Just for the crazy cool factor: This person does not exist & This cat does not exist. Source: over 1 year ago

This website has infinite AI generated images of cats. Source: over 1 year ago

Not sure if anyone else had found this, but while I was doing research for a school project I came across a page, thiscatdoesnotexist.com, it shows a picture of a cat, very time you reload it it shows a different one, If I try to view the source it just shows what to me are just random symbols, maybe a more experienced programmer could find out what they are? And nothing much else to go by, just that, pretty neat... Source: over 1 year ago

Good analysis! I guess there's also the fact that cat pictures are just such an internet phenomenon so there are so. many. pics. To pull from that could end up being cat pics. It's really funny how thiscatdoesnotexist.com almost always delivers a perfectly believable cat photo while thishorsedoesnotexist.com usually struggles to give you anything except Eldritch abominations. Source: almost 2 years ago

Https://thiscatdoesnotexist.com Same thing with horses, chemicals and art. Source: about 2 years ago

We will use the OpenAI embeddings API to convert the content of the blog posts into vector embeddings. You will need to sign up for an API key on the OpenAI website to use the API. You will need to provide your credit card information as there is a cost associated with using the API. You can review the pricing on the OpenAI website. There are alternatives to generate embeddings. Hugging Face provides... - Source: dev.to / 6 days ago

Hugging-face 🤗 is a repository to host all the LLM models available in the world. https://huggingface.co/. - Source: dev.to / 13 days ago

HuggingFaceEmbeddings is a function that we use for converting our documents to vector which is called embedding, you can use any embedding model from huggingface, it will load the model on your local computer and create embeddings(you can use external api/service to create embeddings), then we just pass this to context and create index and store them into folder so we can reuse them and don't need to recalculate it. - Source: dev.to / about 1 month ago

The only requirement for this tutorial is to have an Hugging Face account. In order to get it:. - Source: dev.to / about 2 months ago

Finally, you'll need to download a compatible language model and copy it to the ~/llama.cpp/models directory. Head over to Hugging Face and search for a GGUF-formatted model that fits within your device's available RAM. I'd recommend starting with TinyLlama-1.1B. - Source: dev.to / about 2 months ago