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Source channel @githubtrending · Post #15510 · Feb 20

#go#ai_agents#ai_security_tool#anthropic#autonomous_agents#golang#gpt#graphql#multi_agent_system#offensive_security#open_source#openai#penetration_testing#penetration_testing_tools#react#security_automation#security_testing#security_tools#self_hosted PentAGI is an AI-powered tool that automates penetration testing with smart agents using 20+ pro tools like nmap and metasploit in a safe Docker sandbox. It researches vulnerabilities, executes attacks, stores knowledge for reuse, and creates detailed reports via a simple web UI. Quick setup needs Docker, an LLM API key (OpenAI/Anthropic), and `docker compose up -d`. This saves you hours of manual work, speeds up secure testing, cuts errors, and helps find issues faster for better protection. https://github.com/vxcontrol/pentagi

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Илья AGI TV 🤖

@ilia_plasma · Post #148 · 10/08/2023, 12:16 PM

Пока весь мир ждет доступа к новой модели со зрением GPT-4V(ision), опенсорс команда (пара азитов со степенью PhD из американских вузов) уже выпустили свой аналог и бесплатную версию #LLaVA (Large Language and Vision Assistant), которая выдает результат (не) хуже GPT4V и может работать локально. Вот такая скорость развития и конкуренции в этом новом #AI рынке. 🧠LLava - вебсайт 📄WhitePaper 🧬Github code 🔋Demo для потестить на своих дикпиках 🦒Colab (для запуска у себя на серваке)

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

#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