tMap::Request> request, std::shared_ptr response); void generate_path_service( const std::shared_ptr request, std::shared_ esponse> response); bool set_costmap(const std_msgs::msg::UInt8MultiArray& costmap); Path generate_global_path(Position const& start,Position const& goal); Map map_; int robot_size_; ple_srvs::srv::GetPath>::SharedPtr path_generator_service_; }; ● PathGenerator will be used to generate the path for our robot ● This code was written using example code available from the ROS 2 documentation

context of natural language processing and LLMs, a prompt is an input provided to the model to generate a response or prediction. Prompt Engineering February 2025 8 These prompts can be used to achieve optimally for your task. Output length An important configuration setting is the number of tokens to generate in a response. Generating more tokens requires more computation from the LLM, leading to higher Putting it all together Choosing between top-K, top-P, temperature, and the number of tokens to generate, depends on the specific application and desired outcome, and the settings all impact one another

SomeData& data) { simd_access::loop(0, n, [&](auto i) { const auto point = data.Generate(i); for (int j = 0; j < d; ++j) { Compute( point.GetVelocity(j) ); } }); } • No indexed access std::simd }; template requires(!std::integral) inline decltype(auto) generate_universal(const IndexType& idx, auto&& generator) { return universal_simdGenerate(i); //... } } void ComputePoints(const SomeData& data) { loop(0, n, [&](auto i) { auto point = generate_universal(i, [&](auto sc_i) { return data.Generate(sc_i); });

raises an exception, Tornado will call RequestHandler.write_error to generate an error page. tornado.web.HTTPError can be used to generate a specified status code; all other exceptions return a 500 status must use e.g. traceback.format_exception instead of traceback.format_exc). It is also possible to generate an error page from regular handler methods instead of write_error by calling set_status, writing application: application = tornado.web.Application([ (r"/", MainHandler), ], cookie_secret="__TODO:_GENERATE_YOUR_OWN_RANDOM_VALUE_HERE__") Signed cookies contain the encoded value of the cookie in addition

conclusions 11ConanCenter 12Generate CPS files from existing Conan packages ZLib Demo: Generate CPS files zlib.cps 13 … … Creates CPS Read CPS X zlib.cpsDemo: Generate CPS files $ conan install install . -g CPSDeps cppcon24/pose_ai 15CPSDeps from conan.cps import CPS class CPSDeps: def generate(self): ... mapping = {} for dep in self._conanfile.dependencies.host.values(): configure() cmake.build() # This should generate a zlib.cps def package(self): cmake = CMake(self) cmake.install() # OR this should generate a zlib.cps? def package_info(self):

Toolchains, NDKs - The space is very continuous and large (Android NDK api level) - Not easy to generate a ton of android_ndk_apiXX_archYY.cps - Avoid as much as possible - Regular libraries Focus on PRIVATE protobuf::libprotobuf) protobuf_generate_cpp(PROTO_SRCS PROTO_HDRS TARGET ${PROJECT_NAME}) protobuf_generate(LANGUAGE cpp TARGET ${PROJECT_NAME} PROTOS addressbook.proto) PRIVATE protobuf::libprotobuf) protobuf_generate_cpp(PROTO_SRCS PROTO_HDRS TARGET ${PROJECT_NAME}) protobuf_generate(LANGUAGE cpp TARGET ${PROJECT_NAME} PROTOS addressbook.proto)

the Abstract Machine • Implementation • Tool(s) to verify a program on the abstract machine and generate an executable image (compiler, linker) • Parametrized • Non-deterministic CppCon 2020 - The Abstract the Abstract Machine • Implementation • Tool(s) to verify a program on the abstract machine and generate an executable image (compiler, linker) • Parametrized • Non-deterministic CppCon 2020 - The Abstract the Abstract Machine • Implementation • Tool(s) to verify a program on the abstract machine and generate an executable image (compiler, linker) • Parametrized • Implementation-defined (and documented)

size_t>>(); 14 15 16 17 18 19 20 21 22 23 } 24 14.1PARTICLE FILTER ALGORITHM RESAMPLE 1. Generate random samples (with replacement) 2. Take N of those samples 3. Create a new particle set particles weights 16SAMPLE VIEW DEFINITION Given a particle range and a random number generator (RNG), generate a lazy computed infinite sequence of random samples (with replacement). sample(particles, gen) generator (RNG), generate a lazy computed infinite sequence of random samples (with replacement). sample(particles, gen) ➡ view IMPLEMENTATION std::generator std::views::generate (C++26) std::views::view_interface

reasoning about types; Julia simply exposes it to the programmer. The Julia compiler is able to generate efficient code in the presence of Union types with a small number of types 1, by generating specialized automatic specialization allows you to write generic algorithms and expect that the compiler will generate efficient, specialized code to handle each case you need. In cases where the number of potential = myfilter(A, kernel, Replicate()) # replicate the edge by default Together, these two methods generate an infinite recursion with A constantly growing bigger. The better design would be to define your

reasoning about types; Julia simply exposes it to the programmer. The Julia compiler is able to generate efficient code in the presence of Union types with a small number of types 1, by generating specialized automatic specialization allows you to write generic algorithms and expect that the compiler will generate efficient, specialized code to handle each case you need. In cases where the number of potential = myfilter(A, kernel, Replicate()) # replicate the edge by default Together, these two methods generate an infinite recursion with A constantly growing bigger. The better design would be to define your