Prophet

It is entirely job dependent. I have been in jobs where it was just a grind and going the extra mile simply put a smile on my boss’s face. In jobs like these the best thing you can do is carve out as many hours as possible during the work week to build new skills or apply to other jobs. I’ve also been in jobs where going the extra mile directly contributed meaningful skills to my resume/portfolio and helped me get a new job with way better pay.

I would add Pinta as another drop-in replacement for MS Paint

The guy who leads this group is extremely vocal (almost weirdly so) about white privilege and systemic racism. He is also white. It’s true that many AI models have white-bias. The reasons for this are multi-faceted. Our datasets are grossly imbalanced against racial minorities. I also think I understand that for some darker-skinned races, it is more difficult for the model to extract relevant features from the shitty Flickr photos they scrape for these models.

That said, injecting words into the users prompt to force the model to generate minorities more often is an extremely naive approach. Kind of like if Google added “reddit” to all searches just because it worked for some specific test cases, but ignoring that you now no longer get any site except reddit. Probably the solution here looks like paying a lot of money for high quality datasets as well as investing in user education and more AI explainability of these tools.

How does one even fix this? Is it just dragged out?

I think you have the right idea but came to the wrong conclusion. Why would anyone buy office space if there is no value in employees coming to the office? Hint: they wouldn’t.

Edited to add: these properties may become a liability on their books which would impact their ability to apply for or pay for loans, as well as other negatives for the company.

It’s because we like the challenge. Also how salty people get when they lose to my f-tier character.

I believe that Disney/Star Wars actually owns the trademark on the word “Droid” and they make money on every droid-phone sold.

Seems reasonable. I’ll add in that there are models specifically finetuned for storytelling. You might check out this thread for some other model suggestions. I think you will also likely want to find a framework for RLHF.

Depends on your hardware and how far you’re willing to go. For serious development I think you need at least 12-16 GB of VRAM, but there’s still some things you can do with ~8. If you just have a cpu, you can still test some models but generation will be slow.

I’d recommend trying out the oogabooga webui. This should work with quite a few models on hugging face. Hopefully I don’t get in trouble for recommending a subreddit but r/localllama has a lot of other great resources and us a very active community. They’re doing exactly what you want.

As far as your other questions…

1. Accessing it on your phone is going to be tricky. You would most likely want to host it somewhere but I’m not sure how easy that is for someone without a bit of software background. Maybe there is a good service for this, huggingface might offer something.

2. Cross thread referencing is an interesting idea. I think you would need to create a log store of all your conversations and then embed those into a a vector store (like milvus or weaviate or qdrant). This is a little tricky since you have to decide how to chunk your conversations, but it is doable. The next step is somewhat open ended. You could always query your vector store with any questions that you are already sending your model, and then pass any hits to the model along with your original question. Alternatively, you could tell the model to check for other conversations and trigger a function call to do this on command. A good starting point might be this example, which makes references to a hardware manual in a Q&A style chatbot.

3. Using an LLM with stable diffusion: not especially sure what you are hoping to get out of this. Maybe to reduce boilerplate prompt writing? But yes you can finetune a model to handle this and then have the model execute a function that calls stable diffusion and returns the results. I am pretty sure langchain provides a framework for this. Langchain is almost certainly a tool you will want to become familiar with.