, such as detecting security threats, connecting unrelated incidents across various geographical or logistical contexts, and examining large datasets for subtle attack indicators that are often missed by humans or conventional security systems. While traditional automation is constrained to predefined instructions, intelligent automation leverages artificial intelligence through playbooks and reasoning processes. This enables systems to analyze the outcomes they receive, make suitable decisions, or perform a series of predetermined tasks beyond simple ‘if-then’ rules. A simple example is a system that detects a malicious device and, if appropriate, isolates the bad actors by isolating the device. Such devices can suggest removing the malicious endpoint from the network or implementing a specific set of controls without the manual approval of security personnel.

Kubernetes Scheduler Plugins: Optimizing AI/ML Workloads

Aggregated on: 2026-03-20 17:08:08

Picture this: Enterprises burn $400K monthly on GPU clusters humming at 35% capacity while workloads queue endlessly outside. Why? The stock scheduler thinks GPUs are interchangeable, counting tokens — oblivious to silicon geography, workload personality, or the thundering cost-per-second of idle accelerators. What follows dissects how purpose-built scheduler plugins flip that equation. We're talking technical guts: architectural decisions, deployment mechanics, working code that actually ships. No hand-waving. Just the machinery needed to make GPUs earn their keep.

AI as a SQL Performance Tuning Assistant: A Structured Evaluation

Aggregated on: 2026-03-20 16:08:08

SQL performance tuning has been one of the most technical tasks in present-day software engineering. A query can be logically sound, well-indexed, and well-tested, yet still degrade significantly under production load. Answers to performance issues are found in execution plans, which are usually thick, technical, and hard to understand quickly. The skills and time needed to learn the strategies of joins, the type of scan, cost estimates, cardinality forecasts, and all that cannot be met by the forces of the development team. As large language models (LLMs) have become integrated into the developer workflow, a number of engineers have started piloting the use of AI as a query analysis tool and an interpretation of performance plans. Rather than manually dissecting the complex EXPLAIN ANALYZE results, developers are requesting AI to clarify bottlenecks, recommend indexing plans, and point out the inefficiencies. This brings us to a critical and practical query: Is it possible that AI can really help in optimization of SQLs, or does it only give confident answers devoid of engineering worth?

Why Agentic AI Demands Intent-Based Chaos Engineering

Aggregated on: 2026-03-20 15:53:08

Chaos engineering transformed modern reliability practices. Instead of waiting for systems to fail in production, we began deliberately injecting failure into distributed architectures to observe how they behaved under stress. The philosophy was simple: resilience cannot be assumed; it must be tested. For stateless microservices and horizontally scaled cloud systems, this approach worked remarkably well. Random instance termination, injected latency, and packet loss exposed weaknesses in infrastructure that traditional testing often missed. However, the systems we are building today are fundamentally different from those chaos engineering was originally designed to protect.