News Aggregator

Stop Your GenAI From Burning Cash in Production

Aggregated on: 2025-09-08 16:29:51

Every developer who's deployed GenAI to production knows this moment. The feature works great. Users love it. Then the cloud bill arrives. Your harmless chatbot just cost more than your entire infrastructure. That RAG pipeline you built? It's eating tokens like there's no tomorrow. Welcome to the reality of production GenAI, where every API call has a price tag.

Building an AI-Powered Insurance Q and A Assistant With RAG and Snowflake Cortex

Aggregated on: 2025-09-08 15:29:56

In the insurance industry, there are vast amounts of data stored in documents like policies, claim details, and FAQs. Providing answers to customers' queries quickly and accurately is crucial for satisfaction and efficiency. The objective of this project is to develop an AI-powered Q&A assistant using Retrieval Augmented Generation (RAG) and Snowflake Cortex Search. RAG (Retrieval-Augmented Generation) integrates large language models with external information retrieval. Upon the user asking a question, the system brings back candidate documents from a knowledge base. The documents act as context to the LLM to generate a proper and informative response. This project demonstrates how to build a robust and effective insurance Q&A assistant by combining the strengths of Retrieval Augmented Generation (RAG) with Snowflake Cortex Search. Using Snowflake's semantic search capability, we can quickly retrieve contextually relevant information from an insurance document knowledge base. The retrieved context is then used as input to a Large Language Model (LLM) to generate accurate and informative answers to user queries.

Tuples and Records (Part 2): JavaScript Migration Guide

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

In Part 1 of this series, we explored JavaScript’s Tuples and Records, two immutable data structures designed to improve performance, predictability, and developer experience. We covered their purpose, syntax, key benefits, and where they fit best in modern applications. Now, in Part 2, we’ll shift the focus to migration strategies. Transitioning from traditional objects and arrays to Tuples and Records isn’t just a syntax change. It requires careful planning to avoid unintended side effects and maximize performance gains. In this article, we’ll walk through step-by-step guidance on identifying suitable use cases, incrementally refactoring, ensuring library compatibility, and benchmarking results, so you can confidently adopt these features in production code.

Slimming Down Docker Images: Base Image Choices and The Power of Multi-Stage Builds

Aggregated on: 2025-09-08 13:14:51

Introduction Let's talk about an uncomfortable truth: most of us are shipping Docker images that are embarrassingly large. If you're deploying ML models, there's a good chance your containers are over 2GB. Mine were pushing 3GB until recently. The thing is, we know better. We've all read the best practices. But when you're trying to get a model into production, it's tempting to just FROM pytorch/pytorch and call it a day. This article walks through the practical reality of optimizing Docker images, including the trade-offs nobody mentions.

API Design First: AsyncAPI in .Net

Aggregated on: 2025-09-08 12:14:51

In modern distributed systems, event-driven architectures have become mainstream. While RESTful APIs have well-established design-first practices with OpenAPI/Swagger, event-driven architectures often lack similar standardization. For any team building event-driven systems (in general) with Kafka, the initial promise of decoupling and resilience can quickly be overshadowed by chaos. Without a contract, producers and consumers drift apart, leading to runtime errors, documentation nightmares, and endless debates over topic names and message schemas. AsyncAPI aims to solve these challenges, but its current tooling ecosystem has gaps, particularly for .NET developers working with Kafka, Schema Registry, and Infrastructure as Code practices. This article shares an opinionated path of bridging this gap by creating a custom AsyncAPI template that generates production-ready Kafka clients in C#.

Getting Started With ClickHouse for AI/ML in Python

Aggregated on: 2025-09-08 11:14:51

As artificial intelligence (AI) and machine learning (ML) workloads grow in complexity and volume, traditional databases often struggle to meet the performance needs of large-scale, real-time analytics. ClickHouse, a high-performance, column-oriented OLAP (Online Analytical Processing) database designed to handle petabyte-scale data with lightning-fast query execution, offers a compelling solution for data engineers and ML practitioners alike. Its unique columnar storage, vectorized execution, and support for distributed deployments make it highly suitable for processing massive datasets generated by IoT devices, web platforms, and large-scale enterprise applications. By enabling both high-speed querying and efficient storage, ClickHouse allows organizations to analyze and act on data in near real-time, which is critical for dynamic AI/ML pipelines and feature engineering tasks. In this article, we’ll explore how to get started with ClickHouse and Python for building fast, scalable AI/ML pipelines.

Building a Platform Abstraction for AWS Networks Using Crossplane

Aggregated on: 2025-09-05 20:14:49

Crossplane helps platform engineers develop abstractions for developers. It is an open-source, multicloud control plane that handles interactions with cloud providers’ APIs for you. In this post, I’ll show how developers can create an AWS network (VPC, Subnet, etc.) with just a single YAML request to the Kubernetes API.

How to Run Selenium Tests on Selenium Grid 4 With Jenkins and Docker Compose

Aggregated on: 2025-09-05 19:44:49

Selenium WebDriver, Selenium Grid 4, Jenkins, and Docker Compose are popular and well-known tools. When combined, these tools are a powerful combination for web automation testing. The combination of these tools can help us set up an on-demand local infrastructure, enabling us to spin up the environment as needed for running our web automation tests at scale. Consider a scenario where we need to run multiple web automation tests on different browsers to verify the functionality and stability of the web application. Combining Selenium Grid 4 with Docker Compose can help set up browsers with a single command, allowing us to perform the required test execution smoothly with Jenkins Jobs.

Meta Prompting for Agile Practitioners

Aggregated on: 2025-09-05 18:14:49

TL; DR: Meta Prompting We’ve all been there: You’re preparing for the next Retrospective, and you turn to ChatGPT for help. “Give me some Retrospective ideas,” you type. What do you get back? Generic templates you’ve seen a hundred times before: Set the Stage, Gather Data, Generate Insights, Decide What to Do, and Close the Retrospective. (Kudos to Esther Derby and Diana Larsen for the format!) The problem isn’t the AI.

Making String Search Easier Across Databases

Aggregated on: 2025-09-05 17:29:49

Searching for information in applications is rarely as simple as matching an exact string. Users don’t always remember the full text; instead, they rely on fragments. When buying a product online, for instance, they might type only the brand (“Samsung”) or only the model (“Galaxy S24”), but rarely both together. In financial systems, the same happens when looking up a transaction by just part of the description. This type of partial search has become crucial for modern systems. In e-commerce, it drives product discovery. In finance, it helps locate records quickly. And in countless other domains, it shapes how people interact with data. To meet this demand, databases have evolved to provide capabilities that go beyond strict equality, allowing queries that can check whether text contains, starts with, or ends with a given fragment.

Measuring What Matters: A Strategic Lens on Transformation Metrics

Aggregated on: 2025-09-05 16:14:49

"Only 16% of digital transformations improve performance and sustain gains in the long term." — McKinsey, 2021 Transformation efforts often falter not for lack of ambition but for lack of clarity. Metrics—when used well—serve as navigational tools that align teams, validate progress, and reveal true impact. When misused, they become noise, breeding vanity and confusion.

The Role of Data Governance in Data Strategy: Part 4

Aggregated on: 2025-09-05 15:29:49

In the previous articles of this series, we explored the importance of data governance in managing enterprise data effectively (Part 1), how BigID supports data governance, particularly for data privacy, security, and classification (Part 2), and the role of Data Subject Access Rights (DSAR) in protecting individual privacy (Part 3). Together, these concepts emphasized the importance of visibility, control, and accountability in modern data governance. In this fourth installment, we shift focus to another critical pillar of data governance: Data Retention. While organizations often think of retention simply as storing data for later use, the reality is far more complex. Done right, data retention ensures compliance, cost efficiency, and stronger security. Done poorly, it creates unnecessary risks, ranging from legal exposure and privacy violations to spiraling storage costs and an expanded cybersecurity attack surface.

Change Data Capture for Apache Phoenix Stream

Aggregated on: 2025-09-05 14:29:49

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 - the best of both worlds. Apache Phoenix provides Change Data Capture (CDC) with PHOENIX-7001. The CDC design in Phoenix leverages the write-optimized Uncovered Index as well as Max Lookback features. The changes are captured in the time-ordered event of row level modifications.

Build a RAG Application With LangChain and Local LLMs Powered by Ollama

Aggregated on: 2025-09-05 13:14:49

Local large language models (LLMs) provide significant advantages for developers and organizations. Key benefits include enhanced data privacy, as sensitive information remains entirely within your own infrastructure, and offline functionality, enabling uninterrupted work even without internet access. While cloud-based LLM services are convenient, running models locally gives you full control over model behavior, performance tuning, and potential cost savings. This makes them ideal for experimentation before running production workloads. The ecosystem for local LLMs has matured significantly, with several excellent options available, such as Ollama, Foundry Local, Docker Model Runner, and more. Most popular AI/agent frameworks, including LangChain and LangGraph, provide integration with these local model runners, making it easier to integrate them into your projects.

DevOps as a Platform: How to Help Developers Ship Faster Without the Chaos

Aggregated on: 2025-09-05 12:29:49

Imagine you're an engineer trying to ship a new feature. You need a pipeline to build, deploy, and test your code. You need infrastructure to run it. You need to check permissions, secrets, and compliance boxes. If your company doesn’t have a standardized DevOps setup, you’re probably setting all that up yourself — or copying it from the last project and hoping for the best. Now multiply that by 50 teams. Welcome to DevOps chaos.

Toward Explainable AI (Part 7): Bridging Theory and Practice—SHAP: Bringing Clarity to Financial Decision-Making

Aggregated on: 2025-09-05 11:14:49

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 VI: What LIME Shows, and What It Leaves Out, Strengths and limits of local explanations.

How to Use AI to Enhance Scrum Ceremonies

Aggregated on: 2025-09-04 19:29:49

Among the Agile methodologies, Scrum is the main tool for software development that advances openness, adaptability, and ongoing learning. Scrum ceremonies include the sprint planning, daily stand-up, sprint review, and sprint retrospective. These ceremonies are structured events that drive collaboration, alignment, and delivery. Per Gartner’s report, among the 80% of organizations performing agile development, 87% use Scrum, which makes it the most popular implementation. Gartner defines artificial intelligence as applying advanced analysis and logic-based techniques, including ML, to interpret events, support and automate decisions, and take actions. As per another Gartner report, forecast assumptions are:

Protecting PII in LLM Applications: A Complete Guide to Data Anonymization

Aggregated on: 2025-09-04 18:14:49

Organizations want to leverage the power of LLMs like GPT or PaLM to solve business problems, but they're rightfully hesitant about sending sensitive data—especially Personally Identifiable Information (PII)—over the internet to third-party hosted models. This article explores a powerful mitigation technique using anonymization and de-anonymization to protect sensitive data while still enabling effective LLM usage in enterprise environments.

CI/CD in the Age of Supply Chain Attacks: How to Secure Every Commit

Aggregated on: 2025-09-04 17:14:49

The digital infrastructure we've built resembles a house of cards. One compromised dependency, one malicious commit, one overlooked vulnerability and the entire edifice comes tumbling down. In March 2024, security researchers discovered something terrifying: a backdoor lurking within XZ Utils, a compression library so ubiquitous it had infiltrated thousands of Linux distributions worldwide. The attack vector? A meticulously orchestrated supply chain compromise that turned the very foundation of open-source development against itself. This wasn't an anomaly. It was a wake-up call.

Building AI Agents? 5 Critical Questions to Ask Before You Automate

Aggregated on: 2025-09-04 16:29:49

Agentic AI is a game changer and a hot topic in nearly every boardroom conversation today. There’s no doubt that companies not think AI-first risk becoming irrelevant. But before you rush to build or deploy an AI agent, it’s important to pause and ask some tough questions. Not every problem requires an AI agent, and without the right foundation, especially around data, agentic AI can quickly become a costly and risky mistake. Rushing headlong into AI adoption without thoughtful planning often leads to wasted resources and missed opportunities. Here are five critical questions every organization should ask before automating with agentic AI.

The Endless Cycle of Manual K8s Cost Optimization Is Costing Organizations More Than They Realize

Aggregated on: 2025-09-04 15:14:49

Developers and DevOps teams working in Kubernetes tend to focus primarily on performance and pay little attention to the cost side of things. When workloads are running smoothly and meeting SLAs, budget considerations often take a backseat until some external force (normally in the form of the finance team) demands that it’s optimized. However, the reality is that ignoring cost until finance steps in only leads to inefficiency and wasted resources, and eventually, quite a lot of work on cost optimization. This cycle, where costs are forgotten and then aggressively optimized under pressure, drains considerable time and energy that could be better spent on other strategic initiatives.

Take AI Out of Your Silos—Why Team Experience Trumps Developer Tools

Aggregated on: 2025-09-04 14:14:49

You wouldn't try to design a product by focusing on one piece at a time. The best products in the world solve problems by reimagining, not by incremental improvements. So why, with AI as the largest technical innovation in recent history, are so many products focusing on improving each step individually, when we complete work in teams? You may be saying “What are you talking about? AI is redefining industries right now!” I hope to convince you that many AI implementations are myopic, local optimizations.

Active Learning and Human-in-the-Loop for NLP Annotation and Model Improvement

Aggregated on: 2025-09-04 13:29:48

Natural language processing (NLP) models depend heavily on data, but obtaining high-quality labeled data at scale is one of the biggest hurdles. It quickly becomes clear that throwing more raw data at an NLP problem doesn't really help much - it’s the labeled data that really drives improvement. This is where active learning and a human-in-the-loop approach become invaluable. They help us prioritize which data to label, involve human expertise at critical points, and continuously improve models in production. In this article, we’ll talk about what active learning is, how to implement a human-in-the-loop workflow for NLP annotation, and why this approach accelerates model improvement.

Engineering for Uptime: Observability, Testing, and the Road to Rock-Solid Back-End Services

Aggregated on: 2025-09-04 12:14:49

Background A single mobile tap can trigger a number of events behind the scenes — API calls to microservices, messages/events sent through queues, writes to databases, and retries on transient failures — all before it returns with a success… or an error toast. The user doesn’t see this complexity. They don’t know about your autoscaling policy, cache hit ratios, or dependency graphs. They only know whether their ride was hailed, their payment went through, or their food order was confirmed. And when things go wrong, it’s that hidden complexity that determines how gracefully your system recovers. That’s why reliability can’t just be the SRE team’s job anymore. It’s a shared responsibility — one that should be embedded in the day-to-day decisions of every back-end engineer. From the way we design systems to how we write alerts, ship code, and handle incidents, reliability is engineered — not wished into existence.

CI/CD Is Not Enough: Stop Missing Test Failures With Intelligent Notifications

Aggregated on: 2025-09-04 11:14:48

The Visibility Gap in Enterprise Testing Modern test automation has matured. CI/CD pipelines are well-orchestrated, test coverage is high, and nightly regressions run like clockwork. But even with all this structure, one subtle problem still persists: nobody knows when things fail — at least not fast enough, or by the right people. Here’s how this usually plays out:

Developing a Nationwide Real-Time Telemetry Analytics Platform Using Google Cloud Platform and Apache Airflow

Aggregated on: 2025-09-03 20:29:48

In my tenure at TELUS, I was assigned a prominent project requiring substantial technical expertise: the development of a telemetry analytics platform that could analyze data in real-time from over 100,000 set-top boxes (STBs) deployed throughout Canada. The objective was not just about scale; it aimed to assist teams to make quicker operational decisions and enhance the experience for millions of customers. Initially, I recognized the outdated data infrastructure as a bottleneck, obstructing the data from reaching the teams who required it the most. This article portrays the methodologies we employed to modernize our infrastructure using Google Cloud Platform (GCP), Apache Airflow, and Infrastructure-as-Code tools to surmount the obstacles and deliver a future-proof solution. The Predicament: Ancient Bottlenecks and Unseen Black Spots Prior to this revamp, we predominantly relied on segregated and batch-oriented data pipelines incapable of supporting real-time diagnostics. Key concerns encompassed:

DSLs vs. Libraries: Evaluating Language Design in the GenAI Era

Aggregated on: 2025-09-03 19:29:48

Programming languages are the fundamental tools used to shape the digital world. Every developer has to choose at some point in their careers between general-purpose languages such as Python, Java, and C# and specialized domain-specific languages like SQL, CSS, or XAML. But with the evolution of AI the lines are getting blurred. We are observing shifts in not only how we write code but the definitions of productivity, maintainability, and innovation are beginning to change as well. As a result, the conventional trade-offs between DSLs and libraries are changing, and long-standing issues like expressiveness, integration complexity, and learning curves are being approached from new perspectives. The Traditional DSL vs Library Paradigm General-Purpose Languages (GPLs) are very versatile. They are packed with extensive libraries that allow developers to tackle problems across multiple domains. But this flexibility comes at the cost of writing more code and the need for significant domain knowledge to implement specialized solutions effectively.

Observability for the Invisible: Tracing Message Drops in Kafka Pipelines

Aggregated on: 2025-09-03 18:14:48

When an event drops silently in a distributed system, it is not a bug, it is an architectural blind spot. In high-scale messaging platforms, particularly those serving real-time APIs like WhatsApp Business or IoT command chains, telemetry failures are often mistaken for application errors. But the root cause lies deeper: observability gaps in event streams. This article explores how backend engineers and DevOps teams can detect, debug, and prevent message loss in Kafka-based streaming pipelines using tools like OpenTelemetry, Fluent Bit, Jaeger, and dead-letter queues. If your distributed messaging system handles millions of events, this guide outlines exactly how to make those events accountable.

Simple Efficient Spring/Kafka Datastreams

Aggregated on: 2025-09-03 17:14:48

I had the opportunity to work with Spring Cloud Data Flow streams and batches. The streams work in production and perform well. The main streams used Debezium to send the database deltas to Soap endpoints or provided Soap endpoints to write into the database. The events where send via Kafka. Spring Cloud Data Flow also provides a application to manage the streams and jobs. The streams are build with a data source and a data sink that are separate applications and are decoupled by the events send via Kafka. Stream 1 has a Debezium source and sends the database deltas via Kafka to the sink that transforms the event into a soap request to the application. Stream 2 receives a soap request from the application and sends an event to Kafka. The sink receives the event and creates the database entries for the event.

Understanding Zero-Copy

Aggregated on: 2025-09-03 16:14:48

In the realm of high-performance computing and network applications, efficient data handling is important. Traditional Input/Output (I/O) operations often involve redundant data copies, creating performance bottlenecks that can limit throughput and increase latency. Zero-copy is a powerful optimization technique that minimizes or eliminates these unnecessary data movements, leading to significant performance gains. Traditional Input/Output Path Consider a common scenario: an application needs to read a file from disk and transmit it over a network. In a traditional I/O model, this seemingly straightforward operation entails a series of data copies:

Understanding Apache Spark Join Types

Aggregated on: 2025-09-03 15:29:48

In this article, we are going to discuss three essential joins of Apache Spark. The data frame or table join operation is most commonly used for data transformations in Apache Spark. With Apache Spark, a developer can use joins to merge two or more data frames according to specific (sortable) keys. Writing a join operation has a straightforward syntax, but occasionally the inner workings are obscured. Apache Spark internal API suggests several algorithms for joins and selects one. A basic join operation could become costly if you do not know what these core algorithms are or which one Spark uses.

Container Security Essentials: From Images to Runtime Protection

Aggregated on: 2025-09-03 14:29:48

Container security is all about making sure you run an image that is exceptionally low in vulnerability and malware. I would love to say having zero vulnerabilities, but it is rarely possible in the real world. In the worst case, you at least want to address critical to medium vulnerabilities to have a good night's sleep and avoid potential compromise from bad actors. You could also think of container security like peeling an onion, where each layer adds resilience against potential threats. As part of this article, we will learn what the different steps are that we could take to increase the overall safety of the container infrastructure.

Cryptography Libraries on Ampere®

Aggregated on: 2025-09-03 13:44:48

Overview This white paper aims to provide the best-known practices for using open-source cryptography libraries on Ampere processors, including the Ampere® Altra® family and the AmpereOne® family of processors. Background Cryptography is the science of securing communication and data through mathematical techniques, ensuring confidentiality, integrity, and authenticity. It is widely used in web services, load balance proxies, databases, etc.

Why Zero Trust Is Not a Product but a Strategy You Can’t Ignore in 2025

Aggregated on: 2025-09-03 13:29:48

"We recently purchased a Zero Trust solution." A statement like that makes even the most seasoned security experts cringe. Zero Trust is a ubiquitous notion in 2025, appearing in product packaging, seminars, and sales presentations. However, the fundamental idea is still gravely misinterpreted. There is no such thing as buying Zero Trust. It's a way of thinking, a plan you follow, and a path you dedicate yourself to. In light of growing attack surfaces, heterogeneous workforces, and more complex threat actors, it is not only inefficient but also risky to approach Zero Trust as a checkbox.

File Systems <> Database: Full Circle

Aggregated on: 2025-09-03 12:14:48

File-based systems were the original data storage systems before the invention of database management systems (DBMS). Back in the 1970s, organizations manually stored data across servers in numerous files, such as flat files. These files have a fixed, rigid format and multiple copies of data stored for each department, resulting in data redundancy. These led to various challenges, especially data consistency, sharing, security, and retrieval. Analyzing these files was also challenging if we needed to join multiple files for one end-to-end record. As a result, file-based systems could not keep up with the changing data and innovations. With the invention of DBMS, data transactions comply with ACID properties (atomicity, consistency, isolation, durability), which allows for data consistency, integrity, recovery, and concurrency. In addition, today's advanced DBMS system provides disaster recovery, backup and restore, data searching, and data encryption and security. Even though the DBMS has evolved, due to the advancement of big data, cloud technologies, the Internet, social media, and advancing data formats, file storage is again a hot topic.

Toward Explainable AI (Part 6): Bridging Theory and Practice—What LIME Shows – and What It Leaves Out

Aggregated on: 2025-09-03 11:14:48

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 V: A Hands-On Introduction to LIME: Explaining pneumonia detection step by step.

Stop Leaking Secrets: The Hidden Danger in Test Automation and How Vault Can Fix It

Aggregated on: 2025-09-02 20:14:48

Modern automation frameworks have come a long way—Playwright, Cypress, RestAssured, Cucumber, and Selenium enable teams to run sophisticated end-to-end validations across browsers and services. But under all that progress lies a risk that's still alarmingly common: secrets hardcoded into test code or environment files. These aren’t just theoretical risks. In one large enterprise, a regression test suite for an internal app had a credentials file committed in plain text six months prior. The automation “just worked,” but the secrets were not only stored in .env files—they were also printed to Jenkins console logs, referenced in Postman collections, and distributed across multiple forks. No one noticed until a security audit flagged it.

Technical Deep Dive: Scaling GenAI-Enhanced SBOM Analysis from Trivy Fix to Enterprise DevSecOps

Aggregated on: 2025-09-02 19:14:48

This article demonstrates how a critical Trivy SBOM generation fix (PR #9224) can be scaled into an enterprise GenAI-powered platform, delivering comprehensive DevSecOps automation and millions in cost savings. We will explore the technical implementation from core dependency resolution improvements to enterprise-scale AI-driven vulnerability intelligence. The Foundation: Cross-Result Dependency Resolution in Trivy Problem Statement: Incomplete SBOM Dependency Graphs Original Issue: SBOM dependency graph plotting was missing dependencies that existed across different scan results, particularly in multimodule projects where module B depends on a shared library from module A. The root cause was that dependency resolution only examined individual results, not all results in the report.

Monitoring Java Microservices on EKS Using New Relic APM and Kubernetes Metrics

Aggregated on: 2025-09-02 18:14:47

Amazon EKS makes running containerized applications easier, but it doesn’t give you automatic visibility into JVM internals like memory usage or garbage collection. For Java applications, observability requires two levels of integration: Cluster-level monitoring for pods, nodes, and deployments JVM-level APM instrumentation for heap, GC, threads, latency, etc. New Relic provides both via Helm for infrastructure metrics, and a lightweight Java agent for full JVM observability.

Modernizing Oracle Workloads With Real-Time Analytics

Aggregated on: 2025-09-02 17:14:48

On July 23, 2025, AWS announced Amazon Relational Database Service (Amazon RDS) for Oracle zero-ETL integration with Amazon Redshift, enabling near real-time analytics and machine learning (ML) on petabytes of transactional data. With this launch, you can create multiple zero-ETL integrations from a single Amazon RDS Oracle database, and you can apply data filtering for each integration to include or exclude specific databases and tables, tailoring replication to your needs. You can also use AWS CloudFormation to automate the configuration and deployment of resources needed for zero-ETL integration. Zero-ETL integrations make it simpler to analyze data from Amazon RDS to Amazon Redshift by removing the need for you to build and manage complex data pipelines and helping you derive holistic insights across many applications. Within seconds of data being written to Amazon RDS for Oracle, the data is replicated to Amazon Redshift. Using zero-ETL, you can enhance data analysis on near-real-time data with the rich analytics capabilities of Amazon Redshift, including integrated ML, Spark support, and materialized views.

Prototype for a Java Database Application With REST and Security

Aggregated on: 2025-09-02 16:29:48

Many times, while developing at work, I needed a template for a simple application from which to start adding specific code for the project at hand. In this article, I will create a simple Java application that connects to a database, exposes a few rest endpoints and secures those endpoints with role based access.

Building a Card Layout Using CSS Subgrid

Aggregated on: 2025-09-02 15:14:48

Creating clean, well-aligned card layouts is a common task in web development. In this tutorial, I’ll walk you through building a grid of four cards per row. Each card contains several content blocks — a title, image, price, bullet point list, and a call-to-action (CTA) button — aligned horizontally within the card using CSS Grid and the powerful CSS Subgrid feature. What You’ll Build A card grid layout (max of 4 cards per row). Each card contains multiple content blocks aligned horizontally. Use of CSS Grid for the overall layout. Use of CSS Subgrid for inner alignment of content inside each card. Why Use CSS Subgrid? CSS Subgrid is a relatively new feature that allows a nested grid to inherit the track sizing of its parent grid. This means you can align inner content perfectly with the outer grid without manually calculating or duplicating track sizes.

Autonomous QA Testing With Playwright, LangGraph, and GPT-4o on AWS

Aggregated on: 2025-09-02 14:14:47

Software testing has come a long way — from manual test cases and record-playback tools to modern CI-integrated test automation frameworks. But in an era of continuous delivery, microservices, and fast-changing UIs, even traditional automation struggles to keep up. Writing and maintaining test scripts manually has become a bottleneck, especially when rapid iteration is the norm. The future of testing is autonomous — where tests are not only executed automatically but are written, adapted, and self-corrected by intelligent agents.

PII Leakage Detection and Measuring the Accuracy of Reports and Statements Using Machine Learning

Aggregated on: 2025-09-02 13:14:47

Reports, invoices, and statements play a vital role in sharing weekly, monthly, and annual usage data and its trends with end-users on day-to-day activities. Starting from utility usage, financial trends, credit statements, and medical data are shared with humans in the form of reports and statements, both in electronic and paper formats. These documents contain PII, personally identifiable information, of users, including address, phone number, account numbers, medical history, and Social Security numbers. Data is also represented in tables, a wide variety of charts, and graphs for an enhanced user experience. Problem Organizations and institutions pay fines, penalties, and work on settlements, often now due to PII data breaches and inaccurate data in reports. The majority of organizations use a third-party vendor to generate and send out these statements to their customers. The chances of misdelivery or sharing inaccurate information are relatively high. Using a visual language model and machine learning techniques, we can eliminate the data breach by detecting and fixing it.

Mastering Prompt Engineering for Generative AI

Aggregated on: 2025-09-02 12:14:47

Prompt engineering is rapidly becoming a foundational skill in working with large language models (LLMs) and generative AI. As LLMs permeate software systems-powering chatbots, coding assistants, research agents, and more. the difference between a generic, shallow response and a nuanced, high-value output often comes down to how the model is prompted. For developers, product teams, and engineering leaders, understanding and leveraging state-of-the-art prompt strategies have tangible impacts on product relevance, accuracy, and user experience. This guide explores advanced prompting techniques, from Chain of Thought (CoT) and few-shot learning to retrieval-augmented generation (RAG), and provides practical advice for integrating them into real-world workflows.

Build Smarter Next-Gen AI Apps: A Step-by-Step LangChain v0.3+ Guide

Aggregated on: 2025-09-02 11:14:47

The New Era of LLM Apps In the last year, AI development has shifted rapidly from simple demos to robust, feature-rich applications. At the heart of this movement is LangChain, the open-source toolkit that makes it easier than ever to plug large language models into real-world data, tools, and workflows. If you've ever wanted to move beyond the standard chatbot — say, build a custom app that can analyze documents, retrieve live data, and even call external APIs — the new LangChain has you covered. Companies like Morningstar are already using LangChain to build their Intelligence Engine, allowing analysts to query massive research databases in natural language. Meanwhile, enterprises across industries report deployment cycles that are 3-5× faster when using LangChain compared to building from scratch.

How to Use ALB as a Firewall in IBM Cloud

Aggregated on: 2025-09-01 20:29:47

Do you have a use case where you want to implement a network firewall in IBM Cloud VPC that filters traffic based on hostname? For example, you may want to allow connections only to www.microsoft.com and www.apple.com, while blocking access to all other destinations. Currently, IBM Cloud does not provide a managed firewall service. However, it does support a bring-your-own-firewall approach with vendors such as Fortinet or Juniper, though customers are responsible for deploying and managing these solutions.

From CloudWatch to Cost Watch: Cutting Observability Costs With Vector

Aggregated on: 2025-09-01 19:29:47

Introduction In modern cloud environments, traditional approaches for storing logs in isolated systems have become inadequate. As distributed software systems become more common, where different components run across multiple services and regions, it is essential to continuously collect and forward both system and application logs to a centralized location for in-depth analysis. These logs play an important role in debugging, performance monitoring, and ensuring the overall health and reliability of the infrastructure. In the AWS cloud environment, many such components of the distributed software system are still hosted on Amazon EC2 instances and use an agent-based approach to transmit system and application logs to a centralized service, where this data is ingested and stored for further use by observability platforms. While observability improves operational insight and system reliability, it also increases the cost of data ingestion and long-term storage. Therefore, organizations must maintain a careful balance between observability depth and the financial sustainability of the platform. Selecting a resilient, scalable, and cost-effective ingestion and storage solution has become an important element of any observability strategy, especially when the platform is being used at enterprise scale.

The AI Co-Pilot: How to Lead When Your Team's Best Player Is a Machine

Aggregated on: 2025-09-01 18:14:47

It’s happening in stand-ups and one-on-ones everywhere. An engineer explains how they cleared a mountain of tickets over the weekend. "How'd you get it all done?" you ask. The answer is a quiet admission, almost a confession: "Uh, I was using Copilot." For years, we've managed teams of people. Now, we're managing teams of people who have an incredibly productive, sometimes inscrutable, and tireless new partner. This AI co-pilot can write boilerplate code in seconds, translate complex logic into a new language, and even suggest fixes for bugs that would have taken a junior engineer half a day to track down.

Exploring QtJambi: A Java Wrapper for Qt GUI Development—Challenges and Insights

Aggregated on: 2025-09-01 17:14:47

I recently experimented with QtJambi, a Java wrapper for the well-known Qt C++ library used to build GUIs. Here are some initial thoughts, remarks and observations: Building a QtJambi project can be somewhat challenging. It requires installing the Qt framework, configuring system paths to Qt’s native libraries, and setting proper JVM options. Although it is possible to bundle native libraries within the wrapper JARs, I haven’t tried this yet. The overall development approach is clean and straightforward. You create windows or dialogs, add layouts, place widgets (components or controls) into those layouts, configure widgets and then display the window or dialog to the user. This model should feel familiar to anyone with GUI experience. Diving deeper, QtJambi can become quite complex, comparable to usual Java Swing development. The API sometimes feels overly abstracted with many layers that could potentially be simplified. There is an abundance of overloaded methods and constructors, which can make it difficult to decide which ones to use. For example, the QShortcut class has 34 different constructors. This likely comes from a direct and not fully optimized mapping from the C++ Qt API. Like Swing, QtJambi is not thread-safe. All GUI updates must occur on the QtJambi UI thread only. Ignoring this can cause crashes, not just improper UI refresh like in Swing. There is no code reuse between Java Swing and QtJambi. Even concepts that appear close and reusable are not shared. QtJambi is essentially a projection of C++ Qt’s architecture and design patterns into Java, so learning it from scratch is necessary even for experienced Swing developers. Using AI tools to learn QtJambi can be tricky. AI often mixes Java Swing concepts with QtJambi, resulting in code that won’t compile. It can also confuse Qt’s C++ idioms when translating them directly to Java, which doesn’t always fit. Despite being a native wrapper, QtJambi has some integration challenges, especially on macOS. For example, handling the application Quit event works differently and only catching window-close events behaves properly out of the box. In contrast, native Java QuitHandler support is easier and more reliable there, but it doesn't work with QtJambi. Mixing Java AWT with QtJambi is problematic. This may leads to odd behaviors or crashes. The java.awt.Desktop class also does not function in this context. If you want a some times challenging Java GUI framework with crashes and quirks, QtJambi fits the bill! It brings a lot of power but also some of complexity and instability compared to standard Java UI options. There is a GUI builder that works with Qt, but it is possible to use its designs in QtJambi, generating source code or loading designs at runtime. The only issue: the cost starts from $600 per year for small businesses to >$5,000 per year for larger companies. Notable Applications Built With QtJambi Notable applications built with QtJambi are few. One example is the Interactive Brokers desktop trading platform (IBKR Desktop), which uses QtJambi for its user interface.