Showing 4 open source projects for "programming learning system"

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  • 1
    Compound Engineering

    Compound Engineering

    Official Compound Engineering plugin for Claude Code, Codex, Cursor

    The Compound Engineering plugin project is an AI-driven workflow system designed to improve software development by turning each unit of work into a reusable and compounding asset. It provides a structured set of commands and agents that guide developers through stages such as brainstorming, planning, execution, review, and knowledge capture. The core philosophy is to reduce technical debt by emphasizing thorough planning and continuous learning, ensuring that each iteration improves future work rather than increasing complexity. ...
    Downloads: 18 This Week
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  • 2
    Pro Workflow

    Pro Workflow

    Claude Code learns from your corrections: self-correcting memory

    Pro Workflow is a productivity framework for Claude Code that introduces self-improving workflows through memory, context engineering, and structured agent orchestration. The system learns from user corrections over time, storing feedback and refining its behavior across sessions to improve accuracy and efficiency. It supports advanced development setups such as parallel worktrees, enabling multiple tasks to be handled simultaneously without interference. The framework includes a collection...
    Downloads: 1 This Week
    Last Update:
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  • 3
    Continuous Claude v3

    Continuous Claude v3

    Context management for Claude Code. Hooks maintain state via ledgers

    ...It also includes a layered code analysis pipeline to reduce token usage and maintain relevant context efficiently. This continuous learning environment enables workflows such as bug fixing, refactoring, planning, and exploratory investigation while minimizing the need to re-explain context manually.
    Downloads: 0 This Week
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  • 4
    anti-distill

    anti-distill

    Anti-distillation for employee Skills

    ...The system is particularly relevant in contexts where models are exposed via APIs and risk being reverse-engineered through repeated querying. Its design reflects growing concerns around model security and competitive advantage in AI systems. It may also include experimental benchmarks to evaluate how resistant a model is to distillation attempts.
    Downloads: 0 This Week
    Last Update:
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