News Aggregator

AI Agents Demystified: From Language Models to Autonomous Intelligence

Aggregated on: 2026-02-13 20:23:48

What Exactly Is an AI Agent? Artificial Intelligence has entered a new phase, one where systems no longer just respond, but reason, plan, and act. Language models like GPT, Gemini, or Claude are incredibly powerful, but they live inside a box. They can generate, summarize, and explain, but they can’t take real-world action unless connected to something beyond themselves. That’s where AI agents come in.

A Guide to Parallax and Scroll-Based Animations

Aggregated on: 2026-02-13 19:23:47

Parallax animation can transform static web pages into immersive, interactive experiences. While traditional parallax relies on simple background image movement and tons of JavaScript code, scroll-based CSS animation opens up a world of creative possibilities with no JavaScript at all. In this guide, we’ll explore two distinct approaches: SVG block animation: Creating movement using SVG graphics for unique, customizable effects. Multi-image parallax background: Stacking and animating multiple image layers for a classic parallax illusion. We'll walk through each technique step by step, compare their strengths and limitations, and offer practical tips for responsive design.

Quantum-Safe Trading Systems: Preparing Risk Engines for the Post-Quantum Threat

Aggregated on: 2026-02-13 18:23:48

The Coming Break in Trust Picture this: a structured BRL-USD note is booked and hedged in 2025, stitched across FX triggers, callable steps, and a sovereign curve that looks stable enough to lull even the cautious. Trade capture is clean, risk logs balance, settlement acknowledges signatures, and the desk moves on. Years pass. The note remains live, coupons roll, collateral terms are amended twice, and the position is referenced by downstream analytics and audit trails that assume the original cryptographic guarantees still hold. Then the ground shifts. Adversaries who quietly harvested network traffic in 2025 now possess hardware that can break the RSA and ECC protections that guarded those artifacts. The trade’s lineage—what was agreed, authorized, and attested — no longer rests on unforgeable proofs. It rests on assumptions that no longer apply. This is not a scare line for a compliance deck. It is a systems problem with direct pricing consequences. If a payoff confirmation, margin call message, or risk model artifact can be replayed, altered, or repudiated because yesterday’s signatures are breakable tomorrow, the integrity of the entire lifecycle is at risk. You can mark a curve correctly and still be wrong if the attestation that links a payout to a specific state of the world becomes suspect.

How to Build an MCP Server

Aggregated on: 2026-02-13 17:23:47

Model Context Protocol has been playing a crucial role in integrating various tools with agents in a very streamlined manner. You can expose your tools via APIs and connect to the MCP clients. At the same time, there has been lots of confusion about MCP. Below clarifies the doubt: What MCP Server Is Not MCP is not a framework for building agents. MCP is not a Python library. MCP is not a container. MCP is not a way to code agents. How Model Context Protocol (MCP) Works Ultimately,

Scaling Enterprise RPA With Secure Automation and Robust Governance

Aggregated on: 2026-02-13 16:23:47

Enterprise RPA has matured from “task bots” into a core capability for automating business processes at scale across several domains, including finance operations, customer onboarding, supply chain workflows, HR shared services, and regulated back-office functions. The challenge is no longer whether automation works, but whether it can be scaled predictably without creating new operational risk: credential sprawl, uncontrolled bot changes, fragile UI dependencies, audit gaps, and inconsistent exception handling. This article lays out a blueprint for enterprise RPA that supports scaling robotic process automation across teams, business units, and geographies while delivering secure and compliant RPA solutions under a strong governance model.

Green AI in Practice: How I Track GPU Hours, Energy, CO₂, and Cost for Every ML Experiment

Aggregated on: 2026-02-13 15:23:47

Most data teams track Accuracy, Latency, and maybe GPU Utilization if someone is watching the dashboard. Almost no one tracks: How many GPU-hours a model run consumed How many kWh of electricity that implies How much CO₂ and cloud spend are associated with each experiment Once I started paying attention to these metrics, it completely changed how I design and run experiments.

Introducing Sierra Charts

Aggregated on: 2026-02-13 14:23:47

Sierra is an open-source framework for simplifying the development of Java Swing applications. It is based on the open-source Kilo framework, which has been discussed in previous articles: Writing (Slightly) Cleaner Code With Collections and Optionals Efficiently Transforming JDBC Query Results to JSON Using Schema Annotations to Create and Execute SQL Queries For example, Sierra's UILoader class can be used to easily construct a hierarchy of user interface elements:

The Human Bottleneck in DevOps: Automating Knowledge with AIOps and SECI

Aggregated on: 2026-02-13 13:23:47

In modern IT operations (ITOps), we face a paradox: our infrastructure is dynamic, scalable, and cloud-native, but our operational processes are often static, manual, and dependent on a few hero engineers. When an incident occurs, the mean time to recovery (MTTR) often depends less on the technology stack and more on who is on call. If the expert is unavailable, the system stays down. This is the knowledge bottleneck.

Serverless Is Not Cheaper by Default

Aggregated on: 2026-02-13 12:23:47

The pitch is clean: you pay only for what you use. No servers idling at 3 a.m., burning cash. No capacity planning. Just functions that appear when needed and disappear when done. Serverless feels like the ideal everyone was waiting for — and sometimes it actually is. Then the bill shows up. A developer I know — an experienced guy, not some junior making rookie mistakes — built what looked like a simple proof of concept. AWS Bedrock knowledge base, OpenSearch Serverless backend. Nothing fancy. A few LLM queries, maybe 2 GB of PDFs uploaded for testing. He was expecting maybe twenty or thirty bucks. The invoice came back at over $200. He spent an hour just staring at the line items, trying to figure out what happened. No error, no hack. Just the way it works.

A Developer-Centric Cloud Architecture Framework (DCAF) for Enterprise Platforms

Aggregated on: 2026-02-12 20:23:47

Enterprise-class cloud systems seldom fail because of infrastructure constraints; rather, problems arise when architectural vision cannot scale to match the scale of the business. With the increasing use of the cloud by various teams, geographic locations, and business units, certain recurring scenarios emerge:

Mastering Postback Tracking and S2S Conversion Tracking

Aggregated on: 2026-02-12 19:23:47

Accurate conversion tracking is the backbone of any high-performing affiliate or partner marketing program. Postback tracking, also known as server-to-server (S2S) tracking, offers a privacy-friendly, robust way to record conversions without relying on client-side pixels. This article explains what postback and S2S tracking are, how they work, why they matter, and how to implement, troubleshoot, and choose platforms that support them.

AWS Bedrock Knowledge Bases: Comparing S3 Vector Store, OpenSearch, PostgreSQL, and Neptune for Cost and Performance

Aggregated on: 2026-02-12 18:23:47

Since July 15, 2025, AWS has added support for S3 vector stores for Bedrock knowledge bases, allowing for seamless storage and retrieval of embeddings for RAG workflows. Currently, it supports multiple stores: AWS-Managed Non AWS-Managed OpenSearch MongoDB Atlas S3 vector store Pinecone PostgreSQL Redis Enterprise Cloud Neptune

Building an Identity Graph for Clickstream Data

Aggregated on: 2026-02-12 17:23:47

Clickstream data is easy to collect and hard to use. Every modern system can emit page views, taps, API calls, and application events with timestamps and attributes. The trouble starts when analysis or downstream services require a notion of “user.” In most production systems, identity is incomplete by default. Many events arrive without a logged-in account. Cookies reset. Mobile devices are shared. IP addresses rotate. A single person often appears as several disconnected records, while unrelated users occasionally collide on the same attributes.

Building Trust in LLM-Generated Code Reviews: Adding Deterministic Confidence to GenAI Outputs

Aggregated on: 2026-02-12 16:23:47

In a previous article, Automating AWS Glue Infra and Code Reviews with RAG and Amazon Bedrock, I described how I built a GenAI-powered code review system for AWS Glue jobs using a retrieval-augmented generation (RAG) approach. Given a use case, the system searched all associated jobs, retrieved each job script and a predefined engineering checklist from S3, invoked an LLM, and generated a structured Markdown (.md) review file per job. Each checklist item was evaluated with:

Golden Paths for AI Workloads - Standardizing Deployment, Observability, and Trust

Aggregated on: 2026-02-12 15:23:47

As AI workloads mature from experimental prototypes into business-critical systems, organizations are discovering a familiar problem: inconsistency at scale. Each team deploys models differently, observability varies widely, and operational maturity depends heavily on individual expertise. This is where Golden Paths become essential.

Backing Up Azure Infrastructure with Python and Aztfexport

Aggregated on: 2026-02-12 14:23:47

In an ideal DevOps world, every cloud resource is spawned from Terraform or Bicep. In the real world, we deal with “ClickOps.” An engineer manually tweaks a Network Security Group (NSG) to fix a production outage, or a legacy resource group exists with no code definition at all. When a disaster strikes — such as the accidental deletion of a resource group — you can’t just “re-run the pipeline” if the pipeline doesn’t match reality.

Java Developers: Build Something Awesome with Copilot CLI and Win Big Prizes!

Aggregated on: 2026-02-12 13:23:47

Here’s today’s invitation: join the GitHub Copilot CLI Challenge and build something with Copilot right in your terminal. Visit the challenge page for the rules, FAQ, and submission template. Why I’m Excited About Copilot CLI (especially for Java) If you write Java for a living, you already know the truth: the terminal is where we build and test. It’s where feedback loops are short and where most productivity gains come from “small wins” repeated hundreds of times.

Bootstrapping a Java File System

Aggregated on: 2026-02-12 12:23:47

So, what does a file system mean to you? Most think of file systems as directories and files accessed via your computer: local disk, remotely shared via NFS or SMB, thumb drives, something else. Sufficient for those who require basic file access, nothing more, nothing less. That perspective on file systems is too limited: VCS repositories, archive files (zip/jar), and remote systems can be treated as file systems, potentially accessed via the same APIs used for local file access while still meeting security and data requirements. Or how about a file system that automatically transcodes videos to different formats or extracts audio metadata for vector searches? Wouldn’t it be cool to use standard APIs rather than create something customized? Definitely!

Jakarta EE 12 M2: Entering the Data Age of Enterprise Java

Aggregated on: 2026-02-11 20:08:47

Every major Jakarta EE release tends to have a defining theme. Jakarta EE 11 was about modernization: a new baseline with Java 17, forward compatibility with Java 21, and a decisive cleanup of long-standing technical debt. Jakarta EE 12 builds directly on that momentum, but its direction is different. This release is less about removing the past and more about aligning the future. Jakarta EE 12 is best understood as the Data Age of enterprise Java.

Jakarta NoSQL in Jakarta EE 12 M2: A Maturing Story of Polyglot Persistence

Aggregated on: 2026-02-11 19:08:47

NoSQL databases did not become popular because relational databases failed; relational databases are still alive. They became popular because systems changed. As applications grew more distributed, data volumes increased, and access patterns diversified, the limits of a single persistence model became more visible. Document databases simplified aggregate storage, key-value stores optimized for latency and scale, column databases handled massive datasets efficiently, and graph databases modeled relationships that relational schemas struggled to express. Over time, these technologies moved from experimentation into critical, production-grade use cases, including highly regulated industries such as finance.

Information Security Outsourcing 2.0: Balancing Control, Cost, and Capability

Aggregated on: 2026-02-11 18:08:47

Information security outsourcing involves transferring part or all of an organization’s cybersecurity and IT infrastructure protection responsibilities to external experts. This approach allows companies to reduce the costs associated with maintaining an in-house Security Operations Center (SOC) and dedicated staff, gain access to advanced technologies and global best practices without significant upfront investments, and ensure continuous 24/7 monitoring and incident response. However, outsourcing critical functions also brings new challenges, particularly in areas such as trust, control, and regulatory compliance. The key is to strike the right balance between efficiency, visibility, and accountability.

Building a CRUD Application With Spring and SimpleJdbcMapper

Aggregated on: 2026-02-11 17:08:47

Spring Framework's JDBC core package, designed to simplify database interactions using JDBC, is a popular option for applications to persist data to a relational database. The central classes used are JdbcClient with its fluent API and JdbcTemplate with the older classic API. When using these APIs, the CRUD operations tend to be verbose. The SimpleJdbcMapper mitigates this verbosity and also stays out of the way so you can keep using all the features of JdbcClient/JdbcTemplate.

How AI-Driven Software Automation Reduced Deployment Failures by 40%?

Aggregated on: 2026-02-11 16:08:47

Deployment failures remain one of the most expensive and disruptive challenges in modern software development. Even with advancements in DevOps for traditional software workflows and AI/ML Ops for AI-integrated ones, the majority of organizations still struggle with production incidents and downtime. The result? Millions are being lost in revenue, teams are fatigued, and everybody dreads the production-deployment day.

Why SAP BDC Is a Game Changer for SAP Data

Aggregated on: 2026-02-11 15:23:47

SAP BDC (Business Data Cloud) is considered a game changer for SAP’s data portfolio because it fundamentally changes how SAP data is unified, governed, and consumed for analytics and AI. Instead of data being locked inside individual SAP systems, BDC turns SAP into an open, business-ready data platform. For many years, SAP data was difficult to use. Data lived in different SAP systems such as ECC, S/4HANA, SuccessFactors, and Ariba. Companies had to move data into SAP BW or BW/4HANA to analyze it. This process was slow, costly, and complex.

Playwright Fixtures vs. Lazy Approach

Aggregated on: 2026-02-11 14:23:47

When building scalable test automation frameworks, how you create and manage objects (pages, services, helpers) matters as much as the tests themselves. Two commonly used patterns are the Fixture Approach and the Lazy Approach. Each has its own strengths — and choosing the right one can significantly impact performance, readability, and maintainability. In this blog, we take a deep dive into the Fixture Approach and the Lazy Approach, helping you understand when and why to use each one.

Shift-Left QA With Octopus Deploy: Orchestrating Katalon Tests in Your Pipeline

Aggregated on: 2026-02-11 13:23:46

Octopus Deploy is helpful in planning releases and runbooks across multiple environments. Integrating Katalon with the Katalon Runtime Engine (KRE) and TestOps provides strong, scriptable UI and API testing, reporting, and team workflows. Combining these approaches will allow us to improve release cycles through automated testing, publish artifacts to a single repository, and fail fast when quality is compromised. There are two possible ways of integration:

Database Connection Pooling at Scale: PgBouncer + Multi-Tenant Postgres (10K Concurrent Connections)

Aggregated on: 2026-02-11 12:23:46

Last month, I watched our production Postgres cluster melt down at 3 AM. We’d hit 8,000 concurrent connections, memory usage spiked to 94%, and our carefully tuned indexes became irrelevant. The database was spending more time managing connections than executing queries. Sound familiar? Here’s what the PgBouncer documentation won’t tell you: simply throwing connection pooling at the problem doesn’t magically solve high-concurrency issues. I’ve seen teams install PgBouncer, pat themselves on the back, and then wonder why their database still chokes under load. The reality? Most connection-pooling implementations are fundamentally flawed for multi-tenant architectures at scale.

The AI Firewall: Using Local Small Language Models (SLMs) to Scrub PII Before Cloud Processing

Aggregated on: 2026-02-10 20:08:46

As organizations increasingly rely on powerful cloud-based AI services like GPT-4, Claude, and Gemini for sophisticated text analysis, summarization, and generation tasks, a critical security concern emerges: what happens to sensitive data when it's sent to external AI providers? Personal Identifiable Information (PII) — including names, email addresses, phone numbers, social security numbers, and financial data — can inadvertently be exposed during cloud AI processing. This creates compliance risks under regulations like GDPR, HIPAA, and CCPA, and opens the door to potential data breaches.

OCI Images as Kubernetes Volumes: A New Era for Data Management

Aggregated on: 2026-02-10 19:08:46

A new volume type has recently joined the Kubernetes ecosystem: the image volume. This feature, available starting with version 1.35.0 and currently in beta, promises to change how we manage static data and configurations in our clusters. The relevance of this volume type has been growing in cloud-native environments. Several applications already use container images to store information in OCI (Open Container Initiative) format. Popular tools such as Falco (for security rules), Kyverno (for policies), and FluxCD (for deployment management) are clear examples of this trend. Now, this capability is native to Kubernetes.

Visualizing Exposure Bias Using Simulation

Aggregated on: 2026-02-10 18:08:46

Abstract Randomization is a foundational assumption in A/B testing. In practice, however, randomized experiments can still produce biased estimates under realistic data collection conditions. We use simulation to demonstrate how bias can emerge despite correct random assignment. Visualization is shown to be an effective diagnostic tool for detecting these issues before causal interpretation. Introduction A/B testing is widely used to estimate the causal impact of product changes. Users are randomly assigned to control (C) or treatment (T), and differences in outcomes are attributed to the experiment. Randomization is intended to balance user characteristics across groups when assignment occurs at the user level. However, even with correct random assignment, the observed segment mix can differ because real experiments are often analyzed on a filtered or triggered subset of users. Eligibility rules, exposure conditions, logging behavior, and data availability can vary by variant due to trigger logic, instrumentation loss, device or browser differences, and latency. As a result, treatment and control may represent different effective populations.

A Pattern for Intelligent Ticket Routing in ITSM

Aggregated on: 2026-02-10 17:08:46

In the world of IT Service Management (ITSM), the Service Desk often acts as a human router. A ticket comes in, a coordinator reads it, checks a spreadsheet to see who is on shift, remembers who is good at databases versus networking, and then assigns the ticket. This process is slow, subjective, and prone to cherry-picking (where engineers grab easy tickets and ignore hard ones). It creates a bottleneck that increases Mean Time to Resolution (MTTR).

Designing a Real-Time Data Activation Platform Using Segment CDP, Databricks, and Iterable

Aggregated on: 2026-02-10 16:08:46

The first sign our activation stack was failing wasn’t latency or scale. It was when two internal teams triggered conflicting workflows from the same event, and neither system could explain why. That moment made something clear: once multiple teams depend on the same signals, activation stops being a marketing workflow problem and becomes a software architecture problem.

Query-Aware Retrieval Routing for Analytics on AWS: When to Use Redshift, OpenSearch, Neptune, or Cache

Aggregated on: 2026-02-10 15:08:46

Typically, LLM analytics assistants or chatbots start with retrieval-augmented generation (RAG) and a database connection. That's fine until real users ask a mix of KPI questions, definition lookups, lineage questions, and repeated dashboard-style requests. If everything goes through one retrieval path to access data, you will see three predictable failures. Wrong answers: Metrics that are computed at the wrong grain, wrong joins, missing filters Slow answers: Long prompts, retries Higher cost: More tokens, more queries, more wasted warehouse scans Analytics questions are not the same every time. The backend that is best for one question (e.g., what does active users mean?) may not be the best for another (e.g., which dashboards depend on the product type field?).

Secure Multi-Tenant GPU-as-a-Service on Kubernetes: Architecture, Isolation, and Reliability at Scale

Aggregated on: 2026-02-10 14:08:46

GPUs are a core feature of modern cloud platforms, used to support a wide range of machine learning training, inference, analytics, and simulation workloads. To support this diverse demand, GPUs can no longer be dedicated to a single team or application. Dedicated GPU solutions have quickly become infeasible and very expensive. To meet this demand, organizations are increasingly looking to shared platforms, where many teams can directly consume GPU resources from a shared Kubernetes cluster. GPU-as-a-Service (GPUaaS) platforms provide this capability.

Jakarta Data in Jakarta EE 12 M2: From Repositories to a Unified Data Access Model

Aggregated on: 2026-02-10 13:08:46

Enterprise Java persistence has been expanding its scope over the last few releases, slowly but deliberately moving away from the idea that persistence is synonymous with relational databases. With Jakarta EE 11, that shift became explicit through the introduction of Jakarta Data, a specification that standardizes application-level data access across both SQL and NoSQL databases. Jakarta EE 12 M2 builds on that foundation, not by changing direction, but by completing ideas that were intentionally deferred in the previous release. Jakarta Data did not replace Jakarta Persistence. Instead, it introduced a new abstraction layer, focused on how applications use data rather than how data is stored. This distinction is subtle but fundamental. Jakarta Persistence remains an ORM specification, deeply rooted in relational concepts, SQL semantics, and persistence contexts. Jakarta Data, by contrast, targets a higher level: the repository, where domain logic meets data access.

Jakarta Query: Unifying Queries in a Polyglot Persistence World, the News on Jakarta EE 12 M2

Aggregated on: 2026-02-10 12:08:46

As software architectures grow more complex, persistence ceases to be a purely technical concern and becomes an architectural one. Modern systems rarely rely on a single database or a single data model. Instead, they adopt polyglot persistence, choosing different storage technologies depending on scalability needs, access patterns, and domain boundaries. Relational databases remain essential, but they increasingly coexist with document stores, key-value databases, and other non-relational systems. This reality has already reshaped the Jakarta ecosystem. Specifications like Jakarta NoSQL and Jakarta Data emerged to acknowledge that persistence is no longer synonymous with ORM. With Jakarta EE 12, another important piece joins the picture: Jakarta Query, a new specification designed to unify how Java developers query data across persistence technologies.

The New Testing Pattern: Standardizing Regression for Cloud Migrations

Aggregated on: 2026-02-09 20:23:45

“Cloud Lift” (migrating on-premises systems to the cloud) is often sold as a simple infrastructure change. In reality, for large-scale administrative systems, it is a high-risk operation. When you move a system handling millions of transactions — such as unemployment insurance or tax processing — you cannot afford a single calculation error or performance regression. The challenge lies in validating that the new system behaves exactly like the old one across thousands of business scenarios. Manual testing is too slow, and unit tests often miss the holistic impact of infrastructure changes.

Context Engineering Is a Must-Learn Skill: Here's How Everyone Can Master It

Aggregated on: 2026-02-09 19:23:45

The Rise of Context Engineering In the rapidly evolving landscape of artificial intelligence, a new discipline has emerged that separates those who simply use AI tools from those who truly harness their power: context engineering. While prompt engineering has been the buzzword of the past few years, context engineering represents the next evolutionary step — a more sophisticated, systematic approach to working with large language models (LLMs) and AI systems. Context engineering is the art and science of designing, constructing, and optimizing the information environment in which an AI model operates. It goes far beyond crafting clever prompts; it encompasses the entire ecosystem of data, instructions, examples, and constraints that shape an AI’s understanding and outputs. As AI systems become more powerful and are integrated into critical business processes, mastering context engineering has become not just advantageous—it’s essential.

Distributed Systems and Cloud Efficiency: A Deep Dive

Aggregated on: 2026-02-09 18:23:45

Cost Is a Distributed Systems Bug The first time you watch $18,000 evaporate overnight because someone left autoscaling unbounded on a Kubernetes cluster that decided to provision 400 nodes for a traffic spike that never materialized, you stop thinking about cloud bills as accounting theater. Cost becomes what it always was: a failure mode with teeth. Zoom’s FinOps team saw their AWS spend double from $20K to $40K daily — not gradually, not with warning klaxons, just a jump that would burn through $600K in thirty days if left unaddressed. The mechanics were mundane: a feature rollout triggered cascading retries in a microservice mesh, with each retry spawning EC2 Spot instances that didn’t terminate cleanly. The cost spike manifested before the performance degradation did. Traditional monitoring missed it entirely because nobody had instrumented the bill.

Building a Self-Healing Observability System with AWS Bedrock AgentCore

Aggregated on: 2026-02-09 17:23:45

In today’s fast-paced cloud environments, keeping systems running smoothly isn’t just about monitoring them — it’s about making them smart enough to fix themselves. Enter the world of self-healing observability systems, where AI agents detect issues, analyze root causes, and take corrective actions without human intervention. With AWS Bedrock AgentCore, a powerful platform for building and deploying AI agents at scale, you can create a system that is reliable, secure, and efficient. In this article, we’ll dive deep into how to build such a system from scratch, complete with code examples, practical diagrams, and real-world insights. By the end, you’ll have a blueprint to implement your own self-healing setup.

Agentic DataOps With Guardrails: MCP and MWAA for Pipeline Incident Response

Aggregated on: 2026-02-09 16:08:45

Failure of data pipelines increasingly feels a lot like a security incident. They occur at inconvenient times; dashboards become stale; delays in data availability impact business decisions; and the on-call engineer loses time navigating across various tools, including CloudWatch logs, tickets, chats, code, and the Airflow UI (MWAA), to identify root causes. Some of the questions you ask yourself during this process are: What broke, and why did it break? What are the logs actually saying? What is the safest option to recover? Is it repeating? In most teams, the real cost isn't clicking on retry. It is about finding context: the right DAG, the right task, the right logs, the right log lines, the downstream impact, and the safest next step to the recovery path. Most GenAI pilots in data teams don't help much since they are still passive. They can explain what to do, but can't reliably pull CloudWatch logs, correlate failure across runs, or propose a safe action that you can audit.

An AI-Driven Architecture for Autonomous Network Operations (NetOps)

Aggregated on: 2026-02-09 15:08:45

In the modern enterprise, the divide between Systems Engineering (SE) and Operations (Ops) is growing. SE teams architect complex, zero-trust networks, while Ops teams are left to maintain them with limited visibility and outdated runbooks. When a critical incident occurs, the escalation path is predictable: Ops attempts to troubleshoot, fails due to a lack of deep technical context, and escalates to SE. This creates a bottleneck in which senior architects spend their time fighting fires instead of designing new systems.

Next-Level Persistence in Jakarta EE: How We Got Here and Why It Matters

Aggregated on: 2026-02-09 14:08:45

Enterprise Java persistence has never really been about APIs. It has always been about assumptions. Long before frameworks, annotations, or repositories entered the picture, the enterprise Java ecosystem was shaped by a single, dominant belief: persistence meant relational databases. That assumption influenced how applications were designed, how teams reasoned about data, and how the Java platform itself evolved. This article is inspired by a presentation given by Arjan Tijms, director of OmniFish, titled “Next-level persistence in Jakarta EE: Jakarta Data and Jakarta NoSQL.” Delivered in 2024, the talk offers a clear and pragmatic view of why Jakarta EE persistence needed to evolve, how Jakarta Data fits into the platform, and how it relates to Jakarta Persistence and Jakarta NoSQL. While the presentation provides the technical backbone, this article expands on the historical context and architectural motivations behind that evolution.

Agile’s AI-Driven Paradigm Shift

Aggregated on: 2026-02-09 13:08:45

TL; DR: Agile’s AI-Driven Paradigm Shift The paradigm shift is here. Andrej Karpathy, former Tesla AI director and OpenAI co-founder, recently admitted he has never felt this far behind as a programmer. If Karpathy feels overwhelmed, how should the rest of us feel? This article maps the shift across three levels: strategic, product, and individual. Each level demands different responses, while “good enough Agile” no longer provides an income or perspective. The question is where you are on the journey.

Model Context Protocol Vs Agent2Agent: Practical Integration with Enterprise Data

Aggregated on: 2026-02-09 12:08:45

Model Context Protocol (MCP), introduced by Anthropic in November 2024, and Agent2Agent (A2A), launched by Google in April 2025, are two different ways of designing AI systems that allow language models and agents to work with tools or with each other. While both aim to make AI development faster and more efficient, they solve different problems. MCP focuses on deterministic tool integration for language models, meaning it provides predictable ways for models to interact with external tools. A2A, on the other hand, focuses on asynchronous agent-to-agent communication, allowing multiple agents to coordinate and share information independently.

Understanding AI Agent Types:Guide to 8 Modern AI Architectures

Aggregated on: 2026-02-06 20:23:44

In this article, I will discuss eight major AI agent types: LCM, HRM, LAM, SLM, VLM, LRM, MOE, and GPT, optimized for specific problem domains, technological implementations, and practical applications. By understanding the strengths and limitations of each architecture, practitioners can make informed decisions when designing AI systems for production environments. Introduction The landscape of AI agents has transformed dramatically over the past five years. Rather than developing single models to address all problems, the industry has converged on specialized architectures, each tailored to specific computational and reasoning requirements. This article provides practitioners, researchers, and decision-makers with a detailed roadmap of eight foundational agent types, complete with technological stacks and implementation guidance.

Building Modern Full-Stack Python Applications: MVC Architecture Meets Enterprise-Ready Python

Aggregated on: 2026-02-06 19:23:44

The Model-View-Controller (MVC) architecture has been a cornerstone of software development for decades, but its relevance extends far beyond traditional web applications. As I was developing a curriculum for a free course on Industry Projects with Python aimed at college students, I realized that enterprise use of Python had changed dramatically in the last year, driven by Python's dominance in AI and data science. As Python continues its meteoric rise in AI backends and enterprise development, developers are building full-stack applications using Python across both frontend and backend layers. This article explores how MVC principles apply across diverse software projects, examines Python's growth in AI development, discusses the emerging trend of unified Python language stacks (beyond just TypeScript/Node.js), and highlights modern tooling that makes Python a first-class enterprise language.

Enabling Single-Sign-On in SaaS Application

Aggregated on: 2026-02-06 18:23:44

Introduction of SSO Single sign-on (SSO) allows users to log in to enterprise applications using central organizational credentials, which can be used across multiple internal applications without re-entering them. There will be a central Identity Provider that manages the same credentials across multiple applications. Once credentials are provided, they will remain in session for a set number of hours (6 or 8, as per organizational policy). Once credentials expire, the user has to re-enter them when they try to log in to any application, and again, there will be no login requirement for up to 8 hours. The Identity Provider handles authentication, authorization, and identity management across most aspects. Why SSO Is Important In a multinational organization, there are multiple applications for different purposes. Users of those applications need to maintain separate credentials for each application, which is hard to track, and maintaining safe password requirements across all the applications is hard for users.

The Real Cost of DevOps Backup Scripts

Aggregated on: 2026-02-06 17:23:44

Organizations rely on different methods for data backup, depending on factors such as data criticality. There are several options, ranging from DIY scripts to third-party backup vendors. The effectiveness of these approaches depends on how well they protect data and support timely recovery after an incident. In DevSecOps, data resilience is key, which puts backup and disaster recovery at the center of any effective security architecture.

The Self-Healing Directory: Architecting AI-Driven Security for Active Directory

Aggregated on: 2026-02-06 16:23:44

For over two decades, Active Directory (AD) has been the “central nervous system” of enterprise IT. It manages who gets in, what they can access, and when. Because of this centrality, it is the single most valuable target for an attacker. If you control AD, you control the organization. The traditional security architecture for AD — SIEM logs, manual audits, and rule-based alerts — is broken. It generates too much noise (alert fatigue) and reacts too slowly (long dwell times). Modern attacks like Kerberoasting or “living off the land” use legitimate tools (such as PowerShell) to blend in, making signature-based detection ineffective.