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Things we learned about LLMs in 2024

simonwillison.net

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Re: Things we learned about LLMs in 2024

#81

Post by xyzsparetimexyz »

simonw wrote: post #50
henning wrote: post #37

Spookily good at writing code? LLMs frequently hallucinate broken nonsense shit when I use them. 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).

Did you try learning HOW to get good code out of them? As with all things LLM there's a whole lot of undocumented and under appreciated depth to getting decent results. Code hallucinations are also the least damaging type of hallucinations, because you get fact checking for free: if you run the code and get an error you know there's a problem. A lot of the time I find pasting that error message back into the LLM gets…

Is there more to getting 'good' at them then just copying error messages back in? Like, how do I get them to reason about e.g. whether a data structure compression method makes sense?

Re: Things we learned about LLMs in 2024

#82

Post by whatshisface »

philipkglass wrote: post #44
datadrivenangel wrote: post #33

Earlier quoted context omitted.

It's justified if AGI is possible. If AGI is possible, then the entire human economy stops making sense as far as money goes, and 'owning' part of OpenAI gives you power. 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.

If AGI is possible, then the entire human economy stops making sense as far as money goes, and 'owning' part of OpenAI gives you power. That's if AGI is possible and not easily replicated . If AGI can be copied and/or re-developed like other software then the value of owning OpenAI stock is more like owning stock in copper producers or other commodity sector companies. (It might even be a poorer investment. Even AGI…

This belief comes from confusing the singularity (every atom on Earth is converted into a giant image of Sam Altman) with AGI (a store employee navigates a confrontation with an unruly customer, then goes home and wins at Super Mario).

Re: Things we learned about LLMs in 2024

#83

Post by AnimalMuppet »

simonw wrote: post #50
henning wrote: post #37

Spookily good at writing code? LLMs frequently hallucinate broken nonsense shit when I use them. 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).

Did you try learning HOW to get good code out of them? As with all things LLM there's a whole lot of undocumented and under appreciated depth to getting decent results. Code hallucinations are also the least damaging type of hallucinations, because you get fact checking for free: if you run the code and get an error you know there's a problem. A lot of the time I find pasting that error message back into the LLM gets…

> Did you try learning HOW to get good code out of them?

That is at least somewhat a valid point. Good workers know how to get the best out of their tools. And yet, good tools accommodate how their users work, instead of expecting the user to accommodate how the tool works.

One could also say that programmers were sold a misleading bill of goods about how LLMs would work. From what they were told, they shouldn't have to learn how to get the best out of LLMs - LLMs were AI, on the way to AGI, and would just give you everything you needed from a simple prompt.

Re: Things we learned about LLMs in 2024

#84

Post by sowbug »

Simon has mentioned in multiple articles how cool it is to use 64GB DRAM for GPU tasks on his MacBook. I agree it's cool, but I don't understand why it is remarkable. Is Apple doing something special with DRAM that other hardware manufacturers haven't figured out? Assuming data centers are hoovering up nearly all the world's RAM manufacturing capacity, how is Apple still managing to ship machines with DRAM that performs close enough for Simon's needs to VRAM? Is this just a temporary blip, and PC manufacturers in 2025 will be catching up and shipping mini PCs that have 64GB RAM ceilings with similar memory performance? What gives?

Re: Things we learned about LLMs in 2024

#85

Post by [deleted] »

datadrivenangel wrote: post #33
shihab wrote: post #20

Earlier quoted context omitted.

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.

It's justified if AGI is possible. If AGI is possible, then the entire human economy stops making sense as far as money goes, and 'owning' part of OpenAI gives you power. 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.

[deleted]

Re: Things we learned about LLMs in 2024

#86

Post by minimaxir »

antirez wrote: post #54

About "people still thinking LLMs are quite useless", I still believe that the problem is that most people are exposed to ChatGPT 4o that at this point for my use case (programming / design partner) is basically a useless toy. And I guess that in tech many folks try LLMs for the same use cases. Try Claude Sonnet 3.5 (not Haiku!) and tell me if, while still flawed, is not helpful. But there is more: a key thing with L…

> Claude Sonnet 3.5 (not Haiku!)

A very big surprise is just how much better Sonnet 3.5 is than Haiku. Even the confusingly-more-expensive-Haiku-variant Haiku 3.5 that's more recent than Sonnet 3.5 is still much worse.

Re: Things we learned about LLMs in 2024

#87

Post by mhh__ »

antirez wrote: post #54

About "people still thinking LLMs are quite useless", I still believe that the problem is that most people are exposed to ChatGPT 4o that at this point for my use case (programming / design partner) is basically a useless toy. And I guess that in tech many folks try LLMs for the same use cases. Try Claude Sonnet 3.5 (not Haiku!) and tell me if, while still flawed, is not helpful. But there is more: a key thing with L…

Hopefully things have narrowed but you can see from the trends data just how few people (API may be a different story) use claude relative to chatgpt.

Re: Things we learned about LLMs in 2024

#88

Post by minimaxir »

mhh__ wrote: post #87
antirez wrote: post #54

About "people still thinking LLMs are quite useless", I still believe that the problem is that most people are exposed to ChatGPT 4o that at this point for my use case (programming / design partner) is basically a useless toy. And I guess that in tech many folks try LLMs for the same use cases. Try Claude Sonnet 3.5 (not Haiku!) and tell me if, while still flawed, is not helpful. But there is more: a key thing with L…

Hopefully things have narrowed but you can see from the trends data just how few people (API may be a different story) use claude relative to chatgpt.

Brand awareness is a hell of a drug.

Re: Things we learned about LLMs in 2024

#89

Post by jppope »

hdjjhhvvhga wrote: post #72
nntwozz wrote: post #65

I think John Gruber summed it up nicely: https://daringfireball.net/2024/12/openai_unimaginable OpenAI’s board now stating “We once again need to raise more capital than we’d imagined” less than three months after raising another $6.6 billion at a valuation of $157 billion sounds alarmingly like a Ponzi scheme — an argument akin to “Trust us, we can maintain our lead, and all it will take is a never-ending stream of…

What is funny is that their "lead" is just because of inertia - they were the first to make an LLM publicly available. But they are no longer leaders so their attempts at getting more and more money only prove Altman's skills at convincing people to give him money.

yeah but in business there are really only 2 skills right? Convincing people to give you money and giving them something back to them thats worth more than the money they gave you.

Re: Things we learned about LLMs in 2024

#90

Post by post-it »

sowbug wrote: post #84

Simon has mentioned in multiple articles how cool it is to use 64GB DRAM for GPU tasks on his MacBook. I agree it's cool, but I don't understand why it is remarkable. Is Apple doing something special with DRAM that other hardware manufacturers haven't figured out? Assuming data centers are hoovering up nearly all the world's RAM manufacturing capacity, how is Apple still managing to ship machines with DRAM that perfo…

Apple designs its own chips, so the RAM and CPU are on the same die and can talk at very high speeds. This is not the case for PCs, where RAM is connected externally.
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