serverless-ai-fitness VS liboai

In my fitness app, premium users get access to custom workouts generated by OpenAI. In my Step Function workflow that creates weekly workout schedules, I check the subscription level of every user and add a logic branch based on their subscription level. If they are a free user, the workflow will pull from a cache of existing workouts. If they are premium, then their data is passed to OpenAI to build the custom workouts.

I'll continue iterating on this design. The next big milestone is lifting this to its own domain and putting it behind a subscription model (it is SaaS, after all). But right now, you can access it and use it for free at fitness.readysetcloud.io or you can check out the source code on GitHub.

An AWS Serverless Hero, Allen Helton, did just that and wrote a detailed post about it.

If you want to see an implementation of this function, you can see it in my serverless-ai-fitness project. It is used in a Step Function workflow to build a workout with the equipment I have available in my home gym. It also creates a warmup and cooldown specifically for the generated workout.

This is open-source! If any of it excites you and you want to contribute, please check it out on GitHub! Feel free to fork it or open up a pull request.

So I added an EventBridge rule to the function and now I can send an email with a simple call to PutEvents. Here's an example taken from a Step Function workflow definition in my workout generator.

I am thrilled to take "workout building" off my plate. It's something I've enjoyed for a long time but I'm ready to have someone else do it for me. This was a fun project to build and I hope you take it and run with it. It's fully open-source and available for a PR!

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.