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AI-driven capabilities to implement specific code changes across a codebase and generate diffs.
Distinct from Inline Code Execution: Candidates focus on simple execution or tracking, not the autonomous implementation of features by AI agents.
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This project is a comprehensive collection of Python programming education materials, including tutorials, exercises, and curated code samples. It serves as a learning curriculum and software engineering toolkit, utilizing Jupyter Notebooks to combine executable code with descriptive educational text. The repository provides practical implementation guides for building large language model applications, such as retrieval-augmented generation systems, stateful AI agents, and machine learning workflows. It distinguishes itself by offering a structured approach to agentic coding workflows, cover
Guides the implementation of AI-driven capabilities to autonomously execute code changes and generate diffs across a codebase.