Best AI tools for< Configuring Services >
2 - AI tool Sites
Shipixen
Shipixen is an AI-powered tool that allows users to generate custom Next.js codebases with an MDX blog, TypeScript, and Shadcn UI in minutes. It provides a seamless experience for developers to create beautifully designed SaaS, blogs, landing pages, directories, and more without the hassle of manual setup. Shipixen offers a wide range of features, themes, and components to streamline the web development process and empower users to focus on building rather than configuring. With AI content generation capabilities, customizable branding, and easy deployment options, Shipixen is a valuable tool for both beginners and experienced developers.
AIGUR
AIGUR is a generative AI platform that enables teams to build, collaborate, deploy, and manage generative AI flows. With AIGUR's no-code editor, users can create generative AI flows by dragging and dropping AI blocks and configuring how they interact. AIGUR also provides collaboration tools that allow multiple users to work on the same flow simultaneously. Once a flow is created, it can be integrated into any web or mobile application using a simple API call. AIGUR also provides monitoring tools that give users visibility into the different executions of their flows, as well as their cost, performance, and availability.
20 - Open Source AI Tools
shortest
Shortest is a project for local development that helps set up environment variables and services for a web application. It provides a guide for setting up Node.js and pnpm dependencies, configuring services like Clerk, Vercel Postgres, Anthropic, Stripe, and GitHub OAuth, and running the application and tests locally.
openai-forward
OpenAI-Forward is an efficient forwarding service implemented for large language models. Its core features include user request rate control, token rate limiting, intelligent prediction caching, log management, and API key management, aiming to provide efficient and convenient model forwarding services. Whether proxying local language models or cloud-based language models like LocalAI or OpenAI, OpenAI-Forward makes it easy. Thanks to support from libraries like uvicorn, aiohttp, and asyncio, OpenAI-Forward achieves excellent asynchronous performance.
clearml-server
ClearML Server is a backend service infrastructure for ClearML, facilitating collaboration and experiment management. It includes a web app, RESTful API, and file server for storing images and models. Users can deploy ClearML Server using Docker, AWS EC2 AMI, or Kubernetes. The system design supports single IP or sub-domain configurations with specific open ports. ClearML-Agent Services container allows launching long-lasting jobs and various use cases like auto-scaler service, controllers, optimizer, and applications. Advanced functionality includes web login authentication and non-responsive experiments watchdog. Upgrading ClearML Server involves stopping containers, backing up data, downloading the latest docker-compose.yml file, configuring ClearML-Agent Services, and spinning up docker containers. Community support is available through ClearML FAQ, Stack Overflow, GitHub issues, and email contact.
SamsungAutomationStudio
Samsung Automation Studio is a development tool that provides an environment for easily configuring application logic by connecting Samsung and 3rd party services. The project shares Node-RED nodes developed by Samsung Automation Studio team, enabling users to install and use Samsung's IoT and AI-related services seamlessly. The tool enhances user experience by integrating with their own services.
shipstation
ShipStation is an AI-based website and agents generation platform that optimizes landing page websites and generic connect-anything-to-anything services. It enables seamless communication between service providers and integration partners, offering features like user authentication, project management, code editing, payment integration, and real-time progress tracking. The project architecture includes server-side (Node.js) and client-side (React with Vite) components. Prerequisites include Node.js, npm or yarn, Anthropic API key, Supabase account, Tavily API key, and Razorpay account. Setup instructions involve cloning the repository, setting up Supabase, configuring environment variables, and starting the backend and frontend servers. Users can access the application through the browser, sign up or log in, create landing pages or portfolios, and get websites stored in an S3 bucket. Deployment to Heroku involves building the client project, committing changes, and pushing to the main branch. Contributions to the project are encouraged, and the license encourages doing good.
go2coding.github.io
The go2coding.github.io repository is a collection of resources for AI enthusiasts, providing information on AI products, open-source projects, AI learning websites, and AI learning frameworks. It aims to help users stay updated on industry trends, learn from community projects, access learning resources, and understand and choose AI frameworks. The repository also includes instructions for local and external deployment of the project as a static website, with details on domain registration, hosting services, uploading static web pages, configuring domain resolution, and a visual guide to the AI tool navigation website. Additionally, it offers a platform for AI knowledge exchange through a QQ group and promotes AI tools through a WeChat public account.
ESP32_AI_LLM
ESP32_AI_LLM is a project that uses ESP32 to connect to Xunfei Xinghuo, Dou Bao, and Tongyi Qianwen large models to achieve voice chat functions, supporting online voice wake-up, continuous conversation, music playback, and real-time display of conversation content on an external screen. The project requires specific hardware components and provides functionalities such as voice wake-up, voice conversation, convenient network configuration, music playback, volume adjustment, LED control, model switching, and screen display. Users can deploy the project by setting up Xunfei services, cloning the repository, configuring necessary parameters, installing drivers, compiling, and burning the code.
scrape-it-now
Scrape It Now is a versatile tool for scraping websites with features like decoupled architecture, CLI functionality, idempotent operations, and content storage options. The tool includes a scraper component for efficient scraping, ad blocking, link detection, markdown extraction, dynamic content loading, and anonymity features. It also offers an indexer component for creating AI search indexes, chunking content, embedding chunks, and enabling semantic search. The tool supports various configurations for Azure services and local storage, providing flexibility and scalability for web scraping and indexing tasks.
gpt-translate
Markdown Translation BOT is a GitHub action that translates markdown files into multiple languages using various AI models. It supports markdown, markdown-jsx, and json files only. The action can be executed by individuals with write permissions to the repository, preventing API abuse by non-trusted parties. Users can set up the action by providing their API key and configuring the workflow settings. The tool allows users to create comments with specific commands to trigger translations and automatically generate pull requests or add translated files to existing pull requests. It supports multiple file translations and can interpret any language supported by GPT-4 or GPT-3.5.
dstack
Dstack is an open-source orchestration engine for running AI workloads in any cloud. It supports a wide range of cloud providers (such as AWS, GCP, Azure, Lambda, TensorDock, Vast.ai, CUDO, RunPod, etc.) as well as on-premises infrastructure. With Dstack, you can easily set up and manage dev environments, tasks, services, and pools for your AI workloads.
AgentBench
AgentBench is a benchmark designed to evaluate Large Language Models (LLMs) as autonomous agents in various environments. It includes 8 distinct environments such as Operating System, Database, Knowledge Graph, Digital Card Game, and Lateral Thinking Puzzles. The tool provides a comprehensive evaluation of LLMs' ability to operate as agents by offering Dev and Test sets for each environment. Users can quickly start using the tool by following the provided steps, configuring the agent, starting task servers, and assigning tasks. AgentBench aims to bridge the gap between LLMs' proficiency as agents and their practical usability.
aiavatarkit
AIAvatarKit is a tool for building AI-based conversational avatars quickly. It supports various platforms like VRChat and cluster, along with real-world devices. The tool is extensible, allowing unlimited capabilities based on user needs. It requires VOICEVOX API, Google or Azure Speech Services API keys, and Python 3.10. Users can start conversations out of the box and enjoy seamless interactions with the avatars.
python-whatsapp-bot
This repository provides a comprehensive guide on building AI WhatsApp bots using Python and Flask. It covers setting up a Meta developer account, integrating webhook events for real-time message reception, and using OpenAI for AI responses. The tutorial includes steps for selecting phone numbers, sending messages with the API, configuring webhooks, integrating AI into the application, and adding a phone number. It also explains the process of creating a system user, obtaining access tokens, and validating verification requests and payloads for webhook security. The repository aims to help users create intelligent WhatsApp bots with Python and AI capabilities.
Tools4AI
Tools4AI is a Java-based Agentic Framework for building AI agents to integrate with enterprise Java applications. It enables the conversion of natural language prompts into actionable behaviors, streamlining user interactions with complex systems. By leveraging AI capabilities, it enhances productivity and innovation across diverse applications. The framework allows for seamless integration of AI with various systems, such as customer service applications, to interpret user requests, trigger actions, and streamline workflows. Prompt prediction anticipates user actions based on input prompts, enhancing user experience by proactively suggesting relevant actions or services based on context.
llm_aided_ocr
The LLM-Aided OCR Project is an advanced system that enhances Optical Character Recognition (OCR) output by leveraging natural language processing techniques and large language models. It offers features like PDF to image conversion, OCR using Tesseract, error correction using LLMs, smart text chunking, markdown formatting, duplicate content removal, quality assessment, support for local and cloud-based LLMs, asynchronous processing, detailed logging, and GPU acceleration. The project provides detailed technical overview, text processing pipeline, LLM integration, token management, quality assessment, logging, configuration, and customization. It requires Python 3.12+, Tesseract OCR engine, PDF2Image library, PyTesseract, and optional OpenAI or Anthropic API support for cloud-based LLMs. The installation process involves setting up the project, installing dependencies, and configuring environment variables. Users can place a PDF file in the project directory, update input file path, and run the script to generate post-processed text. The project optimizes processing with concurrent processing, context preservation, and adaptive token management. Configuration settings include choosing between local or API-based LLMs, selecting API provider, specifying models, and setting context size for local LLMs. Output files include raw OCR output and LLM-corrected text. Limitations include performance dependency on LLM quality and time-consuming processing for large documents.
llmops-promptflow-template
LLMOps with Prompt flow is a template and guidance for building LLM-infused apps using Prompt flow. It provides centralized code hosting, lifecycle management, variant and hyperparameter experimentation, A/B deployment, many-to-many dataset/flow relationships, multiple deployment targets, comprehensive reporting, BYOF capabilities, configuration-based development, local prompt experimentation and evaluation, endpoint testing, and optional Human-in-loop validation. The tool is customizable to suit various application needs.
gpt-home
GPT Home is a project that allows users to build their own home assistant using Raspberry Pi and OpenAI API. It serves as a guide for setting up a smart home assistant similar to Google Nest Hub or Amazon Alexa. The project integrates various components like OpenAI, Spotify, Philips Hue, and OpenWeatherMap to provide a personalized home assistant experience. Users can follow the detailed instructions provided to build their own version of the home assistant on Raspberry Pi, with optional components for customization. The project also includes system configurations, dependencies installation, and setup scripts for easy deployment. Overall, GPT Home offers a DIY solution for creating a smart home assistant using Raspberry Pi and OpenAI technology.
kong
Kong, or Kong API Gateway, is a cloud-native, platform-agnostic, scalable API Gateway distinguished for its high performance and extensibility via plugins. It also provides advanced AI capabilities with multi-LLM support. By providing functionality for proxying, routing, load balancing, health checking, authentication (and more), Kong serves as the central layer for orchestrating microservices or conventional API traffic with ease. Kong runs natively on Kubernetes thanks to its official Kubernetes Ingress Controller.
MiniSearch
MiniSearch is a minimalist search engine with integrated browser-based AI. It is privacy-focused, easy to use, cross-platform, integrated, time-saving, efficient, optimized, and open-source. MiniSearch can be used for a variety of tasks, including searching the web, finding files on your computer, and getting answers to questions. It is a great tool for anyone who wants a fast, private, and easy-to-use search engine.
chat-with-your-data-solution-accelerator
Chat with your data using OpenAI and AI Search. This solution accelerator uses an Azure OpenAI GPT model and an Azure AI Search index generated from your data, which is integrated into a web application to provide a natural language interface, including speech-to-text functionality, for search queries. Users can drag and drop files, point to storage, and take care of technical setup to transform documents. There is a web app that users can create in their own subscription with security and authentication.