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    Our Free Plans just got better! | Auth0

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  • 1
    Strix

    Strix

    Open-source AI hackers to find and fix your app’s vulnerabilities

    Strix is an open source agent-driven security platform that uses autonomous AI agents to identify, investigate, and validate vulnerabilities in software applications. The system is designed to mimic the behavior of real attackers by executing dynamic testing and verifying findings through proof-of-concept exploitation. Unlike traditional vulnerability scanners that rely heavily on static analysis, Strix agents actively run code, probe systems, and attempt exploitation to confirm whether...
    Downloads: 27 This Week
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  • 2
    gptcommit

    gptcommit

    A git prepare-commit-msg hook for authoring commit messages with GPT-3

    ...Commit messages are a key channel for developers to communicate their work with others, especially in code reviews. When making complex code changes, it can be tedious to thoroughly document the contents of each change. I often felt the impulse to just title my commit “fix bug” and move on. Surfacing these changes with gptcommit helps the author and reviewer by bringing attention to these additional changes.
    Downloads: 6 This Week
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  • 3
    JSON_REPAIR

    JSON_REPAIR

    A python module to repair invalid JSON from LLMs

    json_repair is an open-source Python library designed to automatically fix malformed JSON data and convert it into valid, parseable structures. The tool is particularly useful in scenarios where JSON output is generated by large language models or external services that may produce syntactically invalid responses. Instead of failing when encountering errors such as missing quotes, trailing commas, or incomplete objects, the library analyzes the malformed data and reconstructs it into valid JSON. ...
    Downloads: 1 This Week
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  • 4
    Agentless

    Agentless

    An agentless approach to automatically solve software development

    ...It then generates multiple candidate patches for the identified locations using language model reasoning and diff-style edits. In the final stage, the framework validates potential patches by running regression tests and additional reproduction tests to confirm whether the fix resolves the original error. Based on these results, the system ranks the candidate patches and selects the most reliable solution to submit.
    Downloads: 1 This Week
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    $300 Free Credits for Your Google Cloud Projects

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  • 5
    NeMo Curator

    NeMo Curator

    Scalable data pre processing and curation toolkit for LLMs

    NeMo Curator is a Python library specifically designed for fast and scalable dataset preparation and curation for large language model (LLM) use-cases such as foundation model pretraining, domain-adaptive pretraining (DAPT), supervised fine-tuning (SFT) and paramter-efficient fine-tuning (PEFT). It greatly accelerates data curation by leveraging GPUs with Dask and RAPIDS, resulting in significant time savings. The library provides a customizable and modular interface, simplifying pipeline...
    Downloads: 0 This Week
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  • 6
    LLM-Aided OCR Project

    LLM-Aided OCR Project

    Enhances Tesseract OCR output using LLMs (local or API)

    ...The system first extracts raw text using OCR engines and then applies language models to analyze and correct recognition errors based on context. This AI-assisted correction process helps reconstruct missing characters, fix formatting mistakes, and produce more coherent text outputs. The project is particularly useful for digitizing historical documents, research papers, and scanned materials where traditional OCR often struggles. It also includes tools for processing batches of images or documents, enabling automated document digitization workflows.
    Downloads: 0 This Week
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  • 7
    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...
    Downloads: 0 This Week
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