fredliu

This is one of the few places with consistent, high-quality setup instructions for essentially every major AI provider in one place — American frontier labs (OpenAI, Anthropic, Google, xAI), Chinese labs (DeepSeek, Alibaba/Qwen, Moonshot, Z.ai), aggregators (OpenRouter), fast inference providers (Groq, Cerebras, Together), and local runtimes (Ollama, LM Studio, LocalAI). This info exists elsewhere but is scattered across each vendor's docs with wildly varying quality. It also covers the common gotcha that a ChatGPT Plus / Claude Pro subscription is not an API key.

LLM: Attention is all you need. Human: Attention is all we have.

We should be careful where we spend our limited attention on.

Thanks for the encouragement! Yes, he's been sharing with his friend family groups. Early feedbacks are pretty good, he's ecstatic that he can actually make this happen!

We are in this transition period where we'll see a lot of these, because of the effort of creating "something impressive" is dramatically reduced. But once it stabilizes (which I think is already starting to happen, and this post is an example), and people are "trained" to recognize the real effort, even with AI help, behind creating something, the value of that final work will shine through. In the end, anything that is valuable is measured by the human effort needed to create it.

Definitely one of the most, if not THE most high quality AI UX out there. Congrats on the launch!

That's definitely my experience as well, sufficiently large context window with a capable enough general purpose LLM solves lots if not all of the problems rag/fine tuning claim to solve.

Yeah... So looks like at least it's still an open question. I guess until we can definitively know how "knowledge" is collectively represented among the weights, it's hard to say either way. The other part of the question is how to evaluate the existence of "knowledge" in an LLM. TFA suggests a way, but still not 100% convinced that's THE way...

Does anyone have real life experience (preferably verified in production environment) of fine-tuning actually adding new knowledge to the existing LLM in a reliable and consistent manner? I've seen claims that fine-tuning only adapt the "forms" but can't adding new knowledge, while some claim otherwise. I couldn't convince myself either way with my limited adhoc/anecdotal experiments.

Would be curious to see if anyone find it really useful. I've tried both Copilot and Codewhisper (Amazon Q now) before, wasn't impressed and uninstalled both. Just tried Q in VSCode again, I can't figure out how to ask questions relevant to the specific workspace that's useful to me. It seems like a bolt-on chat interface to your IDE with a bad UX. Feels like even "clippy" was more useful back in the day...

The Beam feature of bigAGI (IMO one of the best model provider agnostic GenAI UX) enables GenAI users to send same prompt to multiple GenAI models at the same time, and gives the user different approaches to examine, select and fuse the best results into a better answer, through a very intuitive and seamless UX. It has been my go-to way of using GenAI in the past few weeks. IMO the results are better than any individual model's results alone. The best thing is, it could (semi)automatically select the best results from the models, for instance, it used to be the Claude 3 Opus model's results were favored, now that the best results lean more towards gpt-4-turbo-2024-04-09, but you can achieve "best model auto selection" with Beam without having to manually pick one model over the other.

got it!

Exactly my thought, as mentioned in the other thread, Chat's linear conversation style is not fit for reasoning/exploration type of tasks, while Beam's fan-out -> select --> merge is a much better and natural flow!