31–40 of 615 posts
Here is my resume. Make it look nice (some design hints).
They can spit html and css, but not Google doc.
On the other hand, Google results are dominated by SEO spam. You can probably find one usable result on page 10.
The problem is not technology. It's a business model that can support the humans feeding data into the LLM.
> Some of those GPT-4 models run on my laptop That's an indication that most business-sized models won't need some giant data center. This is going to be a cheap technology most of the time. OpenAI is thus way overvalued.
The last OpenAI valuation I read about was 157 billion. I am struggling to understand what justifies this. To me, it feels like OpenAI is at best few months ahead of competitors in some areas. But even if I am underestimating the advantage, it's few years instead of few months, why does it matter? It's not like AI companies are going to enjoy the first-mover advantage internet giants had over the competition.
The non-skeptical interpretation is that it's a threshold function, a flat-out race with an unambiguous finish line. If someone actually hit self-improving AGI first there's an argument that no one would ever catch up.
> Some of those GPT-4 models run on my laptop That's an indication that most business-sized models won't need some giant data center. This is going to be a cheap technology most of the time. OpenAI is thus way overvalued.
The last OpenAI valuation I read about was 157 billion. I am struggling to understand what justifies this. To me, it feels like OpenAI is at best few months ahead of competitors in some areas. But even if I am underestimating the advantage, it's few years instead of few months, why does it matter? It's not like AI companies are going to enjoy the first-mover advantage internet giants had over the competition.
That is of course, assuming AGI is possible and exponential, and that marketshare goes to a single entity instead of a set of entities. Lots of big assumptions. Seems like we're heading towards a slow-lackluster singularity though.
> Some of those GPT-4 models run on my laptop That's an indication that most business-sized models won't need some giant data center. This is going to be a cheap technology most of the time. OpenAI is thus way overvalued.
They run on a laptop, yes - you might squeeze up to 10 token/sec out of a kinda sorta GPT-4 if you paid $5K plus for an Apple laptop in the last 18 months.
And that's after you spent 2 minutes watching 1000 token* prompt prefill at 10 tokens/sec.
Usually it'd be obvious this'd trickle down, things always do, right?
But...Apple infamously has been stuck on 8GB of RAM in even $1500 base models for years. I have 0 idea why, but my intuition is RAM was ~doubling capacity at same cost every 3 years till early 2010s, then it mostly stalled out post 2015.
And regardless of any of the above, this absolutely melts your battery. Like, your 16 hr battery life becomes 40 minutes, no exaggeration.
I don't know why prefill (loading in your prompt) is so slow for local LLMs, but it is. I assume if you have a bunch of servers there's some caching you can do that works across all prompts.
I expect the local LLM community to be roughly the same size it is today 5 years from now.
* ~3 pages / ~750 words; what I expect is a conservative average for prompt size when coding
What is the current status on pushing "reasoning" down to latent/neural space? Seems like a vaste of tokens to let a model converse with itself especially when this internal monologue often has very little to do with the final output so it's not useful as a log of how the final output was derived.
My fav part of the writeup at the end: """ LLMs need better criticism # A lot of people absolutely hate this stuff. In some of the spaces I hang out (Mastodon, Bluesky, Lobste.rs, even Hacker News on occasion) even suggesting that “LLMs are useful” can be enough to kick off a huge fight. I like people who are skeptical of this stuff. The hype has been deafening for more than two years now, and there are enormous quan…
I agree, but I think my biggest issue with LLMs (and a lot of GenAI) is that they act as a massive accelerator for the WORST (and unfortunately most common) type of human - the lazy one. The signal-to-noise ratio just goes completely out of control. https://journal.everypixel.com/ai-image-statistics
Recognize what they do well (generate simple code in popular languages) while acknowledging where they are weak (non-trivial algorithms, any novel code situation the LLM hasn't seen before, less popular languages).
> Some of those GPT-4 models run on my laptop That's an indication that most business-sized models won't need some giant data center. This is going to be a cheap technology most of the time. OpenAI is thus way overvalued.
The last OpenAI valuation I read about was 157 billion. I am struggling to understand what justifies this. To me, it feels like OpenAI is at best few months ahead of competitors in some areas. But even if I am underestimating the advantage, it's few years instead of few months, why does it matter? It's not like AI companies are going to enjoy the first-mover advantage internet giants had over the competition.
> Some of those GPT-4 models run on my laptop That's an indication that most business-sized models won't need some giant data center. This is going to be a cheap technology most of the time. OpenAI is thus way overvalued.
The last OpenAI valuation I read about was 157 billion. I am struggling to understand what justifies this. To me, it feels like OpenAI is at best few months ahead of competitors in some areas. But even if I am underestimating the advantage, it's few years instead of few months, why does it matter? It's not like AI companies are going to enjoy the first-mover advantage internet giants had over the competition.
People are buying shares at $x because they believe they will be able to sell them for more later. I don’t think there’s a whole to more to it than that.