Systems | Development | Analytics | API | Testing

The Complete Hospital Management Software Implementation Checklist: A Step-by-Step Playbook for Hospital Leaders

The healthcare landscape in 2026 is defined by a paradox. While the global healthcare IT market is projected to skyrocket toward a US$ 961.26 billion valuation by 2030 according to MarketsandMarkets, hospital leaders are finding that the shiny new tool syndrome is a recipe for disaster. McKinsey highlights that while agentic AI and ambient listening are transforming administrative workflows, the foundation, the Hospital Management Software (HMS), remains the most frequent point of failure.

AI for DevOps: Fueling Innovation at Scale | Full DBTA Webinar

AI innovation moves fast, but without compliant data access, even the best ML, AI, and analytics initiatives can stall. In this webinar roundtable, experts from Perforce Delphix, 3T Software Labs, and Redgate explore how organizations can accelerate AI delivery without compromising data privacy, security, or compliance. You’ll hear practical insights and real-world examples on how to remove one of the biggest bottlenecks in modern software and data workflows: access to safe, usable, production-like data.

Data Products for Qlik Analytics - Data Quality - Advanced Data Validation Rules - Part 7

Welcome to Part 7 of the “Data Products for Qlik Analytics” series! In this episode, we take Data Quality to the next level by building advanced data validation rules using IF / THEN / ELSE logic within Qlik Analytics. You’ll learn how to create conditional validation logic that evaluates relationships across multiple columns, enabling smarter and more dynamic data quality checks for your data products.

Address the Long Tail of Legacy Applications with AI Modernization

The pressure to scale AI is on, forcing most organizations to take a serious look at their legacy technology stacks and reinstate failed or postponed modernization projects. AI both requires and enables a modern enterprise. Traditional barriers to modernization—such as time, cost, and business disruption—are now significantly reduced with the introduction of AI modernization tools.

Your AI Pilot is Lying to You: Why Enterprise Tech Needs a Trust Score

Understand how to close the gap between AI experimentation and enterprise production. Shub Agarwal, Founder of the AI Trust Lab at USC and author of Successful AI Product Creation: A Nine-Step Framework, shares his AI product management framework for taking enterprise AI strategy from demo to production, drawing on two decades of product leadership at Amazon and Fortune 50 firms. He breaks down why experimentation must tie directly to business OKRs, the four mindset shifts leaders need to scale AI responsibly, and how the AI Trust Lab is building a benchmark evaluation framework for AI model trust and governance.

API Gateway vs AI Gateway - What Actually Changed?

Kong's AI Gateway applies the same architectural pattern as the API Gateway — now governing LLM, MCP, and agent traffic at the infrastructure layer. Just as API gateways abstracted rate limiting, auth, and caching across microservices, AI gateways do the same for large language models and agents — with token budgets, semantic caching, and semantic routing replacing their REST equivalents. Kong breaks this into three layers: LLM Gateway, MCP Gateway for tool calls, and Agents Gateway for agent-to-agent traffic.#Shorts.

What are Virtual Users (VUs) in Load Testing? Definition + Examples

Virtual users (VUs) are the simulated humans that hit your system during a load test. They’re the load. Where real users come from browsers and apps, VUs come from a test harness. JMeter threads, k6 worker goroutines, Locust greenlets. Each VU sends requests, waits for responses, sometimes pauses (“think time”), and repeats. Aggregate enough VUs and you get traffic that looks like a real audience.