Systems | Development | Analytics | API | Testing

As artificial intelligence (AI) adoption has grown over the last several years, it has become increasingly clear to companies and observers that successful AI requires real-time data and context. And it’s not just AI—every critical business function that relies on data inputs needs that data to be as trustworthy and fresh as possible.

‍ Healthcare’s most significant bottleneck in 2025 isn’t technology, it’s people. Across hospitals and clinics, staff shortages, burnout, and scheduling chaos are threatening the quality of care and driving operational costs upward. The U.S. alone is projected to face a shortfall of up to 3.2 million healthcare workers by 2026, according to Mercer.

‍ Healthcare isn’t broken because of a lack of technology. It’s broken because most of the tech running hospitals today is outdated, overloaded, or barely held together with patches. Talk to any healthcare CIO and you’ll hear something like: “Our EHR is older than half our staff, the vendor sunset half the modules, and our IT team hasn’t slept since last quarter.” This isn’t drama, it’s reality.

Cowboy is a small, fast, and modern HTTP server for Erlang/OTP. It particularly shines when handling multiple concurrent connections with minimal overhead. This makes it a perfect choice for building lightweight, real-time streaming services. In this article, we’ll build a tiny real-time text pub/sub server using Cowboy. Our service supports HTTP-based publishing, allowing clients to subscribe using either WebSockets or Server-Sent Events (SSE).

It’s a common idea across the tech industry: AI is fueling a new wave of technological innovation. Test automation is no exception. Over three decades, enterprise QA customers have turned to Tricentis Tosca to transform inefficient testing practices with model-based automation. Now, the company you’ve relied on for scalable, codeless test automation is entering a new era of agentic testing with Tosca’s cloud deployment as its foundation.

DreamFactory's df-spark integration provides a production-ready, plug-and-play connector that automatically generates REST APIs for Apache Spark clusters, Databricks SQL Warehouses, Delta Lake tables, and Unity Catalog resources. This integration enables organizations to expose Spark data through standardized REST endpoints without writing custom API code, supporting both SQL-based workloads and advanced Spark Connect operations.

Annotations are a way to draw, write, and comment directly on top of full-stack session recordings. Now, instead of sketching ideas in isolation, teams can mark up actual user sessions, highlighting specific UI elements, API calls, and backend traces that need attention. Whiteboarding tools are indispensable in system design for visually conveying concepts, ideas, and rough plans. They tap into our natural preference for visual learning.

‍ Think about this: you’re a high-net-worth client logging into your wealth management portal. Instead of a sleek, tailored dashboard, you're greeted by slow loading, opaque fee disclosures, and cryptic charts that require a finance degree to decipher. Frustrating, right? That’s precisely the problem many legacy wealth platforms are saddled with today.

‍ Every day, hidden in plain sight, fraud drains hundreds of billions of dollars in the financial world. In fact, in 2023 alone, losses from fraud-related scams and bank schemes reached an estimated USD 485.6 billion globally. For organisations from fintechs to enterprise incumbents, every transaction, API call, and user onboarding step is now a potential battleground.

Many organizations try to implement driver based forecasting but struggle to deliver accurate or timely forecasts. This happens not because the method is flawed, but because the underlying systems, data structure, and planning models cannot support continuous updates or cross-functional alignment. Teams often rely on spreadsheets, manual drivers, and disconnected EPM tools that make it difficult to apply consistent logic across departments.