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Source channel @githubtrending · Post #14879 · Jun 28

#cplusplus#cpp#hacktoberfest#iot#iot_device#iot_edge#microcontroller#microsoft_for_beginners#python#raspberry_pi#rpi You can learn the basics of the Internet of Things (IoT) through a free 12-week course with 24 lessons that guide you step-by-step in building real projects like plant monitoring, vehicle tracking, and smart cooking timers. Each lesson includes quizzes, instructions, challenges, and solutions to help you understand sensors, cloud connections, security, and AI on devices. The course uses real hardware or virtual options, making it easy to practice hands-on skills. This project-based learning helps you gain practical IoT knowledge useful for many industries, improving your tech skills and job readiness. https://github.com/microsoft/IoT-For-Beginners

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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