Search Results for "model-builder"

...Designed to be self-hosted, it ensures complete data sovereignty by keeping your content local or within your own infrastructure. The platform supports 16+ AI providers—including OpenAI, Anthropic, Ollama, Google, and LM Studio—allowing flexible model choice and cost optimization. Open Notebook enables users to organize and analyze multi-modal content such as PDFs, videos, audio files, web pages, and Office documents. It combines full-text and vector search with context-aware AI chat to deliver insights grounded in your own research materials. With advanced features like multi-speaker podcast generation, customizable content transformations, and a comprehensive REST API, Open Notebook provides a powerful and extensible research environment.

...It supports rich text and Markdown editing alongside outline-style notes, attachments, workspaces, folders, and tags, making it suitable for everything from quick daily journaling to long-term documentation projects. Poznote includes a RESTful API that enables programmatic access and integration with other tools, and it even offers an MCP (Model Context Protocol) server to let AI assistants interact with your notes via natural language commands. In addition to basic note creation and editing, the platform features powerful full-text search, public sharing links, backups and exports, trash management, and multi-user support with profiles isolated per user, giving individuals and teams control over their content.

Super comprehensive deep learning notes is a massive and well-structured collection of deep learning notebooks that serve as a comprehensive study resource for anyone wanting to learn or reinforce concepts in computer vision, natural language processing, deep learning architectures, and even large-model agents. The repository contains hundreds of Jupyter notebooks that are richly annotated and organized by topic, progressing from basic Python and PyTorch fundamentals to advanced neural network designs like ResNet, transformers, and object detection algorithms. It’s not just a dry code repository; it includes theoretical explanations alongside hands-on examples, loss function explorations, optimization routines, and full end-to-end experiments on real datasets, making it highly suitable for both self-study and classroom use.