Showing 3 open source projects for "git:/git.code.sf.net/p/docfetcher/code"

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  • Create stunning, professional email signatures in minutes Icon
    Create stunning, professional email signatures in minutes

    For companies looking to create, assign and manage all their employees email signatures and add targeted marketing banners.

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  • MaintainX is the world-leading mobile-first workflow management platform for industrial and frontline workers. Icon
    MaintainX is the world-leading mobile-first workflow management platform for industrial and frontline workers.

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  • 1
    Claw Code

    Claw Code

    AI agent harness for AI coding agents

    Claw Code is an open-source AI agent harness project focused on building better tools for orchestrating and managing autonomous coding agents. It originated as a clean-room reimplementation inspired by the architecture of Claude Code, aiming to replicate core concepts without using proprietary code. The project provides a Python-based foundation for experimenting with agent workflows, tool integration, and task execution pipelines.
    Downloads: 63 This Week
    Last Update:
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  • 2
    Desloppify

    Desloppify

    Agent harness to make your slop code well-engineered and beautiful

    Desloppify is a utility-focused project aimed at improving the quality, structure, and clarity of generated or written text by removing redundancy, noise, and unnecessary verbosity. It is designed to “clean up” outputs, particularly those produced by AI systems, making them more concise, readable, and professional. The system likely applies heuristics or transformation rules to identify repetitive patterns, filler content, and stylistic inconsistencies. This makes it especially useful in...
    Downloads: 2 This Week
    Last Update:
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  • 3
    OSS-Fuzz Gen

    OSS-Fuzz Gen

    LLM powered fuzzing via OSS-Fuzz

    OSS-Fuzz-Gen is a companion project that helps automatically create or improve fuzz targets for open-source codebases, aiming to increase coverage in OSS-Fuzz with minimal maintainer effort. It analyses a library’s APIs, examples, and tests to propose harnesses that exercise parsers, decoders, or protocol handlers—precisely the code where fuzzing pays off. The system integrates with modern LLM-assisted workflows to draft harness code and then iterates based on build errors or low coverage signals. Importantly, it aligns with OSS-Fuzz conventions, generating corpus seeds, build rules, and sanitizer settings so projects can plug in quickly. Reports highlight what functions were targeted, how coverage evolved, and where manual hints could unlock more paths. ...
    Downloads: 0 This Week
    Last Update:
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