Best AI tools for< Butler >

Content Butler is an AI-powered platform designed to simplify the creative process for content creators by providing ready-to-use, data-driven scripts. The platform offers a vast script library covering current market trends and future insights, with weekly content refreshes to keep users engaged. Leveraging advanced AI analysis, Content Butler helps users analyze and predict trends, enabling them to create fresh and relevant content. The platform also provides comprehensive script analysis with detailed insights and estimated budgets, empowering users to plan effectively. With features focused on forecasting and shaping future trends, Content Butler puts users ahead in the content creation game.

Eve is an AI-powered tool designed for labor and employment lawyers to streamline their casework, increase efficiency, and boost revenue. It acts as a 'second brain' and an extra set of hands, tailored to the lifecycle of cases. Eve assists in case intake and evaluation, drafting documents and letters, propounding discovery, responding to discovery, and more. The tool is trusted by thousands of forward-thinking plaintiff lawyers and offers features like powerful analytics, shared team inboxes, instant customer service, and easy-to-use reports.

Trello is a project management tool that helps teams organize and track their work. It is a visual tool that uses boards, lists, and cards to represent tasks and projects. Trello can be used for a variety of purposes, including project planning, task management, team collaboration, and customer relationship management. It is a cloud-based tool that can be accessed from any device with an internet connection. Trello is free to use for individuals and small teams, and there are paid plans available for larger teams and organizations.

Butterflies.ai is an AI-powered platform that enhances team collaboration and productivity. It leverages artificial intelligence to provide personalized coaching and insights to help individuals and teams improve their communication skills and work dynamics. By analyzing interactions and feedback, Butterflies.ai offers actionable recommendations for professional growth and development. The platform aims to foster a positive and inclusive work environment by promoting effective communication and fostering strong relationships among team members.

Butterfly iQ3 is a handheld, whole-body ultrasound device that provides clear and detailed images for a variety of medical applications. It is the third-generation probe from Butterfly Network, and it features double the processing power and clearer images than its predecessors. Butterfly iQ3 is also equipped with real-time AI, which can help clinicians to identify and diagnose medical conditions more quickly and accurately. The device is lightweight and portable, making it easy to use in a variety of settings, including the clinic, the hospital, and the field. Butterfly iQ3 is a valuable tool for clinicians who want to improve the quality of care they provide to their patients.

ButterReader is an innovative audio widget designed to transform blog texts into engaging, listenable content, making learning and information consumption as smooth as butter. It offers a range of customization options to tailor the widget's appearance and functionality to match your brand's style and audience preferences. With ButterReader, you can add a rich auditory layer to your website and blog posts, making them more accessible and appealing to a diverse audience.

OpenDAN is an open source Personal AI OS that consolidates various AI modules for personal use. It empowers users to create powerful AI agents like assistants, tutors, and companions. The OS allows agents to collaborate, integrate with services, and control smart devices. OpenDAN offers features like rapid installation, AI agent customization, connectivity via Telegram/Email, building a local knowledge base, distributed AI computing, and more. It aims to simplify life by putting AI in users' hands. The project is in early stages with ongoing development and future plans for user and kernel mode separation, home IoT device control, and an official OpenDAN SDK release.

This repository contains an assistant proposal for the Semantic Kernel, allowing the usage of assistants without relying on OpenAI Assistant APIs. It runs locally planners and plugins for the assistants, providing scenarios like Assistant with Semantic Kernel plugins, Multi-Assistant conversation, and AutoGen conversation. The Semantic Kernel is a lightweight SDK enabling integration of AI Large Language Models with conventional programming languages, offering functions like semantic functions, native functions, and embeddings-based memory. Users can bring their own model for the assistants and host them locally. The repository includes installation instructions, usage examples, and information on creating new conversation threads with the assistant.

Zep is a long-term memory service for AI Assistant apps. With Zep, you can provide AI assistants with the ability to recall past conversations, no matter how distant, while also reducing hallucinations, latency, and cost. Zep persists and recalls chat histories, and automatically generates summaries and other artifacts from these chat histories. It also embeds messages and summaries, enabling you to search Zep for relevant context from past conversations. Zep does all of this asyncronously, ensuring these operations don't impact your user's chat experience. Data is persisted to database, allowing you to scale out when growth demands. Zep also provides a simple, easy to use abstraction for document vector search called Document Collections. This is designed to complement Zep's core memory features, but is not designed to be a general purpose vector database. Zep allows you to be more intentional about constructing your prompt: 1. automatically adding a few recent messages, with the number customized for your app; 2. a summary of recent conversations prior to the messages above; 3. and/or contextually relevant summaries or messages surfaced from the entire chat session. 4. and/or relevant Business data from Zep Document Collections.

Awesome-Efficient-LLM is a curated list focusing on efficient large language models. It includes topics such as knowledge distillation, network pruning, quantization, inference acceleration, efficient MOE, efficient architecture of LLM, KV cache compression, text compression, low-rank decomposition, hardware/system, tuning, and survey. The repository provides a collection of papers and projects related to improving the efficiency of large language models through various techniques like sparsity, quantization, and compression.

HuggingLLM is a project that aims to introduce ChatGPT to a wider audience, particularly those interested in using the technology to create new products or applications. The project focuses on providing practical guidance on how to use ChatGPT-related APIs to create new features and applications. It also includes detailed background information and system design introductions for relevant tasks, as well as example code and implementation processes. The project is designed for individuals with some programming experience who are interested in using ChatGPT for practical applications, and it encourages users to experiment and create their own applications and demos.

MOOSE 2.0 is a leaner, meaner, and stronger tool for 3D medical image segmentation. It is built on the principles of data-centric AI and offers a wide range of segmentation models for both clinical and preclinical settings. MOOSE 2.0 is also versatile, allowing users to use it as a command-line tool for batch processing or as a library package for individual processing in Python projects. With its improved speed, accuracy, and flexibility, MOOSE 2.0 is the go-to tool for segmentation tasks.

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.

Langchain.rb is a Ruby library that makes it easy to build LLM-powered applications. It provides a unified interface to a variety of LLMs, vector search databases, and other tools, making it easy to build and deploy RAG (Retrieval Augmented Generation) systems and assistants. Langchain.rb is open source and available under the MIT License.

AIlice is a fully autonomous, general-purpose AI agent that aims to create a standalone artificial intelligence assistant, similar to JARVIS, based on the open-source LLM. AIlice achieves this goal by building a "text computer" that uses a Large Language Model (LLM) as its core processor. Currently, AIlice demonstrates proficiency in a range of tasks, including thematic research, coding, system management, literature reviews, and complex hybrid tasks that go beyond these basic capabilities. AIlice has reached near-perfect performance in everyday tasks using GPT-4 and is making strides towards practical application with the latest open-source models. We will ultimately achieve self-evolution of AI agents. That is, AI agents will autonomously build their own feature expansions and new types of agents, unleashing LLM's knowledge and reasoning capabilities into the real world seamlessly.

关于如何编写大语言模型的prompt的一系列课

InternLM-XComposer2 is a groundbreaking vision-language large model (VLLM) based on InternLM2-7B excelling in free-form text-image composition and comprehension. It boasts several amazing capabilities and applications: * **Free-form Interleaved Text-Image Composition** : InternLM-XComposer2 can effortlessly generate coherent and contextual articles with interleaved images following diverse inputs like outlines, detailed text requirements and reference images, enabling highly customizable content creation. * **Accurate Vision-language Problem-solving** : InternLM-XComposer2 accurately handles diverse and challenging vision-language Q&A tasks based on free-form instructions, excelling in recognition, perception, detailed captioning, visual reasoning, and more. * **Awesome performance** : InternLM-XComposer2 based on InternLM2-7B not only significantly outperforms existing open-source multimodal models in 13 benchmarks but also **matches or even surpasses GPT-4V and Gemini Pro in 6 benchmarks** We release InternLM-XComposer2 series in three versions: * **InternLM-XComposer2-4KHD-7B** 🤗: The high-resolution multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _High-resolution understanding_ , _VL benchmarks_ and _AI assistant_. * **InternLM-XComposer2-VL-7B** 🤗 : The multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _VL benchmarks_ and _AI assistant_. **It ranks as the most powerful vision-language model based on 7B-parameter level LLMs, leading across 13 benchmarks.** * **InternLM-XComposer2-VL-1.8B** 🤗 : A lightweight version of InternLM-XComposer2-VL based on InternLM-1.8B. * **InternLM-XComposer2-7B** 🤗: The further instruction tuned VLLM for _Interleaved Text-Image Composition_ with free-form inputs. Please refer to Technical Report and 4KHD Technical Reportfor more details.

This repository includes papers and blogs about Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation(RAG), and Evaluation for Long Context Modeling.

A curated list of Generative AI projects, tools, artworks, and models

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

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