Best AI tools for< Recommend New Releases >

The One-Stop Natural Language Hotel Recommender is an AI-powered tool that simplifies the process of finding the perfect hotel for your needs. By utilizing natural language processing technology, the tool can understand your preferences and requirements, and provide you with personalized hotel recommendations. It considers factors such as proximity to popular places, top-rated establishments, summarized reviews, and staying within your budget. With this tool, you can easily find the ideal accommodation for your next trip without the hassle of extensive research.

LavieTaste.AI is an AI-powered application that helps users explore and discover the best restaurants offering Singaporean and Japanese cuisine. By simply entering the desired food or place, the tool provides personalized recommendations for top dining experiences. Users can find a variety of options ranging from retro cafes to popular buffets and top steak houses. LavieTaste.AI aims to enhance the culinary journey of users by offering tailored suggestions based on their preferences and location.

WGMI Media is a curated platform focusing on AI, tech, and business content. It offers insights on new business ideas, AI tools, growth hacks, and more through newsletters and articles. The platform aims to provide valuable information to entrepreneurs and visionaries, helping them stay updated on the latest trends and opportunities in the digital landscape.

AI Bookstore is a website that uses AI to help users find books that they want to read. Users can ask the AI questions about what they are looking for, and the AI will recommend books that match their criteria. The AI can also generate personalized recommendations based on a user's reading history.

Vexisai.com is a website that serves as a domain parking page created by the domain owner using Sedo Domain Parking. It provides resources and information related to the domain. The webpage does not have any specific AI tool or application functionality but rather acts as a placeholder for the domain. Please note that Sedo, the service used for domain parking, is not affiliated with any third-party advertisers and does not endorse or recommend any specific services or trademarks.

The website offers AI-powered streaming options to assist users in choosing what to watch. Recommendations are crafted by the innovative team behind the launch of popular platforms like Disney+, ESPN+, FoxSports, and Hulu.

FindAMeal is an AI-powered search engine that helps users discover nearby restaurants based on their personal preferences. By combining data from various food review providers and utilizing AI technology, FindAMeal simplifies the process of finding the perfect dining spot. Users can make any type of query, from finding a date spot to selecting a fancy restaurant for special occasions. The platform aggregates information from sources like Google, Yelp, and Foursquare to offer tailored recommendations, making it easy for users to explore new dining experiences.

AI Music Catalog is an innovative application that utilizes artificial intelligence to curate and organize music based on user preferences. By leveraging advanced algorithms, the app recommends personalized playlists, suggests new tracks, and helps users discover music tailored to their tastes. With a user-friendly interface, AI Music Catalog simplifies the music discovery process, making it easier for users to explore and enjoy a wide range of genres and artists. Whether you're looking for new music or want to rediscover old favorites, this AI tool offers a seamless and personalized music experience.

Vinetribe is a platform that offers personalized wine recommendations from local shops, curated by trusted creators. The platform aims to make wine more accessible by providing a user-friendly experience for discovering new wines. With the Supermarket Wine Finder feature, users can easily find the perfect wine for their preferences and occasions. Vinetribe is founded on the idea of bringing people together through the enjoyment of wine, making the wine selection process less daunting and more enjoyable.

Lancey is an AI copilot for product teams that leverages customer feedback, CRM, and product analytics data to assist in building innovative ideas. It provides insights on themes, sentiment, and feature areas impacted by feedback, enabling continuous product discovery without manual effort. With integrations for data collection and analysis, Lancey helps product teams make better decisions and drive new ARR. The application is praised by product leaders for its ability to surface valuable insights and streamline product development processes.

GPTMyDay is an AI-powered application that helps users plan their day efficiently. By leveraging ChatGPT's AI technology, users can input their preferences and receive personalized daily plans, saving time and effort. The application offers features like activity recommendations, plan generation, plan review, and personalized history tracking. With GPTMyDay, users can explore new activities, restaurants, and hotels hassle-free, making their daily experiences more enjoyable and organized.

Coora AI is an AI-powered discovery shopping platform that offers personalized recommendations to users based on their preferences and browsing history. It utilizes advanced machine learning algorithms to analyze user behavior and provide tailored product suggestions. With a user-friendly interface, Coora AI aims to enhance the online shopping experience by making it more efficient and enjoyable for customers. By leveraging the power of artificial intelligence, Coora AI helps users discover new products and brands that align with their interests, ultimately leading to a more satisfying shopping journey.

Melodisco is an AI-powered music player that allows users to discover new music, create playlists, and share their favorite songs with friends. The platform uses artificial intelligence to recommend songs that users are likely to enjoy, based on their listening history and preferences. Melodisco also offers a variety of tools for creating and managing playlists, making it easy for users to organize their music collection and share it with others.

KissAsian.ai is an AI-powered platform that offers a wide range of Asian dramas and movies for online streaming. Users can access a vast library of content from various Asian countries, including Korea, Japan, China, and more. The platform utilizes artificial intelligence algorithms to recommend personalized content based on user preferences, making it easier for viewers to discover new shows and movies. With a user-friendly interface and high-quality video streaming capabilities, KissAsian.ai provides an immersive entertainment experience for fans of Asian cinema.

Knoiz is an AI-powered music playlist generator that allows users to create custom playlists based on keywords or genres. It uses advanced algorithms to analyze and recommend songs that match the user's preferences. Knoiz is designed to make it easy for users to discover new music and create playlists for any occasion.

WatchNow AI is an AI-powered movie recommendation platform that utilizes advanced algorithms to suggest personalized movie recommendations based on user preferences and viewing history. The platform offers a seamless and user-friendly interface, allowing users to discover new movies tailored to their tastes. With a vast database of movies across genres, WatchNow AI ensures that users find the perfect movie to watch every time. Whether you're a fan of action, romance, comedy, or thriller, WatchNow AI has something for everyone. Say goodbye to endless scrolling through streaming platforms and let WatchNow AI curate a customized movie list for you.

ResearchRabbit is a research tool that helps researchers discover and organize academic papers. It uses artificial intelligence to recommend papers that are relevant to a researcher's interests and to visualize networks of papers and co-authorships. ResearchRabbit also allows researchers to collaborate on collections of papers and to share their findings with others.

Chat Jams is a music streaming platform that allows users to create and share playlists with friends. Users can discover new music, create personalized playlists, and connect with others through music. The platform offers a wide range of music genres and artists to cater to diverse preferences. Chat Jams aims to provide a social and interactive music listening experience for its users.

Blobr is an AI tool designed to optimize ad spend effortlessly. It utilizes AI to analyze ad campaigns, ad copies, and performance to identify optimizations that save time and money. By continuously monitoring keywords, KPIs, and products, Blobr provides deep insights and correlations, suggesting improvements and new keywords. The tool helps in budget allocation, finding duplicates, recommending ad changes, and analyzing ad performance data. Blobr operates 24/7, offering proactive insights and recommendations to enhance ad campaigns and improve ROI.

BookSurfAi is an AI-powered book recommendation tool that helps users discover their next favorite book. By leveraging artificial intelligence technology, the application provides personalized reading suggestions based on individual preferences and reading habits. BookSurfAi aims to enhance the reading experience by offering tailored recommendations that cater to each user's unique tastes and interests. With a user-friendly interface, BookSurfAi makes it easy for book lovers to explore new literary works and expand their reading horizons.

allms is a versatile and powerful library designed to streamline the process of querying Large Language Models (LLMs). Developed by Allegro engineers, it simplifies working with LLM applications by providing a user-friendly interface, asynchronous querying, automatic retrying mechanism, error handling, and output parsing. It supports various LLM families hosted on different platforms like OpenAI, Google, Azure, and GCP. The library offers features for configuring endpoint credentials, batch querying with symbolic variables, and forcing structured output format. It also provides documentation, quickstart guides, and instructions for local development, testing, updating documentation, and making new releases.

Prompt Buddy is a Microsoft Teams app that provides a central location for teams to share and discover their favorite AI prompts. It comes preloaded with Microsoft Copilot and other categories, but users can also add their own custom prompts. The app is easy to use and allows users to upvote their favorite prompts, which raises them to the top of the leaderboard. Prompt Buddy also supports dark mode and offers a mobile layout for use on phones. It is built on the Power Platform and can be customized and extended by the installer.

ChatGPT Desktop Application is a multi-platform tool that provides a powerful AI wrapper for generating text. It offers features like text-to-speech, exporting chat history in various formats, automatic application upgrades, system tray hover window, support for slash commands, customization of global shortcuts, and pop-up search. The application is built using Tauri and aims to enhance user experience by simplifying text generation tasks. It is available for Mac, Windows, and Linux, and is designed for personal learning and research purposes.

Langfuse is a powerful tool that helps you develop, monitor, and test your LLM applications. With Langfuse, you can: * **Develop:** Instrument your app and start ingesting traces to Langfuse, inspect and debug complex logs, and manage, version, and deploy prompts from within Langfuse. * **Monitor:** Track metrics (cost, latency, quality) and gain insights from dashboards & data exports, collect and calculate scores for your LLM completions, run model-based evaluations, collect user feedback, and manually score observations in Langfuse. * **Test:** Track and test app behaviour before deploying a new version, test expected in and output pairs and benchmark performance before deploying, and track versions and releases in your application. Langfuse is easy to get started with and offers a generous free tier. You can sign up for Langfuse Cloud or deploy Langfuse locally or on your own infrastructure. Langfuse also offers a variety of integrations to make it easy to connect to your LLM applications.

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

Aidoku is a free and open source manga reading application for iOS and iPadOS. It offers features like ad-free experience, robust WASM source system, online reading through external sources, iCloud sync support, downloads, and tracker support. Users can access the latest ipa from the releases page and join TestFlight via the Aidoku Discord for detailed installation instructions. The project is open to contributions, with planned features and fixes. Translation efforts are welcomed through Weblate for crowd-sourced translations.

Chatbox is a desktop client for ChatGPT, Claude, and other LLMs, providing a user-friendly interface for AI copilot assistance on Windows, Mac, and Linux. It offers features like local data storage, multiple LLM provider support, image generation with Dall-E-3, enhanced prompting, keyboard shortcuts, and more. Users can collaborate, access the tool on various platforms, and enjoy multilingual support. Chatbox is constantly evolving with new features to enhance the user experience.

LiveBench is a benchmark tool designed for Language Model Models (LLMs) with a focus on limiting contamination through monthly new questions based on recent datasets, arXiv papers, news articles, and IMDb movie synopses. It provides verifiable, objective ground-truth answers for accurate scoring without an LLM judge. The tool offers 18 diverse tasks across 6 categories and promises to release more challenging tasks over time. LiveBench is built on FastChat's llm_judge module and incorporates code from LiveCodeBench and IFEval.

StoryToolkitAI is a film editing tool that utilizes AI to transcribe, index scenes, search through footage, and create stories. It offers features like full video indexing, automatic transcriptions and translations, compatibility with OpenAI GPT and ollama, story editor for screenplay writing, speaker detection, project file management, and more. It integrates with DaVinci Resolve Studio 18 and offers planned features like automatic topic classification and integration with other AI tools. The tool is developed by Octavian Mot and is actively being updated with new features based on user needs and feedback.

Moly is an AI LLM client written in Rust, showcasing the capabilities of the Makepad UI toolkit and Project Robius, a framework for multi-platform application development in Rust. It is currently in beta, allowing users to build and run Moly on macOS, Linux, and Windows. The tool provides packaging support for different platforms, such as `.app`, `.dmg`, `.deb`, AppImage, pacman, and `.exe` (NSIS). Users can easily set up WasmEdge using `moly-runner` and leverage `cargo` commands to build and run Moly. Additionally, Moly offers pre-built releases for download and supports packaging for distribution on Linux, Windows, and macOS.

NeMo Framework is a generative AI framework built for researchers and pytorch developers working on large language models (LLMs), multimodal models (MM), automatic speech recognition (ASR), and text-to-speech synthesis (TTS). The primary objective of NeMo is to provide a scalable framework for researchers and developers from industry and academia to more easily implement and design new generative AI models by being able to leverage existing code and pretrained models.

The fsdp_qlora repository provides a script for training Large Language Models (LLMs) with Quantized LoRA and Fully Sharded Data Parallelism (FSDP). It integrates FSDP+QLoRA into the Axolotl platform and offers installation instructions for dependencies like llama-recipes, fastcore, and PyTorch. Users can finetune Llama-2 70B on Dual 24GB GPUs using the provided command. The script supports various training options including full params fine-tuning, LoRA fine-tuning, custom LoRA fine-tuning, quantized LoRA fine-tuning, and more. It also discusses low memory loading, mixed precision training, and comparisons to existing trainers. The repository addresses limitations and provides examples for training with different configurations, including BnB QLoRA and HQQ QLoRA. Additionally, it offers SLURM training support and instructions for adding support for a new model.

EmotiVoice is a powerful and modern open-source text-to-speech engine that supports emotional synthesis, enabling users to create speech with a wide range of emotions such as happy, excited, sad, and angry. It offers over 2000 different voices in both English and Chinese. Users can access EmotiVoice through an easy-to-use web interface or a scripting interface for batch generation of results. The tool is continuously evolving with new features and updates, prioritizing community input and user feedback.

OpenCompass is a one-stop platform for large model evaluation, aiming to provide a fair, open, and reproducible benchmark for large model evaluation. Its main features include: * Comprehensive support for models and datasets: Pre-support for 20+ HuggingFace and API models, a model evaluation scheme of 70+ datasets with about 400,000 questions, comprehensively evaluating the capabilities of the models in five dimensions. * Efficient distributed evaluation: One line command to implement task division and distributed evaluation, completing the full evaluation of billion-scale models in just a few hours. * Diversified evaluation paradigms: Support for zero-shot, few-shot, and chain-of-thought evaluations, combined with standard or dialogue-type prompt templates, to easily stimulate the maximum performance of various models. * Modular design with high extensibility: Want to add new models or datasets, customize an advanced task division strategy, or even support a new cluster management system? Everything about OpenCompass can be easily expanded! * Experiment management and reporting mechanism: Use config files to fully record each experiment, and support real-time reporting of results.

**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.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

This repository contains self-trained single image super resolution (SISR) models. The models are trained on various datasets and use different network architectures. They can be used to upscale images by 2x, 4x, or 8x, and can handle various types of degradation, such as JPEG compression, noise, and blur. The models are provided as safetensors files, which can be loaded into a variety of deep learning frameworks, such as PyTorch and TensorFlow. The repository also includes a number of resources, such as examples, results, and a website where you can compare the outputs of different models.

Sublayer is a model-agnostic Ruby AI Agent framework that provides base classes for building Generators, Actions, Tasks, and Agents to create AI-powered applications in Ruby. It supports various AI models and providers, such as OpenAI, Gemini, and Claude. Generators generate specific outputs, Actions perform operations, Agents are autonomous entities for tasks or monitoring, and Triggers decide when Agents are activated. The framework offers sample Generators and usage examples for building AI applications.

Forza Mods AIO is a free and open-source tool that enhances the gaming experience in Forza Horizon 4 and 5. It offers a range of time-saving and quality-of-life features, making gameplay more enjoyable and efficient. The tool is designed to streamline various aspects of the game, improving user satisfaction and overall enjoyment.

Your AI guide to the world of Cinema and Film

Efficient movie recommender with user-driven synopsis options.

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

Recommends new Careers and Job Titles based on your skills and interests.

Start with "Recommend me a song"

I recommend international films.

Please upload an image of a book you love, and I will analyze your taste to recommend other great reads. Plus, engage in fascinating discussions about these books. It's time for exploring and talking about books!

Your AI playlist curator

Enter the world of Secret Somm, where intrigue and fine wine meet. Whether you're a rookie or a connoisseur, your personal wine agent awaits—ready to unveil the secrets of the perfect pour. Your mission, should you choose to accept it, will lead to unparalleled wine discoveries.

The beat master with a playlist for every mood. 🎧

Whatever you're going through, this GPT will find music that will resonate.

Your ultimate gaming guide. The Game Wizard will help guide you as you embark on your gaming adventures.

A playlist creation assistant tailoring to tastes.

Succinct, friendly product search aid.

ChatBOOK是一個專門設計來陪伴用戶閱讀的智能機器人。它能與用戶一起讀書，幫助總結書本的關鍵點，並與用戶就內容進行討論。ChatBOOK還能為用戶推薦一些值得閱讀的書籍。

Expert in pool skills, strategy, and game creation.

A virtual bartender offering personalized cocktail recipes and engaging in cocktail exploration.

Your go-to guide for dining in Charlotte, NC

提供游戏清单及详情，提示用户回复数字或输入游戏名称。