I've been trying to keep up with the advances in the world of AI and LLMs. NLP was a world that I knew pretty well 7 years ago, when I knew most of the major NLP libraries, and their various strengths and weaknesses. However, nowadays, I'm having trouble finding good discussions about the real uses of the LLMs.<p>I have gone to Hugging Face, and the amount of data there is overwhelming, but it seems poorly organized:<p>https://huggingface.co<p>Does anyone know a secret that makes that site tractable? I've experimented with a few of the libraries posted there, but I can only sample a tiny fraction of what is there, and what I'm missing is some method for finding the useful stuff while disposing of the junk.<p>7 years ago I could tell you the strengths of weaknesses of the Google's Tensorflow or the Stanford NLP library. But where do I go to get good comparative information now, about the strengths and weaknesses of the various libraries that interact with the new LLM tools?<p>I'm looking to answer practical questions, that I can use in my own work with AI startups.<p>For an example of a question, for which I cannot find an answer, I am aware of a startup that has developed a chat client that, the startup says, can entirely replace a company's customer support team. Among the claims made by the startup is that when their chat client makes a mistake, it can be easily adjusted so it won't make that mistake any more. I am curious, what approaches are the engineers at that startup probably using to fix mistakes? If I search Hugging Face for ways to fix factual errors in LLMs then I see some libraries, but I've no idea what is considered good or bad.<p>So I asked the Hacker News community, how are you keeping up with advances around LLMs and associated tools?<p>Also, every LLM seems to have an embedded finite state machine that remembers the state of the current conversation, so where can I go to learn about the strengths and weaknesses of those finite state machines? How would I go about adjusting them?<p>Or, let me offer another example of the kind of information I want:<p>I've been testing different AI chats by trying to play text adventures with them. For instance:<p>https://huggingface.co/spaces/HuggingFaceH4/zephyr-7b-gemma-chat<p>https://chat.openai.com<p>If I use the same prompt with each of them, I can see how different they are, but how do I know if my observations are general (would other people get similar results) and how do I learn about other AI chats (since I cannot test them all).