News Aggregator

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.

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.