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Source channel @githubtrending · Post #15513 · Feb 20

#python#agents#claude#cursor#databricks#vibecoding The Databricks AI Dev Kit enhances AI-driven development by providing your coding assistant (Claude Code, Cursor, etc.) with trusted Databricks knowledge and best practices. It includes a Python library, MCP server with 50+ tools, markdown skills teaching Databricks patterns, and a web-based builder app. You can build Spark pipelines, jobs, dashboards, knowledge assistants, and deploy ML models faster and smarter. The benefit is that your AI coding assistant gains direct access to Databricks functionality and patterns, enabling you to develop data and AI applications more efficiently with built-in governance and best practices. https://github.com/databricks-solutions/ai-dev-kit

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djangoproject

@djangoproject · Post #153 · 09/03/2016, 08:20 PM

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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.

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