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Source channel @githubtrending · Post #14639 · Apr 27

#python#agent_computer_interface#ai_agents#computer_automation#computer_use#grounding#gui_agents#in_context_reinforcement_learning#memory#mllm#planning#retrieval_augmented_generation Agent S2 is a smart AI assistant that handles computer tasks by breaking them into smaller steps and using specialized tools for each part, making it highly adaptable and efficient across different systems like Windows and Android. It outperforms other AI tools in completing complex tasks, learns from experience, and adjusts plans as needed, helping users automate digital work more reliably and effectively. https://github.com/simular-ai/Agent-S

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djangoproject

@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.

djangoproject

@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.