The fitness industry is no stranger to ‘smart’ equipment, and what distinguishes one product from another ultimately comes down to user experience. Product success depends on stability, something top of mind for developers at Tonal. Ranked as one of New York Magazine’s best smart home training solutions 2022 and Men’s Health’s best connected cable machine 2022, Tonal literally sets the bar for smart home trainers.

Errors are part of building software. Even if you are one of the fabled 10X engineers, errors are still going to happen. When an error does occur, typically you are going to look at the stack trace to understand the why and who for triaging. But figuring out who to assign an issue to just based on the stack trace can be difficult. How many times do you see a stack trace in a Sentry issue, head to GitHub, and try to figure out who changed the line of code in question? Often would be our guess.

Today Codecov is joining the Sentry family. Codecov began as a code coverage reporting tool in 2014 and has since emerged as a market leader in the test analytics space. Codecov makes coverage actionable for over two dozen test frameworks, and has helped over a million software developers improve their approach to testing, coverage, and code reliability. You might be asking, what do test analytics have to do with application monitoring?

Sentry helps every developer diagnose, fix, and optimize the performance of their code, and we need to deliver high quality stack traces in order to do so. You might have noticed a significant improvement in Sentry JavaScript stack traces recently. In this blog post, we want to explain why source maps are insufficient for solving this problem, the challenges we faced, and how we eventually pulled it off by parsing JavaScript.

So you’re building a mobile app that’s performing big data requests; or crunching big data. But now you’re asking yourself: With Sentry’s Custom Instrumentation you can keep an eye on those big data-handling functions. Let’s see how you can implement them in your Storyboard and SwiftUI projects.

Profiling is an important tool in every developer’s toolkit because it provides a granular view into the execution of your program from your production environment. This is an important concept, as performance bottlenecks can often be very hard or even impossible to reproduce locally due to external constraints or loads only seen in a production environment.

Profiling is an important tool in every developer’s toolkit because it provides a granular view into the execution of your program from your production environment. This is an important concept, as performance bottlenecks can often be very hard or even impossible to reproduce locally due to external constraints or loads only seen in a production environment. Python is one of the most popular programming languages available, and it is one of the core technologies we use at Sentry.

Not in a reading mood? You also can watch the talk I gave at DjangoCon 2022. One of Sentries core company values is “for every developer”. We want to support every developer out there with our tools. But not every developer uses the newest or widely adopted tech stack, so we also try to support older versions of libraries and frameworks.

Sentry recently launched Performance Issues, a feature to help developers discover and fix common performance problems in their projects. We tested this project internally and with alpha users, so when we finally turned it on for all Sentry users, we were delighted (and dismayed) to hear from Carlton Gibson, current Django fellow and great human, that Sentry had.

Today we are releasing Dynamic Sampling, available to all new customers, and opt-in for existing customers. This goes beyond a new feature however and is an overhaul to the way we package Sentry’s Performance Monitoring product. We are saying goodbye to the days of static, magic number sampling configured within the SDK and moving to a world of flexibility.