HedgeDoc VS Searx

Nice and simple. I feel the only lacking feature for a basic blog is having unlisted blog posts, which is very handy when you want to share it to proof-readers. This can be done on google doc/hedgedoc [0] for sure, but then when porting there are very often typos creeping in.

[0] https://hedgedoc.org/

Maybe Hedgedoc will fit these needs? You can use markdown to format. https://hedgedoc.org/

If self-hosting is an option for you I would recommend that you go with HedgeDoc. Completely open source, you get all the features you asked for including real time collaboration.

You can give HedgeDoc (https://hedgedoc.org/) a try as a replacement for Google Docs.

It is the one that works best for concurrent editing IMO (but it is markdown which can be a problem for some)

I use one singular HedgeDoc document for that purpose. It's not exactly the same intent as Google Keep, but it's an awesome project I use anyway and fills the role perfectly for me personally.

HedgeDoc is a web-based, self-hosted, collaborative markdown editor. This open-source option allows a team to easily share ideas on notes, graphs and presentations in real-time. troubleshootmertr finds it a good option "for knowledgebase."

HedgeDoc [0] allows you to collaborate in markdown, and also create slides.

[0] https://hedgedoc.org/ and https://demo.hedgedoc.org/slide-example?both

For writing Markdown documents I use Hedgedoc.

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.