aws-cdk-rfcs VS serverless-ai-fitness

This was the most voted RFC.

The scheduler is a refined and improved version of the older rules mechanism. So, why would you use the older rule mechanism for scheduled tasks? I don’t see any reason other than the better CDK constructs support (which is only temporary, see this PR).

CDK released the L1 construct for Pipes in v2.55 back in December 2022. Unfortunately, the L2 construct is still in progress, so we will have to manually specify most of the CloudFormation template but bear with me, it won’t be too painful. The community is working on an L2 construct; you can find the issue on GitHub here.

The experimental cdk feature is described here. The rfc is still open so you might be able to track or influence how it will stabilize.

More info on L1 vs L2 constructs here. I plan to keep an eye on this open RFC.

You could hardcode the stack names. But this is one of the biggest limitations CDK still has: no easy way yet to refactor names like this. There's an open RFC, but it isn't moving much.

It is not very convenient to delete some resources manually and there are several discussions with AWS developers to fix it.

It's worth mentioning that AWS CDK already has an RFC for integration testing so we might end up with something even better. In the meantime, if you are serious about integration testing, time to give AWS CDK and Provider Framework a look!

One apparent limitation of the CDK is that, at least for the nonce, it can only be used with AWS (there are two notable projects in the work right now that will greatly expand CDK's reach: cdk8s and cdktf). With Terraform you can use choose from a plethora of providers. Hell, I was able to set up my home network running on Unifi Dream Machine using Terraform. How cool is that?!

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!