Systems | Development | Analytics | API | Testing

It’s not a surprise that in today’s challenging economic landscape, rising costs pose a significant threat to the telecommunications industry. Consider that in 2022, Bain Capital was predicting that Telcos would grapple with increased personnel and escalating operating costs due to inflation. And here we are.

React Native is an open-source framework that Meta (then Facebook) designed to build native applications for Android, iOS, and other platforms. The framework is built so anyone familiar with React can easily build cross-platform applications without learning a completely different framework or language. However, there are differences in the functionality of the two, including: There are many more differences, but we’ll get into those later in the post.

Ably was proud to celebrate our 8th birthday this year, and we’ve enjoyed reflecting on some of our achievements since launching in 2016. When we launched, we believed that realtime interactions would underpin our everyday digital experiences, rather than just augment them. And so we set out on a mission to build the definitive realtime experience platform.

Market data analytics has always been a classic use case for Apache Kafka. However, new technologies have been developed since Kafka was born. Apache Flink has grown in popularity for stateful processing with low latency output. Streamlit, a popular open source component library and deployment platform, has emerged, providing a familiar Python framework for crafting powerful and interactive data visualizations. Acquired by Snowflake in 2022, Streamlit remains agnostic with respect to data sources.

Businesses that are best able to leverage data have a significant competitive advantage. This is especially true in financial services, an industry in which leading organizations are in constant competition to develop the most responsive, personalized customer experiences. Often, however, legacy infrastructure, data silos, and batch systems introduce significant technical hurdles.

The life sciences industry is rapidly embracing artificial intelligence (AI) to automate complex processes, enhance efficiency, and improve outcomes. From drug development to patient care, AI-driven automation is transforming the landscape. Here are some of the top artificial intelligence use cases for process automation in the pharmaceutical industry.

There are many ways AI can improve government case management processes. Examples include: AI capabilities often depend on a large language model (LLM), an advanced AI system that can understand, analyze, interpret, and generate human language. It uses deep learning techniques to predict and produce coherent text based on input prompts. A large language model trained on government agency data is capable of tasks like text extraction, translation, summarization, and conversational responses.

Google regularly updates its developer policies to make sure Google Play is a safe and trustworthy platform for everyone. While this is unquestionably necessary and essential to protect users and their data, keeping up with the latest changes to ensure applications remain safe and compliant can feel a little overwhelming for developers.

APIs are the bread and butter of the internet. The ability to interact with our applications programmatically enables interoperability and makes our lives as developers easier. Unfortunately, web applications are vulnerable to malicious actors that seek to misuse them or degrade their performance, which is why rate limiting is an important part of any API.

Regardless of whether an engineering team is colocated, remote, or hybrid, they face the same inherent communication pitfalls. This article explores strategies to address them.