News Aggregator

Web App Load Testing Using Maven Plugins for Apache JMeter, and Analyzing the Results

Aggregated on: 2025-12-02 21:11:16

In this article, we will walk you through how to conduct a load test and analyze the results using Java Maven technology. We'll covering everything from launching the test to generating informative graphs and tables. For this demonstration, we'll utilize various files, including Project Object Model (POM) files, JMeters scripts, and CSV data, from the jpetstore_loadtesting_dzone project available on GitHub. This will help illustrate the steps involved and the functionality of the necessary plugins and tools. You can find the project here: https://github.com/vdaburon/jpetstore_loadtesting_dzone.

Apache Phoenix With Variable-Length Encoded Data

Aggregated on: 2025-12-02 19:11:16

Apache Phoenix is an open-source, SQL skin over Apache HBase that enables lightning-fast OLTP (Online Transactional Processing) operations on petabytes of data using standard SQL queries. Phoenix helps combine the scalability of NoSQL with the familiarity and power of SQL. By supporting large-scale aggregate and non-aggregate functionality, Phoenix has evolved into an OLTP and OLAP (Online Analytical Processing) database. This makes it a compelling choice for organizations looking to combine real-time data processing with complex analytical querying in a single, unified system. Phoenix supports several variable-length data types:

JDK 17 Memory Bloat in Containers: A Post-Mortem

Aggregated on: 2025-12-02 18:11:16

When engineering teams modernize Java applications, the shift from JDK 8 to newer Long-Term Support (LTS) versions, such as JDK 11, 17, and soon 21, might seem straightforward at first. Since Java maintains backward compatibility, it's easy to assume that the runtime behavior will remain largely unchanged. However, that's far from reality. In 2025, our team completed a major modernization initiative to migrate all of our Java microservices from JDK 8 to JDK 17. The development and QA phases went smoothly, with no major issues arising. But within hours of deploying to production, we faced a complete system breakdown.

Architecting Cloud Data Migration From Legacy Warehouses

Aggregated on: 2025-12-02 17:11:16

The Legacy Challenge in Enterprise Data For decades, enterprise data platforms were built on Teradata, Oracle, and other legacy systems. They were once the backbone of analytics, providing reliability and scale, but over time, they became rigid, costly, and difficult to evolve. Today, many of these platforms hold petabytes of data, support thousands of reports, and sit at the center of hundreds of dependent processes. What was once an enabler has become a bottleneck. The challenge is not just technology. Over the years, enterprises accumulate thousands of stored procedures, ETL pipelines, and reporting scripts embedded into these systems. Business rules and definitions are often hard-coded into SQL, reporting layers, or application logic. Migration to the cloud cannot be treated as a simple copy-and-paste job. Without a deliberate strategy, companies risk recreating the inefficiencies and inconsistencies of the past on a modern platform.

Beyond Buzzwords: Demystifying Agentic AI

Aggregated on: 2025-12-02 16:11:16

AI discussions today are filled with buzzwords — autonomy, orchestration, reasoning, context-awareness, and more. These terms often get used loosely, yet they are central to understanding the shift toward agentic AI. In this article, I’ll unpack the most common buzzwords tied to AI agents, explain what they really mean, and show how they come together to shape agentic AI. What Are Agents Anyway? First, let us understand what an agent is. Why does everyone want to build an agent?

A Comparative Analysis of AI Tools for Developers in 2025

Aggregated on: 2025-12-02 15:11:16

Overview Nowadays, AI-powered coding assistants transform how developers write, refactor, and comprehend code. This technology blog examines the features, usability, and efficacy of the most cutting-edge AI coding tools, such as GitHub Copilot, Cursor, Cody, Aider, and Windsurf. Most importantly, this article analyzes, evaluates, and suggests the best choice based on practical testing. AI Assistants Evaluated The following AI coding assistants were examined:

From Mechanical Ceremonies to Agile Conversations

Aggregated on: 2025-12-02 14:11:16

TL; DR: Mechanical Ceremonies to Meaningful Events Your Agile events aren’t failing because people lack training. They’re failing because your organization adopted the rituals while rejecting the transparency, trust, and adaptation that make them work. And often, the dysfunction of mechanical ceremonies isn’t a bug. It’s a feature. The Reality of Your “Ceremonies” Let’s stop pretending. Your Daily Scrum is a status report. Your Sprint Planning confirms decisions that a circle of people made last week without you. Your Retrospective surfaces the same three issues it surfaced six months ago, and nothing has changed. Your Sprint Review is a demo followed by polite applause, before everyone happily leaves to do something meaningful.

Phishing 3.0: AI and Deepfake-Driven Social Engineering Attacks

Aggregated on: 2025-12-02 13:11:16

Phishing is no longer an easy-to-detect cyberattack. With the rise of artificial intelligence, attackers now launch AI-driven phishing campaigns to mimic human behavior. They can now generate flawless emails and use deepfake phishing attacks. Email security threats are more prominent now due to AI impersonation attacks and real-time credential phishing. Plus, there is a likelihood of credential harvesting. It can lead to not only monetary fraud but also reputation damage. Plus, organizations can suffer non-compliance and operational interruptions.

Building a Customer Intelligence AI Agent With OpenSearch and LLMs

Aggregated on: 2025-12-02 12:11:16

The Problem You have three types of customer data: You want to support questions like:

Not Just Crashes: Your Observability Stack for the Mobile App

Aggregated on: 2025-12-01 20:26:15

Go beyond Crashlytics by adopting latency tracing, ANR root-cause analysis, and in-app telemetry to understand the end-user journey. If you are a mobile engineer, you have probably felt the same gut punch I have: ship a feature, see the app store rating drop, see reviews say nothing more useful than "app is slow" or "app keeps freezing."

Why Open-Source OpenSearch 3.0 Is More Than Just an Upgrade: An Interview

Aggregated on: 2025-12-01 19:26:15

OpenSearch 3.0 is more of a signal flare than just another version bump. The open-source project, which began as a fork of Elasticsearch, has now grown into a fully differentiated, community-driven search and analytics platform. With performance leaps, modular architecture, and a deeper embrace of AI workloads, OpenSearch 3.0 marks a pivotal shift toward a more scalable, flexible, and future-ready open source engine. To unpack what’s new and what’s next, I spoke with Anil Inamdar, Global Head of Data Services at NetApp Instaclustr. Anil has decades of experience helping enterprises adopt and operate open source data technologies at scale. In this conversation, he explains why 3.0 matters not just for developers already on OpenSearch, but for any engineering team rethinking how they search, monitor, and analyze data in a distributed world.

Building an OWASP 2025 Security Scanner in 48 Hours

Aggregated on: 2025-12-01 18:26:15

OWASP dropped its 2025 Top 10 on November 6th with a brand-new category nobody saw coming: "Mishandling of Exceptional Conditions" (A10). I spent a weekend building a scanner to detect these issues and immediately found authentication bypasses in three different production codebases. The most common pattern? return True in exception handlers, effectively granting access whenever the auth service hiccups. This article walks through building the scanner, what I found, and why this matters way more than you think. Friday Night: OWASP Releases Something Interesting I was scrolling through Twitter when I saw the OWASP announcement. They'd just released the 2025 Top 10 list at the Global AppSec Conference. Most people were talking about Supply Chain Security moving up to #3, but something else caught my eye.

Real-Time Computer Vision on macOS: Accelerating Vision Transformers

Aggregated on: 2025-12-01 17:26:15

Hi mates! For years, "computer vision" meant convolutional neural networks (CNN). If you wanted to detect a cat, you would use a CNN. If you wanted to recognize a face, you used a CNN. But in 2020, the game changed. A paper entitled "An Image is Worth 16x16 Words" introduced the Vision Transformer. Instead of looking at pixels through small sliding windows — convolution — the ViT treats an image like a sequence of text patches. It sees the "whole picture" all at once, and often with better accuracy.

Shield Your Nonprofit: How to Tackle Ransomware Attacks

Aggregated on: 2025-12-01 16:26:15

Set against the backdrop of accelerated growth of technology over the past several decades, notwithstanding large organizations, nonprofits as well have become overly reliant on technology for their day-to-day operations. New data shows that this reliance often presents opportunities for cyber criminals to launch discreet or direct attacks, leading to one of the most threatening scenarios: a ransomware attack. In recent years, there has been a significant uptick in ransomware attacks, in which malicious software or a hacker encrypts or locks down critical files. Post that, they demand huge payments to let users get back their files and access to their systems. These types of attacks can happen anywhere — and at any time of the day — often to the surprise and shock of users. Further, tracking the source of such attacks is often difficult, exacerbating a highly critical situation. The attackers have the potential to slam the brakes on the engine of any large business — and nonprofits aren’t much further away from becoming a victim in such cases — and this can often lead to their shutdown.

AI Ethics in Action: How We Ensure Fairness, Bias Mitigation, and Explainability

Aggregated on: 2025-12-01 15:26:15

Like many challenges, it began with a user who continued receiving the wrong videos on her feed. It appeared to be a mere glitch in our recommendation system, but as we got deeper into it, we found that there was some concealed bias in our code, and it was just causing unfairness in our setup. It was not only a question of bad user experience, but of equity and credibility. Since then, AI ethics has no longer been a blank whiteboard discussion but an actual issue we have had to resolve at the moment. It is not difficult to produce AI with a lot of power; however, to create it in a fair, transparent, and trustworthy way is quite a different matter.

From Chaos to Clarity: Building a Data Quality Framework That Actually Works

Aggregated on: 2025-12-01 14:26:15

The dream of a "data-driven" organization is common to all of them. However, the reality across a wide range of business sectors is that the situation is very opposite; the data is so overwhelming that it can't be managed properly. Even strong data initiatives are sometimes undermined by incomplete records, inconsistent formats, duplicate entries, and obsolete information. The misinterpretation of the situation caused by the use of poor-quality data is the main consequence leading to confusion instead of insight, which is interpreted as missed opportunities, flawed strategies, and wastage of resources. Data chaos doesn't happen all of a sudden; it silently grows from siloed systems, a lack of governance, and unclear ownership. With the increasing number of data sources and automation of processes, the need for a structured approach to data quality management becomes crucial.

Building a Production-Ready MCP Server in Python

Aggregated on: 2025-12-01 13:26:15

The Model Context Protocol (MCP) is rapidly emerging as a fundamental framework for secure AI integration. It effectively links large language models (LLMs) with essential corporate assets, such as APIs, databases, and services. However, moving from concept to production requires addressing several key real-world demands: Governance: Defining clear rules regarding who is authorized to access specific tools Security: Implementing robust practices for managing and protecting tokens and secrets Resilience: Ensuring system stability and performance during high-demand periods or in the face of malicious attacks Observability: Establishing the capability to effectively diagnose and troubleshoot failures across various tools and user environments In this article, we'll focus on these points and upgrade a simple MCP server into a production-grade, robust system. We'll build:

Introducing the Testing Vial: a (better?) alternative to Testing Diamond and Testing Pyramid

Aggregated on: 2025-12-01 12:26:15

Testing is crucial for any application. It can also be important in applications that are meant to be thrown away: in fact, with a proper testing strategy, you can ensure that the application will do exactly what you expect it to do; instead of running it over and over again to fix the parts, by adding some specific tests, you will speed up the development of that throwaway project. The most common testing strategies are the Testing Pyramid and the Testing Diamond. Both useful, but I think that they are not perfect.

How to Gracefully Deal With Contention

Aggregated on: 2025-11-28 20:11:14

The Problem Statement When multiple clients, processes, or threads compete for a limited number of resources simultaneously, causing degraded turnaround time and performance, the system enters a state called contention. This is the most common problem in systems that handle high traffic volumes. Without graceful dealing, contention leads to race conditions and an inconsistent state. Example Scenario Consider buying flight tickets online. There is only one seat available on the flight. Alice and Bob both want this seat and click "Book Now" at exactly the same time.

The Illusion of Deep Learning: Why "Stacking Layers" Is No Longer Enough

Aggregated on: 2025-11-28 19:11:14

Have we reached the limit of what we can achieve with our current AI models? At the very heart of the race for parameters and power conducted by Big Tech players, a fundamental question emerges: Do our AIs truly understand the changing world, or are they simply reciting a frozen past? In the study shared by the Google Research team in their paper "Nested Learning: The Illusion of Deep Learning Architectures" (1), the finding is unequivocal. According to them, our large language models (LLMs) suffer from "anterograde amnesia syndrome." Like patient Henry Molaison, a famous clinical case (2), who was incapable of forming new memories after his operation, our models, once their training is complete, are frozen.

RAG Applications with Vertex AI

Aggregated on: 2025-11-28 18:11:14

Most organizations experimenting with generative AI face a common bottleneck: their LLMs can chat nicely, but they do not consistently know the company’s own data. A customer wants to know a policy clause, or an engineer asks a question about a system diagram, and the model makes something up or simply provides an ambiguous, incomplete response. This won’t work in industries such as healthcare, financial services, or insurance where accuracy is critical. What we want is the creative power of LLMs, but also the ability to reliably know our organization’s stuff. Here, we will explore how Retrieval-Augmented Generation (RAG) gives us those solutions.

Is TOON the Next Lightweight Hero in Event Stream Processing With Apache Kafka?

Aggregated on: 2025-11-28 17:11:14

The data serialization format is a key factor when dealing with stream processing, as it decides how efficiently the data is forwarded on the wire and optimized internally in order to be stored, understood, and processed by a distributed system. The data serialization format is core to stream processing in that it directly influences the speed, reliability, scalability, and maintainability of the entire pipeline. Choosing the right one can eliminate expensive lock-ins and ensure that our streaming infrastructure remains stable as data volume and intricacy evolve. In a stream-processing platform where millions of events per second must be handled with low latency by ingestion systems such as Apache Kafka and processing engines like Flink or Spark, reducing CPU usage is important, as it depends on efficient data formats.

Next-Gen AI-Based QA: Why Data Integrity Matters More Than Ever

Aggregated on: 2025-11-28 16:11:14

Artificial intelligence has changed the way we work across different industries. From chatbots that quickly resolve customer issues to systems that detect equipment failures before they occur, automation is now a standard practice. As these smart systems become more independent, one question keeps emerging: how much can we trust the data behind them? Data integrity may not make the news often, but it supports every AI-driven process. When data is inconsistent, incomplete, or biased, even the best algorithms can fail. In an automated setup, those failures don’t just stay small; they grow, causing flawed predictions, distorted insights, or even unethical results. Bias, safety, disinformation, copyright, and alignment are big problems with AI thus robust data quality matters ever than before.

From Repetition to Reusability: How Maven Archetypes Save Time

Aggregated on: 2025-11-28 15:11:14

Within the discipline of software engineering, practitioners are frequently encumbered by the monotonous ritual of initializing identical project scaffolds — configuring dependencies, establishing directory hierarchies, and reproducing boilerplate code prior to engaging in substantive problem‑solving. Although indispensable, such preliminary tasks are inherently repetitive, susceptible to human error, and inimical to efficiency. Maven, a cornerstone of the Java build ecosystem, furnishes an elegant mechanism to mitigate this redundancy through the construct of archetypes. An archetype functions as a canonical blueprint, enabling the instantaneous generation of standardized project structures aligned with organizational conventions. By engineering bespoke archetypes, development teams can institutionalize consistency, accelerate delivery, and reallocate intellectual effort toward innovation rather than procedural repetition.

Level Up Your API Design: 8 Principles for World-Class REST APIs

Aggregated on: 2025-11-28 14:11:14

You’ve probably built a “REST API” before. But what does “RESTful” truly mean? It’s not just about using JSON and HTTP. It’s a spectrum, best described by the Richardson Maturity Model (RMM). Level 0 (The Swamp): Using HTTP as a transport system for remote procedure calls (RPC). Think of a single /api endpoint where all operations are POST requests. Level 1 (Resources): Introducing the concept of resources. Instead of one endpoint, you have multiple URIs like /users and /orders. Level 2 (HTTP Verbs): Using HTTP methods (GET, POST, PUT, DELETE) and status codes (2xx, 4xx) to operate on those resources. This is where most “REST” APIs live. Level 3 (Hypermedia — HATEOAS): The “holy grail” of REST. The API’s responses include links (hypermedia) that tell the client what they can do next. The client navigates your API by discovering these links, not by hard-coding URLs. The eight principles I’m sharing today are a blend of my own production experience and the pragmatic wisdom from industry-leading guides like Zalando’s. These should help you move your APIs up this maturity ladder, creating designs that are more robust, scalable, and easier to use.

Five Nonprofit & Charity APIs That Make Due Diligence Way Less Painful for Developers

Aggregated on: 2025-11-28 13:11:13

I learned this lesson the hard way. A few years back, I built a donation platform I thought was bulletproof. The design? Slick. Payments? Smooth. I figured, “Alright, I’ve nailed it.”

Running Istio in Production: Five Hard-Won Lessons From Cloud-Native Teams

Aggregated on: 2025-11-28 12:11:13

Istio has established itself as a popular, trusted, and powerful service mesh platform. It complements Kubernetes with powerful features such as security, observability, and traffic management with no code changes. Istio’s several key features strengthen cloud-native and distributed systems, ensuring consistency, security, and resilience across diverse environments. Istio has also recently graduated under the Cloud Native Computing Foundation (CNCF), along with other projects like Kubernetes. In this article, we will cover Istio's best practices for building a production-grade service mesh layer that offers secure, resilient, and durable performance.

Building a Simple MCP Server and Client: An In-Memory Database

Aggregated on: 2025-11-27 20:11:13

If you've been diving into the world of AI-assisted programming or tool-calling protocols, you might have come across Model Context Protocol (MCP). MCP is an open-source standard for connecting AI applications to external systems. It is a lightweight framework that lets you expose functions as "tools" to language models, enabling seamless interaction between AI agents and your code. Think of it as a bridge that turns your functions into callable endpoints for models. In this post, we’ll build a basic in-memory database server using MCP, with code samples to extend and learn from. We'll dissect the code step by step, and by the end, you'll have a working prototype. Plus, I'll ask you to extend it with update, delete, and drop functionalities. Let's turn your terminal into a mini SQL playground!

How To Restore a Deleted Branch In Azure DevOps

Aggregated on: 2025-11-27 19:11:13

Human error is one of the most common causes of data loss or breaches. In the ITIC report, they state that 64 % of downtime incidents have their roots in human errors. If you think that in SaaS environments all your data is safe, you need to think once again. All SaaS providers, including Microsoft, follow the shared responsibility model, which states that the service provider is responsible for the accessibility of its infrastructure and services, while a user is responsible for their data availability, including backup and disaster recovery.

Mastering Fluent Bit: Controlling Logs with Fluent Bit on Kubernetes (update to Part 4)

Aggregated on: 2025-11-27 18:11:13

NOTE: This is a special update to the original Controlling Logs with Fluent Bit on Kubernetes (Part 4) article published previously. The issue requiring this update arose over the weekend when I discovered that Broadcom, who acquired VMWare, who were the custodians of the Bitnami catalog, did something not so nice to all of us.

Solving Real-Time Event Correlation in Distributed Systems

Aggregated on: 2025-11-27 17:11:13

Modern digital platforms operate as distributed ecosystems — microservices emitting events, APIs exchanging data, and asynchronous communication becoming the norm. In such environments, correlating events across multiple sources in real time becomes a critical requirement. Think of payments, orders, customer metadata, IoT sensors, logistics tracking — all flowing continuously.

Run LLMs Locally Using Ollama

Aggregated on: 2025-11-27 16:56:13

Over the past few months, I’ve increasingly shifted my LLM experimentation from cloud APIs to running models directly on my laptop. The reason is simple: local inference has matured to the point where it’s fast, private, offline-friendly, and surprisingly easy to set up. Tools like Ollama have lowered the barrier dramatically. Instead of wrestling with GPU drivers, manually downloading weights, or wiring up custom runtimes, you get a single lightweight tool that can run models such as Llama 3.1, Mistral, Phi-3, DeepSeek R1, Gemma, and many others, all with minimal configuration.

AWS Airflow vs Step Functions: The Data Engineering Orchestration Dilemma

Aggregated on: 2025-11-27 16:11:13

There's a moment in every data engineering project when you realize your growing collection of batch jobs, data transformations, and scheduled tasks needs proper orchestration. You've probably duct-taped together some Lambda functions with CloudWatch Events, maybe written a few shell scripts with cron jobs, and now you're looking at AWS, wondering: should I go with Managed Airflow (MWAA) or Step Functions? I've seen teams make both choices, and here's the truth: neither is universally "better." The right answer depends on what you're actually building, who's maintaining it, and how your data engineering team thinks about workflows.

Automating FastAPI Deployments With a GitHub Actions Pipeline

Aggregated on: 2025-11-27 15:56:13

Deploying FastAPI apps manually gets old fast. You SSH into a server, pull the latest code, restart the service, and hope nothing breaks. Maybe you remember to run tests first. Maybe you don't. One forgotten environment variable or skipped test, and your API is down. Users get 500 errors. You're frantically SSHing back in to fix it.

Optimizing Trino Performance With Materialized Views in a Data Lake

Aggregated on: 2025-11-27 15:11:13

In this article, I share how we improved the performance of our Trino-based data lake by using materialized views. Our service evolved from a dual-storage system built on HBase and Elasticsearch to a simplified, cost-efficient data lake architecture powered by Iceberg, Spark Streaming, and Trino. The transition brought significant advantages but also unexpected performance challenges that we solved through careful use of Trino’s materialized views. Business Description Our service receives data from a Kafka source on three different topics and inserts it into HBase and Elasticsearch. HBase was used for get-by-ID operations, while Elasticsearch handled GraphQL-style search queries. HBase is known for excellent insert performance and fast get-by-ID operations, and Elasticsearch provides powerful full-text search capabilities. Over time, however, we realized that we were not using most of Elasticsearch’s advanced search features. Maintaining both systems was costly, and the operational complexity of supporting two clusters — HBase and Elasticsearch — was high. We decided to migrate to a modern data lake architecture to improve scalability and cost efficiency.

The Fake "Multi" in Multi-Tenant: When SaaS Tenancy Models Backfire at Scale

Aggregated on: 2025-11-27 13:11:13

One SaaS, Many Users, One Big Lie Your “multi-tenant” SaaS architecture is probably a single-tenant app with commitment issues. That sounds harsh until you look at the actual implementation. Customer A gets one deployment with hardcoded settings. Customer B gets the same codebase, but now wrapped in a flag-laden logic bomb. By the time you reach customer C, your team has a 60-page Confluence doc titled “How to onboard a new tenant without waking the VP of Engineering.”

How to Push Docker Images to AWS Elastic Container Repository Using GitHub Actions

Aggregated on: 2025-11-26 20:11:13

GitHub Actions enables the CI/CD, short for continuous integration or continuous deployment, process to build, test, and deploy the code through the workflows within the same GitHub repository. GitHub Actions builds images and pushes them to cloud providers such as AWS and Docker Hub. We can choose the different OS platforms, Windows or Linux, to run the workflows. In this article, we will demonstrate how we can streamline the build and deploy process to push Docker Images to AWS ECR, short for Elastic Container Repository, by using GitHub Actions.

Top 5 Best Practices for Building Dockerized MCP Servers

Aggregated on: 2025-11-26 19:11:13

The Model Context Protocol (MCP) is changing how we build software. It provides the "API" for large language models (LLMs) to interact with the real world. This lets an AI agent query a database, read a file, or call a third-party service. This new capability brings new challenges. MCP servers, the back-end tools the AI uses, are not traditional microservices. Their user is a non-deterministic AI, and they often need access to sensitive systems. How do we build, deploy, and secure these servers reliably? The clear answer is Docker. The entire MCP ecosystem, including Docker's own MCP Toolkit and Catalog, is built around containerization. Running your MCP servers in Docker is not just a good idea; it is a necessary best practice. This article covers five key principles for building production-ready, Dockerized MCP servers.

Overview Of Observability As Code

Aggregated on: 2025-11-26 18:11:13

Observability as Code It is a practice where monitoring, logging, alerting, and observability configurations are defined, managed, and deployed using code-based approaches rather than manual configuration through dashboards or UIs. Core Concept Typically, engineers manually set up alerts for monitoring on the web console. However, with Observability as Code, engineers write code (typically YAML, JSON, or domain-specific languages) that declaratively define:

Rethinking the Software Supply Chain for Agents

Aggregated on: 2025-11-26 17:56:13

A recent MIT study reported that only about 5% of GenAI applications are creating real, measurable business value. In my opinion, that’s not a failure of ambition. If anything, most teams are experimenting aggressively. The issue is that the underlying systems we use to deliver software haven’t adapted to what AI actually is. It has become incredibly easy to build a prototype or demo. A few prompt tweaks, an API call, and you can show something impressive. But turning that prototype into something you can trust in production is a different challenge. That part requires real engineering: reliability, consistency, versioning, monitoring, and guardrails. The problem is that the tools and workflows we’ve relied on for years were never designed to support systems that change their behavior over time.

Building a Local RAG App With a UI, No Vector DB Required

Aggregated on: 2025-11-26 17:11:13

Generative AI, LLMs, and RAG have been at the forefront of technological innovation and discussion. Retrieval-augmented generation (RAG) has emerged as a powerful pattern for building LLM applications that can reason over your data, reducing hallucinations and providing up-to-date, contextually relevant answers. Most of the time, I found the RAG tutorials involve a dedicated vector database like Pinecone, Weaviate, or Chroma. These are fantastic for production systems, but what should I use for local development, rapid prototyping, or smaller-scale applications? The overhead of setting up, managing, and paying for a database service is not a better choice when you just want to build something.

Building AI Agents With Semantic Kernel: A Practical 101 Guide

Aggregated on: 2025-11-26 16:56:13

AI agents are evolving beyond traditional chatbots, taking on complex problem-solving tasks that demand deep contextual understanding and intelligent reasoning. Developers today aim to build systems that can not only respond intelligently but also act autonomously — combining domain knowledge, business logic, and specialized tools to create decision-making agents tailored to specific problems. Achieving this requires a powerful orchestrator capable of coordinating models, tools, and workflows seamlessly. Microsoft’s Semantic Kernel (SK) provides exactly that: a lightweight framework that bridges large language models (LLMs) with your own code, data, and APIs. In this article, we'll build a simple AI agent and explore the key components that make it work.

Scaling Identity Governance Without Connectors: The LDAP Directory IGA Integration Pattern

Aggregated on: 2025-11-26 16:11:13

In Identity Governance and Administration (IGA), connectors help keep user accounts, roles, and access permissions in sync across your applications. What if you don’t deploy a connector? What about legacy and cloud applications that don’t support SCIM, or systems handled by third-party vendors that don’t allow inbound connections?

LLMOps Under the Hood: Docker Practices for Large Language Model Deployment

Aggregated on: 2025-11-26 15:56:13

Large language models (LLMs) are everywhere — powering chatbots, copilots, and AI-driven apps across industries. But if you’ve ever tried to run one outside of a managed service, you know the pain: gigabytes of model weights, conflicting Python dependencies, fragile CUDA versions, and a GPU setup that only seems to work on your machine. This is where Docker shines. By packaging the entire environment — code, libraries, and drivers — into a container, you can run an LLM anywhere, whether it’s your laptop, a cloud GPU node, or a Kubernetes cluster. Containers give you reproducibility, portability, and isolation: exactly what’s needed for the messy world of LLMOps.

Securing Converged AI-Blockchain Systems: Introducing the MAESTRO 7-Layer Framework

Aggregated on: 2025-11-26 12:11:13

Introduction When an AI trading agent exploits a smart contract vulnerability, financial firms can lose millions in seconds. In 2024 alone, more than $1.42 billion vanished through smart contract exploits, with AI-enhanced systems showing particularly troubling weaknesses that traditional security frameworks simply cannot address. As blockchain and AI technologies converge, they create entirely new attack surfaces that existing methodologies like STRIDE and MITRE ATT&CK weren’t designed to handle. Through my experience securing enterprise systems processing trillions in assets, I developed the MAESTRO framework — Multi-Agent Environment, Security, Threat, Risk, and Outcome — as a practical, seven-layer approach specifically designed for AI-blockchain convergence.

Breaking the Chains of the GIL in Python 3.14

Aggregated on: 2025-11-25 20:26:12

For years, developers working in Python have wrestled with a strange paradox: great productivity and ecosystem breadth, but limited multicore throughput in many scenarios. The culprit? The Global Interpreter Lock (GIL). Put simply: in CPython, only one native thread may execute Python bytecode at a time. For IO-bound tasks, this is often fine, but for CPU-bound or highly concurrent workflows, this constraint has been a persistent bottleneck. I have experienced this frustration many times - you design a multithreaded service, spin up 16 threads on a 32-core machine expecting massive throughput, and then watch in horror as CPU utilization flatlines at 100% (effectively one core). You are then forced to switch to multiprocessing, pay the heavy overhead of inter-process communication, or rewrite critical paths in Rust or C++. All this complexity just to get true parallelism.

Vector Databases in Action: Building a RAG Pipeline for Code Search and Documentation

Aggregated on: 2025-11-25 19:26:12

Imagine typing "authentication with JWT tokens" and instantly finding every relevant code snippet across your entire codebase, regardless of variable names or exact phrasing. That's the promise of vector databases combined with retrieval-augmented generation (RAG). After implementing this architecture across multiple production systems, I've learned that the real challenge isn't the theory; it's the practical decisions that make or break your implementation. Traditional keyword search fails spectacularly with code. A developer searching for "validate user input" won't find functions named sanitize_request_data() or check_payload_integrity(), even though they're semantically identical. Vector databases solve this by understanding meaning, not just matching strings. When combined with RAG, they transform how development teams interact with their codebases.

Revamping Real-Time Data Ingestion for Scalable Media Intelligence

Aggregated on: 2025-11-25 18:26:12

In the era of 24/7 media and constant digital noise, the ability to process and act on real-time information is crucial. For any system designed to monitor, classify, and enhance media content, scalable ingestion pipelines are the backbone. This blog outlines a re-engineered real-time ingestion pipeline that successfully scaled to handle over 8 million articles per day, demonstrating a shift from traditional ETL models to AI-augmented streaming architectures. The Problem Space: High-Velocity Media Streams Media monitoring platforms must absorb diverse content formats from countless providers and categorize them in near real time. Traditional monolithic systems or batch ETL jobs fail to meet such latency and reliability demands.

Integrating Lakeflow Connect With PostgreSQL: A Developer’s Complete Hands-On Guide From the Field

Aggregated on: 2025-11-25 17:26:12

Modern data teams want reliable, incremental, near real-time ingestion from PostgreSQL into Databricks Unity Catalog without building costly and fragile CDC jobs, custom pipelines, or manual ETL orchestration. That’s where Lakeflow Connect solves the issue by providing developers with a unified, low-overhead ingestion framework that handles extraction, CDC, schema syncing, and table creation inside Unity Catalog automatically. This post walks through how I have set up Lakeflow Connect with PostgreSQL, including:

How to Test POST Requests With REST Assured Java for API Testing: Part I

Aggregated on: 2025-11-25 16:26:12

REST Assured is a popular API test automation framework in Java. Software teams widely use it for efficiently validating RESTful web services with minimal setup. It simplifies the process of sending requests, verifying responses, and handling JSON or XML paåyloads. With its rich syntax and integration support for tools like TestNG and Maven, REST Assured enables robust, maintainable, and scalable API testing.