News Aggregator

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.

Tuples and Records (Part 4): Optimize React and Prevent Re-Renders

Aggregated on: 2025-09-12 11:14:53

Part 4 dives into React, showing how Tuples and Records can cut down unnecessary re-renders. By using immutable, value-based data structures, React can efficiently detect state changes, keeping your components lean and fast. We’ll explore why re-renders happen and how adopting Tuples and Records simplifies state management while boosting performance. Also read:

Securing LLM Applications: Beyond the New OWASP LLM Top 10

Aggregated on: 2025-09-11 19:14:52

Have you heard of the new OWASP Top 10 for Large Language Model (LLM) Applications? If not, you’re not alone. OWASP is famous for its “Top 10” lists addressing security pitfalls in web and mobile apps, but few realize they’ve recently released a dedicated list for LLM-based systems. With AI chatbots, text generators, and agentic AI architectures proliferating in DevOps pipelines and customer-facing apps, traditional web security scanning tools can’t detect the new vulnerabilities these models introduce. Why? LLMs generate creative responses by iteratively refining a probability distribution to match real-world data. That same “creative” nature means these models can also perform unanticipated or malicious actions if exploited — especially in an environment where they can chain commands or orchestrate other tools.

Optimizing Cost and Carbon Footprint With Smart Scaling on AWS: Part 2

Aggregated on: 2025-09-11 18:14:52

This article builds on the ideas shared in "Optimizing Cost and Carbon Footprint with Smart Scaling," diving deeper into advanced scaling strategies on AWS. We'll take a closer look at the challenges of queue-based scaling and explore how custom load metrics can offer a better solution. Along the way, we'll also walk through how to create these metrics in AWS CloudWatch and highlight other AWS services that play a role in optimizing both cost and carbon footprint.

Azure VM Instance Types and Their Roles in Different Distributed Software Systems

Aggregated on: 2025-09-11 17:14:52

Azure offers a variety of virtual machine (VM) types to cater to different workloads and use cases, including worker and driver nodes for various Azure-hosted technologies such as Azure Databricks, Azure HDInsight, and Azure Kubernetes Service (AKS). Here’s a brief overview of the different VM types and their suitability for worker or driver nodes: General-Purpose VMs B-series (Burstable VMs): Cost-effective VMs suitable for workloads that do not require continuous CPU performance. Use case: Development and test environments, small databases, low-traffic web servers. D-series: Balanced CPU-to-memory ratio, suitable for most production workloads. Use case: Web servers, enterprise applications, and small to medium databases. Compute-Optimized VMs F-series: High CPU-to-memory ratio, suitable for compute-intensive workloads. Use case: Batch processing, web servers, analytics, gaming. Memory-Optimized VMs E-series: High memory-to-CPU ratio, suitable for memory-intensive applications. Use case: Large databases, in-memory analytics, SAP HANA. M-series: Very high memory-to-CPU ratio, suitable for extremely large memory workloads. Use case: Large-scale SAP HANA, data warehousing, in-memory analytics. Storage-Optimized VMs L-series: High disk throughput and IO, suitable for storage-intensive applications. Use case: Big data, SQL, and NoSQL databases, data warehousing. GPU-Optimized VMs NC-series: GPU-enabled VMs for compute-intensive and graphics-intensive workloads. Use case: AI and deep learning, high-performance computing (HPC), rendering. NV-series: GPU-enabled VMs for visualization and graphics-intensive workloads. Use case: Remote visualization, gaming, simulation. High-Performance Compute VMs H-series: High-performance VMs for compute-intensive workloads. Use case: Molecular modeling, fluid dynamics, finite element analysis. Distributed Systems 1. Kubernetes (AKS: Azure Kubernetes Service) Kubernetes is a container orchestration tool that enables the deployment, scaling, and management of containerized applications. Azure Kubernetes Service (AKS) leverages Azure VM instance types for scaling and managing containers.

Jakarta Query: Unifying Queries Across SQL and NoSQL in Jakarta EE 12

Aggregated on: 2025-09-11 16:14:52

When we talk about the history of knowledge and information, it's natural to write and endure. The information is one step; the next step is how to retrieve and search the information storage. This also occurs with the most modern software applications, where we need to handle various databases and different methods for retrieving information. Furthermore, we need to learn how to retrieve information through queries. The good news for Java developers is that our lives can be easier: Imagine writing a single query once and running it seamlessly across different databases, whether SQL or NoSQL. No more translating between dialects, no more adjusting your persistence logic to fit the quirks of one provider or another. The primary goal of the newest specification, Jakarta Query, is to take shape as part of Jakarta EE 12. If successful, it has the potential to dramatically simplify how enterprise Java developers interact with data, making the persistence layer far more consistent, portable, and developer-friendly.

The AI Precision Anti-Pattern

Aggregated on: 2025-09-11 15:14:53

TL; DR: The Generative AI Precision Anti-Pattern Here’s another one for your collection: The Generative AI Precision Anti-Pattern, where organizations wield LLMs like precision instruments when they’re probabilistic tools by design. Sound familiar? It’s the same pattern we see when teams cargo-cult agile practices without understanding their purpose. LLMs excel at text summarization and pattern recognition in large datasets, which helps analyze user feedback or generate documentation drafts, but can they be used for deterministic tasks like calculations? If you are not careful with matching your problem to the right tool, you end up building issues of all kinds into the foundation of your product.

AI on the Fly: Real-Time Data Streaming From Apache Kafka to Live Dashboards

Aggregated on: 2025-09-11 14:14:52

In the current fast-paced digital age, many data sources generate an unending flow of information, a never-ending torrent of facts and figures that, while perplexing when examined separately, provide profound insights when examined together. Stream processing can be useful in this situation. It fills the void between real-time data collecting and actionable insights. It’s a data processing practice that handles continuous data streams from an array of sources. Real-time data streaming has started having an important impact on modern AI models for applications that need quick decisions. We can consider a few examples where AI models need to deliver instant decisions, such as self-driving cars, fraud in stock market trading, and smart factories that utilize technology like sensors, robots, and data analytics to automate and optimize manufacturing processes.

OWASP Top 10 Non-Human Identity Risks for 2025: What You Need to Know

Aggregated on: 2025-09-11 13:14:52

The Open Worldwide Application Security Project, OWASP, has just released its top 10 non-human identities risks for 2025. While other OWASP resources broadly address application and API security, none focus specifically on the unique challenges of NHIs. This new document fills that gap, addressing risks that are often overlooked but have critical implications for organizational security. This release is a significant milestone in the cybersecurity landscape, as one of the most trusted security communities now recognizes the term Non-Human identities (NHIs) and that this is a significant issue that needs to be addressed by the enterprise. Given the growing number of breaches stemming from NHI credential leaks or misuse, this release is very timely.

Toward Explainable AI (Part 9): Bridging Theory and Practice—Conclusion: Explainability Under Real-World Conditions

Aggregated on: 2025-09-11 12:14:52

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 VIII: SHAP: Powerful, But Can We Trust It? Strengths and vulnerabilities of SHAP explanations.

A Practical Guide to API Threat Analytics in Cloud Platforms

Aggregated on: 2025-09-11 11:14:52

Any modern application is centered around APIs. They drive mobile applications, link business systems, and deliver new digital experiences. However, the convenience has its own risks — attackers often use APIs to break into systems. Basic security steps like authentication and rate limits are no longer enough. Attackers now use stolen credentials, automated scripts, and advanced methods to target APIs. To stay safe, organizations need API threat analytics to collect API logs, spot unusual activity, and monitor it in real time with dashboards.

Getting Started With Apple's Vision Framework

Aggregated on: 2025-09-10 19:14:52

The Vision framework was introduced by Apple in 2017 at WWDC as part of iOS 11. Its launch marked a turning point in the evolution of machine vision and image analysis, providing developers with native tools to analyse visual content and perform subsequent processing as needed. In 2017, Vision introduced the following:

Supercharging Your Chatbot With Context-Aware AI on AWS

Aggregated on: 2025-09-10 18:14:52

Today’s online users expect instant, personalized support—whether they’re comparing day to day online products, troubleshooting a technical issue, or just looking for a quick answer. Basic keyword-based chatbots can only take you so far. To truly connect with customers, your bot needs "context". It needs to remember past interactions, anticipate needs, and respond with more than just predefined scripts. In this article, we’ll walk through a scalable, serverless architecture built on AWS, combining Lambda for event logging and real-time logic processing, DynamoDB for session memory, and SageMaker for AI-driven insights. We’ll also cover how to gracefully escalate to human agents via SNS for those critical moments when a bot just isn’t enough or while the bot get additional learning power from more human interaction.

Beyond DORA: Building a Holistic Framework for Engineering Metrics

Aggregated on: 2025-09-10 17:14:52

My journey in the technical field has taken me from hands-on software engineering to the CTO’s role. In my monthly and quarterly routines at my current position, I regularly evaluate the efficiency of contributors: engineers, designers, QA, DevOps, and cross-functional teams overall. And over time, I’ve come to a clear conclusion: traditional engineering metrics like velocity, story points, or arguably lines of code can fail to capture the bigger picture. They are not inherently bad, but can drive wrong results, and their value depends entirely on how we use them. These metrics only make sense when framed against real outcomes, such as customer value delivered, time-to-market improvements, system stability, or cost efficiency. They can be incredibly insightful when used tactically to diagnose patterns, identify bottlenecks, or track improvements within a team. However, as strategic indicators, they can often mislead and derail the process.

Crafting Keys: Best Naming Strategies and Sorting Techniques in Redis

Aggregated on: 2025-09-10 16:14:52

In Redis, everything begins with a key. A key is more than just a string that points to a value. It defines how your data is organized, retrieved, and queried. A well-designed key structure makes it easier to group related records, filter by attributes, and scale queries as datasets grow. On the other hand, poor key design can lead to inefficient lookups, expensive scans, and unnecessary complexity in your application logic. In this guide, we’ll explore different strategies for designing Redis keys using a hotel dataset. You’ll see how simple naming conventions, sets, and sorted sets can shape how data is grouped and retrieved, and when it makes sense to move toward Redis modules like RediSearch for more complex querying.

From HTTP to Kafka: A Custom Source Connector

Aggregated on: 2025-09-10 15:14:52

Recently, I came across an interesting scenario: one application had a cron job constantly polling an API for active offers, just to refresh a Redis cache that powered the offer view. So, I started thinking—isn’t there a better way to handle this? Or at least a way to offload such repetitive tasks outside the application itself? That’s when it hit me: this pattern looks way too similar to the CDC flows we already implement with Kafka Connect JDBC source connectors. So why not apply the same idea to HTTP? After a bit of digging, I found the answer was yes—we can definitely do it. But there’s a catch. The official Confluent HTTP source connector requires a license, and most open-source alternatives are either too complex or don’t quite match the use case.

Vibe Coding: Conversational Software Development — Part 4: Guiding AI Through Iteration

Aggregated on: 2025-09-10 14:14:52

Welcome to the fourth and final post in my Vibe Code series. In the previous article, I explained how system prompts can steer AI behaviour by setting initial expectations and boundaries. But if you have worked on even a mildly complex application, you will know that the first draft is never the final version. You always have some sort of UX tweaks, performance enhancements, or new feature requirements. That is where task-level prompting comes into play. Once your foundation is in place, the next step is to provide instructions to AI to build, improve, and polish step by step. In this blog post, I will share several approaches that have worked well for me.

Using Arrow Flight SQL to Improve Data Transfer Performance in Apache Doris

Aggregated on: 2025-09-10 13:14:52

Data analyst Xiao Hua rubs his sore eyes, staring blankly at the computer screen. He can't help but complain, "This data export is so slow!" Indeed, waiting for MySQL protocol to transfer large volumes of data feels like trying to drink a barrel of water through a straw — when will it ever end?

An Introduction to Artificial Intelligence: Neural Networks, NLP, and Word Embeddings

Aggregated on: 2025-09-10 12:14:52

Part 1: The Very Basics How Does It Really Work? In recent years, artificial intelligence (AI) has become a buzzword, especially with the emergence of tools like ChatGPT. However, despite the widespread conversation about AI, not everyone fully understands what it is. AI tools today can process text, generate images and videos, write code, and automate tasks. Some enthusiasts predict a future where AI replaces programmers, creates entire products independently, and potentially leaves many of us without jobs. While these apocalyptic scenarios seem exaggerated to me, I believe AI is best viewed as a powerful tool that, when used wisely, can make us more efficient and productive. For example, AI can generate small pieces of code, understand simple or vague questions, and provide quick answers — almost like a more advanced version of Google. It can feel like magic, but it’s not (or at least, not entirely).

Tuples and Records (Part 3): Potential ECMAScript Proposals

Aggregated on: 2025-09-10 11:14:52

In Part 1, we introduced JavaScript’s Tuples and Records, highlighting their role as immutable data structures that bring predictability, performance, and safety into everyday development. In Part 2, we explored migration strategies, covering how to transition existing codebases, reduce reliance on third-party libraries, and adopt Tuples and Records incrementally. Now, in Part 3, we’ll look beyond today’s capabilities and explore what’s on the horizon. While Tuples and Records already provide strong foundations, their current feature set is intentionally minimal. To make them even more useful in real-world applications, the JavaScript community is actively discussing potential ECMAScript proposals and speculative enhancements.

Using ChartMuseum as a Helm Repository

Aggregated on: 2025-09-09 20:14:51

ChartMuseum is an open-source, self-hosted Helm Chart repository server that enables users to store and manage Helm charts efficiently. Helm is the standard package manager for Kubernetes, allowing developers to deploy applications seamlessly. While Helm provides public repositories like Artifact Hub, organizations often require private and secure repositories for managing their Helm charts internally. ChartMuseum fills this gap by offering a lightweight and flexible solution. ChartMuseum provides a robust API that allows users to interact with it programmatically, making it an essential tool for automated CI/CD pipelines. It is written in Go and can be deployed as a standalone binary, within a container, or as a Kubernetes deployment.

Tutorial: RAG at Scale With Vector Databases vs. Lakehouse Architectures

Aggregated on: 2025-09-09 19:14:51

Retrieval-Augmented Generation (RAG) is quickly becoming the standard enterprise pattern for deploying large language models (LLMs). Instead of relying solely on pretraining, RAG enriches prompts with fresh, domain-specific information. The result? More accurate answers, fewer hallucinations, and outputs that enterprises can trust. But building RAG at enterprise scale is tricky. You’re not embedding a few PDFs anymore—you’re embedding billions of rows from databases, log streams, or knowledge repositories. That leads to a critical architectural question:

Understanding Table Statistics in SQL Server: Importance, Performance Impact, and Practical Examples

Aggregated on: 2025-09-09 18:14:51

In SQL Server, table statistics are metadata objects that store information about the data distribution within one or more columns of a table. These statistics are crucial for the query optimizer, which uses them to estimate the number of rows that a query's predicates will return. This estimation, known as cardinality estimation, is the foundation of a good execution plan. For example, if a query filters on a column with a skewed data distribution (i.e., some values appear much more frequently than others), the optimizer can use statistics to choose a more efficient access method, such as a clustered index scan over a non-clustered index seek, avoiding a costly lookup operation. As a DBA, I believe keeping statistics up to date is paramount for maintaining optimal query performance. Stale statistics, which don't accurately reflect changes in the underlying data (e.g., due to frequent INSERT, UPDATE, or DELETE operations), can lead the optimizer to make poor cardinality estimates. This results in inefficient execution plans that could use more system resources and run longer than necessary. Regularly updating statistics either manually with UPDATE STATISTICS or by allowing the database engine's automatic update feature (AUTO_UPDATE_STATISTICS) to do so ensures the query optimizer has the most accurate information available. This proactive maintenance helps prevent performance degradation and ensures that queries continue to run efficiently as the data in the database evolves.

Blockchain-Based Authentication: The Future of Secure Identity Verification

Aggregated on: 2025-09-09 17:14:51

Traditional authentication methods — passwords, centralized databases, and third-party identity providers — are plagued by security breaches, identity theft, and data privacy concerns. Blockchain-based authentication offers a decentralized, tamper-proof, and more secure alternative. In this deep dive, we’ll explore:

How to Use Jenkins Effectively With ECS/EKS Cluster

Aggregated on: 2025-09-09 16:14:51

In modern DevOps workflows, Jenkins is the cornerstone for continuous integration and continuous deployment (CI/CD) pipelines. Because of its flexibility and wide-ranging plugins available, it's indispensable in the automation of build, test, and deployment processes. On the other hand, AWS provides Elastic Container Service and Elastic Kubernetes Service as powerful managed services for deploying and managing containerized applications. This article explores how Jenkins can be effectively integrated with both ECS and EKS clusters to optimize the CI/CD process.

Probably Secure: A Look at the Security Concerns of Deterministic vs Probabilistic Systems

Aggregated on: 2025-09-09 15:14:51

Would you rather have determined that you are in fact secure, or are you willing to accept that you are "probably" doing things securely? This might seem like a silly question on the surface, after all, audits don't work on probability; they work on documented facts. We are in an era of rapidly advancing artificial intelligence-driven applications across our organizations, covering a wide variety of use cases. We all love LLMs. There are numerous benefits to applying AI effectively, but like any technology, if we misapply it, we risk opening the door to new dangers. As we further adopt these tools and apply AI in novel ways to our workflows across all departments, it is likely a good idea to take a step back and think through how these systems work, which boils down to most likely getting a correct answer versus repeatedly getting the same expected results based on the same specific inputs.

Secure Your Spring Boot Apps Using Keycloak and OIDC

Aggregated on: 2025-09-09 14:14:51

In this blog, we will take a closer look at Spring Security, specifically in combination with Keycloak using OpenID Connect, all supported with examples and unit tests. Enjoy! Introduction Many applications are supported by means of authentication and authorization. However, it is also something software developers find difficult to grasp. In this blog, Spring Security is introduced, which is Spring's solution for adding security to your Spring applications. By means of examples and unit tests, you will learn the annotations and Spring classes. The end goal is to set up an application using OpenID Connect in combination with Keycloak. An introduction to OpenID Connect and Keycloak can be found in a previous blog. It is advised to read this blog if you are not yet familiar with the concepts.

Is Your AI a Psychopath?

Aggregated on: 2025-09-09 13:14:51

The "Whack-A-Mole" Problem: Why We're Losing the AI Bug War You just added the newest LLM to your main product. The demos were excellent, but now the support tickets are coming in fast. The customer service chatbot's AI is giving strangely passive-aggressive answers. You spend a whole day coming up with a clever meta-prompt to make it more "friendly." Yes! But a week later, you find out that it's now "helpfully" making up product features that don't exist, which confuses many users. You just started the "whack-a-mole" game for fixing AI bugs. Given the structure of the game, the outcome is inevitable.

Cloud Automation Excellence: Terraform, Ansible, and Nomad for Enterprise Architecture

Aggregated on: 2025-09-09 12:14:51

Enterprise cloud architecture demands sophisticated orchestration of infrastructure, configuration, and workload management across diverse computing platforms. The traditional approach of manual provisioning and siloed tool adoption has become a bottleneck for organizations seeking cloud-native agility while maintaining operational excellence. This article explores the strategic integration of three complementary automation technologies: Terraform for infrastructure provisioning, Ansible for configuration management, and HashiCorp Nomad, which serves as a lightweight workload orchestrator, managing application deployment, scaling, and scheduling across diverse infrastructure environments with minimal operational overhead. Unlike monolithic solutions, this ecosystem approach leverages specialized tools that excel in their respective domains while maintaining platform-agnostic capabilities across AWS, Azure, Google Cloud, IBM Cloud, and hybrid environments.

Toward Explainable AI (Part 8): Bridging Theory and Practice—SHAP: Powerful, But Can We Trust It?

Aggregated on: 2025-09-09 11:14:51

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 VII: SHAP: Bringing Clarity to Financial Decision-Making.

The Value Gap After Go-Live: The Agile Advantage in Tech Transformation

Aggregated on: 2025-09-08 19:14:51

Robust change management practice is pivotal for the success and survival of enterprises in the ever-changing technology landscape and technology-driven business world. However, the success of Change Management practices in modern enterprises is not limited to the successful deployment of technological solutions. Technology implementation is only the first step to build the foundation in the broader change management lifecycle, which encompasses adoption, integration, and the realization of sustained value. Despite investing millions into modernization initiatives, large organizations often experience transformation fatigue, stalled adoption, and disillusioned teams.

How to Do Image Recognition With CNNs on the COCO Dataset — a Practical, Step-By-Step Guide

Aggregated on: 2025-09-08 18:29:51

Short summary: This guide walks you from environment setup to a working PyTorch example that trains a Convolutional Neural Network (a pretrained ResNet) to recognize which object categories are present in a COCO image (multi-label image recognition). You’ll learn how to load COCO annotations, build a multi-label dataset, train with BCEWithLogitsLoss, evaluate average precision, and run inference. Run code snippet on a machine with Python + PyTorch. Why COCO and What This Tutorial Does The MS-COCO dataset is a large-scale dataset for object detection, segmentation and captioning; it contains hundreds of thousands of images and 80 common object categories (people, cars, cups, etc.). It’s a standard benchmark for object-level tasks, and we’ll reuse its annotations to turn detection-style labels into a multi-label image recognition task: for each image, predict which of the 80 categories appear. This is a practical way to use a CNN backbone (ResNet) and practice multi-label learning on a real dataset.

Replacing LEADTOOLS Scanner With AWS Textract (Step-by-Step Migration)

Aggregated on: 2025-09-08 17:29:51

Replacing LEADTOOLS Scanner with AWS Textract service is a strategic move if you’re aiming to leverage cloud-native, scalable, and AI-powered document processing. By transitioning from custom or any other tools to AWS, you can take full advantage of its equivalent services. This end-to-end migration offers numerous benefits, including improved efficiency, cost-effectiveness, and advanced capabilities.