News Aggregator

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.

Fine-Tuning LLMs Locally Using MLX LM: A Comprehensive Guide

Aggregated on: 2025-08-04 17:29:32

Fine-tuning large language models has traditionally required expensive cloud GPU resources and complex infrastructure setups. Apple's MLX framework changes this paradigm by enabling efficient local fine-tuning on Apple Silicon hardware using advanced techniques like LoRA and QLoRA. In this comprehensive guide, we'll explore how to leverage MLX LM to fine-tune state-of-the-art language models directly on your Mac, making custom AI development accessible to developers and researchers working with limited computational resources.

Deep Observability in Node.js Using OpenTelemetry and Pino

Aggregated on: 2025-08-04 16:14:32

As applications become increasingly distributed, debugging performance issues or locating failures in a Node.js backend can be challenging. Logging by itself usually provides limited context to comprehend how a request navigates through many layers of your system. Similarly, you cannot correlate trace data with application-specific events if you use tracing without structured logging. That is where OpenTelemetry (OTel) for tracing and Pino for structured logging come in. By combining the two, you get deep observability — blending logs and traces together for an unobstructed view of your system's behavior, thereby speeding up debugging, monitoring, and root cause analysis.

AI for AI: How Intelligent Systems Are Shaping Their Own Evolution

Aggregated on: 2025-08-04 15:14:32

AI for AI, also referred to as AI4AI, is a rapidly growing field that focuses on using artificial intelligence to improve the development, performance, and management of other AI systems. It involves applying AI techniques to automate and optimize various stages of the AI lifecycle, including model selection, training, deployment, and ongoing adaptation. This approach enables AI systems to operate more autonomously and efficiently, reducing the need for constant human intervention while improving scalability and performance across a wide range of domains. Key Aspects of AI for AI AI4AI achieves its goals through powerful techniques that fundamentally transform how intelligent systems are built. Key aspects include:

Self-Managed Keycloak for App Connect Dashboard and Designer Authoring

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

With the release of the IBM® App Connect Operator version 12.1.0, you can now use your existing Keycloak instance to configure authentication and authorization for App Connect Dashboard and Designer Authoring. Building on top of the capability to use Keycloak, which was first available in IBM® App Connect Operator version 11.0.0, this feature extends the supported platforms from Red Hat® OpenShift® Container Platform (OCP) only to also include Kubernetes. It has in addition removed the dependencies on the IBM® Cloud Pak foundational services and IBM® Cloud Pak for Integration operators. It is worth noting that this new feature is only available with App Connect licenses.

Beyond 200 OK: Full-Stack Observability for Developers

Aggregated on: 2025-08-04 13:29:32

You may remember coming out of a feature meeting and saying to yourself, "My React frontend is working fine, the API goes 200 OK, I am done!" Then, a few days later, we get a user complaint: "It's slow. Sometimes I get errors."

How to Build an MCP Server With Java SDK

Aggregated on: 2025-08-04 12:29:32

A previous article [Resource 1] described how an open-source PostgreSQL Model Context Protocol server can be plugged into an AI host and provide additional database context to the interacting LLM. Moreover, quite a few interesting insights on the considered data were inferred by the LLM when natural language prompts were written and responded to. The current article uses the exact same database schema and does a similar experiment, the only significant difference being that the MCP Server is developed from scratch, using the available Java SDK, without involving any additional frameworks.

Real-Time Flight Schedule Changes at Scale: Event-Driven Pipelines With gRPC

Aggregated on: 2025-08-04 11:29:32

Introduction: The Challenge of Flight Schedule Changes Travel aggregators (like online travel agencies or fare comparison platforms) handle data from hundreds of airlines, including frequent flight schedule changes — delays, cancellations, gate changes, etc. Managing these updates in real-time for millions of users is a massive challenge. Traditional approaches (like periodic polling or manual updates) can’t keep up with the volume and immediacy required. For example, if a flight is canceled or delayed, customers and downstream systems expect to know within seconds, not hours. As one source notes, use cases like airline flight cancellations or package delivery updates demand immediate notifications upon any upstream change. To tackle this, modern travel platforms are embracing event-driven architecture (EDA) and pipeline patterns to process flight schedule changes in real-time. In an EDA, changes (events) propagate through a pipeline of microservices that react asynchronously. This decoupled design can scale to millions of events and deliver updates instantly to all interested components. A key enabler in this architecture is gRPC — a high-performance RPC framework — which, alongside message brokers, helps services communicate efficiently and reliably.

Feature Flags in Agile Development: Lessons from Scaling Front-End Platform Releases

Aggregated on: 2025-08-01 20:14:30

Let’s start with the basics: what is a feature flag? A feature flag is a technique that allows developers to control the execution of specific features or code blocks at runtime without redeploying the application. As engineering teams accelerate their adoption of agile practices, feature flagging has become a cornerstone of modern front-end deployment strategies.

A Complete Guide to Creating Vector Embeddings for Your Entire Codebase

Aggregated on: 2025-08-01 19:14:30

As AI-powered development tools like GitHub Copilot, Cursor, and Windsurf revolutionize how we write code, I've been diving deep into the technology that makes these intelligent assistants possible. After exploring how Model Context Protocol is reshaping AI integration beyond traditional APIs, I want to continue sharing what I've learned about another foundational piece of the AI development puzzle: vector embeddings. The magic behind these tools' ability to understand and navigate vast codebases lies in their capacity to transform millions of lines of code into searchable mathematical representations that capture semantic meaning, not just syntax. In this article, I'll walk through step-by-step how to transform your entire codebase into searchable vector embeddings, explore the best embedding models for code in 2025, and dig into the practical benefits and challenges of this approach.

Meta-Learning: The Key to Models That Can "Learn to Learn"

Aggregated on: 2025-08-01 18:14:30

Meta-Learning: The Key to Models That Can "Learn to Learn" As artificial intelligence (AI) systems continue to evolve, one of the biggest challenges we face is getting machines to generalize well from limited data. Traditionally, training an AI model for a specific task requires vast amounts of labeled data, a problem that is not only costly but also time-consuming. However, a breakthrough concept known as meta-learning or "learning to learn" is quickly changing the way we think about AI training. In simple terms, meta-learning aims to train models that can adapt quickly to new tasks with very little data. This technique is poised to make AI systems more flexible and capable of solving a wide range of problems with less effort.

WAN Is the New LAN!?!?

Aggregated on: 2025-08-01 17:14:30

For decades, the Local Area Network (LAN) was the heart of enterprise IT. It represented the immediate, high-speed connectivity within an office or campus. But in today's cloud-first, globally distributed world, the very definition of "local" has expanded. The Wide Area Network (WAN) was considered to be the most expensive link. However, its high agility and intelligent fabric make it more reliable and help make LAN expand globally. The paradigm shift is clear: "WAN is the new LAN". This transformation hasn't happened overnight. A lot of research hours went into this innovation, and it took more than 2 decades for the evolution. It's a journey that began with the limitations of traditional Multiprotocol Label Switching (MPLS) infrastructure, evolved through the revolutionary capabilities of Software-Defined Wide Area Networking (SD-WAN), and is now culminating in the promise of hyper-scale Cloud WAN.

Software Engineer Archetypes: The 5 Branding Styles That Shape Your Tech Career

Aggregated on: 2025-08-01 16:29:30

Some of the most skilled software engineers spend years mastering their craft, contributing to critical systems, and solving complex problems — yet remain invisible outside their immediate circles. Meanwhile, others with average skills gain influence, career momentum, and opportunities. It isn't just about meritocracy; in practice, there are biases, as well as perceptions and positioning. In the modern software industry, your technical skills must be paired with a strong personal brand to ensure your work is recognized, understood, and valued. Reputation amplifies expertise. Our goal is to take the next step in Personal Branding. To understand the reason, I recommend reading the article "Personal Branding for Software Engineers." We explored how branding shapes how others perceive your expertise, values, and long-term potential. It's about communicating your impact clearly and consistently.

ITBench, Part 2: ITBench User Experience – Democratizing AI Agent Evaluation

Aggregated on: 2025-08-01 15:29:30

(Note: A link to the previous article published in this series can be found at the conclusion of this article.) In the first blog post of this series we introduced ITBench, IBM Research's groundbreaking framework that brings scientific rigor to AI agent evaluation in enterprise IT environments.

MCP Logic: How to Make It 40x Simpler

Aggregated on: 2025-08-01 14:29:30

Foreword This document presents a real-world A/B comparison of two approaches to implementing the same business logic requirements. We asked AI to generate both a procedural implementation using conventional code, and a declarative implementation using the LogicBank rules engine. This experiment highlights fundamental differences between the two approaches, and what they mean for building reliable, maintainable systems. It's important, because business logic typically represents nearly half the effort in database projects.

Docker Offload: One of the Best Features for AI Workloads

Aggregated on: 2025-08-01 13:44:30

As I mentioned in my previous post about Docker Model Runner and why it's a game-changing feature. I also mentioned that the best is yet to come, and Docker finally announced during the "WeAreDevelopers" event in Berlin, about their new feature, "Docker Offload." In this article, I will explain what exactly Docker Offload is and why we need it as developers, and why I say it's one of the best features released by Docker in recent times. What Is Docker Offload? If you are like me, who struggled to try out those cool AI models or data processing pipelines locally but were unable to do so due to the limitations of not having a GPU or a powerful machine to run them on, then continue reading. I always end up utilizing cloud resources, which often come with a hefty price tag.

How GitHub Copilot Handles Multi-File Context Internally: A Deep Dive for Developers, Researchers, and Tech Leaders

Aggregated on: 2025-08-01 13:14:30

GitHub Copilot has evolved from a basic autocomplete engine into an intelligent AI assistant capable of understanding and navigating large-scale codebases. One of the most powerful capabilities it brings to developers is the ability to reason across multiple files in a project. This seemingly magical feature is not a trivial extension of autocomplete — it is the result of sophisticated orchestration involving context retrieval, symbol analysis, vector embeddings, token prioritization, and prompt construction under strict limitations. This article presents a deeply technical examination of how GitHub Copilot internally handles multi-file context. The purpose is to demystify its architectural design, explain its data processing pipeline, and highlight the algorithms and data structures powering its context-aware capabilities.

AI-Powered AWS CloudTrail Analysis: Using Strands Agent and Amazon Bedrock for Intelligent AWS Access Pattern Detection

Aggregated on: 2025-08-01 12:14:30

Background/Challenge AWS CloudTrail logs capture a comprehensive history of API calls made within an AWS account, providing valuable information about who accessed what resources and when. However, these logs can be overwhelming to analyze manually due to their volume and complexity. Security teams need an efficient way to: Identify unusual access patterns Detect potential security threats Understand resource usage patterns Generate human-readable reports from technical log data My approach combines AWS native services with generative AI to transform raw log data into actionable security insights. By leveraging the power of Amazon Bedrock and the Strands Agent framework, I have created a scalable, automated system that significantly reduces the manual effort required for CloudTrail analysis while providing more comprehensive results than traditional methods.

KV Caching: The Hidden Speed Boost Behind Real-Time LLMs

Aggregated on: 2025-08-01 11:14:30

Introduction: Why LLM Performance Matters Ever notice how your AI assistant starts snappy but then… starts dragging or slowing down? It’s not just you. That slowdown is baked into how large language models (LLMs) work. Most of them generate text one token at a time using something called autoregressive decoding. And here's the catch - the longer the response gets, the more work the model has to do at every step. So the lag adds up.

AI-Powered Product Recommendations With Oracle CDC, Flink, and MongoDB

Aggregated on: 2025-07-31 20:14:30

Planning a weekend hike? River Runners has you covered, with lightweight pants, trail shoes, and now, eerily good product recommendations. Okay, River Runners isn’t real. It’s a fake outdoor running company I created to show how real-time AI can turn any store into something that feels smart and personalized. The kind of experience where the site seems to know what you need before you do.