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Source channel @githubtrending · Post #15066 · Aug 16

#python#agents#ai#api_gateway#asyncio#authentication_middleware#devops#docker#fastapi#federation#gateway#generative_ai#jwt#kubernetes#llm_agents#mcp#model_context_protocol#observability#prompt_engineering#python#tools The MCP Gateway is a powerful tool that unifies different AI service protocols like REST and MCP into one easy-to-use endpoint. It helps you manage multiple AI tools and services securely with features like authentication, retries, rate-limiting, and real-time monitoring through an admin UI. You can run it locally or in scalable cloud environments using Docker or Kubernetes. It supports various communication methods (HTTP, WebSocket, SSE, stdio) and offers observability with OpenTelemetry for tracking AI tool usage and performance. This gateway simplifies connecting AI clients to diverse services, making development and management more efficient and secure. https://github.com/IBM/mcp-context-forge

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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