News Aggregator

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.

Best Software Engineer Books: Build Your Personal Library

Aggregated on: 2025-09-19 04:15:00

I believe that every one of us, software engineers, should have our own personal library of software engineering books. Whether in old plain-text book form or in a newer, more eco-friendly electronic one is an open question. The important thing is to actually have one. I am one of those strange people who believe that we, in general, should read books. Doing so has multiple benefits, but let's not dive too deep into this and focus on software engineering. Well, there are a couple of problems with software engineer books: They get old rather quickly. There are a lot of them. They are expensive. They have varying levels of quality. Given our limited time, the obvious conclusion is that it is hard to find a book worthy of reading, one we will not waste our money on. Here comes this article. It will be the first in a series focused on what books I recommend you include in your professional library. This particular blog covers books that focus on the softer parts of our job:

LLMs for Debugging Code

Aggregated on: 2025-09-18 18:30:00

Large language models (LLMs) are transforming software development lifecycles, with their utility in code understanding, code generation, debugging, and many more. This article provides insights into how LLMs can be utilized to debug codebases, detailing their core capabilities, the methodologies used for training, and how the applications might evolve further in the future. Despite the issues with LLMs like hallucinations, the integration of LLMs into development environments through sophisticated, agentic debugging frameworks proves to improve developers’ efficiency. Introduction The Evolving Role of LLMs in Coding LLMs have already proven their capabilities beyond their initial applications in natural language processing to achieve remarkable performance in diverse code-related tasks, including code generation and translation. They power AI coding assistants like GitHub Copilot and Cursor, and have demonstrated near-human-level performance on standard benchmarks such as HumanEval and MBPP.

Disabling UseNUMA Flag When CPU and Memory Node Misalign in JDK

Aggregated on: 2025-09-18 17:30:00

Today, Java is still one of the widely used languages to build and run applications, and for the same reason, organizations prioritize measuring its performance. When running a Java application on a multi-NUMA (Non-Uniform Memory Access) memory node, we need to pay attention to the remote accesses, if any, otherwise, that can result in increased latencies and hence result in reduced performance. The libnuma kernel library provides several policies, including localalloc, preferred, membind, and interleave, which enable users to affinitize their applications and run them with optimal utilization of the server nodes as per their requirements.

Blueprint for Agentic AI: Azure AI Foundry, AutoGen, and Beyond

Aggregated on: 2025-09-18 16:30:00

In 2025, AI isn’t just about individual models doing one thing at a time, but it’s about intelligent agents working together like a well-coordinated team. Picture this: a group of AI systems, each with its own specialty, teaming up to solve complex problems in real time. Sounds futuristic? It’s already happening — thanks to multi-agent systems. Two tools that are making this possible in a big way are Azure AI Foundry and AutoGen.

Remote Android Management: A Step-by-Step Guide

Aggregated on: 2025-09-18 15:30:00

The Problem No One Talks About In an era where screens dominate bedtime routines, millions now fall asleep to YouTube videos, podcasts, or streaming apps. However, this habit has a hidden cost: uncontrolled volume exposure, especially for children. As a parent and developer, I faced this problem firsthand — my child’s late-night YouTube binges led to restless sleep and morning irritability. Free apps in the Google Play Store, like Volume Limiter and Volume Control, were a failure: They crashed, had no settings, or were too intrusive. Perhaps commercial apps would be better, but I haven't tested this since they cost money, often quite a bit.

FOSDEM 2025 Recap: Open Source Contributors Unite to Collaborate and Help Advance Apache Software Projects

Aggregated on: 2025-09-18 14:30:00

FOSDEM 2025 has come to a close, and it certainly was not without a lot of content and participation from Apache Software Foundation (ASF) members, committers, and contributors! We asked ASF participants to provide summaries and observations from this year’s premier free software event, to share a small part of the work that ASF community members do for open-source software development. This blog provides a brief overview of their talks, including links to the video recordings. Apache NuttX RTOS Talk: "SBOM Journey for an Open Source Project - Apache NuttX RTOS" (video)

Unified Checkout Experience Through Micro Frontend Architecture

Aggregated on: 2025-09-18 13:15:00

Large retail systems today, much like Walmart, operate multiple types of checkout registers across various services — pharmacy, auto care, fuel stations, photo centers, and more. These checkout points are not just limited to traditional frontend registers for scanning and payment, but encompass a broad array of service-specific interfaces. As the breadth of services grows, retailers are often left managing fragmented checkout solutions. This fragmentation leads to inconsistent user experiences, higher training overhead for staff, and slower development cycles. The need for a unified checkout experience across microapps — one that abstracts underlying service complexity and presents a consistent interface to customers and associates — has never been more critical.

Creating a Distributed Computing Cluster for a Data Base Management System: Part 1

Aggregated on: 2025-09-18 12:15:00

Ideas of creating a distributed computing cluster (DCC) for database management systems (DBMS) have been striking me for quite a long time. If simplified, the DCC software makes it possible to combine many servers into one super server (cluster), performing an even balancing of all queries between individual servers. In this case, everything will appear for the application running on the DCC as if it was running with one server and one database (DB). It will not be dispersed databases on distributed servers, but work as one virtual one. All network protocols, replication exchanges, and proxy redirections will be concealed inside the DCC. At the same time, all resources of distributed servers, in particular RAM and CPU time, will be utilized evenly and in an efficient fashion. For example, in a cloud data processing center (DPC), it is possible to take one physical super server and divide it into a number of virtual DBMS servers. But the reverse procedure was not possible until now, i.e., it is not possible to take a number of physical servers and merge them into a single virtual DBMS super server. In some specified sense, DCC is a technology that makes it possible to merge physical servers into one virtual DBMS super server.

Development of System Configuration Management: Summary and Reflections

Aggregated on: 2025-09-18 11:15:00

Series Overview This article is Part 4 of a multi-part series: "Development of system configuration management." The complete series:

Enable AWS Budget Notifications With SNS Using AWS CDK

Aggregated on: 2025-09-17 19:14:56

Keeping track of AWS spend is very important. Especially since it’s so easy to create resources, you might forget to turn off an EC2 instance or container you started, or remove a CDK stack for a specific experiment. Costs can creep up fast if you don’t put guardrails in place. Recently, I had to set up budgets across multiple AWS accounts for my team. Along the way, I learned a few gotchas (especially around SNS and KMS policies) that weren’t immediately clear to me as I started out writing AWS CDK code. In this post, we’ll go through how to:

Building a Platform Abstraction for EKS Cluster Using Crossplane

Aggregated on: 2025-09-17 18:14:56

Building on what we started earlier in an earlier article, here we’re going to learn how to extend our platform and create a platform abstraction for provisioning an AWS EKS cluster. EKS is AWS’s managed Kubernetes offering. Quick Refresher Crossplane is a Kubernetes CRD-based add-on that abstracts cloud implementations and lets us manage Infrastructure as code.

From Data Growth to Data Responsibility: Building Secure Data Systems in AWS

Aggregated on: 2025-09-17 17:14:56

Enterprise data solutions are growing across data warehouses, data lakes, data lakehouse, and hybrid platforms in cloud services. As the data grows exponentially across these services, it's the data practitioners' responsibility to secure the environment with secure guardrails and privacy boundaries. In this article, we will learn a framework for implementing security protocols in AWS and learn how to implement them across Redshift, Glue, DynamoDB, and Aurora database services.

Anything Rigid Is Not Sustainable: Why Flexibility Beats Dogma in Agile and Project Management

Aggregated on: 2025-09-17 16:14:56

Rigid structures are not sustainable. The same is true in project management and organizational agility: anything rigid is not sustainable — whether it’s a process, a framework, or an architecture. From my experience in leading technology programs across industries, the learning and observation are clear: rigid approaches may deliver in the short term, but adaptability is a must for long-term sustainability.

Terraform Compact Function: Clean Up and Simplify Lists

Aggregated on: 2025-09-17 15:14:56

In Terraform, many configurations are dynamic, and you may build a list using conditional expressions that return null when not applicable. If those null values are passed directly to a resource (for example, in security_group_ids or depends_on), they can cause validation errors. The compact() function ensures that only valid, non-null elements are included, helping prevent such runtime errors during the apply phase.

Development of System Configuration Management: Performance Considerations

Aggregated on: 2025-09-17 14:14:56

Series Overview This article is Part 3 of a multi-part series: "Development of system configuration management." The complete series:

Beyond Retrieval: How Knowledge Graphs Supercharge RAG

Aggregated on: 2025-09-17 13:14:56

Retrieval-augmented generation (RAG) enhances the factual accuracy and contextual relevance of large language models (LLMs) by connecting them to external data sources. RAG systems use semantic similarity to identify text relevant to a user query. However, they often fail to explain how the query and retrieved pieces of information are related, which limits their reasoning capability. Graph RAG addresses this limitation by leveraging knowledge graphs, which represent entities (nodes) and their relationships (edges) in a structured, machine-readable format. This framework enables AI systems to link related facts and draw coherent, explainable conclusions, moving closer to human-like reasoning (Hogan et al., 2021).

Mastering Fluent Bit: Top 3 Telemetry Pipeline Input Plugins for Developers (Part 6)

Aggregated on: 2025-09-17 12:14:55

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.

The AI FOMO Paradox

Aggregated on: 2025-09-17 11:14:55

TL; DR: AI FOMO — A Paradox AI FOMO comes from seeing everyone’s polished AI achievements while you see all your own experiments, failures, and confusion. The constant drumbeat of AI breakthroughs triggers legitimate anxiety for Scrum Masters, Product Owners, Business Analysts, and Product Managers: “Am I falling behind? Will my role be diminished?”

How to Migrate from Java 8 to Java 17+ Using Amazon Q Developer

Aggregated on: 2025-09-16 19:14:55

Replatforming from Java 8 to the newer Java versions has proven to be a huge challenge due to potential compatibility issues and changes in language specifications. The Spring Framework, which provides a programming and configuration model for modern Java applications, has just released its latest major version, Spring Framework 6.2.10, and it requires a baseline of Java 17 or higher. Because of this, migrating from an older version like Java 8 would involve code modifications, which take considerable effort and rigorous testing. Before diving deep into version upgrades for Java applications, let us first discuss what Amazon Q developer is and how it helps developers with application modernization.

Implementing a Weekly Release Cycle for Mobile Apps

Aggregated on: 2025-09-16 18:14:55

Mobile app development has moved from occasional, significant updates to a point where users constantly expect new improvements. While weekly launches for mobile apps can be a substantial benefit, it’s not only about how fast you release. The goal is to keep improving over time, allowing teams to deliver value faster, repair errors faster, and maintain the user base without compromising quality. Still, making a weekly release model sustainable is not only about increasing work speed. It is all about changing the process of creating, testing, releasing, and monitoring your app. Big names in the app world, such as Instagram and Spotify, now release updates each week. This is not because they are more efficient in coding. They can do this because they have perfected a culture of rapid changes with no chaos.

Beyond Code: How to Use AI to Modernize Software Architecture

Aggregated on: 2025-09-16 17:14:55

Enterprise teams today ship more code, more frequently, than ever before — fueled in part by AI-driven coding tools like GitHub Copilot and Amazon Q. But there’s a problem.

Multi-Agent (Multi-Function) Orchestration With AWS Step Functions

Aggregated on: 2025-09-16 16:14:55

Multi-agent orchestration with AWS Step Functions is a robust architectural pattern for coordinating multiple, specialized agents (such as Lambda functions, microservices, or dedicated AI modules) into a unified, scalable workflow. This approach is especially useful when complex tasks require the collaboration of several autonomous agents without hard-coding their interactions — a strategy that not only simplifies development but also enhances reliability and scalability. These agents are going to increase efficiency and productivity, enhance decision-making, improve customer experiences, adaptability, and scalability, and hence reduce operational costs.

Protecting Non-Human Identities: Why Workload MFA and Dynamic Identity Matter Now

Aggregated on: 2025-09-16 15:14:55

We’ve normalized multi-factor authentication (MFA) for human users. In any secure environment, we expect login workflows to require more than just a password — something you know, something you have, and sometimes something you are. This layered approach is now foundational to protecting human identities. But today, the majority of interactions in our infrastructure aren’t human-driven. They’re initiated by non-human entities — services, microservices, containerized workloads, serverless functions, background jobs, and AI agents. Despite this shift, most of these systems still authenticate using a single factor: a secret.

Automating RCA and Decision Support Using AI Agents

Aggregated on: 2025-09-16 14:14:55

With the AI boom over the past couple of years, almost every business is trying to innovate and automate its internal business processes or front-end consumer experiences. Traditional business intelligence tools require manual intervention for querying and interpreting data, leading to inefficiencies. AI agents are changing this paradigm by automating data analysis, delivering prescriptive insights, and even taking autonomous actions based on real-time data. Obviously, it is the humans who set the goals, but it is an AI agent that autonomously decides on the best action to perform these goals.

How TBMQ Uses Redis for Persistent Message Storage

Aggregated on: 2025-09-16 13:14:55

TBMQ was primarily designed to aggregate data from IoT devices and reliably deliver it to backend applications. Applications subscribe to data from tens or even hundreds of thousands of devices and require reliable message delivery. Additionally, applications often experience periods of downtime due to system maintenance, upgrades, failover scenarios, or temporary network disruptions. IoT devices typically publish data frequently but subscribe to relatively few topics or updates. To address these differences, TBMQ classifies MQTT clients as either application clients or standard IoT devices. Application clients are always persistent and rely on Kafka for session persistence and message delivery. In contrast, standard IoT devices — referred to as DEVICE clients in TBMQ — can be configured as persistent depending on the use case.

Geometric Deep Learning: AI Beyond Text and Images

Aggregated on: 2025-09-16 12:14:55

While traditional deep learning techniques have excelled at handling structured data like images and text, they often struggle when faced with irregular, complex data like molecules and networks of data. Geometric deep learning (GDL) is a machine learning methodology suitable for solving problems with irregular and complex data. What Is Geometric Deep Learning? Geometric deep learning extends traditional deep learning approaches to handle non-Euclidean data, data that doesn't fit into regular grid-like or fixed structures like images or text. Geometric deep learning focuses on understanding relationships between data points, regardless of their spatial arrangement. This makes it particularly powerful for analyzing complex structures like molecular compounds, social networks, and meshes, where the connections between elements matter more than their specific locations.

Super Massively Distributed Systems

Aggregated on: 2025-09-16 11:14:55

With a message broker, you can transmit JSON dynamically to different clients, resulting in "an event-driven architecture." The problem with this approach is that the creator of each individual "service" in this network mesh needs to anticipate the usage requirements of all future potential clients, for an infinite amount of possible questions, for an infinite amount of time. This is the equivalent of anticipating every possible answer to every imaginable question that can be phrased using computer languages, and creating a pre-computed list of potential answers. This is not only awkward and sub-optimal, but also impossible, resulting in the internet as a whole becoming a trillion times "dumber" than it needs to be.

From Laptop to Cloud: Building and Scaling AI Agents With Docker Compose and Offload

Aggregated on: 2025-09-15 19:29:55

Running AI agents locally feels simple until you try it: dependencies break, configs drift, and your laptop slows to a crawl. An agent isn’t one process — it’s usually a mix of a language model, a database, and a frontend. Managing these by hand means juggling installs, versions, and ports. Docker Compose changes that. You can now define these services in a single YAML file and run them together as one app. Compose even supports declaring AI models directly with the models element. With one command — docker compose up — your full agent stack runs locally.

Spring Cloud Gateway With Service Discovery Using HashiCorp Consul

Aggregated on: 2025-09-15 18:14:55

This article will explain some basics of the HashiCorp Consul service and its configurations. It is a service networking solution that provides service registry and discovery capabilities, which integrate seamlessly with Spring Boot. You may have heard of Netflix Eureka; here, Consul works similarly but offers many additional features. Notably, it supports the modern reactive programming paradigm. I will walk you through with the help of some applications. Used Libraries Spring Boot Spring Cloud Gateway Spring Cloud Consul Spring Boot Actuator The architecture includes three main components:

Mastering Approximate Top K: Choosing Optimal Count-Min Sketch Parameters

Aggregated on: 2025-09-15 17:14:55

What Is Top K? The "Top K" problem refers to determining the top-k elements with the highest frequencies or relevance scores from vast, rapidly changing data streams. In modern real-time systems — such as e-commerce platforms, social media, and streaming services — it's vital to quickly identify the most relevant items or events. Real-world examples include: Trending Twitter hashtags rapidly shifting based on tweet volume Most-watched Netflix movies updating hourly across regions Top Amazon products ranking sales in real time Popular YouTube videos updating hourly based on view velocity The "Top K" approach is essential for use cases like:

How AI and Machine Learning Are Shaping the Fight Against Ransomware

Aggregated on: 2025-09-15 16:14:55

Ransomware remains one of the biggest threats to individuals and corporations, primarily because cybercriminals relentlessly look for loopholes. With traditional measures struggling to keep pace with cyber threats, the shift to artificial intelligence (AI) and machine learning (ML) can be revolutionary. With such technologies, detection is automated, damage mitigation strategies are devised, and even attacks are predicted ahead of time. In this article, we review the innovative approaches and AI-enabled solutions that enhance cybersecurity strategies against ransomware. The Roles of AI in Prevention and Threat Detection With AI technologies like natural language processing and image recognition, identifying anomalies is faster, more precise, and far better than having to rely on existing systems. By leveraging AI, machine learning algorithms can be combined to identify unique patterns that directly correspond to anomalies. For AI security solutions, the accuracy of cyber attack detection in a real-world environment is reduced by 96% when compared with traditional methods.

Toward Explainable AI (Part 10): Bridging Theory and Practice—Responsible AI: Ambition or Illusion?

Aggregated on: 2025-09-15 15:14:55

Series reminder: This series explores how explainability in AI helps build trust, ensure accountability, and align with real-world needs, from foundational principles to practical use cases. Previously, in Part IX: Conclusion: Explainability Under Real-World Conditions: Comparing LIME and SHAP in practice.

GenAI Beyond Just LLMs

Aggregated on: 2025-09-15 14:14:55

From Words to Molecules: The Expanding Frontier of GenAI Generative AI has changed how we create, work, and even imagine. In just a few short years, tools like ChatGPT, GitHub Copilot, and DALL·E have redefined productivity across industries — from software development and design to education and marketing. But the innovation curve continues to steepen, and it's no longer just about generating text or images. The same technology that can draft an email or write a poem is now designing molecules and discovering new materials. We’re entering a world where AI doesn’t just write about science — it helps do the science. Big tech isn’t sitting this one out. OpenAI, Meta, Google DeepMind, Microsoft, and others are in a tight race, developing large-scale AI models that understand not just language and visuals, but also chemistry, biology, and physics.

Benchmarking Open-Source LLMs: LLaMA vs Mistral vs Gemma — A Practical Guide for Developers Building Private Models

Aggregated on: 2025-09-15 13:14:54

Large language models (LLMs) have transitioned from research labs into the everyday workflows of companies worldwide. While tools like GPT-4 and Claude often steal the spotlight, they come with restrictions such as API rate limits, opaque model behavior, and privacy concerns. This has led to the rise of open-source LLMs like Meta’s LLaMA, Mistral AI’s Mistral, and Google’s Gemma. These models allow developers to build and deploy powerful AI applications without relying on third-party APIs, offering transparency, flexibility, and cost control.

Agentic AI: The Next Evolution of Artificial Intelligence and Autonomous Automation

Aggregated on: 2025-09-15 12:14:54

Artificial intelligence has evolved significantly over the years. In the early days, we had pseudo-AIs like Siri and Alexa — handy tools for playing songs, setting alarms, or answering basic questions. However, their functionality was limited. They operated as reactive voice bots, only responding to direct commands without any real autonomy or initiative. Then came AI-powered assistants like ChatGPT, which businesses quickly adopted for drafting emails, reports, and handling customer inquiries. A marketing team could request a campaign plan, or a support team could automate responses to common questions. While this was a step forward, these tools still required human input to function — they didn’t act on their own. Imagine an assistant that only speaks when spoken to. It executes tasks efficiently but lacks independent thought or initiative.

Tuples and Records (Part 5): Performance Challenges

Aggregated on: 2025-09-15 11:14:54

After exploring Tuples and Records in Parts 1–4—covering JavaScript syntax, immutability, value-based equality, performance benefits, and React optimizations—we now examine why this ambitious proposal was ultimately withdrawn from ES2025. Despite the promise of native immutable data structures, technical challenges around structural equality, memory management, and engine optimization prevented its adoption. In this part, we’ll break down the core reasons for the withdrawal, the alternatives considered, and the lessons for future JavaScript language evolution. The journey of the Tuples and Records proposal in JavaScript represents one of the most significant withdrawals in recent ECMAScript history. After years of development and community anticipation, the proposal was officially withdrawn from the TC39 standardization process in April 2025, marking the end of an ambitious attempt to bring immutable data structures as primitives to JavaScript.

Enhancing AI Privacy: Federated Learning and Differential Privacy in Machine Learning

Aggregated on: 2025-09-12 20:29:53

Privacy-preserving techniques are keeping your data safe in the age of AI. In particular, federated learning (FL) keeps data local, while differential privacy (DP) strengthens individual privacy. In this article, we will discuss challenges associated with this, practical tools, and emerging trends like secure aggregation and personalized FL for stronger privacy in AI. Introduction

Shifting Left in Software Testing: Integrating AI-Driven Early Defect Detection into Agile Development Workflows

Aggregated on: 2025-09-12 19:29:53

Nobody likes bugs. Not the developers who accidentally write them, not the testers who hunt them down, and certainly not the users who stumble into them. But here’s the kicker — according to IBM, 5 to 30% of software defects still slip into production, costing companies up to 30 times more to fix post-release than if caught early. The statistic highlights the essential nature of discovering software defects at the earliest point in development. Testing processes must shift left in development lifecycles according to the shifting left concept.

AI for Ensuring Data Integrity and Simplifying Migration in Microservices Ecosystems

Aggregated on: 2025-09-12 18:14:53

Organizations implement microservices architectures to create adaptable, scalable applications that function efficiently, but must overcome significant challenges to maintain data accuracy during migration. The data sharing mechanism of microservices architecture enables beneficial advantages but produces complex data accuracy and reliability challenges. Artificial intelligence functions as a powerful instrument that enables users to manage data integrity and optimize the data migration process. AI delivers efficient solutions for microservices ecosystems to handle data integrity issues and execute data migration operations effectively.

Deploying AI Models in Air-Gapped Environments: A Practical Guide From the Data Center Trenches

Aggregated on: 2025-09-12 17:14:53

Organizations are eager to harness machine learning and deep learning — but not everyone is racing to the cloud. For highly regulated industries, government entities, and security-first organizations, air-gapped environments remain essential. The question many are now asking is: How do we bring AI into air-gapped or isolated systems, and do it securely, reliably, and scalably? After nearly two decades managing on-prem data centers and private cloud environments, I’ve seen the evolution — from physical servers and VLANs to containerized workloads and AI clusters. In this article, I’ll share practical strategies for deploying AI models in air-gapped environments, with a focus on lessons learned, key technical considerations, and actionable guidance for both engineers and decision-makers.

Security Concerns in Open GPTs: Emerging Threats, Vulnerabilities, and Mitigation Strategies

Aggregated on: 2025-09-12 16:14:53

With the increasing use of Open GPTs in industries such as finance, healthcare, and software development, security concerns are growing. Unlike proprietary models, open-source GPTs allow greater customization but also expose organizations to various security vulnerabilities. This analysis explores real-world breaches, case studies, and advanced security techniques to safeguard Open GPT deployments.

AI for Data Cleaning: How AI Can Clean Your Data and Save You Hours and Money

Aggregated on: 2025-09-12 15:14:53

Dirty data is the bane of the analytics industry. Almost every organization that deals with data has had to deal with some degree of unreliability in its numbers. According to the Pragmatic Institute, data practitioners spend 80% of their time identifying, cleansing, and arranging data and 20% analyzing it. This 80/20 rule is referred to as the Pareto Principle.

Quantum Machine Learning (QML) for Developers: A Beginner's Guide

Aggregated on: 2025-09-12 14:14:53

Quantum computing is transforming artificial intelligence. Traditional AI faces challenges in optimization, large-scale data processing, and security. Quantum machine learning (QML) integrates quantum mechanics with AI, offering advantages such as: Faster AI model training and inference Better pattern recognition and optimization Improved security using quantum cryptography This guide covers practical implementations using:

Demystifying Kubernetes on AWS: A Comparative Analysis of Deployment Options

Aggregated on: 2025-09-12 13:14:53

Kubernetes has become the industry-standard platform for container orchestration, offering automated deployment, scaling, and management of containerized applications. Its ability to efficiently utilize resources, abstract infrastructure complexities, and provide robust enterprise features makes it essential for modern application infrastructure. While Kubernetes can run on-premises, deploying on AWS provides significant advantages, including on-demand scaling, cost optimization, and integration with AWS services for security, monitoring, and operations. With multi-AZ high availability and a global presence in 32 regions, AWS delivers the reliability needed for mission-critical applications.

The Real-time Data Transfer Magic of Doris Kafka Connector's "Data Package": Part 1

Aggregated on: 2025-09-12 12:14:53

Apache Doris provides multi-dimensional data ingestion capabilities. In addition to the built-in Routine Load and Flink's support for reading from Kafka and writing to Doris, the Doris Kafka Connector [1], as an extended component of the Kafka Connect ecosystem, not only supports importing Kafka data into Doris but also relies on the vast Kafka Connect ecosystem to achieve the following features [2]: Rich Format Support Natively parses complex formats such as Avro/Protobuf. Automatically registers and converts schemas. Optimizes the efficient processing of binary data streams. Heterogeneous Integration of Multiple Data Sources Relational databases: MySQL, Oracle, SQL Server, DB2, Informix, etc. NoSQL databases: MongoDB, Cassandra, etc. Message queue systems: ActiveMQ, IBM MQ, RabbitMQ, etc. Cloud data warehouses: Snowflake, Google BigQuery, Amazon Redshift, etc.