News Aggregator

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.

How to Build Permission-Aware Retrieval That Doesn't Leak Across Teams

Aggregated on: 2026-02-18 16:08:50

LLM assistants or chatbots are very good at connecting the dots, which is exactly why they can be dangerous in multi-team organizations. A PM from team A asks, 'Why did the churn rate spike last Wednesday?' The assistant retrieves and displays an answer written by Team B, which includes customer names and contact details. Even if you block the final answer, the leak may have already occurred through retried snippets, intermediate summaries, cached results, etc. If your retrieval layer isn't permission-aware end-to-end, the model can pull context from other teams' documents, tickets, dashboards, or embeddings. This is not just about blocking access. In reality, leaks happen during retrieval, summarization, inside tool traces/logs, or via shared embedding stores.

Testing Legacy JSP Code

Aggregated on: 2026-02-18 15:08:50

JSP might be old, not fancy, or trendy anymore, but many legacy systems still use it, and there are development teams tasked with maintaining and extending systems with a JSP frontend (see https://webtechsurvey.com/technology/javaserver-pages). What can you do when you need to work on a code base that has unit tests for the Java code, but a significant part of the code base is living in (an untested) frontend code and is prone to failures? You can rely on code reviews or pull requests, but that seems insufficient to flag even trivial issues. You can wait for manual testers or automated UI tests to find problems after the change was deployed to the QA environment, but that is way too late and cumbersome.

Why “At-Least-Once” Is a Lie: Lessons from Java Event Systems at Global Scale

Aggregated on: 2026-02-18 14:08:50

At-least-once delivery is treated like a safety net in Java event systems. Nothing gets lost. Retries handle failures. Duplicates are “a consumer problem.” It sounds practical, even mature. That assumption doesn’t survive production.

Functional Scaffolding for LLM Solutions

Aggregated on: 2026-02-18 13:08:50

Slingshot Your Way to Secure, Efficient AI Solutions I have always been fascinated by the elliptical trail maneuvers of gravity-assisted slingshots in rocketry. Somehow, it feels comparable to how we reach our goals in the AI solutions world — moving from a starting point to a target destination. A gravitational slingshot leverages every planet in its path — using each body’s gravity to gain momentum (saving fuel in the process) — until the spacecraft is flung toward its destination. The starting point is analogous to our problem statement, and the destination is the target state we wish to achieve with AI agents. The smarter the agents, the greater the efficiency, the less compute “fuel” burned, and the faster we reach the final solution.

When Kubernetes Forgets: The 90-Second Evidence Gap

Aggregated on: 2026-02-18 12:08:50

The Contradiction At 3:47 AM, your monitoring dashboard shows a healthy Kubernetes cluster — 99.97% availability. Your customers report a complete outage. Ninety seconds later, the pod has self-healed. Metrics look normal. The restart counter reads “1.” But why it restarted — what actually happened — has vanished. This isn’t a tooling failure. The system simply recovered faster than a human could observe.

Trust No Agent: How to Secure Autonomous Tools on Your Machine

Aggregated on: 2026-02-17 21:08:50

Two weeks ago, one of my friends called me and asked if it was a good idea to install OpenClaw on a personal machine. The immediate thought that crossed my mind was how about security and how to reduce the blast radius if the OpenClaw is compromised. Autonomous agent tools are reshaping how we work. Tools like OpenClaw and Picoclaw can write code, make API calls, read files, and interact with external services on your behalf. They're incredibly useful. But they're also a significant security risk if you don't know what you're doing. Over the past few weeks, I have been working with these tools on my Mac and Linux workstations. I have friends running agents with full access to their home directory. They have stored API keys in plaintext environment files. They have connected agent machines to their main network with no isolation. Each time we interact, I realize how quickly things could go wrong.

Diffs Are Dead. Why You Need Scalable Previews.

Aggregated on: 2026-02-17 21:08:50

The software development lifecycle (SDLC) is going through a massive shift as coding agents become increasingly ubiquitous in the industry. While this has very visible impacts that have been widely documented, it also results in more subtle changes that are less obvious. I noticed one of those changes recently: code review has all but died without us noticing. I have seen this happen on my own engineering team. Senior engineers known for meticulous review are approving pull requests (PRs) within minutes.

The Human-in-the-Loop AI: Reviving the Lost Art of Procedure Manuals

Aggregated on: 2026-02-17 20:23:50

We often treat “automation” and “documentation” as enemies. The prevailing DevOps wisdom is that if you have a runbook, you should automate it into a script. If you can’t automate it, you tolerate it as toil. However, in complex cloud operations (CloudOps), there are persistent “Non-Automated Domain” tasks that require human judgment, complex troubleshooting, or legacy system interaction. When these tasks fail, it’s rarely because the engineer lacked skill; it’s because the documentation was ambiguous, outdated, or written by someone who left the company three years ago.

Beyond Ingestion: Teaching Your NiFi Flows to Think

Aggregated on: 2026-02-17 19:23:50

If you are working with data pipelines, chances are you have crossed paths with Apache NiFi. For years, it's been the go-to way for getting data from point A to point B (and often C, D, and E). Its visual interface makes building complex routing, transformation, and delivery flows surprisingly easy, handling everything from simple log collection to intricate IoT data streams across countless organizations. It's powerful, it's flexible, and honestly, it just works really well for shuffling bits around reliably. We set up our sources, connect our processors, define our destinations, and watch the data flow — job done, right? AI Opportunity Well, mostly. While Apache NiFi is fantastic at the logistics of data movement, I started wondering: what if we could make the data smarter while it's still in motion? We hear about AI everywhere, crunching massive datasets after they've landed in a data lake or warehouse. But what about adding that intelligence during ingestion? Imagine enriching events, making routing decisions based on predictions, or flagging anomalies before the data even hits its final storage.

Design and Implementation of Cloud-Native Microservice Architectures for Scalable Insurance Analytics Platforms

Aggregated on: 2026-02-17 18:23:50

Objective Statement This study proposes a scalable, modular, and cloud-native microservice architecture tailored for the insurance industry. The goal is to enable rapid enterprise-wide analytics adoption, seamless AI integration, and real-time data processing through containerization, orchestration, and service-based deployment models that enhance scalability, agility, and system resilience. Problem Context Although insurers are among the earliest adopters of artificial intelligence, fewer than 10% have successfully scaled AI initiatives beyond pilot programs. Most struggle with monolithic legacy systems, fragmented data pipelines, and rigid IT infrastructures that limit agility and interoperability. Insights from Risk & Insurance (2024) and McKinsey & Company (2014–2023) reveal that organizational silos and outdated core technologies prevent carriers from realizing the full business value of analytics. Addressing this gap requires a cloud-native, microservice-based architecture capable of supporting continuous delivery, real-time analytics, and ecosystem-wide integration.

Leading Through the Chaos of Large-Scale Cloud Operations: 7 Best Practices

Aggregated on: 2026-02-17 17:23:50

High-scale systems fail in many unexpected ways that you would never have designed for. Over the past 14 years, I have navigated the layers of physical and virtual networking. I started as an individual contributor writing code for data plane services and later led global teams managing highly distributed services that owned millions of hosts. I have seen a wide range of incidents, including multi-service impacts, single-service impacts, cascading failures, single-customer issues, service failures during incident recovery, service failures post-recovery, and services that cannot auto-recover. The list goes on and on. I have studied the root causes of major outages across the industry’s cloud leaders. There are common failure patterns across the industry. While these events are inevitable, based on my experience, adhering to the best practices below for managing failures will greatly improve your ability to handle them. These are the seven best practices I recommend to keep teams efficient during large-scale incidents and help reduce the impact time.

The Citizen Developer Boom: How Generative AI Lowers the Barrier to Entry

Aggregated on: 2026-02-17 16:23:50

For years, enterprises have chased the dream of “Citizen Development”—empowering non-engineers to build their own tools using low-code/no-code platforms. The promise is enticing: business users solve their own problems, IT backlogs shrink, and innovation accelerates. However, the reality is often different. Adoption stalls because the “low-code” barrier is still too high for the average employee. They get stuck on API integration, basic logic flow, or security compliance.

From Prompts to Platforms: Scaling Agentic AI (Part 1)

Aggregated on: 2026-02-17 15:23:50

The industry is shifting from passive generative AI — systems that simply respond to prompts — to active, goal-driven, agentic AI. This is more than a race toward better model benchmarks; it represents a fundamental change in how we architect platforms for autonomous execution at scale. From building agentic systems that power job seeker, hiring, and sales use cases, I’ve seen firsthand how difficult it is to move from proof of concept to a global ecosystem serving millions of members. Scaling these systems requires addressing latency, cost, and reliability challenges while preserving modularity and extensibility.

Open Notebook: A Secure Alternative to Google NotebookLM

Aggregated on: 2026-02-17 15:08:50

Google NotebookLM is a powerful AI tool for interacting with your documents. However, privacy concerns might prevent you from uploading sensitive data to NotebookLM. There is an open source alternative by means of Open Notebook. All data can be kept local, and you are not restricted to Google's Gemini models. Let's check this out! Introduction Google NotebookLM lets you upload your documents and get insights about the documents using Google's Gemini models. It is a very powerful and convenient tool.

AI Transformation Anti-Patterns (And How to Diagnose Them)

Aggregated on: 2026-02-17 15:08:50

TL;DR: AI Transformation Anti-Patterns AI initiatives fail for the same reasons Agile transformations did: The majority of failures result from people, culture, and processes, not technology. This article gives you a diagnostic checklist of 10 AI transformation anti-patterns to spot where your organization’s initiatives are coming off track. Why Your AI Initiative Is Failing Your organization announced an AI initiative, the leadership bought licenses, and someone launched a pilot. The quarterly review called it a success. Six months later, nobody uses it.

Responding to HTTP Session Expiration on the Front-End via WebSockets

Aggregated on: 2026-02-17 15:08:50

There is no doubt that nowadays software applications and products that have a significant contribution to our well-being are real-time. Real-time software makes systems responsive, reliable, and safe, especially in cases where timing is important — from healthcare and defense to entertainment and transportation. Such applications are helpful as they process and respond to data almost instantly or within a guaranteed time frame, which is critical when timing and accuracy directly affect performance, safety, or even user experience. As a protocol that enables real-time, two-way (full-duplex) communication between a client and a server over a single, long-lived TCP connection, WebSockets are among the technologies used by such applications.

My Learning About Password Hashing After Moving Beyond Bcrypt

Aggregated on: 2026-02-16 20:23:49

For a long time, I thought I had password hashing figured out. Like many Java developers, I relied on bcrypt, mostly because it’s the default choice in Spring Security. It was easy to use, widely recommended, and treated in tutorials as "the secure option." I plugged it in, shipped features, and moved on.

Automatic Data Correlation: Why Modern Observability Tools Fail and Cost Engineers Time

Aggregated on: 2026-02-16 19:23:49

When a production issue hits, it starts a race to find the data that shows you what went wrong. And in many engineering organizations, the data search takes longer than understanding what the bug is — or coding the fix itself. This is what I call the “correlation problem”: the information you need to debug an issue exists, but it’s scattered across multiple tools, systems, and log files.

Breaking the Vendor Lock in Network Automation: A Pure Python Architecture

Aggregated on: 2026-02-16 18:23:49

In the world of Infrastructure as Code (IaC), servers are a solved problem. We spin up thousands of VMs with a single script. But the network layer? That often remains a manual bottleneck. The reason is the “Multi-Vendor Trap.” Enterprise networks are rarely homogeneous. They are a patchwork of routers, switches, and load balancers from different vendors (Cisco, Juniper, F5), each with its own proprietary CLI syntax. This fragmentation makes standard automation difficult, leading to long lead times (often weeks) just to open a VLAN or update a firewall rule.

Stop Fine-Tuning for Everything: A Decision Tree for RAG vs Tuning vs Tools

Aggregated on: 2026-02-16 17:23:49

I used to treat fine-tuning like the “grown-up” step in an LLM project. Prototype with prompts → hit a problem → fine-tune it.

Schema Evolution in Event-Driven Systems: Avro/Protobuf Strategies That Don’t Break Consumers

Aggregated on: 2026-02-16 16:23:49

Most schema-evolution advice is technically correct and still gets teams hurt. It usually stops at “add fields, don’t remove fields,” and skips the parts that cause real incidents: semantic drift, consumer lag, unknown consumers, and silent failures. In an event-driven system, the most dangerous break is the one that doesn’t crash anything — it just produces wrong results quietly.

Building a Self-Correcting GraphRAG Pipeline for Enterprise Observability

Aggregated on: 2026-02-16 15:23:49

The RAG Plateau: Why Vector Search Is Failing the Enterprise In the early days of generative AI, retrieval-augmented generation (RAG) was a revelation. By grounding large language models (LLMs) in external data, we solved the immediate problem of static knowledge. However, as we move through 2026, enterprise developers have hit what I call the "RAG Plateau." Standard RAG relies on vector databases and cosine similarity. This works perfectly for "flat" queries—where the answer exists within a single paragraph of text. But enterprise data isn't flat; it’s a web of interconnected dependencies. If you ask an AI, "Which microservices are at risk if the 'User-Auth' database experiences 500ms latency?", a vector search will find snippets about "User-Auth" and "Latency." It will almost certainly fail to map the three-hop relationship between the database, the authentication service, and the downstream billing gateway.

Automating the DFIR Triage Loop With Memory Forensics and LLMs

Aggregated on: 2026-02-16 14:23:49

Most modern security operations centers (SOCs) face a problem of speed and volume of data collection. While collecting data is no longer the issue in many cases, analyzing it is — especially during high-priority incidents. To collect forensic evidence in many cases, analysts manually run multiple tools: Volatility for memory dumps, YARA for malware signatures, and strings for basic text search. Each tool creates a different output. The combination of all of those outputs is required for meaningful analysis. Manual correlation of these outputs is time-consuming and error-prone. Manual correlation of forensic outputs also contributes to alert fatigue — when the number of alerts becomes so large that they cannot be reasonably processed by humans.

Building Intelligent Agents With MCP and LangGraph

Aggregated on: 2026-02-16 13:23:49

We're at an interesting point in AI development. Language models have become very good at understanding and generating text, but they still can't do much independently. They can't check your calendar, pull data from your database, or send that email you've been meaning to write. Whenever we want to give an AI system a new capability, we have to write custom integration code. It’s like having a brilliant assistant who requires a new instruction manual for every single task. The Model Context Protocol is working to solve this issue, and honestly, it's about time someone stepped up.

The Death of the CSS Selector: Architecting Resilient, AI-Powered Web Scrapers

Aggregated on: 2026-02-16 12:23:49

Introduction: The High Cost of Fragile Data Pipelines For over a decade, web scraping has been a game of cat and mouse. You write a script to scrape a job board, targeting specific DOM elements like div.job-title or span#salary. It works perfectly for a month. Then, the website deploys a frontend update. The class names change to random hashes (common in React/Next.js apps), your selectors fail, and your data pipeline crashes. The hidden cost of web scraping isn't the compute; it's the engineering maintenance hours spent debugging and fixing broken selectors.

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.