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AI-powered virtual escape room puzzles

Generative AI models such as LLMs and image generators can be ingeniously used to create AI-powered virtual escape room puzzles. These models can generate intricate storylines and complex clues, introducing an element of surprise and unpredictability to the game. For instance, using an LLM like ChatGPT, the AI can dynamically generate interactive detailed narratives and clues based on player’s actions and decisions. Simultaneously, image generators can create lifelike virtual environments to enhance immersion.

Moreover, these models can be used to create AI NPCs (Non-Player Characters) which can interact with players, providing hints or misdirection to intensify the challenge. Furthermore, OpenAI's generative models can continuously learn from player interactions, leading to better and more engaging game scenarios over time. Thus, these technologies promise a compelling, immersive, and adaptive player experience increasing the intrigue and appeal of AI-powered virtual escape rooms.

How to build with Clevis

Text Input

Prompt the user to enter the theme or setting for the escape room.

Prompt ChatGPT

Generate a storyline for the escape room based on the user's input from the previous step.

HTTP Request

Fetch a list of riddles or puzzles related to the generated storyline.

Generate Image (DALL-E)

Generate a visual clue or puzzle image using DALL-E based on the storyline.

Display Output

Display the generated storyline, puzzles, and visual clues to the user in a user-friendly format.

This is an example of an application that you can build using Clevis, a tool used for creating AI-powered apps. The app, known as 'AI-powered Virtual Escape Room Puzzles', uses several steps to create an interactive storytelling and puzzle experience for users based on a chosen theme or setting.

To begin with, the app provides a Text Input step where users are prompted to input the desired theme of their virtual escape room. The chosen theme is then passed to ChatGPT, an AI model from OpenAI, which generates a unique storyline based on the input.

In the Http Request step, the app fetches a list of related riddles or puzzles that align with the generated storyline. Simultaneously, DALL·E, another AI model from OpenAI, is used to create a DALL·E step that creates a visual clue or puzzle image fitting the storyline.

Finally, the Display Output step takes the results of all the previous procedures and presents them to the user. The generated storyline, puzzles, and visual clues are displayed in an engaging and user-friendly format.

This scenario exhibits the potential of Clevis to create diversified and engaging apps. Just as this example, more immersive and interactive applications can be crafted within the same domain using the Clevis tool.

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