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at to another you write a pure function that takes two arguments the input and output file names and does the conversion then you submit a bunch of tasks basically function and argument pairs and the executor runs them with maximal parallelism i decided to implement this in pystd to make things simpler it does not yet support futures for return values writing a thread pool executor needs some fundamental building blocks such as threads mutexes and condition variables once those were implemented and working writing the actual business logic turned out to be fairly simple in fact it took less time than writing the png processing code with libpng first you need to write this helper class to register you task for running it also stores all arguments etc that the task needs to do its thing yes this has a bit of that icky 90s java feeling to it but otoh it is fairly simple has unambiguous ownership semantics and compiles massively faster than your typical template monstrosity using this is just a matter of globbing for png files in the input dir and submitting tasks for them the ux is a bit verbose it could probably be made nicer with a helper method or two still not too shabby for something written by hand in a single afternoon for those interested in details the code for this program is in the pngtest branch nibble stew raysan5 raylib 6 2 a simple and easy to use library to enjoy videogames programming raysan5 raylib c futures part 4 what if i want an immediate on thread std async providing the case that the c language won t do the post c futures part 4 what if i want an immediate on thread std async appeared first on the old new thing the old new thing ast grep ast grep 0 51 0 a cli tool for code structural search lint and rewriting written in rust ast grep ast grep ai writes all your code now how will you ever debug it greg law mark williamson accu 2026 accuconf rr debugger rr 5 10 0 record and replay framework rr debugger rr qt creator 21 beta2 released we are happy to announce the release of qt creator 21 beta2 qt blog conan workspaces a monolithic super build of your packages on demand there is a long running argument in c and c organizations should a large codebase be a monorepo with a single monolithic build or a set of independently versioned components each in its own repository consumed as packages each camp has good reasons the component approach gives you per component versioning and releases pre built binaries and builds that only touch what changed the monorepo approach gives you live at head development change a header in a low level library and the next build recompiles every consumer in one build tree the component approach usually loses that developer experience when needing to work simultaneously on multiple different packages but the component approach can be better for large organizations with different teams working at different paces using package releases to work more independently this post is about not having to choose conan workspaces let you keep your components as versioned packages in different git repositories with precompiled binaries for efficient consumption and at the same time bring back multiple components and collapse them into a single monolithic super build for development ux and convenience the problem changing a library deep in the graph let s use a small example graph game is an application that depends on engine which depends on ai and graphics which both depend on mathlib all of them are static libraries each one lives in its own git repository and each is a conan package in our server the repositories are conanci_mathlib conanci_ai conanci_graphics conanci_engine and conanci_game the workspace itself is in conanci_workspace the dependencies are declared with version ranges for example in engine class enginerecipe conanfile name engine version 1 0 requires ai 1 0 graphics 1 0 i want to add a feature to ai an api compatible change that will become ai 1 1 and verify that game still builds and works correctly with it engine sits between the two its code won t change but it s a static library compiled against ai s headers so it has to be rebuilt with plain packages that means conan create in ai then rebuilding engine and game against it and repeating the whole loop through the conan cache for every iteration editable packages conan editable add improve this since engine can consume ai directly from its source folder but they re still independent projects you build ai then engine then game each with its own build tree its own conan_toolchain cmake and often its own ide window editables are also global to the conan cache so it s easy to forget one is active and have an unrelated project pick it up what a workspace is a workspace is a set of locally editable packages defined by a conanws yml and optionally a conanws py at the root of a folder unlike conan editable add these editables are not global they only apply to conan commands running inside that folder a workspace can be used in two ways orchestrated conan builds each package of the workspace separately in the correct order conan workspace build this works even when the packages have different build systems and it can also work when there are multiple final consumer packages in the workspace like different final executable applications belonging to a system monolithic super build all the workspace packages are collapsed into a single node of the dependency graph conan installs only the external dependencies and generates one toolchain and one set of find_package files you then build everything as one project the monolithic mode is where the paradigm changes and it s what the rest of this post is about this is the flow we are going to follow creating the packages everything in this post is reproducible you need conan 2 31 or newer cmake 3 25 or newer git and a c compiler we start from the conanci_workspace repository git clone https github com conan io conanci_workspace git game dev cd game dev python bootstrap py packages created mathlib 1 0 ai 1 0 graphics 1 0 engine 1 0 game 1 0 in a real organization the packages would already be in your server built by ci bootstrap py plays that role it clones each component repository into a temporary folder and runs conan create on them in dependency order any extra arguments are forwarded to conan create for example python bootstrap py s build_type debug adding packages to a workspace packages know where their sources are so a workspace can be built from package references instead of repository urls i want to work on ai and check the result in game engine sits between them and has to be part of the workspace too because its binary in the conan cache was compiled against the old ai conan workspace add ref ai 1 0 cloning git repo checkout 8595303a211ba4556f257017e789e45a515adf50 reference ai 1 0 added to workspace conan workspace add ref engine 1 0 cloning git repo checkout 98aee101ef132b2f7a63fbe99686991955da8817 reference engine 1 0 added to workspace conan workspace add ref game 1 0 cloning git repo checkout 88df8ca649eda1e2f30fa599fdcefc8f7bea4e4c reference game 1 0 added to workspace conan could clone each repository at the exact commit that produced the package not some branch head that may have moved on because the packages capture their git origin url and commit when they are created after these commands the workspace folder looks like this each package folder is still its own git repository game dev conanws yml now lists ai engine and game conanws py super build definition see below cmakelists txt super project see below bootstrap py ai clone of conanci_ai at 8595303 engine clone of conanci_engine at 98aee10 game clone of conanci_game at 88df8ca note adding engine by hand is not strictly necessary after adding ai and game conan workspace complete finds the packages in the conan cache that depend on workspace packages and adds them too cloning them at their recorded commit if one is missing conan workspace super install fails and suggests running it the super build definition besides conanws yml the workspace repository contains two files that define the super build the first is conanws py which defines a minimal conanfile with the cmakedeps and cmaketoolchain generators for the super project and a workspace class the interesting part of the latter is build_order class myworkspace workspace def build_order self order pkgs for level in order for pkg in level pkgs append f pkg ref name pkg folder content set conan_ws_build_order format join pkgs save self build conanws_build_order cmake content build_order receives the topologically sorted list of workspace packages translating it into something the build system understands is up to the workspace here we write a small cmake conanws_build_order cmake file and the second file the super project cmakelists txt at the workspace root consumes it cmake_minimum_required version 3 25 project superbuild cxx include fetchcontent generated by conan workspace super install see conanws py build_order include build conanws_build_order cmake foreach pair conan_ws_build_order string replace parts pair list get parts 0 pkg list get parts 1 folder fetchcontent_declare pkg source_dir cmake_current_list_dir folder system override_find_package fetchcontent_makeavailable pkg get_target_property type pkg type if not type strequal executable just because the original packages didn t properly do it but can be fixed here add_library pkg pkg alias pkg endif endforeach the key is override_find_package when engine cmakelists txt calls find_package ai config required cmake doesn t look for an installed ai config cmake it resolves to the ai subproject in the same build the alias provides the namespaced ai ai target that the package s find_package would have defined none of the packages own cmakelists txt files needed any change their build scripts are identical whether they build standalone as a package in the cache or inside the super build neither of these two files is specific to this workspace they work for any set of cmake based packages so they can live in a shared repository like this one or be generated by your own conan new template installing and building the super build now we install the workspace conan workspace super install requirements ai 1 0 editable engine 1 0 editable game 1 0 editable graphics 1 0 77561e5ec993160f3103f3300ea01102 cache mathlib 1 0 03ead0629a968a8daa7c5ed84418a895 cache collapsing workspace packages collapsed graph graph root conanws py base project conanfile home user game dev requirements graphics 1 0 77561e5ec993160f3103f3300ea01102 cache mathlib 1 0 03ead0629a968a8daa7c5ed84418a895 cache this is what monolithic means in practice ai engine and game are now a single project and graphics and mathlib the parts of the graph that are not in the workspace remain pre built binaries in the conan cache i get the monorepo experience for 3 packages without building the other 2 or the other 300 in a real organization the build_order method of our conanws py has written the build order of the workspace packages to a cmake file which the super project cmakelists txt includes cat build conanws_build_order cmake set conan_ws_build_order ai ai engine engine game game now we can build the whole super project and run the application as usual cmake preset conan release conan default on windows cmake build preset conan release 28 built target ai 57 built target engine 100 built target game build release _deps game build game path is different in win build _deps game build release game exe mathlib 1 0 mathlib maths release ai ws some artificial intelligence for aliens release ai ws intelligence level 0 graphics 1 0 checking if things collide release engine ws computing some game things release game ws fun game release the output tells the two worlds apart mathlib 1 0 and graphics 1 0 are binaries from the cache while ai engine and game were compiled from the workspace sources which the example marks by printing ws instead of a version on windows and other multi config generators the executable is at build _deps game build release game exe making the change now the actual work ai gets a new feature a configurable number of aliens as a new parameter with a default value existing callers don t need any change so this is an api compatible minor change a include ai h b include ai h ai_export void ai int intelligence 0 ai_export void ai int intelligence 0 int aliens 3 a src ai cpp b src ai cpp void ai int intelligence void ai int intelligence int aliens std cout std cout one build command from the workspace root cmake build preset conan release 14 building cxx object _deps ai build cmakefiles ai dir src ai cpp o 28 linking cxx static library libai a 42 building cxx object _deps engine build cmakefiles engine dir src engine cpp o 57 linking cxx static library libengine a 71 linking cxx executable game build release _deps game build game path is different in win build _deps game build release game exe ai ws intelligence level 0 aliens 3 look at the third line of the build engine cpp was recompiled although nobody touched it and git status in the engine repository is clean it s recompiled because it includes ai h and the build system tracks that dependency across what are in reality different repositories this is exactly the monorepo experience the build system decides what has to be rebuilt not the developer that recompilation is also necessary for correctness engine was compiled against the old ai h so as the header changed it has to be rebuilt even if the change is api compatible in an ide you open the workspace root and get a single project go to definition on ai in engine cpp jumps into ai s source rather than into a header in the conan cache and the debugger steps across all three components what to keep in mind for monolithic super builds a monolithic build is one build and that has implications one toolchain for everything when using monolithic super builds all workspace packages share a single conan_toolchain cmake and a single set of dependency files what packages do in their own generate doesn t apply automatically the same goes for options like shared the super build conanfile must define its own options or aggregate the packages ones explicitly subprojects need to be well behaved the fetchcontent approach works best when each package s cmakelists txt is at the root of its repository and defines the same targets its find_package would provide the alias trick covers the most common gap one node per dependency a dependency can t appear twice for example once as a tool_requires in the build context and once in the host context since conan 2 33 conan resolves this kind of ambiguity in favor of the host context and visible requirements and prints a warning when it does cmake is the paved road not the only one conan new workspace generates a cmake super project but nothing in the model is cmake specific an msbuild solution aggregating vcxproj su...