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Source channel @githubtrending · Post #15600 · Apr 4

#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

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@githubtrending · Post #15204 · 10/08/2025, 11:30 AM

#java#cloud#coap#dashboard#iot#iot_analytics#iot_platform#iot_solutions#java#kafka#lwm2m#microservices#middleware#mqtt#netty#platform#snmp#thingsboard#visualization#websockets#widgets ThingsBoard is an open-source IoT platform that helps manage and analyze data from connected devices. It allows users to collect data, create real-time dashboards, and automate tasks using a powerful rule engine. This platform supports various protocols like MQTT and HTTP, making it easy to connect devices. Users can also define relationships between devices and assets, and trigger alarms based on specific conditions. The benefit is that it simplifies IoT project development, making it scalable and efficient for applications like smart farming, smart offices, and more. https://github.com/thingsboard/thingsboard