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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

#541

Post by voidhorse »

phantompeace wrote: post #520
voidhorse wrote: post #463

Earlier quoted context omitted.

Based on your writing you are clearly emotionally invested in this technology, consider how that may affect your understanding. By multiple rewrites, I meant that, to me, at least, it is silly to spend N compute on producing effectively the same summary on demand for the Mth chatbot user when, in some cases, we could much more economically generate one summary once and make it available via distribution channels--to…

I wouldn't say i'm more "emotionally invested" in this tech moreso than annoyed with people who expect it to be 100% perfect, as if they've accepted the snakeoil salesmen at face value and suddenly dismiss all useful applications of it at the first hurdle. Consider that your disdain for these sales people and their oft-exaggerated claims (which i absolutely despise) may cloud your judgement of the actual technology.…

I basically agree with everything you say here, I guess my chief concern surrounds reducing brain rot, and I mostly just worry that we will only increase brain rot through uncritical application of LLMs, rather than decrease it.

At least there is one silver lining: your comments are evidence that not everyone has suffered that brain rot, and some of us are still out there using tools critically—thanks for a good conversation on this!

Re: Things we learned about LLMs in 2024

#542

Post by ninth_ant »

hansvm wrote: post #442
mvdtnz wrote: post #409

Earlier quoted context omitted.

> Every day by automation I feed in the hourly weather forecast my home ollama server and it builds me a nice readable concise weather report. It’s super cool! You feed it a weather report and it responds with a weather report? How is that useful?

It distilled bulk information into a form the author cared about. If nothing else it was probably fun, and a personal report on the things you care about can save minutes each day. I did something similar awhile back without LLMs. I enjoy kayaking, but for a variety of reasons [0] it's usually unwieldy to break out of the surf and actually get out into the ocean at my local beach. I eventually started feeding the dat…

Well said, that’s exactly what I meant.

Re: Things we learned about LLMs in 2024

#543

Post by s1mplicissimus »

mvkel wrote: post #235
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…

I'm surprised at the description that it's "useless" as a programming / design partner. Even if it doesn't make "elegant" code (whatever that means), it's the difference between an app existing at all, or not. I built and shipped a Swift app to the App Store, currently generating $10,200 in MRR, exclusively using LLMs. I wouldn't describe myself as a programmer, and didn't plan to ever build an app, mostly because in…

Would be very interesting to have a look at this app that you wrote using only LLMs. Mind sharing the name?

Re: Things we learned about LLMs in 2024

#544

Post by Teever »

Terr_ wrote: post #454
Teever wrote: post #319

Earlier quoted context omitted.

But what if that AGI can fit inside a humanoid robot and that robot is capable of self replication even if it means digging the sand out of the ground to make silicon with a spade?

We already have humanoid intelligeces that self-assemble and power from common materials, as a colony of incredibly advanced nanobots.

Yes. The goal is to emulate that with different substrates to understand how it works and to have better control over existing self-replicating systems.

Re: Things we learned about LLMs in 2024

#545

Post by HDThoreaun »

richardw wrote: post #134
philipkglass wrote: post #44

Earlier quoted context omitted.

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…

The first AGI will have such an advantage. It’ll be the first thing that is smart and tireless, can do anything from continuously hacking enemy networks to trading across all investment classes, to basically taking over the news cycle on social media. It would print money and power.

Depends on how efficient it is. If it requires more processing power than we have to do all these things competitors will have time to catch up while new hardware is created.

Re: Things we learned about LLMs in 2024

#546

Post by HDThoreaun »

com2kid wrote: post #100
robertlagrant wrote: post #70

Earlier quoted context omitted.

> If AGI is possible, then the entire human economy stops making sense as far as money goes, What does this mean in terms of making me coffee or building houses?

If we can simulate a full human intelligence at a reasonable speed, we can simulate 100 of them and ask the AGI to figure out how to make itself 10x faster. Rinse and repeat. That is exponential take off. At the point where you have an army of AIs running at 1000x human speed it can just ask it to design the mechanisms for and write the code to make robots that automate any possible physical task.

The human brain still has orders of magnitude more processing power than LLMs. Even if we develop superintelligence the current hardware cant run it which gives competitors time to catch up.

Re: Things we learned about LLMs in 2024

#547

Post by catalypso »

scrollaway wrote: post #502
Jensson wrote: post #476

Earlier quoted context omitted.

Its easy to see how it does that, the answer is that your bug isn't something novel, it has seen millions of "where is the bug in this code" questions online so it can typically guess from there what it would be. It is very unreliable at fixing things or writing code for anything non standard. Knowing this you can easily construct queries that trips them up by noticing what it is in your code they notice, so you cons…

Both of your claims are way off the mark (I run an AI lab). The LLMs are good at finding bugs in code not because they’ve been trained on questions that ask for existing bugs, but because they have built a world model in order to complete text more accurately. In this model, programming exists and has rules and the world model has learned that. Which means that anything nonstandard … will be supported. It is trivial…

I just tried it and I'm actually surprised with how well they work even with base64 encoded inputs.

This is assuming they don't call an external pre-processing decoding tool.

Re: Things we learned about LLMs in 2024

#548

Post by simonw »

catalypso wrote: post #547
scrollaway wrote: post #502

Earlier quoted context omitted.

Both of your claims are way off the mark (I run an AI lab). The LLMs are good at finding bugs in code not because they’ve been trained on questions that ask for existing bugs, but because they have built a world model in order to complete text more accurately. In this model, programming exists and has rules and the world model has learned that. Which means that anything nonstandard … will be supported. It is trivial…

I just tried it and I'm actually surprised with how well they work even with base64 encoded inputs. This is assuming they don't call an external pre-processing decoding tool.

The LLM UIs that integrate that kind of thing all have visible indicators when it's happening - in ChatGPT you would see it say "Analyzing..." while it ran Python code, and in Claude you would see the same message while it used JavaScript (in your browser) instead.

If you didn't see the "analyzing" message then no external tool was called.

Re: Things we learned about LLMs in 2024

#549

Post by [deleted] »

com2kid wrote: post #93
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 uses HBM, basically RAM on the same die as the CPU. It has a lot more memory bandwidth than typically PC dram, but still less than many GPUs. (Although the highest end macs have bandwidth that is in the same ballpark as GPUs)

[deleted]

Re: Things we learned about LLMs in 2024

#550

Post by SoftTalker »

cj wrote: post #405
sarchertech wrote: post #394

Earlier quoted context omitted.

Staff plus just means staff or higher. Staff, senior staff, principal, mega ultra principal etc…

Outside of big tech, those titles aren’t common. Level X SWE vs staff vs principal doesn’t mean anything to a lot of people who aren’t in that orbit.

Yes when I started working, "staff" meant entry-level. My first job out of school was a "staff consultant." So I'm always tripped up when I see "staff" used to mean "very senior/experienced"
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