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Source channel @githubredteam · Post #84404 · 5月16日

🚨 GitHub 监控消息提醒 🚨发现关键词:#漏洞#检测#分析 📦项目名称:Open-Audit 👤项目作者:elegent-administrator 🛠开发语言: Python ⭐Star数量: 0 | 🍴Fork数量: 0 📅更新时间: 2026-05-16 03:56:40 📝项目描述: Open Audit是面向企业研发、开发者群体的AI智能明文代码安全审计工具,基于Python语言开发、FastCGI架构搭建,融合Semgrep工具链与自主研发的AI Agent,精准匹配数字时代代码安全审计的市场核心需求。工具直击行业传统审计工具误报率高、扩展能力弱、无法检测逻辑漏洞三大痛点,通过AI Agent深度分析漏洞代码上下文实现误报过滤,开放标准化接口支持企业自定义扩展漏洞检测方向,依托Agent的逻辑推理能力突破常规工具技术瓶颈,实现逻辑漏洞精准审计,同时完成跨平台适配,为中小微企业、互联网研发团队、个人开发者提供高效、可定制、高精准的代码安全审计解决方案,全方位筑牢代码研发安全防线。 🔗点击访问项目地址

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GitHub Trends

@githubtrending · Post #15143 · 2025/09/14 12:00

#python#llms#mlx MLX LM is a Python tool that helps you run and fine-tune large language models (LLMs) efficiently on Apple Silicon Macs. It connects easily to thousands of models on Hugging Face, supports model quantization to save memory, and allows distributed training. You can generate text or chat with models via simple commands or Python code. It also offers features like prompt caching and memory optimization for handling long texts, making it faster and less resource-heavy. This means you can run powerful AI models locally on your Mac without needing expensive cloud services, saving cost and improving speed. https://github.com/ml-explore/mlx-lm

GitHub Trends

@githubtrending · Post #14655 · 2025/05/01 13:30

#typescript#electron#llama#llms#lora#mlx#rlhf#transformers Transformer Lab is a free, open-source tool that lets you easily work with large language models on your own computer, offering one-click downloads for popular models like Llama3 and Mistral, fine-tuning across different hardware (including Apple Silicon and GPUs), and features like chatting, training, and evaluating models through a simple interface—saving you from complex setups like CUDA or Python version issues[1][2][5]. https://github.com/transformerlab/transformerlab-app

GitHub Trends

@githubtrending · Post #15614 · 2026/04/13 11:30

#typescript#ai#cuda#mlx#qwen3_tts#qwen3_tts_ui#voice_ai#voice_clone#whisper Voicebox is a free, open-source voice synthesis studio that lets you clone voices, generate speech in 23 languages, and apply audio effects—all running privately on your computer. You can create realistic voice clones from just seconds of audio, use five different text-to-speech engines for different needs, add effects like reverb and pitch shift, and build multi-voice projects with a timeline editor. The key benefit is complete privacy: your voice data and AI models never leave your machine, unlike cloud-based alternatives. It also includes an API for building voice-powered applications and works across Mac, Windows, and Linux with GPU acceleration support. https://github.com/jamiepine/voicebox

GitHub Trends

@githubtrending · Post #14684 · 2025/05/08 12:00

#python#apple_silicon#audio_processing#mlx#multimodal#speech_recognition#speech_synthesis#speech_to_text#text_to_speech#transformers MLX-Audio is a powerful tool for converting text into speech and speech into new audio. It works well on Apple Silicon devices, like M-series chips, making it fast and efficient. You can choose from different languages and voices, and even adjust how fast the speech is. It also includes a web interface where you can see audio in 3D and play your own files. This tool is helpful for making audiobooks, interactive media, and personal projects because it's easy to use and provides high-quality audio quickly. https://github.com/Blaizzy/mlx-audio

GitHub Trends

@githubtrending · Post #15600 · 2026/04/04 11:30

#python#apple_silicon#florence2#idefics#llava#llm#local_ai#mlx#molmo#paligemma#pixtral#vision_framework#vision_language_model#vision_transformer MLX-VLM lets you run, chat with, and fine-tune Vision Language Models (VLMs) plus audio/video models on your Mac using MLX—install easily with `pip install -U mlx-vlm`. Use CLI for quick text/image/audio generation (e.g., `mlx_vlm.generate --model ... --image photo.jpg`), Gradio UI for chats, Python scripts, or a FastAPI server with OpenAI-compatible endpoints supporting multi-images/videos. Features like TurboQuant cut KV cache memory by 76%, and LoRA/QLoRA fine-tuning works on consumer hardware. You benefit by experimenting with powerful multimodal AI locally—fast, memory-efficient, no cloud costs, perfect for Mac users tweaking models affordably. https://github.com/Blaizzy/mlx-vlm