Best AI tools for< Look Stylish In Winter >

VirtualFit is an AI application that allows users to change their style within seconds using advanced AI technology. Users can upload their photo, specify what they want to change (e.g., clothes, hairstyle), and let the powerful AI algorithm do the rest. VirtualFit offers a range of features such as outfit replacement, image restoration, generative fill, object recoloring, and background removal. The application is designed to provide users with an affordable and user-friendly solution for enhancing their photos without the need for complex editing software like Photoshop.

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.

AI Hairstyle is an AI-powered online platform that offers a virtual hairstyle transformation experience. Users can explore various hairstyles and hair colors using the AI hairstyle generator and color changer. The platform provides personalized hairstyle suggestions and allows users to try on different looks virtually. With features like free storage, users can save their generated images in the cloud for easy access. AI Hairstyle aims to make experimenting with new hairstyles effortless and fun.

LooksMaxx Report is an AI-powered application designed to assist users in enhancing their appearance and achieving a 'glow up'. By leveraging advanced algorithms and image processing technology, the app provides personalized recommendations and insights to help individuals improve their physical attractiveness. Users can access a range of features such as virtual makeovers, facial symmetry analysis, hairstyle suggestions, and skincare tips. With its user-friendly interface and cutting-edge AI capabilities, LooksMaxx Report aims to empower users to boost their confidence and refine their aesthetic appeal.

Perfect365 is an AI makeup application that allows users to virtually try on makeup and hairstyles through advanced augmented reality technology. With over 100 million users, the app offers a seamless way to experiment with different looks, acting as a personal beauty assistant. Users can adjust every aspect of their appearance, from skin tone to eye color, all while maintaining a natural and realistic look. The app employs artificial intelligence algorithms to let users experiment with different makeup looks virtually, without the need for physical products. Perfect365 is a pioneer in the beauty apps sector, providing users with a transformative experience in exploring e-cosmetics.

LookRight.ai is an AI tool designed to provide users with a second pair of eyes for various tasks such as rating outfits, providing roasts, inspiring messages, completing looks, and writing product captions. Users can select a prompt from the list and upload a picture to receive feedback and suggestions. The tool leverages artificial intelligence to analyze images and generate responses to assist users in making decisions and enhancing their content.

Style Imagined is an AI-powered fashion platform designed to enhance your style status. It offers a wide range of user-voted popular fashion styles tailored to different body profiles and budgets. The platform provides AI-based fit recommendations and virtual fittings to visualize how the selected styles will look on you before making a purchase. Users can also participate in styling contests to showcase their creativity and win prizes, transforming their look with a fabulous new style.

Outfit.fm is an AI-powered online dressing room that allows users to try on any outfit they want instantly. It uses AI to create realistic images of the user wearing the clothes, so they can see how they look before they buy. Outfit.fm is free to use while in beta, and no signup is required.

Curlyhair AI is an AI-powered tool that allows users to see how they would look with curly hair. It uses advanced AI algorithms to generate realistic images of users with different curly hairstyles. The tool is easy to use and can be accessed from any device with an internet connection. Users simply need to upload a photo of themselves and select the desired hairstyle. The tool will then generate four variations of the user's photo with different curly hairstyles. The results can be downloaded or shared on social media.

OutfitIdeas is an AI-powered styling tool that offers personalized haircut and outfit recommendations based on individual preferences. Users can upload a photo and answer a simple questionnaire to receive a free lookbook with haircut and outfit designs, face-fit visualizations, expert tips, and a shopping guide. The platform aims to serve as a personal image consultant, helping users save time, effort, and money while achieving their desired style.

My Perfect Hairstyle is an AI-powered tool that helps users find their perfect hairstyle. By utilizing advanced artificial intelligence algorithms, the application analyzes facial features and suggests hairstyles that best suit the user's unique characteristics. Users can experiment with different styles virtually before making a decision, saving time and effort. Whether you're looking for a new haircut, color, or style, My Perfect Hairstyle provides personalized recommendations tailored to your preferences.

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.

Outfit Anyone AI is a free online AI clothes changer that allows users to explore endless fashion possibilities by virtually trying on different outfits. Users can upload their own model photo, choose or customize outfits, and generate new looks within seconds. The application offers a seamless shopping experience, high-quality garment fitting, and personalized styling options. With Outfit Anyone AI, users can transform their appearance from casual to corporate, try on various outfits virtually, and refresh their style for different occasions.

TryHijab is an AI-powered tool that allows users to virtually try on hijabs using their selfies. It provides a realistic preview of how a hijab would look on the user, without the need for a physical hijab. The tool is designed to inspire and empower users to explore their personal style and connect with their spiritual side.

StyleMyRide.ai is an AI-powered platform that allows users to revamp their car's style effortlessly. By uploading a picture of their ride, users can access a wide range of tuning styles to give their vehicle a personalized and unique look. The platform supports various car makes and models, offering different customization options for users to choose from. With features like fine-tuning designs, choosing favorite styles, and accessing more tuning options, StyleMyRide.ai aims to make car customization fun and accessible to all car enthusiasts.

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.

Avumi is an AI-powered 3D Fashion Virtual Try-on tool designed for E-commerce businesses. It allows customers to virtually try on clothes with accuracy, enhancing the online shopping experience. Avumi provides a seamless and interactive way for users to visualize how clothing items would look on them before making a purchase decision. The tool leverages advanced AI technology to create realistic virtual try-on experiences, helping businesses reduce returns and increase customer satisfaction.

Glowup AI is an innovative AI tool that allows users to discover their unique beauty potential through advanced facial analysis technology. By uploading a photo, users can receive personalized recommendations for enhancing their features and achieving their desired look. The app provides insights on skincare, makeup techniques, and hairstyle suggestions tailored to individual facial characteristics. With its user-friendly interface and accurate results, Glowup AI is revolutionizing the beauty industry by empowering users to explore and enhance their natural beauty effortlessly.

This AI-powered age detection tool analyzes your photo to estimate how old you look. It utilizes advanced artificial intelligence technology to assess facial characteristics such as wrinkles, skin texture, and facial features, comparing them against a vast dataset to provide an approximation of your age. The tool is free to use and ensures privacy by automatically deleting uploaded photos after analysis.

How Old Do I Look is a free online AI face age detector that utilizes advanced AI technology to estimate the age of a person based on their uploaded photo. Users can easily upload a photo without the need for registration or login, ensuring a quick and fun experience. The tool provides instant results by analyzing facial features and characteristics through AI-powered algorithms. User privacy is prioritized as photos are securely processed and not stored on the servers. How Old Do I Look offers a unique way to see one's age through the eyes of AI, allowing for entertaining comparisons with friends and family.

WebScout is a versatile tool that allows users to search for anything using Google, DuckDuckGo, and phind.com. It contains AI models, can transcribe YouTube videos, generate temporary email and phone numbers, has TTS support, webai (terminal GPT and open interpreter), and offline LLMs. It also supports features like weather forecasting, YT video downloading, temp mail and number generation, text-to-speech, advanced web searches, and more.

Guardrails is a Python framework that helps build reliable AI applications by performing two key functions: 1. Guardrails runs Input/Output Guards in your application that detect, quantify and mitigate the presence of specific types of risks. To look at the full suite of risks, check out Guardrails Hub. 2. Guardrails help you generate structured data from LLMs.

FaceFusion is a next-generation face swapper and enhancer that allows users to seamlessly swap faces in images and videos, as well as enhance facial features for a more polished and refined look. With its advanced deep learning models, FaceFusion provides users with a wide range of options for customizing their face swaps and enhancements, making it an ideal tool for content creators, artists, and anyone looking to explore their creativity with facial manipulation.

DaKanji is a mobile application that helps you learn Japanese. With DaKanji, you can look up words in many languages, search Kanjis by simply drawing them, add furigana to texts, and much more.

AutoNode is a self-operating computer system designed to automate web interactions and data extraction processes. It leverages advanced technologies like OCR (Optical Character Recognition), YOLO (You Only Look Once) models for object detection, and a custom site-graph to navigate and interact with web pages programmatically. Users can define objectives, create site-graphs, and utilize AutoNode via API to automate tasks on websites. The tool also supports training custom YOLO models for object detection and OCR for text recognition on web pages. AutoNode can be used for tasks such as extracting product details, automating web interactions, and more.

Koko-aio shader is an all-in-one CRT shader tool that can be configured with various parameters to run on different GPUs. It aims to provide visual parameters to make monitors look similar to CRT displays without simulating their internal behavior. The tool includes features such as color corrections, B/W display colorization, antialiasing, noise effects, deconvergence, blurring/sharpening, interlacing, phosphor glow, and more. It also supports ambient lighting, vignette, integer scaling, and various image effects. Koko-aio is designed to enhance the visual experience of low-res content on high-resolution displays.

This Helm chart bootstraps an Airflow deployment on a Kubernetes cluster using the Helm package manager. The version of this chart does not correlate to any other component. Users should not expect feature parity between OSS airflow chart and the Astronomer airflow-chart for identical version numbers. To install this helm chart remotely (using helm 3) kubectl create namespace airflow helm repo add astronomer https://helm.astronomer.io helm install airflow --namespace airflow astronomer/airflow To install this repository from source sh kubectl create namespace airflow helm install --namespace airflow . Prerequisites: Kubernetes 1.12+ Helm 3.6+ PV provisioner support in the underlying infrastructure Installing the Chart: sh helm install --name my-release . The command deploys Airflow on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured during installation. Upgrading the Chart: First, look at the updating documentation to identify any backwards-incompatible changes. To upgrade the chart with the release name `my-release`: sh helm upgrade --name my-release . Uninstalling the Chart: To uninstall/delete the `my-release` deployment: sh helm delete my-release The command removes all the Kubernetes components associated with the chart and deletes the release. Updating DAGs: Bake DAGs in Docker image The recommended way to update your DAGs with this chart is to build a new docker image with the latest code (`docker build -t my-company/airflow:8a0da78 .`), push it to an accessible registry (`docker push my-company/airflow:8a0da78`), then update the Airflow pods with that image: sh helm upgrade my-release . --set images.airflow.repository=my-company/airflow --set images.airflow.tag=8a0da78 Docker Images: The Airflow image that are referenced as the default values in this chart are generated from this repository: https://github.com/astronomer/ap-airflow. Other non-airflow images used in this chart are generated from this repository: https://github.com/astronomer/ap-vendor. Parameters: The complete list of parameters supported by the community chart can be found on the Parameteres Reference page, and can be set under the `airflow` key in this chart. The following tables lists the configurable parameters of the Astronomer chart and their default values. | Parameter | Description | Default | | :----------------------------- | :-------------------------------------------------------------------------------------------------------- | :---------------------------- | | `ingress.enabled` | Enable Kubernetes Ingress support | `false` | | `ingress.acme` | Add acme annotations to Ingress object | `false` | | `ingress.tlsSecretName` | Name of secret that contains a TLS secret | `~` | | `ingress.webserverAnnotations` | Annotations added to Webserver Ingress object | `{}` | | `ingress.flowerAnnotations` | Annotations added to Flower Ingress object | `{}` | | `ingress.baseDomain` | Base domain for VHOSTs | `~` | | `ingress.auth.enabled` | Enable auth with Astronomer Platform | `true` | | `extraObjects` | Extra K8s Objects to deploy (these are passed through `tpl`). More about Extra Objects. | `[]` | | `sccEnabled` | Enable security context constraints required for OpenShift | `false` | | `authSidecar.enabled` | Enable authSidecar | `false` | | `authSidecar.repository` | The image for the auth sidecar proxy | `nginxinc/nginx-unprivileged` | | `authSidecar.tag` | The image tag for the auth sidecar proxy | `stable` | | `authSidecar.pullPolicy` | The K8s pullPolicy for the the auth sidecar proxy image | `IfNotPresent` | | `authSidecar.port` | The port the auth sidecar exposes | `8084` | | `gitSyncRelay.enabled` | Enables git sync relay feature. | `False` | | `gitSyncRelay.repo.url` | Upstream URL to the git repo to clone. | `~` | | `gitSyncRelay.repo.branch` | Branch of the upstream git repo to checkout. | `main` | | `gitSyncRelay.repo.depth` | How many revisions to check out. Leave as default `1` except in dev where history is needed. | `1` | | `gitSyncRelay.repo.wait` | Seconds to wait before pulling from the upstream remote. | `60` | | `gitSyncRelay.repo.subPath` | Path to the dags directory within the git repository. | `~` | Specify each parameter using the `--set key=value[,key=value]` argument to `helm install`. For example, sh helm install --name my-release --set executor=CeleryExecutor --set enablePodLaunching=false . Walkthrough using kind: Install kind, and create a cluster We recommend testing with Kubernetes 1.25+, example: sh kind create cluster --image kindest/node:v1.25.11 Confirm it's up: sh kubectl cluster-info --context kind-kind Add Astronomer's Helm repo sh helm repo add astronomer https://helm.astronomer.io helm repo update Create namespace + install the chart sh kubectl create namespace airflow helm install airflow -n airflow astronomer/airflow It may take a few minutes. Confirm the pods are up: sh kubectl get pods --all-namespaces helm list -n airflow Run `kubectl port-forward svc/airflow-webserver 8080:8080 -n airflow` to port-forward the Airflow UI to http://localhost:8080/ to confirm Airflow is working. Login as _admin_ and password _admin_. Build a Docker image from your DAGs: 1. Start a project using astro-cli, which will generate a Dockerfile, and load your DAGs in. You can test locally before pushing to kind with `astro airflow start`. `sh mkdir my-airflow-project && cd my-airflow-project astro dev init` 2. Then build the image: `sh docker build -t my-dags:0.0.1 .` 3. Load the image into kind: `sh kind load docker-image my-dags:0.0.1` 4. Upgrade Helm deployment: sh helm upgrade airflow -n airflow --set images.airflow.repository=my-dags --set images.airflow.tag=0.0.1 astronomer/airflow Extra Objects: This chart can deploy extra Kubernetes objects (assuming the role used by Helm can manage them). For Astronomer Cloud and Enterprise, the role permissions can be found in the Commander role. yaml extraObjects: - apiVersion: batch/v1beta1 kind: CronJob metadata: name: "{{ .Release.Name }}-somejob" spec: schedule: "*/10 * * * *" concurrencyPolicy: Forbid jobTemplate: spec: template: spec: containers: - name: myjob image: ubuntu command: - echo args: - hello restartPolicy: OnFailure Contributing: Check out our contributing guide! License: Apache 2.0 with Commons Clause

Large Language Models (LLM) are used to browse the Wolfram directory and associated URLs to create the category structure and good word embeddings. The goal is to generate enriched prompts for GPT, Wikipedia, Arxiv, Google Scholar, Stack Exchange, or Google search. The focus is on one subdirectory: Probability & Statistics. Documentation is in the project textbook `Projects4.pdf`, which is available in the folder. It is recommended to download the document and browse your local copy with Chrome, Edge, or other viewers. Unlike on GitHub, you will be able to click on all the links and follow the internal navigation features. Look for projects related to NLP and LLM / xLLM. The best starting point is project 7.2.2, which is the core project on this topic, with references to all satellite projects. The project textbook (with solutions to all projects) is the core document needed to participate in the free course (deep tech dive) called **GenAI Fellowship**. For details about the fellowship, follow the link provided. An uncompressed version of `crawl_final_stats.txt.gz` is available on Google drive, which contains all the crawled data needed as input to the Python scripts in the XLLM5 and XLLM6 folders.

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.

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

The UvA Deep Learning Tutorials repository contains a series of Jupyter notebooks designed to help understand theoretical concepts from lectures by providing corresponding implementations. The notebooks cover topics such as optimization techniques, transformers, graph neural networks, and more. They aim to teach details of the PyTorch framework, including PyTorch Lightning, with alternative translations to JAX+Flax. The tutorials are integrated as official tutorials of PyTorch Lightning and are relevant for graded assignments and exams.

This tool is an AIG install for MSFS ONLY EXCLUDING offline AI flight plans. It provides a solution to model matching for online networks along with providing a tool to inject live traffic to your simulator, directly from Flightradar24. The tool is designed for use with online virtual traffic networks like VATSIM, but it will also work for offline traffic. A VMR File for VATSIM usage has been included in the folder.

Deaddit is a project showcasing an AI-filled internet platform similar to Reddit. All content, including subdeaddits, posts, and comments, is generated by AI algorithms. Users can interact with AI-generated content and explore a simulated social media experience. The project provides a demonstration of how AI can be used to create online content and simulate user interactions in a virtual community.

This repository contains datasets with information about the air raid sirens in Ukraine by each region. It includes official and unofficial alerts collected by volunteers. The datasets are updated daily and can be regenerated manually using provided steps. The goal is to provide valuable information about air raid sirens in Ukraine during the ongoing conflict with Russia.

LocalAIVoiceChat is an experimental alpha software that enables real-time voice chat with a customizable AI personality and voice on your PC. It integrates Zephyr 7B language model with speech-to-text and text-to-speech libraries. The tool is designed for users interested in state-of-the-art voice solutions and provides an early version of a local real-time chatbot.

Transformers_And_LLM_Are_What_You_Dont_Need is a repository that explores the limitations of transformers in time series forecasting. It contains a collection of papers, articles, and theses discussing the effectiveness of transformers and LLMs in this domain. The repository aims to provide insights into why transformers may not be the best choice for time series forecasting tasks.

AICSImageIO is a Python tool for Image Reading, Metadata Conversion, and Image Writing for Microscopy Images. It supports various file formats like OME-TIFF, TIFF, ND2, DV, CZI, LIF, PNG, GIF, and Bio-Formats. Users can read and write metadata and imaging data, work with different file systems like local paths, HTTP URLs, s3fs, and gcsfs. The tool provides functionalities for full image reading, delayed image reading, mosaic image reading, metadata reading, xarray coordinate plane attachment, cloud IO support, and saving to OME-TIFF. It also offers benchmarking and developer resources.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

AI-in-a-Box is a curated collection of solution accelerators that can help engineers establish their AI/ML environments and solutions rapidly and with minimal friction, while maintaining the highest standards of quality and efficiency. It provides essential guidance on the responsible use of AI and LLM technologies, specific security guidance for Generative AI (GenAI) applications, and best practices for scaling OpenAI applications within Azure. The available accelerators include: Azure ML Operationalization in-a-box, Edge AI in-a-box, Doc Intelligence in-a-box, Image and Video Analysis in-a-box, Cognitive Services Landing Zone in-a-box, Semantic Kernel Bot in-a-box, NLP to SQL in-a-box, Assistants API in-a-box, and Assistants API Bot in-a-box.

A fashion-focused guide on winter clothing and style.

Expert in creating stylish buttons for web and software apps using any programming language.

Interested in finding out your kibbe body type? Then you've come to the right place.

Upload your photo and I'll give you an opinion

Upload your photo and I'll give you an opinion

Advisor for hairstyles, top products, and salon recommendations matched with your hair type and location.

Determines your Kibbe body type, so you can find what clothes look best on you.

I suggest hairstyles based on your selfie.

Tour guide with map pins

Look at the Bible through the lens of Behavioral Psychology

Look at the future of your current profession and how it may be affected by Artificial Intelligence

Can look up nutritional information for anything

A look back at past events.

I will make you look...hideous

What does your future look like, would you like to find out?

Find out what your future baby will look like! I will analyze your photo(s) and generate a baby picture using AI.

Expert advice on how to look like you are working when you actually aren't.

Enhances human faces in photos for a more beautiful look.

Assists with PDF and template formatting for a professional look.

MODX GPT is trained on the MODX.com CMS documentation and helps guide you with MODX development tasks. Learn the basics, use me to look up function references, discover MODX Extras, or even help with debugging.