Best AI tools for< Boat Repair Technician >

crewAI is a cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. It provides a flexible and structured approach to AI collaboration, enabling users to define agents with specific roles, goals, and tools, and assign them tasks within a customizable process. crewAI supports integration with various LLMs, including OpenAI, and offers features such as autonomous task delegation, flexible task management, and output parsing. It is open-source and welcomes contributions, with a focus on improving the library based on usage data collected through anonymous telemetry.

The crewAI Tools repository provides a guide for setting up tools for crewAI agents, enabling the creation of custom tools to enhance AI solutions. Tools play a crucial role in improving agent functionality. The guide explains how to equip agents with a range of tools and how to create new tools. Tools are designed to return strings for generating responses. There are two main methods for creating tools: subclassing BaseTool and using the tool decorator. Contributions to the toolset are encouraged, and the development setup includes steps for installing dependencies, activating the virtual environment, setting up pre-commit hooks, running tests, static type checking, packaging, and local installation. Enhance AI agent capabilities with advanced tooling.

This repository provides a guide for setting up tools for crewAI agents to enhance functionality. It offers steps to equip agents with ready-to-use tools and create custom ones. Tools are expected to return strings for generating responses. Users can create tools by subclassing BaseTool or using the tool decorator. Contributions are welcome to enrich the toolset, and guidelines are provided for contributing. The development setup includes installing dependencies, activating virtual environment, setting up pre-commit hooks, running tests, static type checking, packaging, and local installation. The goal is to empower AI solutions through advanced tooling.

AIS-catcher for Android is a multi-platform AIS receiver app that transforms your Android device into a dual channel AIS receiver. It directly accesses a Software Defined Radio USB device to pick up AIS signals from nearby vessels, visualizing them on a built-in map or sending messages via UDP to plotting apps. The app requires a RTL-SDR dongle or an AirSpy device, a simple antenna, an Android device with USB connector, and an OTG cable. It is designed for research and educational purposes under the GPL license, with no warranty. Users are responsible for prudent use and compliance with local regulations. The app is not intended for navigation or safety purposes.

DOM to Semantic Markdown is a tool that converts HTML DOM to Semantic Markdown for use in Large Language Models (LLMs). It maximizes semantic information, token efficiency, and preserves metadata to enhance LLMs' processing capabilities. The tool captures rich web content structure, including semantic tags, image metadata, table structures, and link destinations. It offers customizable conversion options and supports both browser and Node.js environments.

Trinity is an Explainable AI (XAI) Analysis and Visualization tool designed for Deep Learning systems or other models performing complex classification or decoding. It provides performance analysis through interactive 3D projections that are hyper-dimensional aware, allowing users to explore hyperspace, hypersurface, projections, and manifolds. Trinity primarily works with JSON data formats and supports the visualization of FeatureVector objects. Users can analyze and visualize data points, correlate inputs with classification results, and create custom color maps for better data interpretation. Trinity has been successfully applied to various use cases including Deep Learning Object detection models, COVID gene/tissue classification, Brain Computer Interface decoders, and Large Language Model (ChatGPT) Embeddings Analysis.

Phidata is a framework for building AI Assistants with memory, knowledge, and tools. It enables LLMs to have long-term conversations by storing chat history in a database, provides them with business context by storing information in a vector database, and enables them to take actions like pulling data from an API, sending emails, or querying a database. Memory and knowledge make LLMs smarter, while tools make them autonomous.

VideoLLaMA 2 is a project focused on advancing spatial-temporal modeling and audio understanding in video-LLMs. It provides tools for multi-choice video QA, open-ended video QA, and video captioning. The project offers model zoo with different configurations for visual encoder and language decoder. It includes training and evaluation guides, as well as inference capabilities for video and image processing. The project also features a demo setup for running a video-based Large Language Model web demonstration.

Whetstone.ChatGPT is a simple light-weight library that wraps the Open AI API with support for dependency injection. It supports features like GPT 4, GPT 3.5 Turbo, chat completions, audio transcription and translation, vision completions, files, fine tunes, images, embeddings, moderations, and response streaming. The library provides a video walkthrough of a Blazor web app built on it and includes examples such as a command line bot. It offers quickstarts for dependency injection, chat completions, completions, file handling, fine tuning, image generation, and audio transcription.

MisguidedAttention is a collection of prompts designed to challenge the reasoning abilities of large language models by presenting them with modified versions of well-known thought experiments, riddles, and paradoxes. The goal is to assess the logical deduction capabilities of these models and observe any shortcomings or fallacies in their responses. The repository includes a variety of prompts that test different aspects of reasoning, such as decision-making, probability assessment, and problem-solving. By analyzing how language models handle these challenges, researchers can gain insights into their reasoning processes and potential biases.

MFLUX is a line-by-line port of the FLUX implementation in the Huggingface Diffusers library to Apple MLX. It aims to run powerful FLUX models from Black Forest Labs locally on Mac machines. The codebase is minimal and explicit, prioritizing readability over generality and performance. Models are implemented from scratch in MLX, with tokenizers from the Huggingface Transformers library. Dependencies include Numpy and Pillow for image post-processing. Installation can be done using `uv tool` or classic virtual environment setup. Command-line arguments allow for image generation with specified models, prompts, and optional parameters. Quantization options for speed and memory reduction are available. LoRA adapters can be loaded for fine-tuning image generation. Controlnet support provides more control over image generation with reference images. Current limitations include generating images one by one, lack of support for negative prompts, and some LoRA adapters not working.

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Military submarine terminal simulator. Copyright (C) 2023, Sourceduty - All Rights Reserved.

Plan, Cast, Catch!

A virtual sailing instructor who is an expert on MarineVerse

Your must have guide from fishing trip planning to cooking your catch, now with fish ID from images.

Expert in knot tying, ropes, and their applications.

Your guide to sailing and racing on a Hobie Wave.

An assistant for accurate tide timings based on location.

Friendly designer of realistic boats from descriptions using DALLE.

Educational guide on reading maps and navigation