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Source channel @githubtrending · Post #15180 · Sep 29
#other You can use a set of markdown files to guide AI coding assistants step-by-step in building software features. This method breaks down your feature idea into a clear Product Requirement Document (PRD), then into detailed tasks, and finally lets the AI work on each task one at a time while you review and approve progress. This structured workflow helps you keep control, avoid errors, and track progress visually, making AI-assisted development more reliable and manageable. It works with many AI tools and improves the quality and clarity of AI-generated code, saving you time and reducing frustration during complex feature development. https://github.com/snarktank/ai-dev-tasks
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@djangoproject · Post #98 · 07/11/2016, 12:22 PM
https://docs.python.org/3/library/asyncio.html #asyncio #Asynchronous programming is more complex than classical “#sequential” programming: see the Develop with asyncio page which lists common traps and explains how to avoid them. Enable the debug mode during development to detect common issues.
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@djangoproject · Post #153 · 09/03/2016, 08:20 PM
http://wla.berkeley.edu/~cs61a/fa11/lectures/streams.html In this chapter, we continue our discussion of real-world applications by developing new tools to process #sequential#data. In Chapter 2, we introduced a sequence interface, implemented in Python by built-in data types such as #tuple and #list. #Sequences supported two operations: querying their length and accessing an element by index. In Chapter 3, we developed a user-defined implementations of the sequence interface, the Rlist class for representing recursive lists. These sequence types proved effective for representing and accessing a wide variety of sequential #datasets.