One significant advantage of Window is that users can fine-tune their own local model on private data and use it on the web, with full visibility of the prompts. The tool is also a great use case for privacy and becomes portable.
Window offers users a flexible and secure way to use AI models on the web. Users can manage all their model setups in one place, whether it’s an external model like OpenAI, proxied, or local. This ensures they can protect their privacy and use their preferred models without being limited by specific vendors or models. Users can also save their prompt history across apps.
Developers can utilize Window to create multi-model apps without worrying about API costs and limitations. Using the injected window.ai library, they can easily develop apps free from vendor lock-in and create decentralized apps. This allows developers to use their own AI models without being limited by specific vendors or models.
In addition to the Window extension, there is also an aggregator of Window apps called http://skylightai.io. This aggregator features a variety of apps, including three games, a ChatGPT clone, a Toolformer implementation, and a template for users to create their own apps using Next.js.
How does it work?
The ‘Window’ extension allows users to set up their keys and models only once. Subsequently, apps can request permission to send prompts to their chosen model through the window.ai library. This process is transparent to the users as they are always informed about the prompts they receive and when they receive them.
Why should you build with Window?
There are several reasons to consider building apps with the Window extension. Firstly, it eliminates the infrastructure burden associated with model API costs, timeouts, and rate limiting, resulting in reduced server billing time. Additionally, Window enables easy integration of multiple models and takes care of model upgrades and support for other providers.
Furthermore, Window allows users to build privacy-conscious apps that communicate solely with the user’s preferred model, which significantly reduces the liability for the model’s output.
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I am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.