Systems | Development | Analytics | API | Testing

6 Practical Examples of APIs in Everyday Life

APIs are bits of software that act as interpreters for two different programs. They'll connect to each service via endpoints and relay messages back and forth, doing the work of software integration for you. DreamFactory is a secure, self-hosted enterprise data access platform that provides governed API access to any data source, connecting enterprise applications and on-prem LLMs with role-based access and identity passthrough. But how does this actually look in the real world?

Medical Practice Management Software: Features, Development Roadmap, Costs

Running a medical practice today involves much more than treating patients. Clinics must manage scheduling, insurance verification, billing, compliance, and reporting, often across multiple disconnected systems. When these workflows depend on manual processes or outdated tools, administrative work quickly overwhelms staff.

How to scale API standards across large teams | Swagger Studio

When multiple designers and teams contribute APIs, you face inconsistent schemas, divergent patterns, and broken assumptions. However, the "shift-left" approach to API standardization helps you catch issues early, automate compliance, and maintain quality without manual gating – making your API program truly scalable. In this video, SmartBear Senior Solution Engineer Joe Joyce demonstrates how to enforce consistent API standards across large development teams using Swagger Studio's governance, collaboration, and CI/CD integration features.

How does BearQ autonomous QA work? Your top questions answered

Testing software at scale has always been a race against change. Then, AI-coding turned what was once a challenge into a crisis: rapid development cycles accelerated by AI have made it impossible to maintain comprehensive test coverage and catch issues before they impact users. In SmartBear’s Closing the AI Software Quality Gap Study, 60% of software experts told us they experienced quality issues as development outpaces testing.

LLM Output Evaluation & Hallucination Detection

As enterprises transition from experimenting with Generative AI (GenAI) to deploying Large Language Models (LLMs) in production, a critical challenge has emerged: reliability. While LLMs demonstrate remarkable proficiency in automating workflows from drafting executive communications to summarizing complex legal corpora, their susceptibility to "hallucinations" remains a significant operational risk. The scale of this challenge is non-trivial.