Agencies typically have older procurement systems or too many systems. In either case, they are data entry intensive and error-prone, negatively impacting the procurement process and outcomes. The Office of Federal Procurement Policy’s Procurement Acquisition Lead Time memo from 2021 specifically calls for government modernization by “leveraging technology to modernize operations and help the workforce move from low- to high-value activities.”

As software development evolves, test automation plays an increasingly significant role in improving efficiency and quality. This is particularly true in the field of Continuous Integration and Continuous Deployment (CI/CD), where CI CD test automation has become a crucial component. Recent data from the CDF highlights the impact of these practices. Developers using CI/CD tools are consistently outperforming their peers, with at least a 15% higher likelihood of being top performers.

The Apache Flink community released Apache Flink 1.20 this week. In this blog post, we'll highlight some of the most interesting additions and improvements. You’ll find a comprehensive rundown of all of the updates in the official release announcement. Recent Flink releases have emphasized improving Flink’s usability, not only for developers, but also for those operating Flink clusters, and this theme continues in this latest release.

Data fabric technology can help in a significant way, which is why it’s becoming a go-to method of choice for data management. Data fabric technology integrates information scattered across disparate sources and creates a unified data model through a virtual data layer. Data fabrics let you leave your existing data architecture intact, pulling directly from your current data sources and updating them in real time. So why is a data fabric approach the solution to your complex data challenges?

It seems that there’s an email service provider (ESP) for everyone these days, whatever your marketing goals or industry. But this wide range of options also makes it harder for small and medium businesses to upgrade to the right advanced option. Without an enterprise’s broad budget, you need to balance your marketing needs with your costs carefully. These factors also make it important to know when it’s worth switching to a more complex platform.

Mobile users are unforgiving, and discovering an issue in the wild might result in users abandoning the app for good. Therefore, mobile apps and websites must be rock solid before launching to meet market demands. In this article, we explore the top 10 mobile testing challenges that app developers and testers face, as well as recommended strategies and best practices for addressing them effectively.

BT Group's Smart Event Mesh: Centralized Event Streaming With Decentralized Customer Experience, Automation, and a Foundation Built on Confluent. BT Group is a British multinational telecommunications holding company headquartered in London, England. It has operations in around 180 countries and is one of the largest telecommunications companies in the world, providing a range of products and services including fixed-line, broadband, mobile, and TV.

Legacy enterprises can often resist software testing modernization. An established brand presence and loyal customer base intensify the pressure to introduce new features and technology and can act as barriers to change.

For many Gen AI-based applications, usage-based billing is crucial. It is especially important if you are using third-party models that charge you for tokens used. You want to ensure that your cost of tokens used is covered by your customers, in addition to charging for your own value add. In this tutorial, we will build a quick Chat Bot API using OpenAI’s ChatGPT, and then use Moesif and Stripe to meter the number of tokens used and report the usage to Stripe to be charged.

Businesses are seeking ways to collect all the data from their digital sources and draw out patterns that can reinterpret human participation with automation. A vanguard in this attempt is the emergence of large language models (LLMs) powered by artificial intelligence (AI).