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Source channel @githubtrending · Post #15404 · Jan 9

#shell Superpowers turns a coding agent into a disciplined helper that first clarifies what you want, then designs, plans, and implements features using clear steps and strict test‑driven development. It automatically manages branches, breaks work into tiny tasks, uses sub‑agents with built‑in reviews, and enforces quality checks before merging. You benefit by getting more reliable code, less babysitting of the AI, safer experimentation in isolated branches, and a repeatable workflow that feels like working with a careful junior engineer who always follows best practices. https://github.com/obra/superpowers

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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,