OpenAI DALL-E-2 Top Builders

Explore the top contributors showcasing the highest number of OpenAI DALL-E-2 app submissions within our community.


DALL-E 2 is a new version of DALL-E, a generative language model that creates corresponding original images from sentences. DALL-E 2 has 3.5B parameters, making it large but not nearly as large as GPT-3. Interestingly, it is smaller than its predecessor, which had 12B parameters. Despite its size, DALL-E 2 generates images 4 times better in resolution than DALL-E. Additionally, human judges prefer DALL-E 2 over DALL-E 70% of the time in caption matching and photorealism.

Relese dateApril 6, 2022
TypeAutoregressive, Transformer, Image generation model

Start building with DALL-E 2

We have collected the best DALL-E 2 libraries and resources to help you get started to build with DALL-E 2 today. To see what others are building with DALL-E 2, check out the community built DALL-E 2 Use Cases and Applications.

OpenAI DALL-E 2 Tutorials


A curated list of libraries and technologies to help you build great projects with DALL-E 2.

  • Blog Open AI's blog post about DALL-E 2 API
  • Documentation DALL-E 2 Documentation
  • API DALL-E 2 API Documentation

OpenAI DALL-E-2 Hackathon projects

Discover innovative solutions crafted with OpenAI DALL-E-2, developed by our community members during our engaging hackathons.



Our app is a unique platform that offers both content creators and users an innovative way to generate and access various types of content. The app has two interfaces: Explorer and Creator, where visitors can access various types of content, including videos, articles, audios, and tweets while creators can upload, edit and use AI tools to generate content. Our app aims to solve the problem of time-consuming content creation and fragmented content discovery. By offering multiple types of content in a single platform, we aim to increase user engagement and retention while offering creators an opportunity to monetize their content. Market: The global content creation and discovery market is expected to reach $892.5 billion by 2027, with an annual growth rate of 16.8%. The increasing demand for video content, podcasts, and other forms of digital media presents a significant opportunity for our app to succeed in the market. Competitive analysis: Our app faces competition from established content creation and discovery platforms such as YouTube, Medium, and Spotify. However, our unique value proposition of offering multiple types of content in a single platform, along with AI generative tools for creators, sets us apart from competitors. Marketing strategy: Our app will be marketed primarily through social media, paid advertising, and partnerships with content creators and publishers. We will also offer referral programs to incentivize users to invite their friends and family to use the app. Revenue model: We plan to generate revenue through a freemium model, where the app is free to access for users, but creators pay for premium tools and features. We will also offer subscription plans for users to access premium content and an advertising model, where advertisers can display ads on the app.

WeCare Caretaker Assistant

WeCare Caretaker Assistant

We have built a solution for agencies which provide the caretaker services for parents who are in search of babysitters for their child. When users call the agency after business hours or when agents are not available for assistance, we are routing them to leave a voicemail with their babysitter requirement and contact number. With this solution, agents can focus on more complex tasks rather than manually retrieving voicemails, analysing them and coming up with a resolution. When the caller dials the agency phone number during office closed hours or peak hours when agents are not available to serve them, we route the caller to the voicemail menu where we ask them to leave a voicemail with babysitting requirements and their contact details, etc. Once the voicemail is available, we extract it and convert this speech to text using OpenAI’s whisper API which gives us the voicemail transcription. After that, we meticulously perform the prompt engineering for ChatGPT API to provide us all the required information from voicemail like intent, sentiment, babysitting date and time, etc in JSON format. Using this information, we query the EmployeeSchedule table which is in the H2 database. Once we have the information about availability of babysitters, we query RedisJSON to get the employee profile information like employee name, contact details, date of birth, languages spoken, image, etc. We then build a PDF document using itext library. This PDF containing available babysitter information will be sent on the caller’s WhatsApp. After this, we send an SMS to the agency as an alert notification about the customer enquiry and ask them to get in touch with the customer. Github link - Video link - Presentation - DEMO is at the end of the video.