Just from the wording, ignoring the numbers, this looks like the control connection (red +5v,black ground and the last one signal).
From your description is guess this connects to the boiler on a dedicated port which provides the DC and reads the signal. 5v is quite common for sensors so that doesn't seem off.
Thanks for sharing but I still don't know what this actually does? As it's win only at the moment I can't give it a test run but the readme is so high level that I'm not sure.
Like, does it interface with my ollama models? Is it integrating with remote models? What's it locating actually?
Ah that would make sense, thanks!
I haven't found (while cross reading ) details about why the "highly improved" didn't make it to upstream openwrt?
According to their page it's a pure searxng instance. I didn't see anything on my own instance changing so there are three options I see:
And then there's the obligatory "none or all of the above".
Personally I'd guess it's just a fluke. I gave it a few searches from Firefox mobile on "all languages" and had a mix of mainly English and a bit of German und French in there as results.
Edit: if you're comfortable with that feel free to share some search terms and we can compare results. Would be curious myself!
That... What you describe is a mesh wifi. APs plus roaming. That's a meshed network.
The screenshot had has the criteria included though. Relevant part: either be for children or for everyone.
I use lemmy in two ways: Whitelist: show me my subscriptions and only those (subscribed) Or blacklisted: show me everything else except the things I want to never see.
The latter lead me to this thread! It's two different experiences for me and I get a bit out of my interest bubble from time to time.
Because it's basically axiomatic: ssh uses all keys it knows about. The system can't tell you why it's not using something it doesn't know it should be able to use. You can give a -i for the certificate to check if it doesn't know it because the content is broken or the location.
That said: this doesn't make -v more useful for cases like this, just because there's a reason!
I'd try chat gpt for that! :)
But to give you a very brief rundown. If you have no experience in any of these aspects and are self learning you should expect a long rampup phase! Perhaps there is an easier route but I'm not familiar with it if there is.
First, familiarize yourself with server setups. If you only want to host this you won't have to go into the network details but it could become a cause for error at one point so be warned! The usual tip here is to get yourself familiar enough with docker that you can read and understand docker compose files. The de facto standard for self hosting are linux machines but I have read of people who used Macos and even windows successfully.
One aspect quite unique to themodel landscape is the hardware requirements. As much as it hurts my nvidia despicing heart at this point in time they are the de facto monopolist. Get yourself a card with 12GB VRAM or more (everything below will be painful if you get things running at all. I've tried and pulled or smaller models on a 8GB card but experienced a lot of waiting time and crashes). Read a bit about Cuda on your chosen OS and what their drivers need.
Once you can understand this whole port, container, path mapping and environment variable things.
Then it's going to the github page linked, following their guide and starting a container. Loading models is actually the easier part once you have the infrastructure running.
No offense intended, possible that I miss read your experience level:
I hear a user asking developer questions. Either you go the route of using the publicly available services (dalle and Co) or you start digging into hosting the models yourself. The page you linked hosts trained models to use in your own contexts, not for a "click button and it works".
As a starting point for image generation self hosting I suggest https://github.com/AUTOMATIC1111/stable-diffusion-webui.
For the training part, I'll be very blunt: if you don't indent to spend five to six digit sums on hardware or processing power, forget it. And even then you'd need the raw training data to pull it of.
Perhaps what you want to do use fine tune a pretrained model, that's something I only have a. It of experience in LLMs thohfn(and even there I don't have the hardware to get beyond a personal proof of concept).
The bankruptcy scenario is correct but the first part isn't: you don't have X shares as collateral that you can liquidate. Instead, you have collateral to cover sum Y.
As long as the collateral contract covers enough stock positions the bank won't lose.
That said all of this is assuming standard contracts. If y bank wrote "0% interest and instead 50% of the revenue growth of Twitter" then this would be an easy way to lose money.
Haven't heard of a stupid banker yet, though, so what would the chances be?