Systems | Development | Analytics | API | Testing

We started 2023 by closing our fiscal year and achieving hypergrowth — once again. We’ve surpassed 600 customers, increased our employee base by 22% year over year, opened our newest office in Singapore following rapid growth in the Asia Pacific region, and expanded meetups to 46 countries and more than 12,000 attendees.

The past few years—accelerated by the pandemic—have seen people move more of their work and personal lives online. There’s a huge growth in teams working remotely, either on a full or part-time basis. And many companies have embraced the remote-first model - offering remote-first or hybrid positions as standard. However this shift to remote, online-first interaction brings challenges. For remote workers, one of the biggest impacts has been in the way that they work together.

Generics in Go refers to a language feature that allows creating functions, data structures, and interfaces that can work with different types. In other words, generics enable the creation of code that is not limited to a specific type or data structure. Before the introduction of generics in Go, developers had to write multiple functions to handle different types of data. This approach was often cumbersome and led to code duplication.

Have you ever wondered what happens when you visit a website or use an app that takes a long time to load? It seems to take an eternity at times! This is where software testing comes in. Use our load test tool you can test how well a website or app can handle a large number of people, all while using it at the same time.

The future of data integration is no-code data integration platforms, and we have the research to prove it.

There are many factors to keep in mind when thinking about how to keep systems and applications secure. First and foremost is the actual environment where the application is deployed. The environment consists of the Operating System (OS), libraries included with the OS, and software that runs in the background. In a perfect world, an application would have very few or zero dependencies on the environment, thereby eliminating the need to keep dependencies up to date.

Lost in the talk about OpenAI is the tremendous amount of compute needed to train and fine-tune LLMs, like GPT, and Generative AI, like ChatGPT. Each iteration requires more compute and the limitation imposed by Moore’s Law quickly moves that task from single compute instances to distributed compute. To accomplish this, OpenAI has employed Ray to power the distributed compute platform to train each release of the GPT models.

In today's data-driven world, product analytics is crucial in understanding user behavior, improving product features, and driving business growth. However, product analytics alone may not provide a complete picture of user interactions and business performance. Integrating product analytics with other data sources and systems is essential to gain deeper insights and make more informed decisions.