Twilio Flex is a React-based web app that lets you run your contact center as a service, and years ago, while working at a previous company, I was tasked with setting it up with Sentry for application observability and error monitoring. To help you set up Flex with Sentry— and using all the lessons I learned along the way— I’ve teamed up with Bruno, Solutions Engineer, from Twilio to build a new Twilio Flex integration.

Dan Mindru is a Frontend Developer and Designer who is also the co-host of the Morning Maker Show. Dan is currently developing a number of applications including PageUI, Clobbr, and CronTool. As a developer, few things are more frustrating than an API that’s slower than molasses. You know the code works, but you know it can’t possibly be a good user experience anymore. I had one of those and looked the other way for a couple of weeks. However, some issues become personal after a while.

Today we’re launching Fair Source, a new approach to software sharing that is safe for companies to adopt and developers to use. Before Fair Source, companies that wanted to engage the developer community with their core products often did not know how to do so while maintaining control over their roadmap and business model. The result is that most software products today are closed-source. With Fair Source, companies have a new option. The Fair Source option is not theoretical.

Over the past few months, we introduced several new features and capabilities. While we released larger product updates like Trace Explorer, Insights modules, and our JavaScript V8 SDK (to name a few), it’s the smaller, iterative improvements that really make a big difference in your debugging workflow. Let’s dive into 8 recent updates that you should know about.

Monitoring tools are great at letting you know when something is broken and the overall impact. We should know, we make an error monitoring tool. Observability tools are good for well, observing. But here’s the thing, you (we) don’t observe code. We (you) push code. So what the collective “we” need is a tool that makes it easy to ship, improve, and maintain reliable and performant code.

Fetch Waterfalls are scenarios where multiple fetch requests are invoked in a sequential manner, not in parallel. This leads to some serious performance degradation. Here’s how they look: In this case, the second and third requests can be fetched in parallel, which will improve the page load and data display by 4.053 seconds. The negative performance impact with fetch waterfalls is also stacking - meaning the more requests there are, the worse the performance impact is going to be.