igel VS Quarkus

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

Meanwhile, if you're building something smaller than Netflix, I'm writing a book just for that (https://opinionatedlaunch.com/).

It's about mobile apps, but I talk about backend at great length, especially since my background is Java. The book is called "opinionated" because I cover Quarkus (https://quarkus.io/), monolith, Fly.io, and no K8s.

Quarkus is a Java based framework tailored for GraalVM and HotSpot, which results in an amazingly fast boot time while having an incredibly low memory footprint. It offers near instant scale up and high density memory utilization which can be very useful for container orchestration platforms like Kubernetes or Serverless runtimes like AWS Lambda.

Other useful articles related to Quarkus extension development can be found under the Writing Extensions guide category on the Quarkus.io website.

Quarkus is one of Java frameworks for microservices development and cloud-native deployment. It is developed as container-first stack and working with GraalVM and HotSpot virtual machines (VM).

If you GraalVM Native Image or one of the frameworks based on it then bootstrap cost disappears:

https://quarkus.io

We are open to practice using any open-source project, however, we want to set a sharp focus on projects maintained by the Red Hat, and our own projects in the Caravana Cloud organization on github. If there is no reason to do differently, we'll build using technologies such as OpenShift, Quarkus, Ansible and related projects.

The Quarkus you are talking about is this one? https://quarkus.io/