Popular Experimental AI tools in Fun Tools include How Normal Am I?, Human or Not?, Tinybots.net, Bjoernkarmann, Infinite Convo, and CrayEye, helping you work more efficiently.
An inspirational portfolio by interaction designer Bjørn Karmann, showcasing groundbreaking projects that merge AI, art, and technology. Discover speculative designs and tangible prototypes like the Paragraphica context-to-image camera and the Project Alias privacy parasite, exploring the future of human-AI interaction.
Generative Art
Tinybots.net is a collection of creative and experimental AI projects. Its flagship tool, ArtBot, provides free, unlimited AI image generation using the power of a distributed Stable Diffusion network (AI Horde). Users can create art from text, images, and even live drawings without registration. The collection also includes unique bots for generating satirical news, Bob Ross-style paintings, and procedural art, showcasing the diverse and fun applications of artificial intelligence.
Image Generation
CrayEye is a free, open-source multimodal AI tool that lets you create and share vision prompts enriched with real-world context from your device's sensors (like camera, GPS) and APIs (like weather). Experiment with visual models to interpret your environment in new, context-aware ways.
Open Source
A social Turing game where you chat with a stranger for two minutes and guess if they are a human or an AI. This engaging experiment by AI21 Labs tests your ability to distinguish between human and machine conversation, using advanced AI models like GPT-4 and Claude.
Research
Infinite Convo is a unique AI-powered project featuring a never-ending talk show between two distinct AI personalities, Juno and Olive. They engage in a continuous, live conversation on deep philosophical and societal topics, offering a thought-provoking and endless stream of content for viewers.
Philosophy
An interactive documentary that uses AI to analyze your face through your webcam. It assesses metrics like beauty, age, and BMI to reveal the biases and unreliability of algorithmic profiling systems in a thought-provoking and privacy-respecting way.
TechnologyExperimental AI tools are applications designed to explore the cutting edge of artificial intelligence, often showcasing novel concepts or unproven algorithms. These tools typically emerge from academic research or independent projects, prioritizing innovation and creative potential over stability and predictability. They provide a glimpse into future AI capabilities, allowing users to interact with new models and unconventional ideas. As a subset of Fun Tools, their primary value lies in discovery, inspiration, and the sheer novelty of their function.
These tools are primarily used by AI researchers, developers, digital artists, and hobbyists. Researchers use them to validate new theories, artists find inspiration in their unique outputs, and developers prototype innovative features for future applications. They are also valuable in educational settings for demonstrating complex AI principles in an accessible way.
When selecting an Experimental AI tool, prioritize the novelty of the idea and its potential for inspiration. Evaluate the underlying technology—is it based on a specific new model or research paper you want to explore? Consider its accessibility (web demo vs. API) and whether there is a community or documentation to provide context. Reliability and feature-richness are secondary to the tool's ability to push creative and technical boundaries.
A digital artist facing a creative block uses an experimental image generation tool that merges unrelated concepts. By inputting prompts like "the sound of a forgotten memory" or "a city built from liquid light," the artist generates a series of abstract, unpredictable visuals. These images, while not polished, serve as powerful starting points and mood boards for a new digital painting series, helping the artist break free from conventional styles and explore novel aesthetic directions.
An AI researcher wants to understand the practical capabilities of a newly published language model. They use an experimental tool that provides direct, unfiltered access to this model. The researcher tests its performance on complex tasks like logical reasoning, poetry generation, and code explanation. This hands-on interaction allows them to identify the model's strengths, weaknesses, and inherent biases far more effectively than just reading the research paper.
A mobile app developer is brainstorming a unique feature for a wellness application. They find an experimental AI tool that generates calming ambient music from user-written text. They use the tool's API to build a quick prototype, allowing users to type their feelings and receive a personalized soundscape. This allows the developer to test the core concept with users and gather feedback on its viability before investing significant resources into building a production-ready version.
A sound designer working on an indie game needs otherworldly audio for a fantasy environment. Instead of using standard sound libraries, they turn to an experimental AI that generates audio from images. They upload concept art of the game's alien flora and fauna, and the tool produces strange, organic, and completely unique sound textures. These sounds add a distinctive and memorable audio identity to the game that would be difficult to create manually.
An architecture student is working on a project focused on futuristic, sustainable cities. They use an experimental 3D modeling tool that generates structural forms based on natural patterns, like coral reefs or crystal growth. The student inputs parameters for space, materials, and environmental factors. The AI generates dozens of conceptual blueprints that challenge traditional architectural norms, providing a rich source of inspiration for their final design.
A university professor teaching an "Introduction to AI" course needs a simple way to explain how generative adversarial networks (GANs) work. They use a web-based experimental tool that visualizes the interaction between the generator and discriminator in real-time as it learns to create simple images. This interactive demo makes an abstract concept tangible for students, leading to a deeper understanding than a textbook diagram could provide.
Experimental AI tools are applications that showcase new, unproven, or unconventional AI technologies. They are often developed by researchers or hobbyists to explore the boundaries of what AI can do. Unlike production-ready software, their focus is on innovation and demonstrating a novel concept rather than providing a stable, polished user experience. They are excellent for inspiration and learning about the future of AI.
The key difference lies in their purpose and maturity. Mainstream AI tools (like a standard chatbot or image editor) are built for reliability, efficiency, and solving specific, well-defined problems. Experimental tools, on the other hand, are built for exploration. They often have:
Experimental AI tools are best suited for users who are curious and willing to tolerate unpredictability. This includes:
They are generally not recommended for critical business operations that require consistent and reliable results.
Yes, there are several factors to be aware of. Since these tools are not fully vetted, they may produce inaccurate, biased, or nonsensical information. They often lack robust security or privacy features, so it's wise to avoid inputting sensitive personal data. Furthermore, these projects may be abandoned by their creators at any time, meaning the service could disappear without notice. They should be used for exploration, not for mission-critical tasks.
Evaluating an experimental tool requires a different mindset than choosing a standard application. Instead of focusing on efficiency or features, consider these points:
