Best AI tools for< Emulate Ai Artists >

Artist Interview.ai is an AI tool that generates realistic AI-powered answers to interview questions. Users can input their questions and receive responses generated by advanced AI models like GPT-3.5 and GPT-4. The website offers a selection of AI artists to emulate in the generated interviews, ranging from music legends like Bob Dylan and Paul McCartney to contemporary stars like Taylor Swift and Beyoncé. Artist Interview.ai aims to provide a unique and engaging experience for users looking to simulate artist interviews using cutting-edge AI technology.

Browse AI is an AI tool that offers the easiest way to extract and monitor data from any website without the need for coding. Users can train a robot in just 2 minutes to extract specific data in spreadsheet format or monitor data on a schedule. With over 7,000 integrations, Browse AI allows users to scrape structured data, run multiple robots simultaneously, emulate user interactions, handle pagination, and more. Trusted by over 370,000 individuals and teams, Browse AI is a powerful tool for data extraction and monitoring tasks.

AI-scripts is a repository containing various AI scripts used for daily tasks. It includes tools like 'holefill' for filling code snippets in VIM, 'aiemu' for emulation purposes, and 'chatsh [model]' for terminal-based ChatGPT functionality. The repository aims to streamline AI-related workflows and enhance productivity by providing convenient scripts for common tasks.

FeedCraft is a powerful tool to process your rss feeds as a middleware. Use it to translate your feed, extract fulltext, emulate browser to render js-heavy page, use llm such as google gemini to generate brief for your rss article, use natural language to filter your rss feed, and more! It is an open-source tool that can be self-deployed and used with any RSS reader. It supports AI-powered processing using Open AI compatible LLMs, custom prompt, saving rules to apply to different RSS sources, portable mode for on-the-go usage, and dock mode for advanced customization of RSS sources and processing parameters.

Instrukt is a terminal-based AI integrated environment that allows users to create and instruct modular AI agents, generate document indexes for question-answering, and attach tools to any agent. It provides a platform for users to interact with AI agents in natural language and run them inside secure containers for performing tasks. The tool supports custom AI agents, chat with code and documents, tools customization, prompt console for quick interaction, LangChain ecosystem integration, secure containers for agent execution, and developer console for debugging and introspection. Instrukt aims to make AI accessible to everyone by providing tools that empower users without relying on external APIs and services.

mjai.app is a platform for mahjong AI competition. It contains an implementation of a mahjong game simulator for evaluating submission files. The simulator runs Docker internally, and there is a base class for developing bots that communicate via the mjai protocol. Submission files are deployed in a Docker container, and the Docker image is pushed to Docker Hub. The Mjai protocol used is customized based on Mortal's Mjai Engine implementation.

avante.nvim is a Neovim plugin that emulates the behavior of the Cursor AI IDE, providing AI-driven code suggestions and enabling users to apply recommendations to their source files effortlessly. It offers AI-powered code assistance and one-click application of suggested changes, streamlining the editing process and saving time. The plugin is still in early development, with functionalities like setting API keys, querying AI about code, reviewing suggestions, and applying changes. Key bindings are available for various actions, and the roadmap includes enhancing AI interactions, stability improvements, and introducing new features for coding tasks.

An Open-Source Engineering Guide for Prompt-in-context-learning from EgoAlpha Lab. 📝 Papers | ⚡️ Playground | 🛠 Prompt Engineering | 🌍 ChatGPT Prompt | ⛳ LLMs Usage Guide > **⭐️ Shining ⭐️:** This is fresh, daily-updated resources for in-context learning and prompt engineering. As Artificial General Intelligence (AGI) is approaching, let’s take action and become a super learner so as to position ourselves at the forefront of this exciting era and strive for personal and professional greatness. The resources include: _🎉Papers🎉_: The latest papers about _In-Context Learning_ , _Prompt Engineering_ , _Agent_ , and _Foundation Models_. _🎉Playground🎉_: Large language models（LLMs）that enable prompt experimentation. _🎉Prompt Engineering🎉_: Prompt techniques for leveraging large language models. _🎉ChatGPT Prompt🎉_: Prompt examples that can be applied in our work and daily lives. _🎉LLMs Usage Guide🎉_: The method for quickly getting started with large language models by using LangChain. In the future, there will likely be two types of people on Earth (perhaps even on Mars, but that's a question for Musk): - Those who enhance their abilities through the use of AIGC; - Those whose jobs are replaced by AI automation. 💎EgoAlpha: Hello! human👤, are you ready?

RAFT (Retrieval-Augmented Fine-Tuning) is a method for creating conversational agents that realistically emulate specific human targets. It involves a dual-phase process of fine-tuning and retrieval-based augmentation to generate nuanced and personalized dialogue. The tool is designed to combine interview transcripts with memories from past writings to enhance language model responses. RAFT has the potential to advance the field of personalized, context-sensitive conversational agents.

M.I.L.E.S. (Machine Intelligent Language Enabled System) is a voice assistant powered by GPT-4 Turbo, offering a range of capabilities beyond existing assistants. With its advanced language understanding, M.I.L.E.S. provides accurate and efficient responses to user queries. It seamlessly integrates with smart home devices, Spotify, and offers real-time weather information. Additionally, M.I.L.E.S. possesses persistent memory, a built-in calculator, and multi-tasking abilities. Its realistic voice, accurate wake word detection, and internet browsing capabilities enhance the user experience. M.I.L.E.S. prioritizes user privacy by processing data locally, encrypting sensitive information, and adhering to strict data retention policies.

Galah is an LLM-powered web honeypot designed to mimic various applications and dynamically respond to arbitrary HTTP requests. It supports multiple LLM providers, including OpenAI. Unlike traditional web honeypots, Galah dynamically crafts responses for any HTTP request, caching them to reduce repetitive generation and API costs. The honeypot's configuration is crucial, directing the LLM to produce responses in a specified JSON format. Note that Galah is a weekend project exploring LLM capabilities and not intended for production use, as it may be identifiable through network fingerprinting and non-standard responses.

This repository contains a collection of papers related to tool learning with large language models (LLMs). The papers are organized according to the survey paper 'Tool Learning with Large Language Models: A Survey'. The survey focuses on the benefits and implementation of tool learning with LLMs, covering aspects such as task planning, tool selection, tool calling, response generation, benchmarks, evaluation, challenges, and future directions in the field. It aims to provide a comprehensive understanding of tool learning with LLMs and inspire further exploration in this emerging area.

PaperQA is a minimal package for question and answering from PDFs or text files, providing very good answers with in-text citations. It uses OpenAI Embeddings to embed and search documents, and includes a process of embedding docs, queries, searching for top passages, creating summaries, using an LLM to re-score and select relevant summaries, putting summaries into prompt, and generating answers. The tool can be used to answer specific questions related to scientific research by leveraging citations and relevant passages from documents.

llama.cpp is a C++ implementation of LLaMA, a large language model from Meta. It provides a command-line interface for inference and can be used for a variety of tasks, including text generation, translation, and question answering. llama.cpp is highly optimized for performance and can be run on a variety of hardware, including CPUs, GPUs, and TPUs.

Backend.AI is a streamlined, container-based computing cluster platform that hosts popular computing/ML frameworks and diverse programming languages, with pluggable heterogeneous accelerator support including CUDA GPU, ROCm GPU, TPU, IPU and other NPUs. It allocates and isolates the underlying computing resources for multi-tenant computation sessions on-demand or in batches with customizable job schedulers with its own orchestrator. All its functions are exposed as REST/GraphQL/WebSocket APIs.

AWS AI Stack is a full-stack boilerplate project designed for building serverless AI applications on AWS. It provides a trusted AWS foundation for AI apps with access to powerful LLM models via Bedrock. The architecture is serverless, ensuring cost-efficiency by only paying for usage. The project includes features like AI Chat & Streaming Responses, Multiple AI Models & Data Privacy, Custom Domain Names, API & Event-Driven architecture, Built-In Authentication, Multi-Environment support, and CI/CD with Github Actions. Users can easily create AI Chat bots, authentication services, business logic, and async workers using AWS Lambda, API Gateway, DynamoDB, and EventBridge.

Awesome Responsible AI is a curated list of academic research, books, code of ethics, courses, data sets, frameworks, institutes, newsletters, principles, podcasts, reports, tools, regulations, and standards related to Responsible, Trustworthy, and Human-Centered AI. It covers various concepts such as Responsible AI, Trustworthy AI, Human-Centered AI, Responsible AI frameworks, AI Governance, and more. The repository provides a comprehensive collection of resources for individuals interested in ethical, transparent, and accountable AI development and deployment.

LLM-as-HH is a codebase that accompanies the paper ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution. It introduces Language Hyper-Heuristics (LHHs) that leverage LLMs for heuristic generation with minimal manual intervention and open-ended heuristic spaces. Reflective Evolution (ReEvo) is presented as a searching framework that emulates the reflective design approach of human experts while surpassing human capabilities with scalable LLM inference, Internet-scale domain knowledge, and powerful evolutionary search. The tool can improve various algorithms on problems like Traveling Salesman Problem, Capacitated Vehicle Routing Problem, Orienteering Problem, Multiple Knapsack Problems, Bin Packing Problem, and Decap Placement Problem in both black-box and white-box settings.

Emulating Steve Jobs' visionary style

Emulates David Ogilvy's persuasive, clear writing style.

Emulates Harry Stebbings' insights on business and startups

Emulates Paul Graham's insights based on his essays.

Emulates MS-DOS 6 commands

Emulates Gordon Ramsay's style for roasting dishes. Upload a picture of your food.

Emulates Greger Teigre Wedel's style, focusing on leadership and tech product development.

Emulates Oleg Gennadyevich Torsunov's expertise in family psychology, personal growth, and natural health.

Emulates Patrick Collison; advises on startups, tech progress, and book recommendations.