igel VS rupy

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 have been running a Raspberry 2 cluster for 10 years: http://host.rupy.se

A few weeks back the first SD card to fail got so corrupted it failed to reboot!

My key learning is use oversized cards, because then the bitcycle will wear slower!

I'm going from 32GB to 256/512/1024!

How this article does not mention SSE, comet or chunking escapes me.

What does their definition of event-driven really look like in practice.

Nobody has a clue.

Here is the ideal event driven system, it's async-to-async: https://github.com/tinspin/rupy/wiki/Fuse

The example is not working because I had to shut down the services for multiple reasons, but the high level of it is that you use 4 (potentially different) threads to do one request/response middle man transaction.

That way you have _zero_ io-wait or idling. I'm surprised nobody has copied this approach since I invented it 10 years ago. I understand why though you need your entire chain to be async and that means rewriting everything and that is a big risk when it's hard to debug.

But if you succeed you can build something that is 10x perf/watt than all other implementations. Which is going to be important when interest rates go higher and crash our entire industry.

The hardware is peaking.

So software is where you can make the difference: http://host.rupy.se

I think we're all confused over the definition. Also one might understand what all the proponents are talking about better if they think about this more as a process and not some technological solution:

https://github.com/tinspin/rupy/wiki/Process

All input I have is you want your code to run on many machines, in fact you want it to run the same on all machines you need to deliver and preferably more. Vertically and horizontally at the same time, so your services only call localhost but in many separate places.

This in turn mandates a distributed database. And later you discover it has to be capable of async-to-async = no blocking ever anywhere in the whole solution.

The way I do this is I hot-deploy my applications async. to all servers in the cluster, this is what a cluster node looks like in practice (the name next to Host: is the node): http://host.rupy.se if you click "api & metrics" you'll see the services.

With this not only do you get scalability, but also redundancy and development is maintained at live coding levels.

I have hosted my own web server both physically and codevise since 2014.

It's on a Raspberry 2 cluster:

http://host.rupy.se

Since 2016 i have my own database also coded from scratch:

http://root.rupy.se

We need to implement HTTP/1.1 with less bloat, a C non-blocking web server that can share memory between threads is probably the most interesting project for humans right now, is anyone working on that?

I have one in Java: https://github.com/tinspin/rupy

Here is the 2000 lines of code of the entire database: http://root.rupy.se/code?path=/Root.java

And here you can try it out: http://root.rupy.se

The smallest PaaS you have ever seen is one order of magnitude larger than mine: https://github.com/tinspin/rupy

And I bet you the same goes for performance, if not two!

The data is here: http://fuse.rupy.se/about.html

Under Performance. Per watt the fuse/rupy platform completely crushes all competition because of 2 reasons:

- Event driven protocol design, averages at about 4 messages/player/second (means you cannot do spraying or headshots f.ex. which is another feature in my game design opinion).

- Java's memory model with atomic concurrency which needs a VM and GC (C++ copied that memory model in C++11, but it failed completely because they lack both VM and GC, but that model is still to this day the one C++ uses), you can read more about this here: https://github.com/tinspin/rupy/wiki

You can argue those points are bad arguments, but if you look at performance per watt with some consideration for developer friendlyness, I'm pretty sure in 100 years we will still be coding minimalist JavaSE on the server and vanilla C (compiled with C++ compiler) on the client.