News Aggregator

Data Mesh Security: How to Protect Decentralized Data Architectures

Aggregated on: 2025-08-11 17:14:36

The rise of data mesh architectures redefines how modern organizations have approached the concept of data security. Standard best practices dictate that data should be centralized, allowing it to be collected, stored, and governed within monolithic systems, such as data warehouses. enabled centralized access control, governance, and 'auditability'. The data mesh model, however, disrupts this architecture and decentralizes data ownership. Now, instead of a centralized team governing data access, data mesh empowers domain-oriented teams to treat data as a product, allowing them to manage it independently. While this new approach offers speed and flexibility, it also introduces serious data mesh security challenges.

Getting Started With Android UI Development With Jetpack Compose

Aggregated on: 2025-08-11 16:14:36

I was contacted by my friend Aditya last month with tremendous enthusiasm regarding a new Android feature he had discovered. "Mohit, you won't believe it! I just used Jetpack Compose and it's insane!" At first, I was like, "Whatever dude, another Google framework that will be obsolete next year." But then, Dan showed me his project, and frankly, I was kind of blown away. Why We Started Working With Jetpack Compose Aditya had been struggling with a dating app he was building using XML layouts for roughly six months. The UI would always get broken on different devices, and animations were a nightmare. Within two weeks of using Compose, he rewrote the entire thing, and it worked way better.

Designing Resilient Systems: What Every Engineering Manager Should Know

Aggregated on: 2025-08-11 15:14:36

Introduction: Why Resiliency Matters If you've ever launched a major feature, only to watch it spectacularly break under pressure, you know exactly why resiliency matters. As a Software Development Manager (SDM) at Amazon, I've been on the front lines of creating systems that handle real-world chaos gracefully. Let's talk resiliency — focusing on critical architectural patterns in plain language: Retries, Circuit Breakers, and Bulkheads.

DSPy Framework: A Comprehensive Technical Guide With Executable Examples

Aggregated on: 2025-08-11 14:14:36

Building AI applications today needs the crafting of each prompt carefully balanced, but one small change can bring the whole system crashing down. Traditional prompt engineering is brittle, unpredictable, and exhausting to maintain. That’s where DSPy (Declarative Self-improving Python) comes in. Developed by Stanford NLP researchers, DSPy takes a totally different approach. Instead of manually tweaking prompts and hoping for the best, it treats language models as programmable components like any other part of your software stack. With DSPy, you declare what you want your AI to do, not how to prompt it. The framework then automatically optimizes prompts, handles errors gracefully, and ensures reliable outputs, all while letting you focus on the bigger picture.

How to Set Up Selenium Grid 4 With Docker Compose

Aggregated on: 2025-08-11 13:29:36

Selenium WebDriver is a popular web automation tool. It automates browsers and enables software teams to perform web automation testing across multiple popular browsers, including Google Chrome, Mozilla Firefox, Microsoft Edge, and Safari. To scale this testing across different platforms and browser versions, Selenium Grid 4 can be utilized. It works seamlessly with Selenium WebDriver, allowing tests to run in parallel across different browsers, making cross-browser and cross-platform testing faster and efficient.

Testing Approaches for Java Enterprise Applications With Jakarta NoSQL and Jakarta Data

Aggregated on: 2025-08-11 12:29:36

When discussing software development and its history, we often hear quotes emphasizing the importance of testing; however, in practice, we usually prioritize it as the last step, perhaps ahead of documentation. Without proper testing, ensuring the quality of your software is nearly impossible. Tests work as a safety certification, catching issues early and ensuring that your software behaves as expected. Despite their clear advantages — improved code quality, easier refactoring, and better adaptability for future changes — tests are often neglected. The reality is that testing is a long-term investment, and many software engineers and tech leaders tend to underestimate its importance. This article aims to highlight why testing should be an integral part of your development workflow, particularly when working with Jakarta EE.

The Immune System of Software: Can Biology Illuminate Testing?

Aggregated on: 2025-08-11 11:29:36

"It is one of the philosophical enigmas of immunity that the self exists largely in the negative — as holes in the recognition of the foreign." — Siddhartha Mukherjee, The Song of the Cell In biology, the immune system is not simply a defence mechanism. It is a highly sophisticated, adaptive intelligence system that defines the organism by what it does not attack. In other words, immunity is not only about fighting pathogens; it is also about recognizing what to ignore. This paradox — that the self is defined by its invisibility to immune detection — offers profound insight, not just in biology but also in systems engineering, particularly software testing.

Agentic AI vs Traditional AI Agents: What Makes Them Autonomous?

Aggregated on: 2025-08-08 20:14:34

Artificial intelligence has developed, and the appearance of agentic artificial intelligence is a significant turning point. Unlike conventional AI agents, which depend too much on human cues, agentic AI systems (such as OpenAI’s Auto-GPT 3.0, Google’s Gemini Pro 1.5, and Meta’s LLaMA 3) demonstrate autonomy, initiative, and adaptive decision-making. This blog post examines how agentic AI differs from traditional agents, what innovations are leading the transformation, and what this means for the world of automation and work.

LangGraph Orchestrator Agents: Streamlining AI Workflow Automation

Aggregated on: 2025-08-08 19:14:34

In AI-driven applications, complex tasks often require breaking down into multiple subtasks. However, the exact subtasks cannot be predetermined in many real-world scenarios. For instance, in automated code generation, the number of files to be modified and the specific changes needed depend entirely on the given request. Traditional parallelized workflows struggle unpredictably, requiring tasks to be predefined upfront. This rigidity limits the adaptability of AI systems. However, the Orchestrator-Workers Workflow Agents in LangGraph introduce a more flexible and intelligent approach to address this challenge. Instead of relying on static task definitions, a central orchestrator LLM dynamically analyses the input, determines the required subtasks, and delegates them to specialized worker LLMs. The orchestrator then collects and synthesizes the outputs, ensuring a cohesive final result. These GenAI services enable real-time decision-making, adaptive task management, and higher accuracy, ensuring that complex workflows are handled with smarter agility and precision.

The Adaptive Modular Monolith Concept

Aggregated on: 2025-08-08 18:14:34

The Modular Monolith architecture is reshaping how software systems are built and evolved. Although splitting modules into independent microservices typically requires significant efforts - such as repackaging, redeployment, and reconfiguration - an ideal architecture would allow modules to be spun off as independent services effortlessly, without these manual steps. This capability distinguishes the Adaptive Modular Monolith concept from both traditional monoliths and microservices, providing a seamless, evolutionary path from simple development to scalable, flexible architectures on demand. It would enable starting with a unified, modular application and later extracting modules as standalone services with minimal overhead, combining the benefits of both monoliths and microservices in one system.

State Machines Behind the Scenes of Flight Booking and Payments

Aggregated on: 2025-08-08 17:14:34

Modern flight booking and payment systems are composed of numerous steps spanning multiple services. For example, an airline booking might involve one service to reserve a seat, another to process payment, and a third to issue the ticket (confirm the seat). All these steps must succeed to complete the booking; if any step fails, the prior steps’ effects should be undone to avoid inconsistencies. In a monolithic system, a single ACID transaction might handle this. But in a distributed microservices architecture, no single transaction can easily encompass seat inventory and payment across systems. As one article notes, in a flight booking scenario, a seat-reservation microservice cannot acquire a lock on the payment database (often an external service), so a different approach to transaction management is required – one that embraces loose coupling and eventual consistency . This is where state machines and the saga pattern come into play. A state machine models a process as a series of discrete states and transitions in response to events. We can define states corresponding to each stage of booking (seat selection, fare held, payment processing, ticket issued, etc.) and transitions triggered by events like “payment successful” or “seat hold expired.” For example, a travel booking flow might have states such as Booking Flight, Booking Hotel, Booking Car, Confirmation, and Error. Events then drive transitions between these states: e.g. a Flight booked event moves from Booking Flight to the next state, whereas a Flight booking failed event transitions to an Error state. Time-based events like Fare hold timeout are also part of the model . By enumerating all success and failure events (including timeouts), engineers can explicitly capture how the system should react at each step, ensuring no outcome is overlooked.

API Testing With Playwright: A Guide for QA Engineers and Developers

Aggregated on: 2025-08-08 16:14:34

Ensuring the quality and reliability of backend services through API testing is as crucial as testing the user interface. APIs act as the backbone for data exchange between different components and systems. Without robust API tests, defects can propagate unnoticed, leading to faulty features and unhappy users. Playwright, widely acclaimed as a next-generation browser automation framework, extends its capabilities beyond UI testing to support comprehensive API testing. By leveraging Playwright for API testing, QA engineers and developers benefit from a unified framework that seamlessly integrates API and UI tests. This article explores how to effectively use Playwright for API testing—from basics to advanced techniques—while offering practical advice and real-world examples.

Boosting Algorithms Demystified: A Deep Dive into XGBoost With Code and Explanation

Aggregated on: 2025-08-08 15:29:34

Boosting algorithms have become a staple in the machine learning world, particularly for structured/tabular data. Among these, XGBoost (Extreme Gradient Boosting) stands out as one of the most widely used and effective techniques. From winning Kaggle competitions to production-level applications, XGBoost consistently delivers top-tier performance. This post aims to provide a comprehensive and technically detailed exploration of boosting, focusing specifically on XGBoost, complete with concepts, practical insights, and experimental strategies. The Foundation: What Is Boosting? Boosting is an ensemble technique designed to convert a set of weak learners into a strong one. It builds models sequentially, each new model attempting to correct the errors made by the previous ones. The core idea is not just averaging predictions (like bagging) but optimizing the overall model by learning from residuals or gradients.

Designing Secure APIs: A Developer’s Guide to Authentication, Rate Limiting, and Data Validation

Aggregated on: 2025-08-08 14:29:34

APIs have emerged as the cement of the contemporary application. APIs are at the heart of the movement of data, and the interaction of systems, whether in the form of mobile apps and web frontends or microservices and third-party integrations. However, along with this omnipresence there is exposure. Malicious actors will usually start with APIs to exploit low-security authentication, rate-limit bypass, and malicious payload injection. This article will examine some of the most important concepts that developers should use to create secure APIs; namely authentication, rate limiting, and input validation. Authentication: Controlling Access at the Door Authentication defines who can access your API and improper authentication is one of the most frequent reasons of data leakage. Although internal API use cases continue to use static API keys, newer systems are using tokens to provide more granular and scalable control, e.g. JWT (JSON Web Tokens) or OAuth2.

CSS Hide and Seek: Choosing Between display: none and visibility: hidden

Aggregated on: 2025-08-08 13:29:34

When it comes to hiding elements in CSS, developers often face a fundamental choice: should they use display: none or visibility: hidden? While both properties can make elements disappear from view, they behave very differently under the hood. Understanding these differences is crucial for creating efficient, accessible, and maintainable web applications. The Core Difference The primary distinction between display: none and visibility: hidden lies in how they affect the document flow and layout:

VB6 vs. C#: How to Migrate and Modernize Your Legacy Code

Aggregated on: 2025-08-08 12:29:34

Introduction to VB6 and C#: Why Modernization Matters Visual Basic 6 (VB6) was once a popular choice for Windows application development. However, as technology evolved, VB6 became outdated, with Microsoft officially ending support in 2008. Today, businesses relying on VB6 applications face increasing security risks, compatibility issues, and maintenance challenges. In contrast, C#—a modern, object-oriented programming language developed by Microsoft—offers significant advantages, including enhanced security, cross-platform compatibility, and seamless integration with modern technologies like cloud computing and microservices.

Databricks DBRX vs OpenAI GPT-4o vs Claude 3: Which LLM Is Best for Enterprise Use Cases?

Aggregated on: 2025-08-08 11:29:34

Enterprise AI has been shaped in new ways due to the fast development of large language models (LLMs). More companies are starting to use these models to enhance their approach to workflow, improve automated communication, make analyzing data easier, and develop smart applications. There are three leading language models in this fast-changing environment. Databricks DBRX, OpenAI’s GPT-4, and Anthropic’s Claude 3. Every model offers a separate solution to the needs of enterprises with open-source flexibility, multi-modality, or ethical consideration. Databricks DBRX: The Open-Source Powerhouse Databricks’ DBRX gives businesses a chance to manage and adapt their infrastructure more effectively. Only a part of its parameters are used for making inferences, so inference becomes quick and efficient. This is possible because of the design, which ensures it is very precise and smooth. DBRX has gained recognition for being open-source, giving users a chance to modify and edit the model they download for free. You may install the model in either the company’s private cloud or on their servers, which helps it to comply with their security protocols. Furthermore, using the Databricks Data Intelligence Platform allows companies to scale their use of LLM models easily and make sure they are properly managed and used. Those who have their data and systems internally can use DBRX to guarantee both transparency and excellent results.

Exploring C++23: Multidimensional Subscript Operator

Aggregated on: 2025-08-07 20:29:34

C++23, the latest iteration of the C++ programming language standard, brings a plethora of exciting features and improvements to the table. Among these is the multidimensional subscript operator, a long-awaited addition that promises to simplify and enhance the way we interact with multidimensional arrays and containers. In this article, we'll delve into the world of multidimensional subscript operators, exploring their syntax, usage, and benefits. We'll also examine how this feature integrates with existing C++ constructs and what implications it has for our coding practices.

JWT Policy Enforcement, Rate Limiting, IP White Listing: Using Mulesoft, API Security, Cloudhub 2.0

Aggregated on: 2025-08-07 18:14:34

This tutorial is all about implementing JWT Policy Enforcement in API Manager using a sample RAML-based project. It's especially helpful when applying policies through the API Manager in the Anypoint Platform. Along the way, you’ll also learn how to secure a specific API endpoint using a third-party Auth Provider like Auth0. In this project, the following policies have been applied to enhance API security and traffic control:

Boost Your Coding Agent and Understand Its Reasoning with 3 Simple Prompts

Aggregated on: 2025-08-07 17:14:39

Use the custom prompts from this article and the linked repository to have the agent (1) plan, (2) implement, and (3) review any code before considering it complete. These are straightforward, proven client-side prompt engineering techniques. This approach consistently improves results, regardless of the LLM used.

Impact of Artificial Intelligence on Network Routers and Switches

Aggregated on: 2025-08-07 16:14:34

Disclaimer: Readers should not infer that Google is using or planning to use any of these technologies in its products or services. Introduction The ever-increasing demand for data-driven applications and services has placed pressure on networking infrastructures. As the primary source for data flow, network routers and switches play an important role in ensuring seamless communication, data management, and network reliability. Traditionally, these devices relied on static configurations and manual management, which often led to challenges in scalability, efficiency, and security. However, Artificial Intelligence (AI) is changing the domain of networking by introducing intelligent, autonomous, and adaptive capabilities to routers and switches.

Availability to Accountability: Running AI Workloads Responsibly in the Cloud

Aggregated on: 2025-08-07 15:14:34

AI exists everywhere, from personal assistants to autonomous systems, while the cloud serves as its fundamental foundation. The great power creates actual operational difficulties. The cloud enables the rapid growth of AI workloads because it serves as the main platform for hosting and training these systems at a large scale. The management of AI systems within cloud environments requires specific operational challenges. Engineers, together with architects, need to solve essential problems regarding system availability, reliability, observability, and responsibility. The following discussion examines these operational challenges and provides effective solutions to address them. Availability: More Than Just Compute Power The compute-intensive nature of AI workloads necessitates dedicated cluster groups (DCGs) to ensure performance. The clusters need to stay within the same proximity group to reduce latency, thus preventing multi-region distribution. The financial limitations often determine cluster dimensions, which leads to reduced scalability when demand increases. The process of cluster provisioning and updates becomes difficult because of worldwide hardware shortages. The process of identifying availability problems remains difficult to accomplish. The absence of built-in diagnostic tools and dependence on outside vendors leads to extended service disruptions. Cloud providers provide buffer capacity for demand increases, yet this capability requires additional expenses.

What the AI Coding Experience Senior Software Engineers Want

Aggregated on: 2025-08-07 14:14:34

AI coding assistants or editors, such as Cursor, Windsurf, Lovable, and GitHub Copilot, are transforming how developers write code. You can now turn an idea into a working app in minutes just by typing a few prompts. That’s exciting but also risky. Many new developers can now build features without really understanding how the code works. Can you trust what the AI writes? Will you or your team understand it later? In some cases, the AI is making big decisions about how the software architecture is built, not the developer. Usually, senior engineers do not jump straight into coding without considering domain knowledge, architecture, or code reusability. They know when a piece of code fits and when it doesn’t. To be useful for real projects, AI tools need to provide developers with more structure, control, and ways to test and trust what gets built.

Introduction to Data-Driven Testing With JUnit 5: A Guide to Efficient and Scalable Testing

Aggregated on: 2025-08-07 13:14:34

When discussing the history of software development, we can observe an increase in software complexity, characterized by more rules and conditions. When it comes to modern applications that rely heavily on databases, testing how the application interacts with its data becomes equally important. It is where data-driven testing plays a crucial role. Data-driven testing helps increase software quality by enabling tests with multiple data sets, which means the same test runs multiple times with different data inputs. Automating these tests also ensures scalability and repeatability across your test suite, reducing human error, boosting productivity, saving time, and guaranteeing that the same mistake doesn't happen twice.

How to Configure a Jenkins Job for a Maven Project

Aggregated on: 2025-08-07 12:14:33

Jenkins is a widely used automation server that plays a big role in modern software development. It helps teams streamline their continuous integration and continuous delivery (CI/CD) processes by automating tasks like building, testing, and deploying applications. One of the key strengths of Jenkins is its flexibility. It easily integrates with a wide range of tools and technologies, making it adaptable to different project needs.

Understanding Agentic AI: From Simple Chatbots to Autonomous Decision-Making Systems

Aggregated on: 2025-08-07 11:29:33

This comprehensive guide breaks down the concept using real-world examples and practical code implementations to help you understand the evolution from basic chatbots to sophisticated autonomous AI systems. The Evolution: From RAG to Agentic AI Stage 1: RAG-Based AI Systems Consider a company with 75+ employees needing an HR assistant to answer policy questions like "How many vacation days do I have per year?" or "What is the policy on sick leave?" The traditional approach involves building a retrieval-augmented generation (RAG) chatbot that pulls information from PDF policy documents and provides answers.

Cloud Sprawl Is a Given; Cloud Complexity Doesn’t Have to Be

Aggregated on: 2025-08-06 21:44:33

Less than a decade ago, most teams ran dev, staging, and production in a single cloud account. Today, that seems unimaginable. Now, you start your cloud journey with at least 10 AWS accounts. One for each environment: one for networking, one for logging, one for security, one for… you get the idea. And if you have multiple business units or products? Multiply all that by at least three.

Automating Node.js Deployments With a Custom CI/CD Server

Aggregated on: 2025-08-06 20:14:33

It is possible that managing and deploying Node.js applications can become a bottleneck as projects grow. Having a properly designed Continuous Integration and Continuous Deployment (CI/CD) pipeline can help reduce the burden of frequent updates, simplify dependency management, and eliminate the need for manual restart processes, thereby avoiding these bottlenecks. In this tutorial, we will create a custom CI/CD server that listens to GitHub webhook events and performs deployments using GitHub Actions, PM2, and shell scripting. This enables us to:

jBPM as AI Orchestration Platform

Aggregated on: 2025-08-06 19:14:33

Disclaimer: The views expressed in this document reflect the author's subjective perspective on the current and potential capabilities of jBPM. This text presents jBPM as a platform for orchestrating external AI-centric environments, such as Python, used for designing and running AI solutions. We will provide an overview of jBPM’s most relevant functionalities for AI orchestration and walk you through a practical example that demonstrates its effectiveness as an AI orchestration platform:

Building Scalable, Resilient Workflows With State Machines on GCP

Aggregated on: 2025-08-06 18:14:33

Modern backend architectures often consist of many microservices and serverless functions working together. In such distributed systems, orchestrating complex processes reliably can be challenging. This is where state machines come into play. A state machine models a process as a series of defined states and transitions, enabling predictable sequences, loops, branching, and error handling in workflows. In practice, state machines let us implement robust workflows – essentially the flowcharts of business logic – with clear steps and outcomes. They are crucial for backend systems that require scalable, resilient coordination of tasks across services. On Google Cloud Platform (GCP), developers have managed services to build these workflows without managing servers. GCP’s Workflows service is a fully managed orchestration engine that executes steps (states) in order, calling various services and APIs. This is analogous to AWS Step Functions – Workflows follows a similar state machine model to connect services in a durable, stateful execution. Combined with event-driven services like Eventarc, messaging like Pub/Sub, and compute platforms like Cloud Functions and Cloud Run, GCP provides powerful tools to implement state machine patterns. The result is scalable and fault-tolerant workflows for tasks such as order processing, data pipelines, and long-running processes with human or external triggers.

Strategies for Robust Engineering: Automated Testing for Scalable Software

Aggregated on: 2025-08-06 17:14:33

During the last few years, I have been developing software that needs to scale up to hundreds of thousands of requests per second. Another issue that has been at the forefront of my mind has not been only creating scalable software but also making sure that the testing infrastructure scales with it. Most teams today concentrate on unit tests and functional tests as standalone entities without considering that these tests also have to be designed for growth. Through years of improving my testing strategies, I have learned a way that goes beyond the typical test automation frameworks. I created a self-adaptive testing layer that is a testing system that modifies tests on the fly based on actual application performance. It’s like a neural network that tunes itself for test automation.

Model Context Protocol (MCP): A Comprehensive Guide to Architecture, Uses, and Implementation

Aggregated on: 2025-08-06 16:14:33

Large language models (LLMs) have shown massive growth in reasoning, summarization, and natural language understanding tasks. OpenAI’s GPT-4, for instance, scored 86.4% on the MMLU benchmark, surpassing the average human baseline of 89.8% across professional and academic tasks [1]. However, LLMs is limited in enterprise deployment because of their inability to access or manipulate structured operational data. According to McKinsey’s 2023 global AI survey, 55% of enterprises identified integration complexity as a primary barrier to production-scale AI implementation, particularly when models must interact with real-time data, APIs, or enterprise systems [2]. Forrester 2024 report said that 64% of IT decision-makers reported delays in LLM deployments due to the absence of standardized model-to-application interfaces [3]. In environments governed by regulatory constraints, such as healthcare or finance, integration risks also raise compliance concerns. Cisco’s Enterprise Security Report (2023) said that over 41% of AI-enabled systems lack structured authorization layers which increases the chances of privilege escalation in loosely integrated model environments [4].

Build Your Own Customized ChatGPT Using OpenAI

Aggregated on: 2025-08-06 15:14:33

AI-powered chatbots are everywhere nowadays, taking over manual tasks and helping businesses and individuals with productive and efficient solutions. Companies like OpenAI (ChatGPT), Anthropic (Claude), Google DeepMind (Gemini), Meta (Llama), and Mistral AI are leading the way in developing these intelligent assistants. But here’s the exciting part — you don’t need to be a programmer to create your own customized chatbot! OpenAI makes it incredibly easy to personalize ChatGPT without writing a single line of code. Whether you want an AI assistant for customer support, content creation, or industry-specific tasks, you can build one in just a few steps.

Mastering Fluent Bit: Developer Guide to Service Section Configuration (Part 5)

Aggregated on: 2025-08-06 14:14:33

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 idea is that each article can stand on its own, but that they also lead down a path that slowly increases our abilities to implement solutions with Fluent Bit telemetry pipelines. Let's take a look at the topic of this article, using Fluent Bit tips and tricks for developers. In case you missed the previous article, check out using a Fluent Bit pipeline on a Kubernetes cluster to take control of all the logs being generated.

Integration Testing for Go Apps Using Testcontainers and Containerized Databases

Aggregated on: 2025-08-06 13:14:33

Integration testing has always presented a fundamental challenge: how do you test your application against real dependencies without the complexity of managing external services? Traditional approaches often involve either mocking dependencies (which can miss integration issues) or maintaining separate test environments (which can be expensive and difficult to manage consistently). Hello Testcontainers! Testcontainers solves this problem elegantly by providing a way to run lightweight, throwaway instances of databases, message brokers, web servers, and other services directly within your test suite. Instead of complex setup scripts or shared test environments, you can spin up real services in Docker containers that exist only for the duration of your tests. The core value proposition is compelling: write tests that run against the actual technologies your application uses in production, while maintaining the isolation and repeatability that good tests require. When your tests complete, the containers are automatically cleaned up, leaving no trace behind.

Handling Password-Protected PDFs in JavaScript

Aggregated on: 2025-08-06 12:14:33

PDF is one of the simplest formats for sharing documents. They are portable and can provide basic access control through password protection. In this post, we will discuss one of many ways to unlock and open password-protected PDF documents in JavaScript. This post uses PDF.js and client-side JavaScript tools built into modern browsers to:

Enriching AI With Real-Time Insights via MCP

Aggregated on: 2025-08-06 11:29:33

As each Large Language Model (LLM) has a training cut-off-date, their accuracy is highly impacted when real-time or future data is requested. This phenomenon is observed even in cases when users write thoroughly engineered prompts because the answers are generated from items predicted based on a static knowledge foundation. Such a situation is not always acceptable. To overcome this, AI assistants (chatbots) are now being enhanced with Internet access, which allows them to articulate more relevant and up-to-date “opinions”. In the case of Anthropic, as of April 2025, web search has been made available to all Claude AI paid plans. This is definitely a step forward, as the pieces of information received by users can now be decorated with additional “contemporary” details and thus, their accuracy increased.

AI/ML for Engineering Managers: Enhancing Productivity and Quality in Fintech

Aggregated on: 2025-08-05 20:29:32

The fintech landscape is rapidly evolving every day and that puts engineering managers in immense pressure to maintain delivery speed, product/engineering quality, and compliance simultaneously. Artificial Intelligence and Machine Learning (AI/ML) techniques offer very helpful and transformative solutions to these challenges by automating repetitive tasks, enhancing code quality, and streamlining regulatory compliance. As a senior engineering manager with deep experience building a neobank back office technology solutions, I've observed firsthand how strategically applied AI/ML can significantly help solve the current challenges to the degree the organization is willing to invest. Why AI/ML Matters in Fintech Engineering AI and ML technologies uniquely address fintech challenges such as compliance and governance requirements, fraud detection and prevention, and complex risk management beyond simple rule based systems. Traditional fintech engineering workflows often rely heavily on manual testing, repetitive reviews, multiple checkpoints with approvals, and intensive documentation—areas ripe for optimization through AI-driven automation with necessary guardrails. Additionally, given the high stakes associated with financial systems, maintaining superior quality through robust, proactive monitoring and building circuit breakers are critical.

Docker Multi-Stage Builds: Optimizing Development and Production Workflows

Aggregated on: 2025-08-05 19:29:32

Hey there, fellow Docker enthusiasts! If you've been containerizing applications for a while, you've probably run into this all-too-familiar frustration: your Docker images are absolutely massive, they take forever to build and deploy, and you're left wondering if there's got to be a better way. Trust me, I've been there—staring at a 1.4GB image thinking "surely this can't be right?" After years of wrestling with bloated containers (and some very unhappy DevOps teammates), I finally embraced multi-stage builds—and honestly, it's been a complete game-changer. In this article, I'll share what I've learned about this powerful but often overlooked Docker feature that could revolutionize your containerization workflow.

Tail Sampling: The Future of Intelligent Observability in Distributed Systems

Aggregated on: 2025-08-05 18:14:32

Observability has become a critical component for maintaining system health and performance. While traditional sampling methods have served their purpose, the emergence of tail sampling represents a paradigm shift in how we approach trace collection and analysis. This intelligent sampling strategy is revolutionizing the way organizations handle telemetry data, offering unprecedented precision in capturing the most valuable traces while optimizing storage costs and system performance. Understanding the Sampling Landscape Before diving into tail sampling, it's essential to understand the broader context of sampling strategies. Traditional head-based sampling makes decisions at the beginning of a trace's lifecycle, determining whether to collect or discard telemetry data based on predetermined criteria such as sampling rates or simple rules. While effective for reducing data volume, this approach often results in the loss of critical information about error conditions, performance anomalies, or rare but important system behaviors.

Why I Abandoned My 30-Year Open-Source Project

Aggregated on: 2025-08-05 17:14:32

Note: A Human wrote this article. Other than proofreading and sentence-level style suggestions, no AI was utilized. This is one of the last surviving members of its kind. Introduction I started an open-source project in 1996, I am abandoning now. It was not my first OSS project and certainly not the last one. It definitely was the one that lasted the longest and that I had the most faith in having an impact on the industry.

AWS SNS (Amazon Simple Notification Service) and Spring Boot With Email as Subscriber

Aggregated on: 2025-08-05 16:14:32

The concepts of "topic" and "subscribe" are often linked, especially in contexts like messaging systems, event-driven architectures, or content platforms. Publisher: This is the source or entity that produces messages or events. The publisher doesn't need to know who will consume its messages. Topic: This acts as a channel or intermediary that categorizes messages. Publishers post messages to specific topics, and subscribers listen to those topics. It's used in systems like message brokers (e.g., RabbitMQ, AWS SNS, Apache Kafka) to allow publishers to send messages without worrying about who will receive them. Subscriber: These are the entities that consume the messages from the topics they're interested in. Subscribers can dynamically choose topics to receive only the information they need. Amazon SNS (Simple Notification Service) Topic Amazon SNS provides message delivery from publishers to subscribers using the pub/sub pattern. Publishers send messages to an SNS topic, and subscribers receive those messages through their chosen endpoints.

Scalable Distributed Architectures in E-Commerce: Proven Case Studies

Aggregated on: 2025-08-05 15:14:32

Modern e-commerce platforms must handle massive scale – from flash sales driving sudden traffic spikes to global user bases demanding low-latency experiences. Achieving this reliability and performance at scale requires robust distributed architectures. In this article, I’ll share three case studies of scalable e-commerce architectures that I’ve analyzed and worked with, each leveraging a different tech stack: Serverless microservices on AWS – how Amazon’s cloud (Lambda, SQS, DynamoDB, etc.) solved real-world scaling problems for an online retailer. Containerized services on Google Cloud – using GCP’s serverless containers (Cloud Run, Firestore, Pub/Sub, BigQuery) for high traffic and maintainability in a retail scenario. Open-source cloud-native stack – a Kubernetes, Kafka, Redis, PostgreSQL architecture that scaled a large online retail platform with open source tooling. Each example will include an architecture diagram, key components (with a table where helpful), the challenges faced, and how the design addressed them – along with deployment and operations insights. As an engineering lead, I’ll also highlight practical takeaways from these architectures. Let’s dive in.

Create Your Own Custom LLM: Essential Steps and Techniques

Aggregated on: 2025-08-05 14:14:32

We will start by defining the most fundamental building block of LLMs: Language modeling, which dates back to early statistical NLP methods in the 1980s and 1990s and was later popularized with the advent of neural networks in the early 2010s. In its simplest form, language modeling is essentially about learning to predict the next word in a sentence. This task, known as next-word prediction, is at the core of how LLMs learn language patterns. The model accomplishes this by estimating the probability distribution over sequences of words, allowing it to predict the likelihood of any given next word based on the context provided by the preceding words.

Estimating Software Projects: A Practical Approach for Tech Leads

Aggregated on: 2025-08-05 13:29:32

Introduction Accurately estimating software projects has been a big challenge for technical leads for quite some time. While there are many established techniques in the market that explain how to estimate a task at hand, they don’t often provide a systematic process to break down tasks, account for unknowns, and track and revisit the estimates. In practice, estimation is an ongoing exercise that keeps evolving with requirements, spikes, and development. A successful estimation requires an organized approach including collaborating with product managers and architects to clarify and align initial requirements, conducting spikes to reduce uncertainty, systematically revisiting and refining estimates, and incorporating effort for testing, code reviews, and deployment tasks into the planning. It is also important to account for buffers to handle unforeseen delays.

LangGraph Orchestrator Agents: Streamlining AI Workflow Automation

Aggregated on: 2025-08-05 12:29:32

In AI-driven applications, complex tasks often require breaking down into multiple subtasks. However, the exact subtasks cannot be predetermined in many real-world scenarios. For instance, in automated code generation, the number of files to be modified and the specific changes needed depend entirely on the given request. Traditional parallelized workflows struggle unpredictably, requiring tasks to be predefined upfront. This rigidity limits the adaptability of AI systems. However, the Orchestrator-Workers Workflow Agents in LangGraph introduce a more flexible and intelligent approach to address this challenge. Instead of relying on static task definitions, a central orchestrator LLM dynamically analyses the input, determines the required subtasks, and delegates them to specialized worker LLMs. The orchestrator then collects and synthesizes the outputs, ensuring a cohesive final result. These Gen AI services enable real-time decision-making, adaptive task management, and higher accuracy, ensuring that complex workflows are handled with smarter agility and precision.

Building a Simple AIOps Monitoring Dashboard With Prometheus and Grafana

Aggregated on: 2025-08-05 11:29:32

Machine learning (ML) is being used by AIOps (Artificial Intelligence for IT Operations) to find problems, predict failures, and automate reactions. This is changing how businesses handle their IT environments. This guide will show you how to make a simple monitoring dashboard that uses Prometheus to collect data and Grafana to demonstrate it. We'll also add some basic AIOps tools to the panel to make it better by adding anomaly detection, which will let you keep an eye on things before they go wrong.

Why Developers Should Pay Attention to Internal Directory Security

Aggregated on: 2025-08-04 20:14:32

Most developers don’t start their day thinking, “Is our internal directory secure?” They’ve got builds to run, bugs to squash, maybe a pull request or five to review. But internal directories (like Active Directory or Azure AD) aren’t just a concern for IT admins. They’re the nervous system of any organization with more than, say, a handful of people and passwords.

WebAssembly: From Browser Plugin to the Next Universal Runtime

Aggregated on: 2025-08-04 19:14:32

For decades, the digital world has converged on a single, universal computing platform: the web browser. This remarkable piece of software, present on nearly every device, promised a "write once, run anywhere" paradigm, but with a crucial limitation, it only spoke one language natively: JavaScript. While incredibly versatile, JavaScript's nature as a dynamically typed, interpreted language created a performance ceiling. For computationally intensive tasks, developers often hit a wall, unable to achieve the raw speed of native applications. This limitation also meant that the vast, mature ecosystems of code written in languages like C++, C, and Rust were largely inaccessible on the web without cumbersome and often inefficient cross-compilation to JavaScript. Into this landscape emerged WebAssembly (Wasm). Often referred to as a fourth standard language for the web alongside HTML, CSS, and JavaScript, Wasm was not designed to replace JavaScript but to be its powerful companion. It is a binary instruction format, a low-level, assembly-like language that serves as a portable compilation target. This simple yet profound idea meant that developers could take existing code written in high-performance languages, compile it into a compact Wasm binary, and run it directly within the browser at near-native speeds. This breakthrough unlocked a new class of applications that were previously impractical for the web, from sophisticated in-browser tools to full-fledged 3D gaming engines.

Is Your Team AI-Ready? 5 Strategies to Upskill Your Engineers

Aggregated on: 2025-08-04 18:29:32

The pressure is on. Every leader, from startups to Fortune 100s, is being asked the same question: "What's our AI strategy?" But behind that question is a more fundamental one that keeps engineering leaders like us up at night: "Is my team ready for AI?" It’s one thing to buy a new tool or spin up a new service; it’s another thing entirely to transform a team’s skills and mindset. The truth is, most engineering teams aren't AI-ready out of the box. And that's okay. The journey from a traditional software team to one that can confidently build, deploy, and manage AI-powered features is a marathon, not a sprint.