Not necessarily. While of course in many many cases, open source is a volunteer effort, there's usually some implicit transaction going on. Whether that's improving the software for yourself and passing that on to others, being a business and improving a library or something you use that helps your project generate revenue, or even a straight up commercial transaction.

But in all these cases, the open source project can be taken by you (or others) and you can do whatever you want with it. In the case of Winamp here, you cannot do any of that. It would be different if they were paying for contributions. But they're not, so.

A lot of times immigrants to Iceland in low paying jobs like this do not understand their rights. Wouldn't surprise me if this guy has gotten away with it before. Possibly more than once.

Iceland isn't perfect. If a business wants to get rid of someone, they'll find a way to do it. But it is illegal to prevent someone from joining a union, or issue threats like this. Companies over a certain size (50+ I think?) are actually required to have a union representative.

Somebody is going to get steamrolled by Icelandic labor laws. And it's not going to be the employee.

Edit: like this is seriously illegal in Iceland. Also, if you're going to be a corrupt and immoral business owner (evil really in this case), the number one thing you DON'T do is broadcast your nefarious intentions over a recordable medium.

They basically want free labor.

That is exactly the plan.

You can right click the URL bar for sites that support the OpenSearch XML standard. Which I guess is what they wanted to replace it with. But I don't really know why they removed the button to a about: config setting. Could at least be a checkbox or something to enable.

Returns the add custom search engine button. Which for some reason, has been hidden by default.

Anyone have any suggestions for bulk options in the Netherlands?

Is it possible to use ollama or an arbitrary OpenAI-compatible endpoint with the chatbot feature yet? Or only the cloud providers?

That would probably be a task for regular machine learning. Plus proper encryption shouldn't have a discernible pattern in the encrypted bytes. Just blobs of garbage.

Not to mention the face of the kid.

That's being generous.

How much speed are you actually getting on Mixtral (I assume that's the 8x7b). I have 64 GB of RAM and an AMD RX 6800 XT with 16 GB of VRAM. I get like 4 tokens per second with Q5_K_M quant.

Depends on the continuity and who's writing it, but often yes. He was notably portrayed this way in the Justice League cartoon.

The only problem I really have, is context size. It's harder to get larger than 8k context size and maintain decent generation speed with 16 GB of VRAM and 16 GB of RAM. Gonna get more RAM at some point though, and hope ollama/llamacpp gets better at memory management. Hopefully the distributed running from llamaccp ends up in ollama.

I do have a local setup. Not powerful enough to run Mixtral 8x22b, but can run 8x7b (albeit quite slowly). Use it a lot.

No trying to get around anything. No funny instructions like my grandma singing a lullaby about illegal activities. Just using instructions to tell a story. Even things like having a superhero in a fight is enough to trigger this. Also doesn't explain why regen makes it continue.

A vector search converts your query into magic numbers, and then searches the database for other magic numbers that are "similar" (closet to it in the vector space, which is basically an N-dimensional graph of points and directions). These results are then returned as snippets to the LLM and it does stuff from that point.

The effectiveness of the vector search depends on how Open WebUI breaks up the documents into smaller sections, and how good the embeddings are.

I'm not exactly sure what you want to achieve, but you might have success in using an LLM to summarize the documents beforehand, using a specific prompt to extract the info you want, then feed that into the vector DB. This would require some scripting, of course.

The easiest thing to do is try it. See if Open WebUI's vector search is able to handle it. Make sure to use a good embedding model like nomic-embed-text (can be found on ollama.com). You can change the vector search settings in the documents settings from the workspace on OpenWebUI.

Edit: https://ollama.com/library/nomic-embed-text

Open WebUI's document management loads everything into a vector database. When you use the hashtag, it will trigger a search against the vector database based on your prompt. These results are run feed into the LLM. Open WebUI should generate a hashtag that can reference all the documents. But the quality of the results will be influenced by the embeddings and the LLM that responds to you.

Install ollama. It has ROCm support (on Linux at least). Then hook it up to your favorite client. It has its own API and an openai compatible one.