I am delighted to announce the 3.4 release of Austin. If you haven't heard of Austin before, it is an open-source frame stack sampler for CPython, distributed under the GPLv3 license. It can be used to obtain statistical profiling data out of a running Python application without a single line of instrumentation. This means that you can start profiling a Python application straight away, even while it's running in a production environment, with minimal impact on performance. https://github.com/P403n1x87/austin The Austin VS Code extension provides a smooth interactive profiling experience, with interactive flame graphs straight into the text editor to allow you to quickly jump to the source code with a simple click. You can find the extension on the Visual Studio Marketplace and install it directly from VS Code: https://marketplace.visualstudio.com/items?itemName=p403n1x87.austin-vscode To see how to make the best of Austin with VS Code to find and fix performance issues, check out this blog post, which shows you the editor extension in action on a real Python project: https://p403n1x87.github.io/how-to-bust-python-performance-issues.html This latest release builds on top of the significant performance improvements introduced in the 3.3 release. Benchmarking figures (see https://github.com/P403n1x87/austin/pull/126#issuecomment-1279765640 for some of the numbers) show that Austin 3.4 continues to provide a high sample rate at low sampling intervals, with improvements of about 4x compared to Austin 3.2. One major feature of this new release is support for Python 3.11. Profiles of applications run with this version of the interpreter will have fully qualified scope names, which makes for more granular profiling data. Besides the new support for Python 3.11, the other major feature of this new release is the new MOJO binary output format that builds on top of the just-mentioned performance improvements to generate much more compact sample output files. The VS Code extension provides support for the new format starting from version 0.11.0 and so it has you covered already! More details about the MOJO file format can be found on the Wiki: https://github.com/P403n1x87/austin/wiki/The-MOJO-File-Format Other utilities to convert between file formats can be found in the austin-python Python package https://github.com/P403n1x87/austin-python More details about what's new and bugfixes can be found in the changelog https://github.com/P403n1x87/austin/blob/master/ChangeLog Austin is a pure C application that has no dependencies other than the C standard library. Its source code is hosted on GitHub at https://github.com/P403n1x87/austin The README contains installation and usage details, as well as some examples of Austin in action. Details on how to contribute to Austin's development can be found at the bottom of the page. Austin can be installed easily on the following platforms and from the following sources: Linux: - Snap Store - AUR - Conda Forge macOS: - Homebrew - Conda Forge Windows: - Chocolatey - Scoop An Austin docker image, based on the latest Ubuntu image, is also available from Docker Hub: https://hub.docker.com/r/p403n1x87/austin Austin is also simple to compile from sources as it only depends on the standard C library, if you don't have access to the above-listed sources. You can stay up-to-date with the project's development by following Austin on Twitter (https://twitter.com/AustinSampler). Austin is a free and open-source project. A lot of effort goes into its development to ensure the best performance and that it stays up-to-date with the latest Python releases. If you find it useful, consider sponsoring this project on GitHub at https://github.com/sponsors/P403n1x87. All the best, Gabriele <p><a href="https://github.com/P403n1x87/austin">Austin 3.4</a> - frame stack sampler for CPython. (30-Oct-22)</p>