serverless-ai-fitness VS client-sdk-javascript

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!

Next, we have to install the necessary packages. For TypeScript, we use @gomomento/sdk and openai.

Looking for examples? Check out this fully functional chat application built with Topics in Next.js. You can also try our work-in-progress game Acorn Hunt, built on both Momento Cache and Topics.

The introduction of the chat concept was intriguing. I had originally incorporated my ChatGPT function in a Step Function workflow, which has a relatively low maximum data size. So passing around a collection of questions and answers between states didn't seem like a great idea. With this in mind, I decided to store the conversation history in a cache using the Node.js Momento cache client.

To run the experiment, I used the Node.js Momento SDK to get and set color-hex combinations in a cache. The Momento SDK allows you to provide an auth token targeted at a specific region, so I ran a set of 1,000 API calls in the following regions.