emlearn VS tera

> My big gripe, and for obvious reasons, is that we need to step away from cloud-based inference, and it doesn't seem like anyone's working on that.

I think there are steps being taken in this direction (check out [1] and [2] for interesting lightweight transpile / ad-hoc training projects) but there is a lack of centralized community for these constrained problems.

[1] https://github.com/emlearn/emlearn

Examples: Gaussian Mixture Models (GMM) for anomaly detection or clustering Mahalanobis distance (EllipticEnvelope) for anomaly detection Decision trees and tree ensembles (Random Forest, ExtraTrees) Feed-forward Neural Networks (Multilayer Perceptron, MLP) for classification Gaussian Naive Bayes for classification

https://github.com/emlearn/emlearn is a ML inference engine for microcontrollers and embedded systems, allowing to deploy models to any platform with a C99 compiler. Has also been used for network traffic analysis as a Linux kernel module, and embedded in Android apps.

The C64 has 64 kB of RAM. That is more than many contemporary microcontrollers. Using something like https://github.com/emlearn/emlearn allows to generate portable C code of ML models for such targets. Should be able to classify digits (MNIST) no problem on such hardware. Assuming there is a workable C compiler available.

Disclosure: Maintainer of emlearn project.

I built emlearn, a Machine Learning inference engine for microcontrollers and embedded systems. It allows converting traditional ML models to simple and portable C99, following best practices in embedded software (no dynamic allocations etc). https://github.com/emlearn/emlearn

You can also use HTML templating with crates like askama, tera and maud! This can be combined with the power of lightweight JavaScript libraries like htmx to speed up time to production. You can read more about this on our other article about using HTMX with Rust which you can find here.. We also collaborated with Stefan Baumgartner on an article for serving HTML with Askama!

Tera, based on Jinja, as the next two

Tera

An authentication system is an integral part of modern applications. It's so important that almost all modern applications have some sort of it. Because of their critical nature, such systems should be secure and should follow OWAP®'s recommendations on web security and password hashing as well as storage to prevent attacks such as Preimage and Dictionary attacks (common to SHA algorithms). To demonstrate some of the recommendations, we'll be building a robust session-based authentication system in Rust and a complementary frontend application. For this article series, we'll be using Rust's actix-web and some awesome crates for the backend service. SvelteKit will be used for the frontend. It should be noted however that what we'll be building is largely framework agnostic. As a result, you can decide to opt for axum, rocket, warp or any other rust's web framework for the backend and react, vue or any other javascript framework for the frontend. You can even use rust's yew, seed or some templating engines such as MiniJinja or tera at the frontend. It's entirely up to you. Our focus will be more on the concepts.

Or Flask!

My guess is that "fastest" refers to the request-response loop.

I'd be interested in knowing how fast it is once you tack your favourite template rendering engine on top.

It would be nice if it supported Tera, the Rust template engine that is inspired by Jinja2:

https://github.com/Keats/tera

supports sophisticated templating using Tera,

As you might've noticed I have a static www folder with all my files. If I go to /, /login, /register I want to respond with my templated HTML. If the browser asks for another file, such as index.js or something.png I want to serve it from the static folder. I someone wants to access the raw template HTML, such as index.html I want to response with a 404 message.

tera for email templates.

I've been using Tera and Chromium Oxide to generate and render reports to PDF and its been very needs suiting. It can also render to a PNG file.