News Aggregator

Mastering GitHub Copilot in VS Code: Ask, Edit, Agent and the Build–Refine–Verify Workflow

Aggregated on: 2026-02-26 13:08:54

Most developers meet GitHub Copilot as a “smart autocomplete” that occasionally guesses the next line of code. Used that way, it’s nice — but you’re leaving a lot of value on the table. Inside VS Code, Copilot offers multiple modes of interaction designed for different stages of development:

OAuth Gone Wrong: The Hidden Token Issue That Brought Down Our Login System

Aggregated on: 2026-02-26 12:08:54

Imagine deploying a Node.js/TypeScript backend for user authentication that works flawlessly in development, only to watch users get mysteriously logged out or unable to log in shortly after launching to production. Everything ran fine on your local machine, but in the live environment, users start losing their sessions en masse. Requests to protected endpoints begin failing with “Unauthorized” errors. Panic sets in as your login system, the gatekeeper of your application, is effectively brought down by an invisible foe. In our case, the culprit was a hidden OAuth token issue involving how we handled refresh tokens. A tiny mistake in token management, something that went unnoticed during development, led to a chain reaction of authentication failures in production.

A Unified Framework for SRE to Troubleshoot Database Connectivity in Kubernetes Cloud Applications

Aggregated on: 2026-02-25 20:23:54

The ability to have an application or business connect with the right information at the right time is key to making informed decisions in today’s digital and AI world. Having an efficient, reliable connection between an application and its database enables businesses to best serve their customers. Traditional troubleshooting methods used on many enterprise systems are no longer sufficient to troubleshoot these complex, multi-layered Kubernetes systems. The layered troubleshooting framework described in this article can be used by developers, cloud architects, and site reliability engineers (SREs) as a structured approach to quickly determine the root cause of failures and achieve stability in production environments. A layered approach to troubleshooting is necessary to provide an understanding of how all the different components of a system relate to one another, which is critical to being able to resolve problems quickly and efficiently. Troubleshooting the communication layer between an application and its database is one of the most complex tasks for developers, cloud architects, and SREs working with Kubernetes-based cloud-native applications.

From Keywords to Meaning: The New Foundations of Intelligent Search

Aggregated on: 2026-02-25 19:23:53

I still remember a moment that should have been simple. A product team wanted a search experience that felt obvious to users. Type “red running shoe” and get red running shoes. We had the catalog, filters, indexing, and engineers (including me) confidently saying, “This is straightforward.”

How We Cut AI API Costs by 70% Without Sacrificing Quality: A Technical Deep-Dive

Aggregated on: 2026-02-25 18:23:54

The Wake-Up Call I'll be honest — we screwed up. Like a lot of engineering teams, we built our AI features fast and worried about costs later. "Later" came faster than expected when our finance team flagged our OpenAI bill crossing five figures monthly. The real problem wasn't just the dollar amount. It was that we had zero visibility. We didn't know:

Cutting P99 Latency From ~3.2s To ~650ms in a Policy‑Driven Authorization API (Python + MongoDB)

Aggregated on: 2026-02-25 17:23:53

Modern authorization endpoints often do more than approve a request. They evaluate complex policies, compute rolling aggregates, call third‑party risk services, and enforce company/card limits, all under a hard latency budget. If you miss it, the transaction fails, and the failure is customer-visible. This post walks through a practical approach to take a Python authorization API from roughly ~3.2s P99 down to ~650ms P99, using a sequence of changes that compound: query/index correctness, deterministic query planning, connection pooling and warmup, and parallelizing third‑party I/O.

Chunking Is the Hidden Lever in RAG Systems (And Everyone Gets It Wrong)

Aggregated on: 2026-02-25 16:23:53

Most RAG discussions fixate on embedding models, vector databases, or which LLM to use. In real systems, especially document-heavy ones, the highest-leverage decision is simpler and far less glamorous, which happens early in the pipeline: it's chunking. This happens before embeddings, before retrieval, before generation, making its failures invisible until they cascade downstream as retrieval misses or hallucinations that seem to originate elsewhere. By the time your system exhibits poor quality, the damage is already baked into your index. This is why treating chunking as a post hoc optimization rather than a core architectural decision is a systematic blind spot in many production RAG deployments. The most effective systems treat chunking not as a preprocessing step to be minimized, but as a primary design lever, the one that deserves as much engineering rigor and iterative refinement as your vector database or embedding model selection.

Cagent: Dockers newest low code Agentic Platform

Aggregated on: 2026-02-25 15:23:53

Cagent is the new open-source framework from Docker that makes running AI agents seamless and lightweight. With Cagent, you can start with simple “Hello World” agents and scale all the way to complex, multi-agent processing workflows. It provides core agent capabilities such as autonomy, reasoning, and action execution, while also supporting the Model Context Protocol (MCP), integrating with Docker Model Runner (DMR) for multiple LLM providers, and simplifying agent distribution through the Docker registry. Unlike traditional agentic frameworks that treat AI agents as programmatic objects requiring extensive Python or C# code, Cagent incorporates a declarative, configuration-first philosophy. So, instead of managing complex dependencies and writing custom orchestration logic, developers define their agent’s persona and capabilities within a single, portable YAML file, effectively decoupling logic from the underlying infrastructure.

Edge Computing's Infrastructure Problem: What Two Years of Factory Visits Actually Revealed

Aggregated on: 2026-02-25 14:23:53

Last March, a factory tour outside Stuttgart clarified something I'd been suspecting for months. The plant manager walked me through their edge deployment — industrial PCs bolted next to production lines, each one supposedly processing sensor data locally to catch equipment problems in real time. Clean installation, solid hardware, confident presentation. Then I asked about their network topology. That's when things got interesting.

How to Configure JDK 25 for GitHub Copilot Coding Agent

Aggregated on: 2026-02-25 13:23:53

GitHub Copilot coding agent runs in an ephemeral GitHub Actions environment where it can build your code, run tests, and execute tools. By default, it uses the pre-installed Java version on the runner — but what if your project needs a specific version like JDK 25? In this post, I'll show you how to configure Copilot coding agent's environment to use any Java version, including the latest JDK 25, ensuring that Copilot can successfully build and test your Java projects.

Backend Graph DB for Custom File System

Aggregated on: 2026-02-25 12:23:53

This post is based on what I learned implementing Neo4Jfs, a customized Java file system built with a graph database (Neo4J) backend. In this post, I’ll identify the challenges in creating a custom file system, in particular, file tree management, and propose an alternative. If you're intrigued but unsure what creating a Java file system actually means, you may find "Bootstrapping a Java File System" helpful. Overview Hands up: how many of you have received a similar feature request from your product team?

How to Integrate an AI Chatbot Into Your Application: A Practical Engineering Guide

Aggregated on: 2026-02-24 20:08:53

AI chatbots are increasingly part of modern application architectures, not as standalone features but integrated interaction layers. When designed correctly, a chatbot can simplify user workflows, reduce friction, and act as a controlled interface to backend systems. This guide focuses on how to integrate an AI chatbot into an application from an engineering perspective, covering architecture, implementation steps, and operational considerations without relying on vendor-driven narratives.

Integration Reliability for AI Systems: A Framework for Detecting and Preventing Interface Mismatch at Scale

Aggregated on: 2026-02-24 19:08:53

Integration failures inside AI systems rarely appear as dramatic outages. They show up as silent distortions: a schema change that shifts a downstream feature distribution, a latency bump that breaks a timing assumption, or an unexpected enum that slips through because someone pushed a small update without revalidating the contract. The underlying services continue to report “healthy.” Dashboards stay green. Pipelines continue producing artefacts. Yet the system behaves differently because components no longer agree on the terms of cooperation. I see this pattern repeatedly across large AI programs, and it has nothing to do with model performance. It is the natural consequence of distributed teams modifying interfaces independently without enforced boundaries.

Swagger UI in a BFF World: Making Swagger UI Work Natively With BFF Architectures

Aggregated on: 2026-02-24 17:23:53

This article introduces a Swagger UI plugin designed specifically for Backend-for-Frontend (BFF) architectures, along with working demos (with and without OIDC) that validate the approach end to end. The Rise (and Return) of the BFF Modern web security has shifted away from storing "tokens in the browser." XSS attacks, stricter browser privacy policies, and evolving OAuth recommendations have made Backend-for-Frontend (BFF) architectures the gold standard for secure web apps.

Building Event-Driven Data Pipelines in GCP

Aggregated on: 2026-02-24 16:23:53

The old-fashioned batch processing is not applicable in the current applications. Pipelines need to respond to events in real time when businesses rely on real-time data to track user behavior, to process financial transactions, or to monitor Internet of Things (IoT) devices, instead of hours after the event. Why Event-Driven Architecture Matters Event-driven processing versus batch processing is a paradigm shift in the flow of data through the systems. With batch pipelines, data is idle until it is run. In event pipelines, each change is followed by an immediate response. This difference is crucial in the development of fraud detection systems that demand sub-second response times or in systems that offer recommendations that are updated in real-time according to who is currently using it.

The DevSecOps Paradox: Why Security Automation Is Both Solving and Creating Pipeline Vulnerabilities

Aggregated on: 2026-02-24 15:23:53

The numbers tell a troubling story. Forty-five percent of cyberattacks in 2024 exploited weaknesses in CI/CD pipelines, according to industry tracking data. Not application code. Not user credentials. The build and deployment infrastructure itself. This represents a fundamental shift in how attackers think. Why spend weeks crafting an exploit for production systems when you can compromise the pipeline that deploys to those systems? Poison the well, and every downstream service drinks contaminated water.

Performance-Centric Platform Engineering: Shared Responsibility, Guardrails, and Tenant Isolation

Aggregated on: 2026-02-24 14:23:53

Platform engineering today isn't just about making deployments faster; it's also about making sure that performance is always predictable, reliable, and scalable. As companies start using internal developer platforms (IDPs) on Kubernetes, performance engineering must shift from being an application-level concern to a platform-embedded discipline. This is where Performance by Design emerges: a model where responsibility is shared, guardrails are enforced, and tenant isolation ensures fairness and reliability across workloads.

How to Extract Document Summaries in C#/.NET

Aggregated on: 2026-02-24 13:23:53

Document Summarization: A Significant Value-Add in the AI Boom C# developers are often saddled with building document-heavy processing workflows across enterprise systems. When we think about what “document processing” means, we probably focus on things like extraction, validation, conversion, storage, etc., but in many modern systems, being able to quickly parse an understanding of what a document contains is often just as important as parsing data from it. Whether we’re processing PDFs from an upload workflow, or tapping into an email server to handle inbound email attachments, or even dealing with hand-scanned images from mobile devices, producing short, readable document summaries can drastically improve how downstream systems and users interact with those documents. A concise summary paragraph can help with anything from document approval & review to search indexing or triage (among many other examples).

The AI4Agile Practitioners Report 2026

Aggregated on: 2026-02-24 12:23:53

TL;DR: The AI4Agile Practitioners Report 2026 83% of Agile practitioners use AI, but most spend 10% or less of their time with it because they do not know where it fits. Our survey of 289 Agile practitioners identifies the real adoption barriers and shows where AI creates value you can act on. We asked 289 Agile practitioners how they use AI. Most of them barely do.

Azure SLM Showdown: Evaluating Phi-3, Llama 3, and Snowflake Arctic for Production

Aggregated on: 2026-02-23 20:08:53

In the rapidly evolving landscape of Generative AI, the industry is witnessing a significant shift. While the “bigger is better” mantra once dominated, the tide is turning. As organizations move from experimental pilots to production-grade applications, the focus has shifted toward small language models (SLMs). These models offer lower latency, reduced compute costs, and the ability to run on edge devices, while maintaining performance that rivals massive models like GPT-4 for specific tasks. Microsoft Azure has positioned itself as a premier destination for these models, offering them through the Model-as-a-Service (MaaS) framework and the Azure AI Model Catalog. In this article, we provide a technical deep dive into three of the most prominent SLMs available on Azure: Microsoft’s Phi-3, Meta’s Llama 3 (8B), and Snowflake Arctic. We analyze their architectures, benchmark performance, deployment strategies, and cost efficiency to help you decide which model best fits your workload.

Supply Chain Security for Tools and Prompts

Aggregated on: 2026-02-23 19:08:53

It's very easy to talk about secure GenAI. But did you ever think about whether your agents are running only the prompts, tool schemas, router rules, and semantic models you intended — especially after many weeks of rapid iteration? It is very hard to prove this. Most teams freeze application code and container images, but they leave one thing open: the fast-moving agent configuration supply chain. Prompts and tool definitions change more often in production. Router rules are updated daily. Tool schemas get expanded just to add one field. A semantic model gets tweaked to fix a metric edge case. These types of edits can silently weaken policy controls, enable data-exfiltration paths, or simply reduce faithfulness until you are shipping wrong answers with confidence. For governed, trustworthy AI data systems, you must treat agent configuration like top-tier software artifacts, such as reviewing, signing, versioning, promoting, and auditing, and not like loose text files in a repository.

Data Driven API Testing in Java with Rest-Assured and TestNG: Part 1

Aggregated on: 2026-02-23 18:08:53

Data-driven testing, also known as parameterized testing, is a technique that uses a data table to drive test execution by defining inputs, expected outputs, and test environment settings. It separates test data from test logic. Rather than creating multiple test cases for different input values, a single test case is executed repeatedly using different data sets. The TestNG framework provides the @DataProvider annotation, which supplies data sets to tests. There are multiple ways to perform data-driven API testing using TestNG’s @DataProvider. In this article, we will learn how to pass test data using an Object array.

The Quantum Computing Mirage: What Three Years of Broken Promises Have Taught Me

Aggregated on: 2026-02-23 17:08:53

I've lost count of how many quantum computing briefings I've sat through where executives project timelines on screens that quietly shift right every six months. The promises sound identical to what I heard in 2022, except the dates change. Quantum advantage was coming in 2024. Then 2025. Now it's 2026. Next year, I'll probably hear 2027. Google's announcement about their Willow processor in late 2024 followed a script I could recite from memory. A hundred-plus qubits. Performance beyond classical supercomputers. A calculation verified as correct. The press release carefully avoids mentioning that the calculation serves no purpose beyond demonstrating the machine works. It's a benchmark divorced from any application someone would pay to run.

Designing Predictable Logic in User-Driven Web Tools

Aggregated on: 2026-02-23 16:08:53

When users interact with a web application, they are rarely thinking about implementation details. They enter values, click buttons, and expect the result to make sense. When it doesn’t, trust breaks immediately. Trust isn’t built in the interface; it’s built in the logic underneath. In many user-driven tools, the hardest part is not layout or formatting — it is designing logic that behaves consistently, explains itself through outcomes, and holds up across unexpected inputs.

Agentic AI vs Copilots: The Architectural Shift from Assistance to Autonomy

Aggregated on: 2026-02-23 15:08:53

The Hook We are currently crossing a threshold in AI development: moving from "generative" tools that predict the next token, to "agentic" systems that predict the next action. This article dissects the engineering differences between copilots and agents, explores the risks of autonomous loops, and explains how to architect your codebase for the agentic future. Introduction For the past two years, developers have treated LLMs as "autocomplete on steroids." Tools like GitHub Copilot or ChatGPT reside in our IDEs, waiting for a prompt and delivering a one-shot response. The human is the driver; the AI is the navigator.

From Prompt Loops to Systems: Hosting AI Agents in Production

Aggregated on: 2026-02-23 14:08:53

An agent can reason well and still fail badly. Most teams do not notice this during early experiments because nothing is under pressure yet. The model calls tools, answers questions, and produces outputs that look correct. From the outside, the system works. The problems surface later, once the agent is expected to run continuously instead of intermittently. Restarts become normal, context has to survive across runs, external services are often involved, and their actions are not always closely monitored. That is where the difference shows. At that point, outcomes depend far less on how the agent reasons and far more on how it is hosted, because hosting determines what happens when execution is interrupted, state disappears, or permissions suddenly block an action.

Building a Sentiment Analysis Pipeline With Apache Camel and Deep Java Library (DJL)

Aggregated on: 2026-02-23 13:08:53

Sentiment analysis is now a key part of many applications. Whether you’re processing customer feedback, sorting support tickets, or tracking social media, knowing how users feel can be just as important as knowing what they say. For Java developers, the main challenge isn’t finding machine learning models, but applying them within the existing or new Java applications without relying on Python. Most NLP models are shown in Python notebooks, while real systems use file pipelines, routing, retries, fallbacks, and monitoring. Many teams find it hard to connect these pieces smoothly.

Azure AI Search at Scale: Building RAG Applications with Enhanced Vector Capacity

Aggregated on: 2026-02-23 12:08:53

In the rapidly evolving landscape of Generative AI, the Retrieval-Augmented Generation (RAG) pattern has emerged as the gold standard for grounding Large Language Models (LLMs) in private, real-time data. However, as organizations move from proof of concept (PoC) to production, they encounter a significant hurdle: scaling. Scaling a vector store isn’t just about adding more storage; it’s about maintaining low latency, high recall, and cost efficiency while managing millions of high-dimensional embeddings. Azure AI Search (formerly Azure Cognitive Search) has recently undergone major infrastructure upgrades, specifically targeting enhanced vector capacity and performance.

Observability Without Cost Telemetry Is Broken Engineering

Aggregated on: 2026-02-20 20:23:51

I've run production systems where we could tell you the p99 latency of any endpoint down to the microsecond, but couldn't explain why our AWS bill jumped $40,000 in a single weekend. That disconnect — between operational visibility and financial reality — is where most observability strategies quietly fail. The orthodox telemetry trinity (metrics, logs, traces) gives you performance. Error rates. Request volumes. Latency distributions that let you argue about whether 250 ms is acceptable for a search API. What it won't tell you is that the microservice you just optimized for speed now costs $0.03 per invocation instead of $0.002, and at scale, that rounding error becomes someone's quarterly budget.

From Command Lines to Intent Interfaces: Reframing Git Workflows Using Model Context Protocol

Aggregated on: 2026-02-20 19:23:51

My recent journey into agentic developer systems has been driven by a desire to understand how AI moves from passive assistance to active participation in software workflows. In an earlier article, AI Co-creation in Developer Debugging Workflows, I explored how developers and AI systems collaboratively reason about code. As I went deeper into this space, I came across the Model Context Protocol (MCP) and became keen to understand what this component is and why it is important. I noticed that MCP was frequently referenced in discussions about agentic systems, yet rarely explained in a concrete, developer-centric way. This article is a direct outcome of that learning process, using a practical Git workflow example to clarify the role and value of MCP in intent-driven developer tooling. What Is an MCP Server? At a conceptual level, an MCP server acts as a control plane between an AI assistant and external systems. Rather than allowing an LLM to issue arbitrary API calls, the MCP server implements the Model Context Protocol and exposes a constrained, well-defined set of capabilities that the model can invoke.

The Missing Primitive in Data Platforms: Agent Contracts for Tool Calls

Aggregated on: 2026-02-20 18:23:51

Analytics agents are moving from answering questions to doing things — running SQL, resolving metrics, fetching lineage, creating exports, and triggering workflows. This shift breaks a common assumption in GenAI projects: that production will be fine if the agent’s prompt is good. In reality, once an agent can call tools, you are operating a distributed system whose behavior can drift with every model upgrade, prompt change, routing adjustment, or schema change. Most teams respond by adding a few guardrails, tuning prompts, or rate-limiting tool access. That helps, but it doesn’t address the failure mode that matters most in data platforms: the same question leading to different tool behavior over time. A small change can turn a safe metric lookup into raw SQL, increasing retries and introducing silent correctness drift without any explicit error. Traditional data platforms solved this problem with data contracts, which consist of SLOs, explicit interfaces, controlled rollouts, and ownership.

Amazon Q Developer for AI Infrastructure: Architecting Automated ML Pipelines

Aggregated on: 2026-02-20 17:23:51

The landscape of Machine Learning Operations (MLOps) is shifting from manual configuration to AI-driven orchestration. As organizations scale their AI initiatives, the bottleneck is rarely the model architecture itself, but rather the underlying infrastructure required to train, deploy, and monitor these models at scale. Amazon Q Developer, a generative AI–powered assistant, has emerged as a critical tool for architects and engineers looking to automate the lifecycle of AI infrastructure. Traditionally, setting up a robust ML pipeline involved complex Infrastructure as Code (IaC), intricate IAM permissioning, and manual tuning of compute resources like NVIDIA H100s or AWS Trainium. Amazon Q Developer streamlines this by translating high-level architectural requirements into production-ready scripts, optimizing resource allocation, and troubleshooting connectivity issues within the AWS ecosystem. This article explores the technical architecture of using Amazon Q for ML infrastructure and provides practical implementation strategies.

Architecting Zero-Trust Database Access in Kubernetes With Vault Dynamic Secrets

Aggregated on: 2026-02-20 16:23:51

The Death of the Static Credential: An Operational Imperative In modern software architecture, speed is the primary driver of innovation. We deploy faster and scale wider, yet this velocity introduces a parallel vector of risk: complexity. Amidst this, one vulnerability remains persistently simple: the static database credential. For decades, the "database password" was a fixed artifact. In the monolithic era, this was manageable. In the era of Kubernetes and ephemeral infrastructure, it is a liability. Zero trust is now an architectural mandate: trust is never granted implicitly based on network location. In the database layer, this necessitates the elimination of "standing privileges."

Hurley: A High-Performance HTTP Client and Load Testing Tool Engineered in Rust

Aggregated on: 2026-02-20 15:23:51

Introduction and Motivation This article examines the technical architecture, capabilities, and use cases of hurley, a project developed in Rust that functions as both a general-purpose HTTP client and a performance testing tool. It explores the efficiency advantages gained by managing API testing and performance analysis through a unified tool within software development processes. With the proliferation of microservices architectures and distributed systems, communication via the HTTP protocol has become the lifeblood of the software ecosystem. In this context, developers face two fundamental needs: (1) A flexible HTTP client to verify the functional correctness of API endpoints, and (2) Performance testing tools to analyze system behavior under load.

Why End-to-End Testing Fails in Microservice Architectures

Aggregated on: 2026-02-20 14:23:51

End-to-end testing is often described as the ultimate safety net for modern software systems. In theory, it validates real user workflows across all components and ensures that the system behaves correctly as a whole. In practice, however, end-to-end testing frequently fails to deliver reliable value in microservice architectures. Teams invest heavily in E2E suites, only to face slow pipelines, flaky tests, and low confidence in test results.

A Unified Defense Against MITRE’s Top Injection Attacks

Aggregated on: 2026-02-20 13:23:51

This is how I created a Go library to address 41 actively exploited vulnerabilities. The Problem That Keeps Security Teams Up at Night On December 11, 2025, MITRE released its annual 2025 CWE Top 25 Most Dangerous Software Weaknesses list, analyzing 39,080 CVE records from the past year. The results should concern every developer.

Queueing Theory for LLM Inference

Aggregated on: 2026-02-20 12:23:51

If you are deploying LLM inference in production, you are no longer just doing machine learning. You are doing applied mathematics plus systems engineering. Most teams tune prompts, choose a model, then wonder why latency explodes at peak traffic. The root cause is usually not the model. It is load, variability, and the queue that forms when the arrival rate approaches the service capacity.

From Prompts to Platforms: Scaling Agentic AI (Part 2)

Aggregated on: 2026-02-19 20:08:51

The tenets I introduced in Part 1 covered the functional mechanics — the core features that power an AI platform. But in production, functionality is only half the battle. These next six Operational Tenets are about how the platform survives the chaos of the real world and scales without breaking under its own complexity. Here are the pillars critical to operating an AI platform at scale:

AWS SageMaker HyperPod: Distributed Training for Foundation Models at Scale

Aggregated on: 2026-02-19 19:08:51

The landscape of Artificial Intelligence has undergone a seismic shift with the emergence of Foundation Models (FMs). These models, characterized by billions (and now trillions) of parameters, require unprecedented levels of computational power. Training a model like Llama 3 or Claude is no longer a task for a single machine; it requires a coordinated symphony of hundreds or thousands of GPUs working in unison for weeks or months. However, managing these massive clusters is fraught with technical hurdles: hardware failures, network bottlenecks, and complex orchestration requirements. AWS SageMaker HyperPod was engineered specifically to solve these challenges, providing a purpose-built environment for large-scale distributed training. In this deep dive, we will explore the architecture, features, and practical implementation of HyperPod.

Automating Unix Security Across Hybrid Clouds

Aggregated on: 2026-02-19 18:08:51

In modern DevOps, we automate deployments, testing, and infrastructure provisioning. Yet, in many organizations, server patching remains a manual, high-friction event. Admins log in late at night, run scripts, and hope services restart correctly. This manual approach is a security liability. The longer the gap between a vulnerability disclosure and a patch application, the wider the attack window.

Tools for Building Deterministic LLM Systems

Aggregated on: 2026-02-19 17:08:50

It’s hard to imagine a world without LLMs nowadays. I rarely reach for Google when ChatGPT can provide me a much more curated answer with almost all the context it could need. However, these daily use cases often lean in creative directions. In the context of B2B systems, that same creativity that provides so much usefulness in day-to-day is not acceptable.

Mastering Serverless Data Pipelines: AWS Step Functions Best Practices for 2026

Aggregated on: 2026-02-19 16:08:50

AWS Step Functions has evolved from a simple state machine orchestrator into the backbone of modern serverless data engineering. As organizations move away from brittle, monolithic scripts toward event-driven architectures, Step Functions provides the reliability, observability, and scalability required for complex ETL (Extract, Transform, Load) processes and data workflows. However, building a “working” pipeline is different from building a “production-grade” pipeline. In this guide, we will explore industry-standard best practices for building robust serverless data pipelines, focusing on performance, cost efficiency, and maintainability.

Jetpack Navigation 3: A New Era for Navigation in Compose-Driven Android Apps

Aggregated on: 2026-02-19 15:08:50

In late 2025, Google released Jetpack Navigation 3, a major redesign of the Android Navigation library built specifically for Jetpack Compose and modern UI architectures. This is not a typical incremental update. Navigation 3 fundamentally changes how navigation state is modeled, owned, and rendered in an application. If you have been using the Navigation Component with Compose through navigation-compose, the new version may feel unfamiliar at first. However, it aligns much more naturally with Compose’s reactive programming model and solves many long-standing limitations around state ownership, adaptive layouts, and complex navigation flows.

The Future of Agentic AI

Aggregated on: 2026-02-19 14:08:50

The era of passive AI chatbots is ending. We are now entering the age of agentic AI: systems that actively reason, plan, and execute tasks. For organizations, this represents a potential leap in productivity, but it also introduces new engineering challenges. Moving from a simple prompt to a reliable agent ecosystem requires a new, robust architecture.

The Developer’s Guide to Local LLMs: Building, Running, and Scaling With Ollama

Aggregated on: 2026-02-19 13:08:50

Firstly, LLMs are already widely used for working with unstructured natural data in general. Additionally, they excel at extracting information and working with semi-structured data, such as JSON files and other lengthy configuration files. It allows us to use them that way to interact with relational data, for example. Cloud-based LLMs are effective and powerful, but they have some limits. That's where locally based LLMs come into play. Local LLMs: Pros and Cons I first realized the need to use local LLMs while developing software for a critical industry (healthcare), where Personal Health Information is strictly regulated and, accordingly, the use of cloud-based LLMs is very limited. So, privacy is the first benefit of using the local LLMs.

Embedding Store as a Platform on AWS: OpenSearch + Bedrock + S3 Needs SLAs, Governance, and Quotas

Aggregated on: 2026-02-19 12:08:50

When teams say they are building RAG, they often mean they are adding a vector database. On AWS, this typically looks like using S3 for documents, Amazon Bedrock to generate embeddings, and Amazon OpenSearch for vector search. It functions when you set it up, embed a few thousand chunks, and perform a similarity search, and it works. Everyone is happy!

Production-Ready Observability for Analytics Agents: An Open Telemetry Blueprint Across Retrieval, SQL, Redaction, and Tool Calls

Aggregated on: 2026-02-18 20:08:50

An analytics agent works great in demos: ask a question, and it fetches context, runs SQL queries, and summarizes the results. Then the real incident happens: a VP challenges a number, the security team asks whether restricted fields were exposed, or an auditor requests to see how the answer was produced and which controls were applied. Most teams can’t answer confidently because their observability was built for latency and debugging — not governance. They either:

Mastering the Gemini 3 API: Architecting Next-Gen Multimodal AI Applications

Aggregated on: 2026-02-18 19:08:50

The landscape of large language models (LLMs) has shifted from text-centric interfaces to truly multimodal reasoning engines. With the release of the Gemini 3 API, Google has introduced a paradigm shift in how developers interact with artificial intelligence. Gemini 3 isn’t just an incremental update; it represents a fundamental advancement in native multimodality, expanded context windows, and efficient agentic workflows. In this technical deep dive, we will explore the architecture of Gemini 3, compare its capabilities with previous generations, and walk through the implementation of a production-ready AI feature: a Multimodal Intelligent Research Assistant.

10 Go Best Practices Every Backend Developer Should Know

Aggregated on: 2026-02-18 18:08:50

Go has become a cornerstone language for building scalable backend services, cloud-native applications, and DevOps tooling. While Go’s simplicity is one of its greatest strengths, writing production-ready Go code requires more than just knowing the syntax. This guide distills practical best practices that teams can adopt to improve code quality, consistency, and maintainability. If you're new to Go, start with the official Go documentation and Effective Go. This article builds on those foundations with team-specific patterns that work well in enterprise environments.

From On-Call to On-Guard: Hardening Incident Response Against Security-Driven Outages

Aggregated on: 2026-02-18 17:08:50

The pager doesn't care why production is burning. A compromised credential chain triggering mass file encryption demands the same midnight scramble as a misconfigured load balancer taking down the payment gateway. Yet most organizations still maintain separate playbooks, separate escalation trees, separate war rooms for "technical incidents" versus "security incidents" — as if attackers politely wait for the right team to clock in. This artificial boundary is killing response times when every minute counts.