Best AI tools for< Try Dresses >

DressMeUp is an AI-powered virtual dressing room platform that allows users to virtually try on dresses from the comfort of their own home. The platform offers personalized recommendations based on body shape and size, providing a seamless shopping experience with endless options to explore. Users can browse through an extensive collection of dresses, visualize their perfect fit, and receive tailored suggestions. DressMeUp aims to revolutionize the way people shop for clothing by leveraging AI technology to enhance the online shopping experience.

Try it on AI is a professional AI photography studio that offers stunning AI headshots and portraits for various purposes such as LinkedIn, resume, acting & modeling portfolios, and social media. The platform provides high-quality AI headshots tailored to individual needs, with features like Express AI for quick headshots, Creative Studio for custom portraits, and Edit Your Own Tools for image enhancement. Try it on AI is widely used by professionals worldwide to meet their portrait needs, offering a seamless experience for creating on-brand portraits for teams and individuals across different industries.

Try it on AI is an online platform that uses artificial intelligence to generate professional-quality headshots and portraits. The platform offers a variety of tools to meet all your portrait needs, including Express AI for quick AI Headshots, Creative Studio to create custom portraits, and Edit Your Own Tools to enhance your own images. Try it on AI is perfect for individuals, teams, and businesses of all sizes. With over 500,000+ professionals around the world using Try it on AI, you can be sure that you're getting the best possible results.

Try On Hairstyles is a website that allows users to try on different hairstyles using artificial intelligence. Users can upload a photo of themselves and then choose from a variety of hairstyles to see how they would look. The website also offers a variety of hair care tips and advice.

Kolors Virtual Try On is an AI-powered fashion fitting tool that allows users to virtually try on clothing and accessories. By uploading images of themselves and the garments they want to try, users can quickly generate realistic virtual try-on results. The cutting-edge AI technology behind Kolors Virtual Try On ensures fast and accurate processing, providing users with a diverse range of clothing options to experiment with. The tool offers a secure platform with advanced encryption to protect user privacy, making it a convenient and reliable solution for exploring different fashion styles.

Why Try AI is a hands-on, no-hype guide to generative AI, providing valuable insights and practical tips for individuals interested in understanding and utilizing AI technologies. With over 6,000 subscribers, the newsletter offers a well-written and hype-free approach to AI, catering to a wide audience of normal folk looking to explore the world of artificial intelligence. The content covers various AI tools, offering readers the opportunity to learn and apply new techniques in their projects and endeavors. By registering on the website, users gain access to a wealth of information and resources related to AI, presented in an engaging and accessible format.

ElevenLabs is an AI audio platform that offers Text to Speech, AI Voice Generator, and more. It provides high-quality, human-like speech in 32 languages, suitable for audiobooks, video voiceovers, commercials, and various other applications. The platform also includes features like Voice Changer, Dubbing, Voice Cloning, and Conversational AI tools. ElevenLabs aims to bridge language gaps, enhance storytelling, and make digital interactions more human through its AI audio solutions.

QuillBot is an AI-powered paraphrasing tool that helps students and professionals rewrite, edit, and change the tone of their text to improve clarity and comprehension. It offers various features such as customization, synonym suggestions, and integrations with other writing tools. QuillBot's paraphrasing tool is designed to enhance writing fluency, vocabulary, tone, and style, making it suitable for a wide range of writing tasks.

Fetcher is an AI-powered recruitment platform that simplifies the hiring process by providing curated batches of talented candidates tailored to specific needs. It eliminates manual sourcing by leveraging advanced AI technology and human expertise to deliver high-quality candidate profiles directly to the user's inbox. With features like personalized email outreach, recruitment analytics, and diversity metrics monitoring, Fetcher optimizes the recruitment pipeline and enhances candidate engagement. Trusted by top recruiting teams worldwide, Fetcher offers unlimited candidate connections and email performance analytics to streamline the hiring strategy.

PropertyPen is an AI-powered platform designed to help real estate agencies create professional property listings quickly and efficiently. It offers customizable features, high-quality content generation, and optimization for real estate needs. The tool utilizes advanced language models and natural language processing to ensure listings are written in persuasive and grammatically correct language. PropertyPen stands out for its speed, flexibility, and ability to generate listings tailored to specific preferences.

Flowith is an AI productivity tool powered by GPT-4. It features a unique node-based interaction that allows for a more fluid and collaborative experience with AI. With Flowith, you can access advanced AI models, upload and analyze files, and create and share custom AI agents. It is designed to enhance productivity and simplify tasks across various domains, including writing, research, coding, and more.

The website is a comprehensive AI-powered automation and AI toolkit designed for social marketers and networkers. It offers a wide range of features to streamline social selling and networking processes, automate daily actions, reach more customers, and manage business growth effectively. The platform provides tools for content creation, list building, social media management, email marketing, automations, workflows, and more. With a focus on scalability and user-friendliness, Social Connector aims to empower marketers at every stage of their journey with AI-generated content, unlimited forms, and list building tools.

Jobo is an AI-powered career assistant that helps you find your ideal job, enhance your resume, and apply for jobs, all in one go. It uses OpenAI's technology to personalize and optimize your job search, making it easier for you to land your dream job.

AI Hairstyles is a website that allows users to try on different hairstyles and colors using artificial intelligence. Users can upload a selfie and choose from a variety of styles and colors to see how they would look with a new hairstyle. The website also offers a privacy-first approach, deleting user images after 30 days of inactivity.

Hairstyle AI is an AI-powered application that allows users to virtually try out new hairstyles before getting a real haircut. With over 155,760 hairstyles already created for 1,298 happy customers, Hairstyle AI provides a platform for users to experiment with different haircuts and styles, helping them feel more confident in their appearance. The application offers a range of features such as generating AI hairstyles indistinguishable from real photos, providing a variety of hairstyle options, and ensuring user privacy and security.

Outfits AI is an AI-powered application that allows users to virtually try on different outfits using advanced artificial intelligence technology. With a user-friendly interface, the application enables over 100,000 happy users to experiment with various clothing styles and combinations effortlessly. Whether you are looking for a new look or simply want to explore different outfit ideas, Outfits AI provides a fun and interactive platform to enhance your fashion experience.

Outfit Changer is a revolutionary AI-powered platform that transforms the fashion experience with its virtual try-on technology. It empowers users to explore a vast collection of clothing styles and virtually try them on, accommodating various body types and offering a realistic and inclusive experience. Outfit Changer's advanced AI ensures high-quality garment fitting, enhanced detailing, and the ability to create outfits for animation characters, making it a versatile tool for fashion enthusiasts and professionals alike.

IDM-VTON online is an AI virtual try-on tool that allows users to try on clothes virtually. Users can upload their human picture and garment picture, select the garment category, and quickly obtain an AI-generated photo of themselves wearing the selected clothing item. The tool is designed to enhance the online shopping experience, personalized recommendations, social media interactions, and fashion design displays.

Easy-Peasy.AI is an all-in-one AI platform that offers a variety of AI tools and solutions to assist users in content generation, copywriting, chatbot creation, image creation, audio transcription, and text-to-speech tasks. The platform provides a user-friendly interface and powerful technology to help users create high-quality content, improve writing skills, and automate various tasks using AI technology.

Freed is an AI medical scribe tool designed to assist clinicians in transcribing and documenting patient encounters efficiently. It listens, transcribes, and writes notes for clinicians, saving them time and allowing them to focus more on patient care. With over 15,000 clinicians and 400+ health organizations trusting Freed, it aims to improve clinician happiness and streamline the documentation process in healthcare settings. The platform is HIPAA compliant, ensuring data security and privacy for users.

MagicMirror is an AI-powered tool that allows users to instantly try on new faces, hairstyles, and outfits with a simple drag and drop interface. It runs smoothly on standard computers without the need for dedicated GPU hardware, ensuring privacy with completely offline processing. The tool is ultra-lightweight with a small installer size and model files, providing a fun and easy way to experiment with different looks.

llamafile is a tool that enables users to distribute and run Large Language Models (LLMs) with a single file. It combines llama.cpp with Cosmopolitan Libc to create a framework that simplifies the complexity of LLMs into a single-file executable called a 'llamafile'. Users can run these executable files locally on most computers without the need for installation, making open LLMs more accessible to developers and end users. llamafile also provides example llamafiles for various LLM models, allowing users to try out different LLMs locally. The tool supports multiple CPU microarchitectures, CPU architectures, and operating systems, making it versatile and easy to use.

HuixiangDou is a **group chat** assistant based on LLM (Large Language Model). Advantages: 1. Design a two-stage pipeline of rejection and response to cope with group chat scenario, answer user questions without message flooding, see arxiv2401.08772 2. Low cost, requiring only 1.5GB memory and no need for training 3. Offers a complete suite of Web, Android, and pipeline source code, which is industrial-grade and commercially viable Check out the scenes in which HuixiangDou are running and join WeChat Group to try AI assistant inside. If this helps you, please give it a star ⭐

Haystack is an open-source framework for building production-ready LLM applications, retrieval-augmented generative pipelines, and state-of-the-art search systems that work intelligently over large document collections. It lets you quickly try out the latest models in natural language processing (NLP) while being flexible and easy to use.

LangStream is a tool for natural language processing tasks, providing a CLI for easy installation and usage. Users can try sample applications like Chat Completions and create their own applications using the developer documentation. It supports running on Kubernetes for production-ready deployment, with support for various Kubernetes distributions and external components like Apache Kafka or Apache Pulsar cluster. Users can deploy LangStream locally using minikube and manage the cluster with mini-langstream. Development requirements include Docker, Java 17, Git, Python 3.11+, and PIP, with the option to test local code changes using mini-langstream.

The Latent Browser is a desktop application designed like a web browser, which hallucinates web search results (the resultds are fictious and are generated by a LLM) and web pages. It is a web application designed to run locally on your machine and is 99% React, Tailwind, TypeScript, and NextJS. The runtime is Tauri, which is written in Rust. The Latent Browser is still under development and some things may be broken when you try it.

Dataflow-oriented robotic application (dora-rs) is a framework that makes creation of robotic applications fast and simple. Building a robotic application can be summed up as bringing together hardwares, algorithms, and AI models, and make them communicate with each others. At dora-rs, we try to: make integration of hardware and software easy by supporting Python, C, C++, and also ROS2. make communication low latency by using zero-copy Arrow messages. dora-rs is still experimental and you might experience bugs, but we're working very hard to make it stable as possible.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access a GPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval. The repo includes sample data so it's ready to try end to end. In this sample application we use a fictitious company called Contoso Electronics, and the experience allows its employees to ask questions about the benefits, internal policies, as well as job descriptions and roles.

The LLM Twin Course is a free, end-to-end framework for building production-ready LLM systems. It teaches you how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices. The course is split into 11 hands-on written lessons and the open-source code you can access on GitHub. You can read everything and try out the code at your own pace.

Vespa documentation is served using GitHub Project pages with Jekyll. To edit documentation, check out and work off the master branch in this repository. Documentation is written in HTML or Markdown. Use a single Jekyll template _layouts/default.html to add header, footer and layout. Install bundler, then $ bundle install $ bundle exec jekyll serve --incremental --drafts --trace to set up a local server at localhost:4000 to see the pages as they will look when served. If you get strange errors on bundle install try $ export PATH=“/usr/local/opt/[email protected]/bin:$PATH” $ export LDFLAGS=“-L/usr/local/opt/[email protected]/lib” $ export CPPFLAGS=“-I/usr/local/opt/[email protected]/include” $ export PKG_CONFIG_PATH=“/usr/local/opt/[email protected]/lib/pkgconfig” The output will highlight rendering/other problems when starting serving. Alternatively, use the docker image `jekyll/jekyll` to run the local server on Mac $ docker run -ti --rm --name doc \ --publish 4000:4000 -e JEKYLL_UID=$UID -v $(pwd):/srv/jekyll \ jekyll/jekyll jekyll serve or RHEL 8 $ podman run -it --rm --name doc -p 4000:4000 -e JEKYLL_ROOTLESS=true \ -v "$PWD":/srv/jekyll:Z docker.io/jekyll/jekyll jekyll serve The layout is written in denali.design, see _layouts/default.html for usage. Please do not add custom style sheets, as it is harder to maintain.

STORM is a LLM system that writes Wikipedia-like articles from scratch based on Internet search. While the system cannot produce publication-ready articles that often require a significant number of edits, experienced Wikipedia editors have found it helpful in their pre-writing stage. **Try out our [live research preview](https://storm.genie.stanford.edu/) to see how STORM can help your knowledge exploration journey and please provide feedback to help us improve the system 🙏!**

Cognita is an open-source framework to organize your RAG codebase along with a frontend to play around with different RAG customizations. It provides a simple way to organize your codebase so that it becomes easy to test it locally while also being able to deploy it in a production ready environment. The key issues that arise while productionizing RAG system from a Jupyter Notebook are: 1. **Chunking and Embedding Job** : The chunking and embedding code usually needs to be abstracted out and deployed as a job. Sometimes the job will need to run on a schedule or be trigerred via an event to keep the data updated. 2. **Query Service** : The code that generates the answer from the query needs to be wrapped up in a api server like FastAPI and should be deployed as a service. This service should be able to handle multiple queries at the same time and also autoscale with higher traffic. 3. **LLM / Embedding Model Deployment** : Often times, if we are using open-source models, we load the model in the Jupyter notebook. This will need to be hosted as a separate service in production and model will need to be called as an API. 4. **Vector DB deployment** : Most testing happens on vector DBs in memory or on disk. However, in production, the DBs need to be deployed in a more scalable and reliable way. Cognita makes it really easy to customize and experiment everything about a RAG system and still be able to deploy it in a good way. It also ships with a UI that makes it easier to try out different RAG configurations and see the results in real time. You can use it locally or with/without using any Truefoundry components. However, using Truefoundry components makes it easier to test different models and deploy the system in a scalable way. Cognita allows you to host multiple RAG systems using one app. ### Advantages of using Cognita are: 1. A central reusable repository of parsers, loaders, embedders and retrievers. 2. Ability for non-technical users to play with UI - Upload documents and perform QnA using modules built by the development team. 3. Fully API driven - which allows integration with other systems. > If you use Cognita with Truefoundry AI Gateway, you can get logging, metrics and feedback mechanism for your user queries. ### Features: 1. Support for multiple document retrievers that use `Similarity Search`, `Query Decompostion`, `Document Reranking`, etc 2. Support for SOTA OpenSource embeddings and reranking from `mixedbread-ai` 3. Support for using LLMs using `Ollama` 4. Support for incremental indexing that ingests entire documents in batches (reduces compute burden), keeps track of already indexed documents and prevents re-indexing of those docs.

Databend is an open-source cloud data warehouse built in Rust, offering fast query execution and data ingestion for complex analysis of large datasets. It integrates with major cloud platforms, provides high performance with AI-powered analytics, supports multiple data formats, ensures data integrity with ACID transactions, offers flexible indexing options, and features community-driven development. Users can try Databend through a serverless cloud or Docker installation, and perform tasks such as data import/export, querying semi-structured data, managing users/databases/tables, and utilizing AI functions.

unsight.dev is a tool built on Nuxt that helps detect duplicate GitHub issues and areas of concern across related repositories. It utilizes Nitro server API routes, GitHub API, and a GitHub App, along with UnoCSS. The tool is deployed on Cloudflare with NuxtHub, using Workers AI, Workers KV, and Vectorize. It also offers a browser extension soon to be released. Users can try the app locally for tweaking the UI and setting up a full development environment as a GitHub App.

PDFMathTranslate is a tool designed for translating scientific papers and conducting bilingual comparisons. It preserves formulas, charts, table of contents, and annotations. The tool supports multiple languages and diverse translation services. It provides a command-line tool, interactive user interface, and Docker deployment. Users can try the application through online demos. The tool offers various installation methods including command-line, portable, graphic user interface, and Docker. Advanced options allow users to customize translation settings. Additionally, the tool supports secondary development through APIs for Python and HTTP. Future plans include parsing layout with DocLayNet based models, fixing page rotation and format issues, supporting non-PDF/A files, and integrating plugins for Zotero and Obsidian.

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: ::= | ::= | | ; Where: ; is a valid real number expressed in one of a number of given formats ; is a string of valid characters enclosed in quotes ; is one of the literal strings 'true', 'false', or 'null' (unquoted) ::=