igel VS lowdefy

Link to the repo: https://github.com/nidhaloff/igel

So I have a project that made it to the GitHub trending list. The project is in English and the spoken language is set to English on my Profile/settings. However, I can only see the project in the trending list if I set the spoken language in the trending tab to "any". If I set it to English then my project is not listed anymore in the trending list. How can this be?

Igel is a machine learning tool that makes it very easy to prototype and create/experiment with ML models on the fly. Igel helps you automate many tasks from cleaning your dataset to evaluating the trained model and finally serve it by creating a REST server that is production-ready.

Thanks for the feedback! When I first started the project, it was not thought for production. Just for fast prototyping and experimenting with no efforts at all. However, users liked the tool and started requesting more features including support for serving models and eventually deploying (e.g this issue https://github.com/nidhaloff/igel/issues/62)

I agree with your point of vue. However, igel is fairly new and evolving fast. Using igel to serve trained model is a new feature that was implemented in the new release so igel has a long way to go in order to be a solid product for production use.It will surely get more mature with time.

Finally, notice that I didn't recommend running it in production. Just mentioned that it is possible and takes no efforts at all. However, if the user generated a trained model then anything can be done with it from there. Technically, the user can implement his/her own server and use the model as wanted. Obviously, users should do that if they want more control ;)

igel is a fairly new machine learning package that allows you to create ML prototypes on the fly. You can use igel from the terminal without writing any code or from python if you want to. I tried to keep the API simple enough and flexible as possible.

Hi everyone, I wanted to share with you the new release/features of the igel machine learning package

I think that most companies that sponsor projects are companies that are using the projects. IIUC https://github.com/nidhaloff/igel is your most popular project. Who is using it?

Don't expect the companies to pay. You can not force them to pay. It's a project with a MIT license. (Perhaps this is obvious for you, but a few days ago someone posted a rant by another developer because some companies were using his MIT-license project and only making a $500 annual money contribution.)

I think one possibility is to write blog post about examples of using the project to solve interesting problems. It's important that they are interesting to get traction here and in other platforms. At the bottom, add a remake explaining that you are the main developer of the project and you'd like sponsors. (I can't guaranty that this will work.)

Also, this helps as an extended documentation of the project and to get more traffic from google and to get more users. All of that can help to increase the user base and hopefully find an sponsor. (I can't guaranty that this will work.)

- igel: https://github.com/nidhaloff/igel a delightful tool that allows using ML without writing code. I'm also working on an even simpler cross-platform frontend for it written in electronjs (check it here https://github.com/nidhaloff/igel-ui)

Last year I built deep-translator https://github.com/nidhaloff/deep-translator

I wanted a tool where multiple translators are integrated and where I can get translations from different sources but only using one tool. I then tried to build a cross platform mobile app using python (which is not the best language for this, I know) https://github.com/nidhaloff/Translator-pp

Probably the best project I built/started last year is the machine learning package igel: https://github.com/nidhaloff/igel

Show HN: Igel – A CLI tool to run machine learning without writing code: https://github.com/nidhaloff/igel

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.