SenseCraft AI is a free, web-based platform designed for beginners, focusing on a no-code approach and application-orientation to simplify and accelerate the creation of AI applications.
This article provides a step-by-step guide on how to configure model output using MQTT for the XIAO ESP32S3 Sense board on the SenseCraft AI platform.
Learn how to build Llama 3.2-Vision locally in a chat-like mode, and explore its Multimodal skills on a Colab notebook.
Google DeepMind introduced PaliGemma 2, a new family of Vision-Language Models with parameter sizes ranging from 3 billion to 28 billion, designed to address challenges in generalizing across different tasks and adapting to various input data types, including diverse image resolutions.
A new study reveals how the brain compensates for rapid eye movements, maintaining a stable visual perception despite dynamic visual input. Researchers found that this stability mechanism breaks down for non-rigid motion like rotating vortices.
Microsoft has released the OmniParser model on HuggingFace, a vision-based tool designed to parse UI screenshots into structured elements, enhancing intelligent GUI automation across platforms without relying on additional contextual data.
Simon Willison explains how to use the mistral.rs library in Rust to run the Llama Vision model on a Mac M2 laptop. He provides a detailed example and discusses the memory usage and GPU utilization.
Meta releases Llama 3.2, which features small and medium-sized vision LLMs (11B and 90B) alongside lightweight text-only models (1B and 3B). It also introduces the Llama Stack Distribution.
This project demonstrates how to use the ESP32-CAM to capture an image of a vehicle's license plate, send it to a cloud server for recognition, and display the recognized number plate on an OLED screen. The project includes setup instructions, code, and component details.
MLX-VLM: A package for running Vision LLMs on Mac using MLX.
