Systems | Development | Analytics | API | Testing

This blog explores how DreamFactory leverages its robust features to perform database analysis with AI, ensuring secure and efficient data operations. We will discuss the platform’s ability to generate dynamic APIs, provide real-time data insights, and maintain stringent security measures to protect data integrity.

We are thrilled to announce Tricentis Copilot solutions, a collection of advanced generative AI capabilities available across our products that help customers boost their efficiency throughout the entire testing lifecycle. With Tricentis Copilot solutions, you can autogenerate manual tests from requirements, optimize your portfolio, autogenerate custom code, and get meaningful insights.

If you were to stop someone walking down the street and ask them how long artificial intelligence, or AI, has been a hot topic, they might say it’s something that’s emerged mostly in recent years. But AI has been around for a long time, with the term first being coined as long ago as 1955. Generative AI however is a different beast, and one that's largely responsible for moving the topic of AI to the tip of everyone’s tongues – from consumers to enterprises alike.

From the end user’s perspective, the microapps and microservices often seem to provide the same functionality. However, development teams need to think about microapps vs. microservices when building products. Before you commit time to microapps or microservices architecture, take some time to consider how these options will affect user experience (UX), user interface (UI), and security of your websites, web apps, and mobile apps.

When comparing the types of APIs (application programming interfaces), API architecture types and different API categories are incredibly important. The following article will discuss API architecture and protocol types (REST and SOAP) and three types of API categories (open, internal, and partner). Here’s the key things to know about the types of APIs your organization might need: Table of Contents.

Support for model fine-tuning in BigQuery takes input text (the prompt) and the expected output, and teaches the model to mimic the behavior.

The topic of testing AI and ensuring its responsibility, safety, and security has never been more urgent. Controversy and incidents of AI misuse have increased 26-fold since 2021, highlighting growing concerns. As users quickly find out, AI tools are not infallible; they can make mistakes, display overconfidence, and lack critical questioning. The reality of the market is that AI is prone to error. This is exactly why testing AI is crucial. But how do we test AI?

We all love the ease and affordability of SaaS (Software-as-a-Service) tools. We expect our SaaS apps to be available whenever we need them, building our business processes around this assumption. At WSO2, we are grateful that many organizations trust our SaaS solutions, and we continually work to ensure this trust is well-founded. It’s worth taking a moment to understand the impact when those mission-critical apps go offline. Just one minute of downtime can cost businesses thousands of dollars.

Extracting values from server responses is a common operation in scripting scenarios for performance testing. In most cases it is a mandatory step in order to reproduce realistic traffic using dynamic values generated on server side like sessionIds or Tokens. The most known way to extract values is using regular expression. While regular expressions are efficient for raw text-based responses, they are less efficient for structured responses like XML or JSON.