AI tools for how to use this
Related Tools:

EduWriter.ai
EduWriter.ai is an AI essay writing tool that offers an undetectable AI essay writer for free users. It provides users with the ability to generate high-quality essays and papers quickly and efficiently. The tool offers features such as unlimited AI writer, bypass AI, plagiarism reports, paraphrasing, and human editors. Users can choose from different AI writer models based on their needs and preferences. EduWriter.ai is designed to assist students and teachers in writing and researching, offering original content while promoting academic integrity.

Free Yes or No Tarot
This website offers a simplified form of tarot card reading that focuses on providing quick and straightforward answers to specific yes-or-no questions. It combines traditional tarot practice with modern technology to provide users with a unique and insightful experience. The website guides users through a step-by-step process to select cards and receive a detailed interpretation tailored to their query.

AI Dating Coach
AI Dating Coach is an AI-powered application designed to help individuals improve their dating skills and success on dating apps. It offers personalized coaching, practice conversations, and gamified learning to enhance communication skills and boost confidence in the dating world. The application combines data-driven insights with gamification elements to provide tailored advice, strategies, and real-time feedback to users. With features like smart recommendations, profile analysis, and app-specific strategies, AI Dating Coach aims to transform users' dating experiences through AI-powered coaching and chat simulations.

Artificial Intelligence: Foundations of Computational Agents
Artificial Intelligence: Foundations of Computational Agents, 3rd edition by David L. Poole and Alan K. Mackworth, Cambridge University Press 2023, is a book about the science of artificial intelligence (AI). It presents artificial intelligence as the study of the design of intelligent computational agents. The book is structured as a textbook, but it is accessible to a wide audience of professionals and researchers. In the last decades we have witnessed the emergence of artificial intelligence as a serious science and engineering discipline. This book provides an accessible synthesis of the field aimed at undergraduate and graduate students. It provides a coherent vision of the foundations of the field as it is today. It aims to provide that synthesis as an integrated science, in terms of a multi-dimensional design space that has been partially explored. As with any science worth its salt, artificial intelligence has a coherent, formal theory and a rambunctious experimental wing. The book balances theory and experiment, showing how to link them intimately together. It develops the science of AI together with its engineering applications.

Brancher.ai
Brancher.ai is a platform that enables users to connect and use AI models to create powerful apps without the need for coding knowledge. With Brancher.ai, users can create AI-powered apps quickly and easily, allowing them to tap into the potential of AI and build unique, sophisticated applications. The platform also offers the opportunity for users to monetize and share their creations, allowing them to potentially earn from their work.

Meteron AI
Meteron AI is an all-in-one AI toolset that helps developers build AI-powered products faster and easier. It provides a simple, yet powerful metering mechanism, elastic scaling, unlimited storage, and works with any model. With Meteron, developers can focus on building AI products instead of worrying about the underlying infrastructure.

Parafrasear.ai
Parafrasear.ai is an advanced AI-powered paraphrasing tool that helps users rewrite existing text, ideas, and information using different words. It offers various modes of rewriting, such as general, anti-plagiarism, fluency, academic, blog, and formal, catering to different user needs. The tool ensures contextual accuracy in paraphrased content, making it ideal for writers, SEO executives, students, marketers, and researchers. With features like free usage, compatibility across devices, plagiarism-free rewriting, customization options, and user-friendly interface, Parafrasear.ai stands out as a reliable platform for text paraphrasing in Spanish.

Metamorph Labs
Metamorph Labs is an AI Resources Curation Platform where the AI Community can explore Technical & Non-Technical/General AI Resources gathered from the Internet. It offers a comprehensive resource aggregation platform for the AI Community to unleash the power of AI. Users can discover a curated collection of cutting-edge AI resources consisting of both Technical & Non-technical Materials.

Awan LLM
Awan LLM is an AI tool that offers an Unlimited Tokens, Unrestricted, and Cost-Effective LLM Inference API Platform for Power Users and Developers. It allows users to generate unlimited tokens, use LLM models without constraints, and pay per month instead of per token. The platform features an AI Assistant, AI Agents, Roleplay with AI companions, Data Processing, Code Completion, and Applications for profitable AI-powered applications.

Picarta AI
Picarta AI is an image geolocalization solution that uses artificial intelligence to find where a photo has been taken in the world. By uploading a photo, users can get the GPS location, latitude, longitude, time stamp, and camera details of the image. Picarta AI also offers a map view of the image location and allows users to download the map. The company's vision is to empower individuals and businesses with the most accurate and reliable image geolocalization solution, unlocking new possibilities for exploration, research, and decision-making.

SaaS Business Ideas
SaaS Business Ideas is a free SaaS business idea generator that helps users brainstorm ideas for their next SaaS product. The website provides a list of over 700 ideas, which can be filtered by category. Users can also sign up for a newsletter to receive additional tips and resources on building a SaaS business.

JustConvert
JustConvert is an AI-powered conversion optimization tool that analyzes your website to provide actionable suggestions for improving your conversion rate, design, and copywriting. It is designed to be easy to use and affordable, even for small businesses and startups. With JustConvert, you can quickly and easily identify areas for improvement on your website and get specific recommendations on how to fix them. This can lead to a significant increase in your conversion rate, which can mean more sales, leads, or signups for your business.

Table to JSON
我們經常在看 REST API 參考文件,文件中呈現 Request/Response 參數通常都是用表格的形式,開發人員都要手動轉換成 JSON 結構,有點小麻煩,但透過這個 GPT 只要上傳截圖就可以自動產生 JSON 範例與 JSON Schema 結構。

GPT Genius
This ChatGPT helps you brainstorm how to make more GPTs. The irony is next level.

Screenshot To Code GPT
Upload a screenshot of a website and convert it to clean HTML/Tailwind/JS code.

Brainstormer
Brainstormer takes in a notion, applies a bunch of perspective agents set to improve it by seeking novelty, and spits out a bunch of improved creative ideas.

Fishing Pro Ultimate Advanced GPT
Your must have guide from fishing trip planning to cooking your catch, now with fish ID from images.

slack-bot
The Slack Bot is a tool designed to enhance the workflow of development teams by integrating with Jenkins, GitHub, GitLab, and Jira. It allows for custom commands, macros, crons, and project-specific commands to be implemented easily. Users can interact with the bot through Slack messages, execute commands, and monitor job progress. The bot supports features like starting and monitoring Jenkins jobs, tracking pull requests, querying Jira information, creating buttons for interactions, generating images with DALL-E, playing quiz games, checking weather, defining custom commands, and more. Configuration is managed via YAML files, allowing users to set up credentials for external services, define custom commands, schedule cron jobs, and configure VCS systems like Bitbucket for automated branch lookup in Jenkins triggers.

Build-your-own-AI-Assistant-Solution-Accelerator
Build-your-own-AI-Assistant-Solution-Accelerator is a pre-release and preview solution that helps users create their own AI assistants. It leverages Azure Open AI Service, Azure AI Search, and Microsoft Fabric to identify, summarize, and categorize unstructured information. Users can easily find relevant articles and grants, generate grant applications, and export them as PDF or Word documents. The solution accelerator provides reusable architecture and code snippets for building AI assistants with enterprise data. It is designed for researchers looking to explore flu vaccine studies and grants to accelerate grant proposal submissions.

serverless-chat-langchainjs
This sample shows how to build a serverless chat experience with Retrieval-Augmented Generation using LangChain.js and Azure. The application is hosted on Azure Static Web Apps and Azure Functions, with Azure Cosmos DB for MongoDB vCore as the vector database. You can use it as a starting point for building more complex AI applications.

HuggingFaceGuidedTourForMac
HuggingFaceGuidedTourForMac is a guided tour on how to install optimized pytorch and optionally Apple's new MLX, JAX, and TensorFlow on Apple Silicon Macs. The repository provides steps to install homebrew, pytorch with MPS support, MLX, JAX, TensorFlow, and Jupyter lab. It also includes instructions on running large language models using HuggingFace transformers. The repository aims to help users set up their Macs for deep learning experiments with optimized performance.

aws-bedrock-with-rag-and-react
This solution provides a low-code ReactJS application to prototype and vet business use cases for GenAI using Retrieval Augmented Generation (RAG). It includes a backend Flask application that uses LangChain to provide PDF data as embeddings to a text-gen model via Amazon Bedrock and a vector database with FAISS or Kendra Index. The solution utilizes Amazon Bedrock as the only cost-generating AWS service.

LLM-LieDetector
This repository contains code for reproducing experiments on lie detection in black-box LLMs by asking unrelated questions. It includes Q/A datasets, prompts, and fine-tuning datasets for generating lies with language models. The lie detectors rely on asking binary 'elicitation questions' to diagnose whether the model has lied. The code covers generating lies from language models, training and testing lie detectors, and generalization experiments. It requires access to GPUs and OpenAI API calls for running experiments with open-source models. Results are stored in the repository for reproducibility.

vasttools
This repository contains a collection of tools that can be used with vastai. The tools are free to use, modify and distribute. If you find this useful and wish to donate your welcome to send your donations to the following wallets. BTC 15qkQSYXP2BvpqJkbj2qsNFb6nd7FyVcou XMR 897VkA8sG6gh7yvrKrtvWningikPteojfSgGff3JAUs3cu7jxPDjhiAZRdcQSYPE2VGFVHAdirHqRZEpZsWyPiNK6XPQKAg RVN RSgWs9Co8nQeyPqQAAqHkHhc5ykXyoMDUp USDT(ETH ERC20) 0xa5955cf9fe7af53bcaa1d2404e2b17a1f28aac4f Paypal PayPal.Me/cryptolabsZA

amber-data-prep
This repository contains the code to prepare the data for the Amber 7B language model. The final training data comes from three sources: RedPajama V1, RefinedWeb, and StarCoderData. The data preparation involves downloading untokenized data, tokenizing the data using the Huggingface tokenizer, concatenating tokens into 2048 token sequences, merging datasets, and splitting the merged dataset into 360 chunks. Each tokenized data chunk is a jsonl file containing samples with 2049 tokens. The repository provides scripts for downloading datasets, tokenizing and concatenating sequences, validating data, and merging subsets into chunks.

github-pr-summary
github-pr-summary is a bot designed to summarize GitHub Pull Requests, helping open source contributors make faster decisions. It automatically summarizes commits and changed files in PRs, triggered by new commits or a magic trigger phrase. Users can deploy their own code review bot in 3 steps: create a bot from their GitHub repo, configure it to review PRs, and connect to GitHub for access to the target repo. The bot runs on flows.network using Rust and WasmEdge Runtimes. It utilizes ChatGPT/4 to review and summarize PR content, posting the result back as a comment on the PR. The bot can be used on multiple repos by creating new flows and importing the source code repo, specifying the target repo using flow config. Users can also change the magic phrase to trigger a review from a PR comment.

json_repair
This simple package can be used to fix an invalid json string. To know all cases in which this package will work, check out the unit test. Inspired by https://github.com/josdejong/jsonrepair Motivation Some LLMs are a bit iffy when it comes to returning well formed JSON data, sometimes they skip a parentheses and sometimes they add some words in it, because that's what an LLM does. Luckily, the mistakes LLMs make are simple enough to be fixed without destroying the content. I searched for a lightweight python package that was able to reliably fix this problem but couldn't find any. So I wrote one How to use from json_repair import repair_json good_json_string = repair_json(bad_json_string) # If the string was super broken this will return an empty string You can use this library to completely replace `json.loads()`: import json_repair decoded_object = json_repair.loads(json_string) or just import json_repair decoded_object = json_repair.repair_json(json_string, return_objects=True) Read json from a file or file descriptor JSON repair provides also a drop-in replacement for `json.load()`: import json_repair try: file_descriptor = open(fname, 'rb') except OSError: ... with file_descriptor: decoded_object = json_repair.load(file_descriptor) and another method to read from a file: import json_repair try: decoded_object = json_repair.from_file(json_file) except OSError: ... except IOError: ... Keep in mind that the library will not catch any IO-related exception and those will need to be managed by you Performance considerations If you find this library too slow because is using `json.loads()` you can skip that by passing `skip_json_loads=True` to `repair_json`. Like: from json_repair import repair_json good_json_string = repair_json(bad_json_string, skip_json_loads=True) I made a choice of not using any fast json library to avoid having any external dependency, so that anybody can use it regardless of their stack. Some rules of thumb to use: - Setting `return_objects=True` will always be faster because the parser returns an object already and it doesn't have serialize that object to JSON - `skip_json_loads` is faster only if you 100% know that the string is not a valid JSON - If you are having issues with escaping pass the string as **raw** string like: `r"string with escaping\"" Adding to requirements Please pin this library only on the major version! We use TDD and strict semantic versioning, there will be frequent updates and no breaking changes in minor and patch versions. To ensure that you only pin the major version of this library in your `requirements.txt`, specify the package name followed by the major version and a wildcard for minor and patch versions. For example: json_repair==0.* In this example, any version that starts with `0.` will be acceptable, allowing for updates on minor and patch versions. How it works This module will parse the JSON file following the BNF definition: <json> ::= <primitive> | <container> <primitive> ::= <number> | <string> | <boolean> ; Where: ; <number> is a valid real number expressed in one of a number of given formats ; <string> is a string of valid characters enclosed in quotes ; <boolean> is one of the literal strings 'true', 'false', or 'null' (unquoted) <container> ::= <object> | <array> <array> ::= '[' [ <json> *(', ' <json>) ] ']' ; A sequence of JSON values separated by commas <object> ::= '{' [ <member> *(', ' <member>) ] '}' ; A sequence of 'members' <member> ::= <string> ': ' <json> ; A pair consisting of a name, and a JSON value If something is wrong (a missing parantheses or quotes for example) it will use a few simple heuristics to fix the JSON string: - Add the missing parentheses if the parser believes that the array or object should be closed - Quote strings or add missing single quotes - Adjust whitespaces and remove line breaks I am sure some corner cases will be missing, if you have examples please open an issue or even better push a PR How to develop Just create a virtual environment with `requirements.txt`, the setup uses pre-commit to make sure all tests are run. Make sure that the Github Actions running after pushing a new commit don't fail as well. How to release You will need owner access to this repository - Edit `pyproject.toml` and update the version number appropriately using `semver` notation - **Commit and push all changes to the repository before continuing or the next steps will fail** - Run `python -m build` - Create a new release in Github, making sure to tag all the issues solved and contributors. Create the new tag, same as the one in the build configuration - Once the release is created, a new Github Actions workflow will start to publish on Pypi, make sure it didn't fail Bonus Content If you need some good Custom Instructions (System Message) to improve your chatbot responses try https://gist.github.com/mangiucugna/7ec015c4266df11be8aa510be0110fe4 Star History [Star History Chart](https://api.star-history.com/svg?repos=mangiucugna/json_repair&type=Date)

ai-models
The `ai-models` command is a tool used to run AI-based weather forecasting models. It provides functionalities to install, run, and manage different AI models for weather forecasting. Users can easily install and run various models, customize model settings, download assets, and manage input data from different sources such as ECMWF, CDS, and GRIB files. The tool is designed to optimize performance by running on GPUs and provides options for better organization of assets and output files. It offers a range of command line options for users to interact with the models and customize their forecasting tasks.

landingai-python
The LandingLens Python library contains the LandingLens development library and examples that show how to integrate your app with LandingLens in a variety of scenarios. The library allows users to acquire images from different sources, run inference on computer vision models deployed in LandingLens, and provides examples in Jupyter Notebooks and Python apps for various tasks such as object detection, home automation, satellite image analysis, license plate detection, and streaming video analysis.

raycast-g4f
Raycast-G4F is a free extension that allows users to leverage powerful AI models such as GPT-4 and Llama-3 within the Raycast app without the need for an API key. The extension offers features like streaming support, diverse commands, chat interaction with AI, web search capabilities, file upload functionality, image generation, and custom AI commands. Users can easily install the extension from the source code and benefit from frequent updates and a user-friendly interface. Raycast-G4F supports various providers and models, each with different capabilities and performance ratings, ensuring a versatile AI experience for users.

LibreOffice-Content-Generator
LibreOffice AI Content Generator is a simple Python macro script that enables users to generate content from selected words/sentences using OpenAI or Google AI. The script allows users to perform various tasks such as generating content, translating to other languages, summarizing long content, improving content, and custom tasks like solving math questions. It requires APSO, OpenAI API Key, Google AI API Key, zenity for handling progress bars, and specific Python modules. Users need a little knowledge of LibreOffice macros and Python to use this tool effectively.

incubator-kie-optaplanner
A fast, easy-to-use, open source AI constraint solver for software developers. OptaPlanner is a powerful tool that helps developers solve complex optimization problems by providing a constraint satisfaction solver. It allows users to model and solve planning and scheduling problems efficiently, improving decision-making processes and resource allocation. With OptaPlanner, developers can easily integrate optimization capabilities into their applications, leading to better performance and cost-effectiveness.