mitosis VS autocannon

Builder.io Mitosis -

In this case, we combined a fine-tuned LLM, a custom compiler that we wrote, and a custom-trained model.

The heart of Visual Copilot lies in its AI models and a specialized compiler. The initial model, trained with over 2 million data points, transforms flat design structures into code hierarchies. Our open-source compiler, Mitosis, takes this structured hierarchy and compiles it into code. In the final pass, a finely tuned Large Language Model (LLM) refines the code to match your specific framework and styling preferences. This multi-stage process ensures that the generated code is high-quality and tailored to meet the requirements of your project.

Mitosis (https://github.com/BuilderIO/mitosis) is a fantastic project, but is still in its early stages, and in need of some build tooling. It allows you to "write once, run everywhere" in the sense that you can write a UI component and have it compiled to a React component for use in a React project, a Svelte component for use in a Svelte project, and so on. It does not, however, support bundling.

create-multi-lib (run like so `npx create-multi-lib ) is my attempt to contribute a much needed bundling-layer on top of Mitosis.

It also comes with E2E testing included, and your resulting packages include type-declarations.

Depending on your needs, check out Mitosis that let you write components and compile them to svelte, angular, react. https://github.com/BuilderIO/mitosis

Saying that, you could check-out Mitosis.

I see you was inspired by https://github.com/BuilderIO/mitosis was there no way to extend there AST?

I discovered https://github.com/BuilderIO/mitosis, and it has changed my front-end development workflow with the ability to create one development and export and test metrics on multiple front-ends delivering the best MVP possible.

Autocannon GitHub Repository

To make things interesting, let’s send some requests to this server with autocannon:

Using autocannon, I did the following script to simulate 500 concurrent connections over 30 seconds:

We then used autocannon to measure the throughput (requests per second) of each runtime server-rendering our React app.

You could also use tools like autocannon to see how many requests/second you can achieve with various methods. : https://github.com/mcollina/autocannon

I used autocannon to test the function at 100 concurrent executions for 30 seconds. I also used Lambda Power Tuning to find the ideal memory configuration, which proved to be 512MB. All the results are available.

Moreover, if your blocking issue is appearing only on heavy load, you can easily test it using the very nice --autocannon CLI param (see it with clinic flame --help) where you can specificy autocannon options to generate some HTTP load on your web service.

I packaged them both with Docker and deployed them to an EC2 instance, each behind an Nginx reverse proxy I setup in docker-compose. I'm currently testing performance using autocannon from my laptop like this: `autocannon -a 5000 -w 10 URL` (5000 requests with 10 workers), and both apps complete in around 40 seconds. The EC2 instance is in Oregon and I'm testing from Toronto.

You can test it by yourself using console.time(). You can use autocannon to stress-test your http server to see what is really the best options.

When I test this with autocannon making three simultaneous requests (autocannon --connections 3 --amount 3 --timeout 10000 --no-progress http://localhost:5678/):