Moodle VS Searx

Are you looking for a hassle-free way to deploy Moodle™ Learning Management Systems (LMS) on Kubernetes? Look no further! Krestomatio presents the LMS Moodle Operator, an open-source Kubernetes Operator designed to simplify the deployment and management of Moodle instances on Kubernetes clusters. Let's dive into what makes this tool a great choice for Moodle administrators and developers alike.

One example is: https://moodle.org/

It's probably copyright violation to copy their exercise sets (depends on the license and where they got them). There are existing maths quiz grading systems that could be used for similar purposes (based on your description; I haven't done that Udacity course), and I'd recommend STACK [1], a Moodle [2] plugin. Both are open source.

[1] https://stack-assessment.org/

[2] https://moodle.org/

do yourself a favor and look at the code: https://github.com/moodle/moodle/blob/master/notes/index.php (I will go and search which plugins are not checking for the "course-login" now...)

Why do not use proper LMS like Moodle https://moodle.org/?

What are the differences between moodle.net and moodle app?

I'm working on this with a friend since yesterday. We've reinstalled several different versions of moodle from moodle.org with different PHP Versions, having this as our best outcome.. We get the proper Moodle look just before Installation, after installing it looks like this

I think searx was largely built by a single person.

https://github.com/searx/searx

I use Recoll to index all of it. Recoll WebUI exposes an API, which I plugged into Searx.

The quality of Google search results has been awful for many years now, but if you still want to use it, the only usable way is via a frontend like Searx[1]. Using any of Google's frontends for any of their services is an exercise in frustration from dodging ads and fighting their hostile UI.

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

RSS-Bridge has a Google search adapter. You can also fake it with Searx (which offers RSS feeds of search results).

I've recently wondered how effective local models were at chaining together thoughts as proposed in https://ofir.io/self-ask.pdf. Turns out they are indeed capable of doing so while also creating reasonable chains of thoughts that are easily as good as OpenAI's models. To make it completely free to run I used SearX running inside a Docker container with a second model curating the search results for the main model to get answers from the web.