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.

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.

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.

Many organizations are turning to SharePoint to store their files and share documents. Allowing your systems to access these files might seem like a challenge at first, but with the help of the Microsoft Graph API, you can quickly download the file to start processing on your back-end. Download the example solution to get started. Use the created functions in your solution to simplify Microsoft Graph integration.

Today Snowflake is launching and open-sourcing with an Apache 2.0 license the Snowflake Arctic embed family of models. Based on the Massive Text Embedding Benchmark (MTEB) Retrieval Leaderboard, the largest Arctic embed model with only 334 million parameters is the only one to surpass average retrieval performance of 55.9, a feat only less practical to deploy models with over 1 billion parameters are able to achieve.

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