A summary of a workshop presented at PyCon US on building software with LLMs, covering setup, prompting, building tools (text-to-SQL, structured data extraction, semantic search/RAG), tool usage, and security considerations like prompt injection. It also discusses the current LLM landscape, including models from OpenAI, Gemini, Anthropic, and open-weight alternatives.
This article details a new plugin, llm-video-frames, that allows users to feed video files into long context vision LLMs (like GPT-4.1) by converting them into a sequence of JPEG frames. It showcases how to install and use the plugin, provides examples with the Cleo video, and discusses the cost and technical details of the process. It also covers the development of the plugin using an LLM and highlights other features in LLM 0.25.
A review of the Qwen2.5-VL-32B large language model, noting its performance, capabilities, and how it runs on a 64GB Mac. Includes a demonstration with a map image and performance statistics.
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.
