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Source channel @githubtrending · Post #14858 · Jun 23

#other#ai#bolt#copilot#cursor#cursorai#devin#devinai#github_copilot#lovable#open_source#replit#system_prompts#trae#trae_ai#trae_ide#v0#vscode#windsurf#windsurf_ai You can access a huge collection of over 7000 lines of official system prompts and internal tools from many AI models and agents like v0, Manus, Cursor, Replit Agent, and more. These prompts guide AI to work better by giving clear instructions, which helps the AI give more accurate and useful answers. Using these prompts can save you time, improve AI performance, and make your interactions with AI smoother and more productive. Plus, there’s a free AI security audit service to help protect your AI systems from leaks and hacks, keeping your data safe. Supporting this project helps keep these valuable resources updated. https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools

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djangoproject

@djangoproject · Post #77 · 07/05/2016, 07:46 AM

https://docs.python.org/2/library/logging.html This module defines functions and classes which implement a flexible event logging system for applications and libraries. The key benefit of having the #logging_API provided by a standard library module is that all Python modules can participate in logging, so your application log can include your own messages integrated with messages from third-party modules. The module provides a lot of functionality and flexibility. If you are unfamiliar with #logging, the best way to get to grips with it is to see the tutorials (see the links on the right). The basic classes defined by the module, together with their functions, are listed below. #Loggers expose the interface that application code directly uses. Handlers send the log records (created by loggers) to the appropriate destination. Filters provide a finer grained facility for determining which log records to output. Formatters specify the layout of log records in the final output.