WearableIntelligenceSystem VS liboai

This architecture is pretty much what we've built out as a smart glasses software framework here: https://github.com/emexlabs/WearableIntelligenceSystem

Smart glasses are a novel interface in a few key ways. Most obviously, they bring the screen directly in front of the user, so they are able to access a visual information stream at all times. Not as commonly thought-about, but perhaps more important, is the access to always on point-of-view (POV) sensory data. This allows the machine to "see what you see" and "hear what you hear". The video above is a quick display of a few basic features that smart glasses can provide. We'd love to know where you see this technology going? Of course spatial computing and AR is cool and hype now, but what use cases do you think you'll use in your everday life? What could enhance your intelligence and capabilities by taking advantage of this new form factor? We're trying to answer these questions, and building a system that makes it easy for developers to build their own smart glasses applications: https://github.com/emexlabs/WearableIntelligenceSystem

Also, the Open Source smart glasses software stack Wearable Intelligence System has this transcription/translation built-in: https://github.com/emexlabs/WearableIntelligenceSystem

Here's the open source system: https://github.com/emexlabs/WearableIntelligenceSystem

Soon there will be! I am working with a team right now to develop an app for smart glasses that I am designing in a way that will help me with my ADHD. Here's the project.

We're trying to answer these questions, and building a system that makes it easy for developers to build their own smart glasses applications: https://github.com/emexlabs/WearableIntelligenceSystem

Imagine you have an AI-powered personal alerting chat assistant that interacts using up-to-date data. Whether it's a big move in the stock market that affects your investments, any significant change on your shared SharePoint documents, or discounts on Amazon you were waiting for, the application is designed to keep you informed and alert you about any significant changes based on the criteria you set in advance using your natural language. In this post, we will learn how to build a full-stack event-driven weather alert chat application in Python using pretty cool tools: Streamlit, NATS, and OpenAI. The app can collect real-time weather information, understand your criteria for alerts using AI, and deliver these alerts to the user interface.

1.OpenAI API

OpenAI Account – make an OpenAI Account here

Their big advantage is that they can quickly deal with data they’ve never seen before and scale. NVIDIA and Open AI are currently leading providers of such technologies. Read more.

You need an API key https://openai.com/api

To run ChatGPT, you need access to OpenAI’s API3, which provides a cloud-based platform for interacting with various models, including ChatGPT. You can sign up for an account and request access to the API here: https://openai.com/api/

To get started, go to OpenAI and create an account. After that, click on your profile on the top right corner, and select view API keys.

As far as I know the ChatGPT API isn’t public yet. However you can use the GPT3 version from OpenAI which do have an API you can connect to. The Davinci-003 text model is pretty damn good. https://openai.com/api/ for more info

GPT-3: GPT-3 is an advanced language generation model developed by OpenAI. It is capable of generating high-quality human-like text with a high degree of coherence and coherence. The model has been trained on a vast corpus of text, making it capable of generating text across a range of topics. GPT-3 can be used for a variety of content creation tasks such as blog posts, articles, and social media updates. One of the most significant advantages of using GPT-3 is that it can save a significant amount of time when creating content. The model can generate text much faster than a human writer, and the output can be used as a starting point for further refinement.