GraphQL is a query language and runtime for APIs, developed by Facebook in 2012 and later open-sourced in 2015. And it has changed the way we fetch data from our server. Typically, most front-end clients – like React, Angular, Vue, or mobile apps like iOS and Android – use REST APIs to fetch data from the server. REST APIs require more HTTP calls than GraphQL, which leads to over and underfetching.
Large Language Models (LLMs) have now evolved to include capabilities that simplify and/or augment a wide range of jobs. As enterprises consider wide-scale adoption of LLMs for use cases across their workforce or within applications, it’s important to note that while foundation models provide logic and the ability to understand commands, they lack the core knowledge of the business. That’s where fine-tuning becomes a critical step.
The telecom industry is undergoing a monumental transformation. The rise of new technologies such as 5G, cloud computing, and the Internet of Things (IoT) is putting pressure on telecom operators to find new ways to improve the performance of their networks, reduce costs and provide better customer service. Cost pressures especially are incentivizing telecoms to find new ways to implement automation and more efficient processes to help optimize operations and employee productivity.
If you’re building an application today, one of your top product priorities for 2024 is almost certainly adding an AI copilot to your application. AI copilots – AI assistants powered by large language models (LLMs) and deeply embedded into applications – offer one of the most compelling opportunities to reimagine applications since the dawn of the internet.
Data is key to building resilience and achieving operational excellence—but first, your data must be intelligible. Luckily, modern BI solutions have intuitive interfaces that allow business users to build interactive data visualizations and contextual data stories. With this knowledge at their fingertips, your entire organization is empowered to make data-driven decisions.
Today, efficient and scalable testing solutions are crucial. As applications grow in complexity and the frequency of releases increases, the need for a more robust, automated, and integrated testing framework becomes critical. To meet these challenges, we created Katalon TestCloud, a cloud-hosted solution that delivers on-demand, flexible, and secure multi-browser/platform and mobile testing environments.
Realtime data is rapidly becoming a standard in many consumer applications. From responsive chat applications to low latency financial applications, nobody wants to refresh their browser for new data. With lots of data bouncing around Kafka behind a firewall, it begs the question of how you can serve this information to your users without sacrificing on latency. Ably provides a seamless way to serve this data to your end users devices, globally, through a direct integration with Confluent Cloud.
To update a document in MongoDB, I used to fetch it, update the values, and save back the entry. I would question the need for an update method. Looking back, it's evident that performance optimizations were hardly a concern when working on a personal project. Working with a larger dataset is a whole different story, though. This is where no-code tools can't help. In this article, I'll share some of my learnings when it comes to working in MongoDB with millions of documents.
