emlearn VS lowdefy

> 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

I'm really enjoying reading through the docs and the tutorial. We've created Lowdefy, a config web-stack which makes it really simple to build quite advanced web apps. We're writing everything in YAML, but it has it's limitations, specifically when doing config type checking and IDE extensions that go beyond just YAML.

I've been looking for a way to have typed objects in the config to do config suggestions and type checking.. PKL looks like it can do this for us. And with the JSON output we might even be able to get there with minimal effort.

Is there anyone here with some PKL experience that would be willing to answer some technical questions re the use of PKL for more advanced, nested config?

See Lowdefy:

https://lowdefy.com/

https://github.com/lowdefy/lowdefy

Awsome! With Lowdefy we tried to build a low-code framework that works like code. We’ve developed a schema in which to define applications and we’ve built all kinds of apps for enterprise customers. Massive, advanced CRM systems, call centre solutions, ticketing systems, a light MRP, all kinds of survey apps and so many dashboards. Even our docs and our website are Lowdefy apps!

Give Lowdefy a try and reach out it you have any questions or want to see what is possible :) (We need to invest a lot more into content and examples, bootstapping is a grind!)

https://github.com/lowdefy/lowdefy

Also add Lowdefy onto the list https://github.com/lowdefy/lowdefy

co-founder here :)

100% this. And yes, good documentation takes a lot of investment but it pays off like compound interest. But with that done, it becomes even more important not to pull the carpet for no good reason, you are building a tower and documentation is at the foundation.

We’ve built Lowdefy [1] as an open source project and documented it with all effort, 200 pages of docs. I often forget why or how something works and then jump to the docs. This investment keeps on paying of as we use Lowdefy to build customer apps, new devs in the team typically take less than two week to get up to speed and start making contributions, the sharp ones, just a two or three days.

This year, we’re extended our documentation onto customer apps aswell, with flow diagrams, state machine definitions, detailed field level explication schema definitions, and end user test procedures. The key here for this documentation is detail. It should be easier to reach for the docs and the the answer, than to dive in the code and interpret it.

1 - https://github.com/lowdefy/lowdefy

https://github.com/lowdefy/lowdefy Co-Founder here.

Check out https://lowdefy.com/ they even have a sample survey app as one of their examples.

You can build an app that would do this

Lowdefy is a low code tool that uses AG Grid as a block component, allowing you to create apps which render data in AG Grid without a lot of coding knowledge. There is a Lowdefy example using AG Grid here.

Shameless plug, also check out Lowdefy - https://github.com/lowdefy/lowdefy

I’m also going to jump in here and say try Lowdefy https://github.com/lowdefy/lowdefy - co-founder here.

We take a different angle and believe that low code should still work like code. We focus on a developer first approach.