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Source channel @githubtrending · Post #15215 · Oct 12

#csharp#agent#ai#avalonia#chat#claude#deepseek#gpt_oss#grok#llm#mcp#ollama#openai#rag#ui_automation Everywhere is an AI assistant that works directly on your screen without needing screenshots or app switching. You just press a shortcut and it understands the context instantly to help you with tasks like fixing errors, summarizing articles, translating text, or improving your writing tone. It supports many AI models and runs on Windows, with macOS and Linux versions coming soon. This tool saves you time and effort by giving quick, relevant help exactly where you need it, making your work and browsing smoother and more efficient. It also supports multiple languages and has a modern, easy-to-use interface. https://github.com/DearVa/Everywhere

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djangoproject

@djangoproject · Post #118 · 08/08/2016, 11:44 AM

https://docs.python.org/3/library/multiprocessing.html multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows. The #multiprocessing module also introduces #APIs which do not have analogs in the #threading#module. A prime example of this is the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data #parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using Pool,