News Aggregator

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.

Hybrid Vector Graph with AI Agents for Software Test Case Creation

Aggregated on: 2026-02-06 15:23:44

The Problem: Manual Test Case Creation Doesn’t Scale Modern software development faces a critical bottleneck in test case creation. As applications become increasingly complex — with microservices architectures, API integrations, and distributed systems — manually creating comprehensive test cases becomes time-intensive and error-prone. Key challenges include:

Best Java GUI Frameworks for Modern Applications

Aggregated on: 2026-02-06 14:23:44

Java has become one of the world’s most versatile programming languages, chosen for its adaptability, stability, and platform independence. Its extensive ecosystem encompasses virtually every application type, from web development to enterprise solutions, game design, the Internet of Things (IoT), and beyond. With an estimated 51 billion active Java Virtual Machines (JVMs) globally, it goes without question that Java powers a substantial portion of modern software infrastructure.

ITSM Uncovered: How IT Teams Keep Businesses Running Smoothly

Aggregated on: 2026-02-06 13:23:44

In today’s digital environment, incidents can have an immediate impact on revenue, customer trust, and team productivity. Traditional IT Service Management (ITSM) approaches often struggle to keep pace with cloud-native, distributed, and AI-driven ecosystems. Organizations are now rethinking ITSM not as a process-heavy function, but as an adaptive platform that blends automation, collaboration, and intelligence. As organizations modernize, ITSM isn’t disappearing — it’s evolving from ticket queues into intelligent automation platforms that bridge the gap between development, operations, and business continuity.

How to Achieve More Accurate Data Extraction From Invoices

Aggregated on: 2026-02-06 12:23:44

Extracting structured data from invoices looks straightforward until you run it at scale. Invoices arrive as PDFs, scans, and photos; they follow different layouts, languages, and fonts, and many contain tables, stamps, handwritten notes, or low-quality images. Even when the information is present, it is often split across lines, repeated in multiple places, or labeled inconsistently, which makes simple pattern matching unreliable. Moreover, we can face issues in numeric and alphanumeric fields, such as VINs and invoice numbers, which are especially error-prone because visually similar characters get swapped, for example, o and 0, w and v, 5 and s, or i and l. The hardest part is that small errors are costly. A single misread character in an invoice number, a swapped decimal separator in a total amount, or a billing address confused with a shipping address can break downstream automation and trigger manual review. A robust solution usually combines several layers: document ingestion and preprocessing, classic OCR and PDF text extraction, rule-based parsing for predictable patterns, business validation rules such as total consistency and identifier checks, and a workflow that routes low-confidence cases to human review.

Architecting Immutable Data Integrity with Amazon QLDB and Blockchain

Aggregated on: 2026-02-05 20:27:55

In the current landscape of ransomware and sophisticated SQL injection attacks, standard database security is no longer sufficient. We rely heavily on cryptographic hashes (such as SHA-256) to verify data integrity. The logic is simple: if the hash changes, the data was altered. But there is a flaw in this logic. If an attacker gains administrative access to your database, they can modify the data and the stored hash simultaneously. The “seal” is broken, and you have no way of proving the original state of the document.

What Is DevOps Automation? A Beginner-Friendly Guide

Aggregated on: 2026-02-05 19:27:55

Modern software teams are expected to deliver features faster, fix issues quickly, and keep systems reliable at scale. Doing all of this manually is no longer realistic. This is where DevOps automation becomes essential. For beginners, DevOps automation can sound complex or overwhelming. In reality, it’s about removing repetitive manual work from software delivery and replacing it with reliable, repeatable processes. This guide explains DevOps automation in simple terms, why it matters, and how teams actually use it in real-world environments.

When Your Cloud Bill Becomes an Outage

Aggregated on: 2026-02-05 18:27:55

The Lambda function ran perfectly. Every request returned in under 200 ms, the error rate held at 0.02%, and the SLO dashboard glowed green. Then accounting called: last month’s AWS bill had jumped from $340 to $6,200. The service hadn’t failed — it had just quietly bankrupted its budget while meeting every technical metric anyone thought to measure. Traditional site reliability engineering watches three gods: latency, errors, and availability. But modern infrastructure has added a fourth that most teams still treat as an afterthought. Cost doesn’t appear in runbooks or incident channels until someone’s Excel sheet starts screaming. By then, you’re debugging last month’s architecture decisions with this month’s invoice.

Principles for Operating Large-Scale Global Production Systems with AI Innovation Across the Stack

Aggregated on: 2026-02-05 17:27:55

Today’s global digital platforms are powered by hundreds of microservices that run behind the front-end users interact with. These services must operate at scale in conjunction with each other. Consequently, the ultimate user experience is determined by the composite availability of these systems, engineered so that the final service continues to operate even if subsystems experience outages. When discussing availability standards like “five nines,” systems available 99.999% of the time are allowed only about 5 minutes of downtime per year (out of 525,600 minutes). Engineering teams must rigorously focus on availability, latency, performance, efficiency, change management, monitoring, deployments, capacity planning, and emergency response planning to meet these goals. High availability is crucial because the digital economy thrives on these services, and any downtime directly translates to lost revenue for small and medium businesses. To coordinate effectively, services establish a shared operational framework on SLIs, SLOs, error budgets, SEV guidelines, and escalation protocols.

Safe Vibe Coding in 2026: Mastering the Workflow With Cursor and Automated Guardrails

Aggregated on: 2026-02-05 16:27:55

Last year, our team got a little too excited about vibe coding. We’d heard the hype — Andrej Karpathy’s “vibe coding” tweet from early 2025 had blown up, Cursor was everywhere, and everyone was posting about shipping features in days instead of weeks. So we said, “Let’s go all in.” For a few months, that’s exactly what we did. We’d gather around a screen, throw a big natural-language prompt at Cursor’s agent — “Build a document management dashboard with folder tree, searchable list view, real-time previews, and dynamic filters” — and watch it crank out components, hooks, and even some backend stubs. It felt magical. Features that used to take two sprints were landing in a couple of days. Juniors were contributing production code faster than ever. We were moving fast, shipping often, and honestly having fun.

Automating Behavioral Evaluations for LLMs: A Practical Guide to Bloom

Aggregated on: 2026-02-05 15:27:55

If you've ever deployed a large language model (LLM) in production, you might know the uncertainty that comes with it. Will the model refuse a legitimate request? Will it be too agreeable when it shouldn't be? How does one even test for behaviors that emerge only in specific, hard-to-predict scenarios? Manual red-teaming and hand-crafted evaluation suites have been the standard approach, but they can be very hard to scale. They're expensive, time-consuming, and worst of all, they become obsolete the moment they're published, since models can be trained on them.

From PDFs to Embeddings: Rebuilding Enterprise Knowledge for the LLM Era

Aggregated on: 2026-02-05 14:27:55

For twenty years, the contract between developers and documentation was simple: write a page or a PDF, throw it on a CMS or Confluence, and users will find it via keyword search. That contract is dead. Large language models, retrieval-augmented generation (RAG) pipelines, and multimodal reasoning engines no longer “read” pages — they retrieve and synthesize meaning from small semantic chunks stored as embeddings. If those chunks are poorly formatted, outdated, or semantically noisy, the model either hallucinates or returns no useful output.

AI RAG Architectures: Comprehensive Definitions and Real-World Examples

Aggregated on: 2026-02-05 13:27:55

Large language models (LLMs) are highly capable, but they are not reliable on their own in the enterprise world. Language models tend to hallucinate, and they are not only deprived of new or proprietary information inputs but are also inefficient in areas such as governance, traceability, and expenditure management. Retrieval-Augmented Generation (RAG) came to the fore as an effective approach to anchor model responses to external knowledge sources. There is a tendency among various teams to consider RAG as a single pattern of implementation. Something I quickly discovered is that RAG is not one architecture, but several. Indeed, a system that is adequate for a simple “search assistance” scenario is not sufficient for scenarios involving multi-step reasoning, tool execution, or multiple data sources. It is important to treat different RAG architectures differently in order to avoid fragile or overly engineered systems that are difficult to run in production environments.

String to Unicode Converter Utility

Aggregated on: 2026-02-05 12:27:55

This is a technical article for Java developers. It describes a Java utility that can convert strings to Unicode sequences and back. There are many websites and other services that allow various text conversions. This utility allows you to do the conversion in Java code. It allows converting any string into a String containing a Unicode sequence that represents characters from the original string. The utility can do backwards conversion as well — convert a Unicode sequence String into a textual String. Just to show an example, a String "Hello World" can be converted into "\u0048\u0065\u006c\u006c\u006f\u0020\u0057\u006f\u0072\u006c\u0064".

Context Engineering: The Missing Layer for Enterprise-Grade AI

Aggregated on: 2026-02-04 20:12:54

Enterprises are eager to develop RAG systems, chatbots, and AI copilots, yet many encounter a similar challenge: while the system performs well in demonstrations, it struggles with the complexities of real-world scenarios. Inconsistencies arise in responses, the tone can shift unexpectedly, hallucinations emerge, and accuracy diminishes as the number of documents increases. The underlying issue isn't the model, the vector database, or the retrieval strategy. Rather, it lies in the absence of context engineering, which involves the deliberate design of what information the model accesses, how it interprets it, and the constraints under which it reasons. By implementing context engineering, AI evolves from an unpredictable text generator into a dependable, policy-aware, role-sensitive intelligence layer that functions like a true enterprise system. This distinction separates a superficial proof of concept from a trustworthy, production-ready AI platform.

UX Research in the Age of AI: From Validation to Anticipation

Aggregated on: 2026-02-04 19:12:54

With pressure to integrate AI into every corner of the digital experience, one phrase keeps showing up in product teams: “We just need to validate this AI feature.” I hear this constantly, and it worries me. This seemingly harmless sentence reveals a deeper problem. It assumes the solution exists. That the need is known. That the user is understood. And that the job of UX research is to rubber-stamp usability rather than ask hard questions about whether the thing should exist in the first place.

Rate Limiting Beyond “N Requests/sec”: Adaptive Throttling for Spiky Workloads (Spring Cloud Gateway)

Aggregated on: 2026-02-04 18:12:54

Most teams add rate limiting after an outage, not before one. I’ve done it both ways, and the “after” version usually looks like this: someone picks a number (say 500 rps), wires up a filter, and feels safer. Then the next incident happens anyway — because the problem wasn’t the number. The real problems tend to be:

Running Granite 4.0-1B Locally on Android

Aggregated on: 2026-02-04 17:12:54

This started the way these things usually do — watching a podcast instead of doing something productive (I ended up writing this blog, so maybe it was productive after all). I was listening to a Neuron AI episode about IBM’s new Granite 4 model family, with IBM Research’s David Cox as the guest. During the discussion on model sizes and deployment targets, they talked about Granite 4 Nano, models designed specifically for edge and on-device use cases. At some point, the discussion turned to running these models on your phone.

Semantic Contracts: The Missing Layer Between Good Data and Reliable AI

Aggregated on: 2026-02-04 16:12:54

Modern data platforms are objectively better than they were five years ago. Schemas are versioned. Pipelines are tested. Data quality checks catch nulls, range violations, and anomalies. Lineage is tracked. Observability dashboards exist.

Automating Lift-and-Shift Migration at Scale

Aggregated on: 2026-02-04 15:12:54

For many enterprises, the “lift-and-shift” (rehost) strategy remains the most pragmatic first step into the cloud. It offers speed and immediate data center exit capabilities without the complexity of refactoring applications. However, doing this manually for hundreds of workloads introduces human error, security gaps, and “migration fatigue.” To solve this, we need to treat migration not as a series of manual tasks, but as a manufacturing process. We need a Migration Factory.

AI Governance for AI Agents: Ship Fast, Stay Safe

Aggregated on: 2026-02-04 14:12:54

When I started deploying autonomous AI agents in production, I quickly learned that governance wasn’t just about compliance — it was a matter of survival. Today, autonomous scripts, smart automations, and conversational assistants make real decisions, act on data, and integrate into production environments. As an engineer and product leader, I’ve often faced one dominant tension: how to deploy AI agents rapidly without sacrificing compliance, security, or ethical accountability. That’s the problem. Here’s the fix. In this article, I’ll share why AI governance is no longer a choice, how to design it into the development process, and what a “governance-first” mindset looks like when done right.

I Built AIBrowser With Claude Code: A Desktop Version of Manus

Aggregated on: 2026-02-04 13:12:54

AI Browser (Altas) is an open-source Electron app that lets you control a browser using plain English (or any language). Just describe what you want to do, and the AI figures out how to do it. GitHub: https://github.com/DeepFundAI/ai-browser Try download it: https://www.deepfundai.com/altas Why I Built This As a developer, I got tired of:

Oracle Data Loading Reimagined: Performance Strategies for Modern Workloads

Aggregated on: 2026-02-04 12:12:54

After spending 15 years in database administration, primarily with SQL Server but also working extensively with Oracle environments, I've discovered that efficient data loading remains one of the most critical yet challenging aspects of database performance tuning. Data loader jobs often represent the foundation of business operations, from nightly ETL processes to real-time data ingestion pipelines. When these jobs run slowly, they create a cascading effect of problems: missed SLAs, extended maintenance windows, stale reporting data, and frustrated end users. Today, I'll share practical strategies for optimizing Oracle data loader jobs based on real-world implementations I've overseen across various industries. Understanding Oracle's Data Loading Utilities Oracle provides several methods for loading data, each with distinct performance characteristics. SQLLoader, Oracle's primary bulk-loading utility, offers extensive configuration options for performance tuning. I once worked with a telecommunications company that was loading 50 million call detail records daily using SQLLoader in conventional path mode. By switching to direct path loading, we bypassed the buffer cache and reduced load times from 4 hours to just under 40 minutes. The syntax was straightforward:

Building a 300 Channel Video Encoding Server

Aggregated on: 2026-02-03 20:12:53

Snapshot Organization: NETINT, Supermicro, and Ampere® Computing Problem: The demand for high-quality live video streaming has surged, putting pressure on operational costs and user expectations. Legacy x86 processors struggle to handle the intensive video processing tasks required for modern streaming.

AI-Powered Spring Boot Concurrency: Virtual Threads in Practice

Aggregated on: 2026-02-03 19:12:53

Modern microservices face a common challenge: managing multiple tasks simultaneously without putting too much pressure on the systems that follow. Adjusting traditional thread pools often involves a lot of guesswork, which usually doesn't hold up in real-world situations. However, with the arrival of virtual threads in Java 21 and the growth of AI-powered engineering tools, we can create smart concurrency adapters that scale in a safe and intelligent way. This article provides a step-by-step guide to a practical proof-of-concept using Spring Boot that employs AI (OpenAI/Gemini) to assist in runtime concurrency decisions. It also integrates virtual threads and bulkheads to ensure a good balance between throughput and the safety of downstream systems.

How to Verify Domain Ownership: A Technical Deep Dive

Aggregated on: 2026-02-03 18:12:53

Domain ownership verification is a fundamental security mechanism that proves you control a specific domain. Whether you're setting up email authentication, SSL certificates, or integrating third-party services, understanding domain verification methods is essential for modern web development. In this article, we'll explore the three most common verification methods, their trade-offs, and practical implementation patterns. I recently built domain verification for allscreenshots.com, a screenshot API I work on, to enable automatic OG image generation — and I’ll share what I learned along the way.

Rapid Prototyping for Multimodal AI Agents in Enterprise Collaboration

Aggregated on: 2026-02-03 17:12:54

Gartner's latest research paints a striking picture: 40% of enterprise applications will have task-specific AI agents by 2026. Right now, we're at 5%. That's not gradual adoption. That's a landslide. And yet McKinsey found that while 88% of enterprises have AI running somewhere in their operations, only 6% are seeing real financial returns across the business. Everyone's adopting. Almost no one's scaling. The bottleneck isn't technology anymore. It's figuring out whether what you're building actually works for the people who have to use it. The Validation Gap Nobody Talks About The pitch sounds great: AI that joins your meetings, transcribes everything, writes up the recap, and flags who owes what to whom. Some of these tools even jump in when the conversation stalls. Technically, it's remarkable work. But here's what gets glossed over in product demos: this isn't software that behaves the way software usually behaves. You can ask the same thing twice and get different answers both times. That's not a bug. That's how language models function.

Distributed Task Queue With Python asyncio + Redis (A Celery Replacement)

Aggregated on: 2026-02-03 16:12:53

Celery has been the de facto standard for background task processing in Python for over a decade. It’s powerful, battle-tested, and feature-rich, but it also comes with significant complexity: brokers, result backends, worker pools, configuration overhead, serialization quirks, and sometimes opaque debugging. With the rise of asyncio, high-performance Redis clients, and modern Python runtimes, many teams are asking a simple question: Do we really need Celery for every background job use case?

Building Resilient Industrial AI: A Developer’s Guide to Multi-ERP RAG

Aggregated on: 2026-02-03 15:12:53

The Integration Reality When someone says "AI agent for supply chain," it’s tempting to think first about prompts and setting windows. But in real enterprises, the hard part isn’t generating text — it’s surviving the desegregation reality. Engineers in manufacturing inherit many systems with multiple issues: ERP sprawl across regions, unstructured truth hidden in emails, text files, spreadsheets, and notes, and complex data lineage where SKUs vary by region.

Token-Efficient RAG: Using Query Intent to Reduce Cost Without Losing Accuracy

Aggregated on: 2026-02-03 14:12:53

In this article, we will examine the RAG optimization technique to reduce the number of tokens required to generate a response while maintaining response accuracy. Before we dig deeper into RAG, let us review a few basic terms. What Is an LLM (Large Language Model)? Large language models (LLMs) are very large deep learning models that are pre-trained on vast amounts of data. They are capable of performing tasks ranging from simple to complex, such as content generation, text classification, text mining, and summarization.

Building SRE Error Budgets for AI/ML Workloads: A Practical Framework

Aggregated on: 2026-02-03 13:12:53

Here's a problem I've seen happen far too often: your recommendation system is functioning, spitting out results in milliseconds, and meeting all its infrastructure SLAs. Everything is looking rosy in the dashboard world. Yet engagement has plummeted by 40% because your model has been pointless for several weeks. On behalf of your traditional error budget? You're golden. According to your product team? The system is broken.

How Global Payment Processors like Stripe and PayPal Use Apache Kafka and Flink to Scale

Aggregated on: 2026-02-03 12:12:53

The recent announcement that Global Payments will acquire Worldpay for $22.7 billion has once again put the spotlight on the payment processing space. This move consolidates two giants and signals the growing importance of real-time, global payment infrastructure. But behind this shift is something deeper: data streaming has become the backbone of modern payment systems. From Stripe’s 99.9999% Kafka availability to PayPal streaming over a trillion events per day, and Payoneer replacing its existing message broker with data streaming, the world’s leading payment processors are redesigning their core systems around streaming technologies. Even companies like Worldline, which developed its own Apache Kafka management platform, have made Kafka central to their financial infrastructure.

Agentic Commerce: A Developer's Guide to Google's Universal Commerce Protocol (UCP)

Aggregated on: 2026-02-02 20:12:53

Online shopping just got its biggest upgrade in years. On January 11, 2026, Google CEO Sundar Pichai announced the Universal Commerce Protocol (UCP) at the National Retail Federation conference — a new open standard co-developed with Shopify, Walmart, Etsy, Target, Wayfair, and others (with endorsements from Stripe, Visa, Mastercard, and more). UCP is designed for the era of agentic commerce, where AI agents handle the full shopping journey: discovery, comparison, cart management, discounts, checkout, and even post-purchase support. No more humans clicking through tabs, managing carts, or entering payment details. Instead, AI agents act as trusted proxies, communicating directly with merchant systems via a standardized protocol. For developers and architects building e-commerce backends, integrations, or AI tools, this shift means rethinking how you expose data — not for human eyes, but for machines.

Selenium Test Automation Challenges: Common Pain Points and How to Solve Them

Aggregated on: 2026-02-02 19:27:53

You have written your first Selenium test suite, watched it pass locally, and felt the satisfaction of automation success. Then you pushed it to CI. The next morning, half your tests failed for reasons that made no sense. Welcome to the real world of Selenium test automation. Selenium remains one of the most widely adopted web automation frameworks for good reason. It offers unmatched flexibility, supports multiple programming languages, and benefits from a massive community that has been refining best practices for nearly two decades. But adopting Selenium is just the beginning. The real challenge starts when you scale beyond a handful of test cases and discover that writing tests is the easy part. Keeping them running reliably is where teams struggle.

How Audiences Become Addressable in Programmatic Advertising: Identity, Data Flows, and Addressability

Aggregated on: 2026-02-02 18:27:53

The goal is to establish a shared mental model for identity, addressability, and precision, one that holds up across environments (web, app, CTV, retail media) and remains valid as technology and regulation evolve. This first article lays the foundation: how programmatic advertising works end-to-end, how identity enters the system, and why metrics like match rate exist at all. Subsequent articles will build on this to explore precision loss, experimentation, and governance.

ML Performance Monitoring Metrics: A Simple Guide for Every Model Type

Aggregated on: 2026-02-02 17:27:53

Machine Learning Models Don’t Fail Loudly — They Fail Quietly Machine learning failures rarely announce themselves with errors or crashes. Most of the time, models fail silently — when data slowly changes, users behave differently, or real-world assumptions drift away from what the model was trained on. The system keeps running, predictions keep flowing, dashboards look “green,” and yet business impact quietly degrades.

From Test Automation to Autonomous Quality: Designing AI Agents for Data Validation at Scale

Aggregated on: 2026-02-02 16:27:53

For a long time, quality engineering has been about building better nets to catch bugs after they fall out of the system. We wrote more tests, added more rules, and built bigger dashboards. And for a while, that worked. Then data systems grew teeth.

Building Real-Time KPI Intelligence with Self-Service BI: From Static Dashboards to Proactive Control Systems

Aggregated on: 2026-02-02 15:27:53

In today's fast-paced, data-driven world, Key Performance Indicators (KPIs) are the backbone of smart decision-making, whether for day-to-day operations or planning for the future. They indicate business health, highlight areas of efficiency, and reveal opportunities for growth. But here’s the catch — even with all the sophisticated BI tools available, many organizations still encounter roadblocks. Issues take too long to resolve, performance trends are often unclear, and teams frequently rely on IT for even minor adjustments.

A Generic MCP Database Server for Text-to-SQL

Aggregated on: 2026-02-02 14:27:53

Text-to-SQL is quickly becoming one of the most practical applications of large language models (LLMs). The idea is appealing: write a question in plain English, and the system generates the correct SQL query. But in practice, the results are mixed. Without structured schema information, models often:

Modern Vulnerability Detection: Using GNNs to Find Subtle Bugs

Aggregated on: 2026-02-02 13:27:53

For over 20 years, static application security testing (SAST) has been the foundation of secure coding. However, beneath the surface, many legacy SAST tools still operate using basic techniques such as regular expressions and lexical pattern matching; essentially, sophisticated versions of the Unix command grep. As a result, most SAST tools suffer from what I call “false positive fatigue.” These tools report every occurrence of a strcpy() (or similar) regardless of whether the buffer is mathematically proven to be safe. This article explores an innovative method for detecting vulnerabilities using graph neural networks (GNNs). In contrast to viewing source code as a linear string of characters, GNNs represent code as a structured graph of logical and data-flow structures. As such, we can now develop models that understand how a user’s input at line 10 in the code ultimately relates to a database query at line 50, even when variable names are changed three times between those two points in the code.

Mastering Fluent Bit: Developer Guide to Routing to Prometheus (Part 13)

Aggregated on: 2026-02-02 12:27:53

This series is a general-purpose getting-started guide for those of us wanting to learn about the Cloud Native Computing Foundation (CNCF) project Fluent Bit. Each article in this series addresses a single topic by providing insights into what the topic is, why we are interested in exploring that topic, where to get started with the topic, and how to get hands-on with learning about the topic as it relates to the Fluent Bit project.

From LLMs to Agents: How BigID is Enabling Secure Agentic AI for Data Governance

Aggregated on: 2026-01-30 20:12:52

Understanding Large Language Models (LLMs) Large Language Models (LLMs) form the foundation of most generative AI innovations. These models are predictive engines trained on massive datasets, often spanning hundreds of billions of tokens. For example, ChatGPT was trained on nearly 56 terabytes of data, enabling it to predict the next word or token in a sequence with remarkable accuracy. The result is an AI system capable of generating human-like text, completing prompts, answering questions, and even reasoning through structured tasks. At their core, LLMs are not databases of facts but statistical predictors. They excel at mimicking natural language and surfacing patterns seen in their training data. However, they are static once trained. If a model is trained on data that is five or ten years old, it cannot natively answer questions about newer developments unless it is updated or augmented with real-time sources. This limitation makes pure LLMs insufficient in enterprise contexts where accuracy, compliance, and timeliness are critical.

Testcontainers Explained: Bringing Real Services to Your Test Suite

Aggregated on: 2026-01-30 19:12:52

Building robust, enterprise-grade applications requires more than just writing code — it demands reliable automated testing. These tests come in different forms, from unit tests that validate small pieces of logic to integration tests that ensure multiple components work together correctly. Integration tests can be designed as white-box (where internal workings are visible) or black-box (where only inputs and outputs matter). Regardless of style, they are a critical part of every release cycle. Modern enterprise applications rarely operate in isolation. They often have to interact with external components like databases, message queues, APIs, and other services. To validate these interactions, integration tests typically rely on either real instances of components or mocked substitutes.

ToolOrchestra vs Mixture of Experts: Routing Intelligence at Scale

Aggregated on: 2026-01-30 18:12:52

Last year, I came across Mixture of Experts (MoE) through this research paper published in Nature. Later in 2025, Nvidia published a research paper on ToolOrchestra. While reading the paper, I kept thinking about MoE and how ToolOrchestra is similar to or different from it. In this article, you will learn about two fundamental architectural patterns reshaping how we build intelligent systems. We'll explore ToolOrchestra and Mixture of Experts (MoE), understand their inner workings, compare them with other routing-based architectures, and discover how they can work together.

Ralph Wiggum Ships Code While You Sleep. Agile Asks: Should It?

Aggregated on: 2026-01-30 17:12:52

TL; DR: When Code Is Cheap, Discipline Must Come from Somewhere Else Generative AI removes the natural constraint that expensive engineers imposed on software development. When building costs almost nothing, the question shifts from “can we build it?” to “should we build it?” The Agile Manifesto’s principles provide the discipline that these costs are used to enforce. Ignore them at your peril when Ralph Wiggum meets Agile. The Nonsense About AI and Agile Your LinkedIn feed is full of confident nonsense about Scrum and AI.

Essential Techniques for Production Vector Search Systems, Part 3: Filterable HNSW

Aggregated on: 2026-01-30 16:12:51

After implementing vector search systems at multiple companies, I wanted to document efficient techniques that can be very helpful for successful production deployments of vector search systems. I want to present these techniques by showcasing when to apply each one, how they complement each other, and the trade-offs they introduce. This will be a multi-part series that introduces all of the techniques one by one in each article. I have also included code snippets to quickly test each technique.

TPU vs GPU: Real-World Performance Testing for LLM Training on Google Cloud

Aggregated on: 2026-01-30 15:12:51

As large language models (LLMs) continue to grow in scale, the underlying hardware used for training has become the single most critical factor in a project’s success. The industry is currently locked in a fascinating architectural battle: the general-purpose power of NVIDIA’s GPUs versus the purpose-built efficiency of Google’s Tensor Processing Units (TPUs). For engineers and architects building on Google Cloud Platform (GCP), the choice between an A100/H100 GPU cluster and a TPU v4/v5p pod is not merely a matter of cost — it is a decision that impacts software architecture, data pipelines, and convergence speed. This article provides a deep-dive technical analysis of these two architectures through the lens of real-world LLM training performance.

Automating TDD: Using AI to Generate Edge-Case Unit Tests

Aggregated on: 2026-01-30 14:12:51

The Problem: The "Happy Path" Trap in TDD Test-driven development (Red-Green-Refactor) is the gold standard for reliable software. However, it has a flaw: The quality of your code is capped by the imagination of your test cases. If you are building a payment processing function, you will naturally write a test for "valid payment." You might even remember "insufficient funds." But will you remember to test for: