We continue to make exciting progress developing new libraries for inclusion in Boost, and expanding those already available. New Libraries int128 Int128 (https://github.com/cppalliance/int128) is a small library that has more or less fallen out of work on Decimal. It provides two type: an unsigned 128-bit integer and a signed 128-bit integer. Since my last post the library should now be ready for beta, and subsequently production use. Much effort was put into optimizing every operation on...

Mailman3 Deployment The Boost mailing list has been in place for around 25 years. During at least the last 3 years we have been exploring an upgrade to mailman3 which is a completely new framework based on Django and multiple other components. The new server finally went live in June 2025 at the same URL: https://lists.boost.org. Mail archives going back to 2004 were imported into the new system. Static file copies of all archives are also hosted at listarchives.boost.org. Thanks to some o...

I’m happy to announce that I’m now a co-maintainer of Boost.Redis, a high-level Redis client written on top of Asio, and hence sister of Boost.MySQL. I’m working with its author, Marcelo, to make it even better than what it is now (and that’s a lot to say). First of all, we’re working on improving test coverage. Boost.Redis was originally written making heavy use of asio::async_compose. If you have a JavaScript or Python background, this approach will feel natural to you, since it’s similar ...

Introduction Boost libraries typically maintain granular unit tests using Boost.Build (B2). B2 provides a run rule that makes it easy to define many independent test targets from a single source file or executable. Each test case can be listed, invoked, and reported separately, which improves developer workflow, test clarity, and CI diagnostics. However, Boost’s CMake integration has lacked this granularity. When Boost libraries are built with CMake, the typical approach is to define a sing...

AI generated code has some fun side effects. Whilst generating a lot of code and testing it using Microsoft Visual Studio I had the odd experience of giving the AI model a line of buggy code, and it forthrightly states to replace this faulty line with this new line. On close inspection both lines are identical! Ha - in the AI world this is known as a “hallucination” - amusing but not the answer I was looking for. Other odd side effects of AI generated code include having unused “#include <...