Systems | Development | Analytics | API | Testing

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Top Benefits of API Observability for Modern Applications

API observability is essential for enhancing performance, speeding up issue resolution, and tightening security. An API observability tool is crucial for understanding and managing the complex web of API interactions within modern enterprise applications. With the benefits of API observability, you can dive deep into API behavior, ensuring reliability and better user experiences.

S1.E10: Unshifted performance testing | QA Therapy Podcast

With the increasing prevalence of visual elements in websites and mobile apps, ensuring their integrity is crucial to retaining user engagement and preventing churn. To address these challenges, we welcome Andrew Knight, aka Automation Panda, our QA Therapist, to provide expert guidance on visual testing. Andy serves as a developer advocate at Applitools and is renowned as the director of Test Automation University.

iOS App Development Cost: A Detailed Estimation

If you compare the world 10 years ago with today's era, you can easily see the difference. From the difference, we don’t mean how the world looks, rather we mean how it functions now and used to function then. What do you think has changed in how the world functions now compared to back then? Is it purely due to digitalization? Or is it just a mere change? Keeping these questions aside, let us tell you that recent data suggests that people spend around 3 hours and 15 minutes on their phones daily.

Using Moesif, AWS, and Stripe to Monetize Your AI APIs Part-2: Setting up Metering and API Access

In the previous article, we set up the AI API with AWS Lambda and Gateway, integrated it with Moesif, and then connected Stripe with Moesif. We now have the infrastructure to begin billing for API usage. In this article, we move on to configuring Moesif with the following steps in the API monetization journey: First, let’s set the prices we want to charge for API usage in Moesif.

Ensuring Accuracy and Reliability with ML Model Validation

As demand for machine learning (ML) grows, rigorous testing and quality assurance are crucial. ML models need quality training data and robust algorithms. Without thorough testing, inaccurate outcomes can occur, especially in sectors like healthcare, finance, and transportation. A 2023 ScienceDirect report found data leakage in 294 academic publications across 17 disciplines, highlighting the need to address this issue in ML-based science.

RAG vs Fine-Tuning: Navigating the Path to Enhanced LLMs

RAG and Fine-Tuning are two prominent LLM customization approaches. While RAG involves providing external and dynamic resources to trained models, fine-tuning involves further training on specialized datasets, altering the model. Each approach can be used for different use cases. In this blog post, we explain each approach, compare the two and recommend when to use them and which pitfalls to avoid.

How To Write Test Cases? Detailed Guide With Examples

Test case is the backbone of any testing project. The art of software testing is to write the right test case. Firstly it’s not about how you write it, but rather what scenarios you are writing for. After that, we need to closely tie our test cases with test design and test execution. Let’s explore how to write test cases in the most strategic fashion.