Over the past two years, the pace of innovation for AI code assistance has been nothing short of astounding. We’ve moved from “enhanced autocomplete” systems to ecosystems of AI agents capable of completing complex tasks and cranking out prodigious amounts of code. At the same time, developers are being asked to build, test, and deploy … continue reading
Many organizations believe they’ve modernized their data architectures, yet still struggle with latency, scaling, and AI readiness. Despite major investments in cloud infrastructure, data systems often remain constrained by assumptions and architectures rooted in an earlier era. As data continues to underpin nearly every digital experience (including agentic AI), enterprises are reexamining the foundations of … continue reading
For years, software security focused on the final product: the code that ships. Today, attackers are increasingly targeting the systems that build the software itself. The shift is logical. Breaching a single app yields limited returns, whereas compromising the infrastructure that builds thousands of apps can quietly scale impact across an organization. As application security … continue reading
In many ways, generative AI has made finding information on the Internet a lot easier. Instead of spending time scrolling through Google search results, people can quickly get the answers they’re looking for with a simple natural language prompt. However, sometimes people have questions that require recent information, and because LLMs are trained on past … continue reading
If bad data in e-commerce costs money, bad data in healthcare tech costs lives. As the industry races to comply with federal interoperability mandates (like the ONC’s Cures Act rules) and adopts FHIR (Fast Healthcare Interoperability Resources) standards, developers are battling a massive architectural headache: the “duplicate patient” crisis. When hospital networks merge, or when … continue reading
Right now, there’s a massive opportunity hiding in plain sight for most engineering teams. While AI coding assistants have become standard equipment in software development, our first-party research shows that only 23% of those teams are actually extracting meaningful productivity gains from these tools. The remaining 77% have the same powerful technology at their disposal, … continue reading
The rise of AI-infused applications, particularly those leveraging Large Language Models (LLMs), has introduced a major challenge to traditional software testing: non-determinism. Unlike conventional applications that produce fixed, predictable outputs, AI-based systems can generate varied, yet equally correct, responses for the same input. This unpredictability makes ensuring test reliability and stability a daunting task. A … continue reading
It’s easy to convert a physical address, like 12 Main Street, into its latitude and longitude coordinates, but there are many situations where you might want to do the opposite: get the closest physical address of coordinates. This process, called reverse geocoding, is accomplished by performing a geospatial proximity search of coordinates against a database … continue reading
Companies that are collecting data need to ensure that data is correct in order to gain value from it. It’s easy for a user to fill out a form incorrectly—whether intentionally or accidentally—so to help companies validate the names in their lists, the data company Melissa offers a name verification API called Global Name. Global … continue reading
Contact data can be an extremely valuable resource for a business, but only if the data is actually correct and up-to-date. Names, addresses, phone numbers, and email addresses are all data points that can change in the years since a customer first fills out a form. Melissa’s SmartMover Cloud API provides a way to validate addresses … continue reading
AI is transforming the software landscape, with many organizations integrating AI-driven workflows directly into their applications or exposing their functionality to external, AI-powered processes. This evolution brings new and unique challenges for automated testing. Large language models (LLMs), for example, inherently produce non-deterministic outputs, which complicate traditional testing methods that rely on predictable results matching … continue reading
AI promised to simplify project management. In practice, it often did the opposite. Instead of fewer tools and clearer execution, teams now juggle “AI add-ons” layered onto fragmented chat platforms, task trackers, and reporting systems that were never designed to work together. The result is familiar: constant context switching, duplicated effort, and project managers spending … continue reading
The adoption of AI in enterprise organizations is causing an evolution in the practice of strategic portfolio management (SPM). The changes reshaping this — lean portfolio management, shorter application delivery cycles and the rise of agentic AI — are redefining how organizations align investment with execution. Many organizations that have brought AI into their operations … continue reading
In an increasingly interconnected business world, being able to connect business intelligence (BI) tools to internal applications or data sources is a must. Fortunately, much of the industry has standardized around REST APIs, which provides a starting point for making these connections, but it’s not a perfect system as it stands today. Progress Principal Sales … continue reading
