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.

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.

With the ever-increasing pressure to release faster and better due to the growing user expectations about quality, reliability, and zero tolerance for defects, effective test management in the development of applications has never been so critical. Xray is a Jira-native application designed to streamline test management processes within your project management tool.

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

Organizations that monitor their LLMs will benefit from higher performing models at higher efficiency, while meeting ethical considerations like ensuring privacy and eliminating bias and toxicity. In this blog post, we bring the top LLM metrics we recommend measuring and when to use each one. In the end, we explain how to implement these metrics in your ML and gen AI pipelines.

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.

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?

BrowserStack is a popular web and mobile testing platform. While the cross-browser testing tool has been the go-to for Quality Assurance (QA) teams and product managers since 2011, many are now turning to other solutions to simplify and automate their testing processes. But how do you pick the right BrowserStack alternative testing tool? Do you rely on user reviews, popularity, or the most budget-friendly option?

In March, Snowflake announced exciting releases, including advances in AI and ML with new features in Snowflake Cortex, new governance and privacy features in Snowflake Horizon, and broader developer support with the Snowflake CLI. Read on to learn more about everything we announced last month.