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Source channel @githubtrending · Post #14807 · Jun 8

#other#agents#agi#ai#anthropic#artifacts#awesome#awesome_list#bots#chatbot#chatgpt#claude#exploit#gemini#google#gpt#hack#jailbreak#openai#prompts#spam AI tools like autonomous software engineers can help developers by completing tasks independently or working alongside them. This can increase productivity by automating repetitive tasks, allowing developers to focus on more complex and creative work. AI also helps reduce errors and improves code quality, making the development process faster and more efficient. Overall, using AI in software development can lead to better outcomes and more innovative solutions. https://github.com/friuns2/BlackFriday-GPTs-Prompts

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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,