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2025: The Year in LLMs

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Re: 2025: The Year in LLMs

#231

Post by Kerrick »

windexh8er wrote: post #221
CamperBob2 wrote: post #172

Earlier quoted context omitted.

That's all very impressive, to be sure. But are you sure you're getting the point? As of 2025, LLMs are now very good at writing new code, creating new imagery, and writing original text. They continue to improve at a remarkable rate. They are helping their users create things that didn't exist before. Additionally, they are now very good at searching and utilizing web resources that didn't exist at training time. So…

> They are helping their users create things that didn't exist before. That is a derived output. That isn't new as in: novel. It may be unique but it is derived from training data. LLMs legitimately cannot think and thus they cannot create in that way.

That is a pedantic distinction. You can create something that didn't exist by combining two things that did exist, in a way of combining things that already existed. For example, you could use a blender to combine almond butter and sawdust. While this may not be "novel", and it may be derived from existing materials and methods, you may still lay claim to having created something that didn't exist before.

For a more practical example, creating bindings from dynamic-language-A for a library in compiled-language-B is a genuinely useful task, allowing you to create things that didn't exist before. Those things are likely to unlock great happiness and/or productivity, even if they are derived from training data.

Re: 2025: The Year in LLMs

#232

Post by coffeebeqn »

ksec wrote: post #228

All these improvement in a single year, 2025. While this may seem obvious to those who follows along the AI / LLM news. It may be worth pointing out again ChatGPT was introduced to us in November 2022. I still dont believe AGI, ASI or Whatever AI will take over human in short period of time say 10 - 20 years. But it is hard to argue against the value of current AI, which many of the vocal critics on HN seems to have…

Seems like Nvidia will be focusing on the super beefy GPUs and leaving the consumer market to a smaller player

Re: 2025: The Year in LLMs

#233

Post by sanderjd »

syndacks wrote: post #89

I can’t get over the range of sentiment on LLMs. HN leans snake oil, X leans “we’re all cooked” —- can it possibly be both? How do other folks make sense of this? I’m not asking for a side, rather understanding the range. Does the range lead you to believe X over Y?

As usual, somewhere in between!

Re: 2025: The Year in LLMs

#234

Post by Kerrick »

windexh8er wrote: post #220
handoflixue wrote: post #175

Earlier quoted context omitted.

Seriously, all that familiarity and you think an LLM "literally" can't invent anything that didn't already exist? Like, I'm sorry, but you're just flat-out wrong and I've got the proof sitting on my hard drive. I use this supposedly impossible program daily.

Do you also think LLMs "think"? From what you've described an LLM has not invented anything. LLMs that can reason have a bit more slight of hand but they're not coming up with new ideas outside of the bounds of what a lot of words have encompassed in both fiction and non. Good for you that you've got a fun token of code that's what you've always wanted, I guess. But this type of fantasy take on LLMs seems to be more…

Hang on, you're now saying that if something has ever been described in fiction it doesn't count as invention? So if somebody literally developed a working photon torpedo, that isn't new because "Star Trek Did It"?

Re: 2025: The Year in LLMs

#235

Post by zingar »

windexh8er wrote: post #221
CamperBob2 wrote: post #172

Earlier quoted context omitted.

That's all very impressive, to be sure. But are you sure you're getting the point? As of 2025, LLMs are now very good at writing new code, creating new imagery, and writing original text. They continue to improve at a remarkable rate. They are helping their users create things that didn't exist before. Additionally, they are now very good at searching and utilizing web resources that didn't exist at training time. So…

> They are helping their users create things that didn't exist before. That is a derived output. That isn't new as in: novel. It may be unique but it is derived from training data. LLMs legitimately cannot think and thus they cannot create in that way.

Could you give us an idea of what you’re hoping for that is not possible to derive from training data of the entire internet and many (most?) published books?

Re: 2025: The Year in LLMs

#236

Post by cloudking »

the_mitsuhiko wrote: post #6

> The (only?) year of MCP I like to believe, but MCP is quickly turning into an enterprise thing so I think it will stick around for good.

For connecting agents to third-party systems I prefer CLI tools, less context bloat and faster. You can define the CLI usage in your agent instructions. If the MCP you're using doesn't exist as a CLI, build one with your agent.

Re: 2025: The Year in LLMs

#237

Post by ManuelKiessling »

PeterHolzwarth wrote: post #207
syndacks wrote: post #89

I can’t get over the range of sentiment on LLMs. HN leans snake oil, X leans “we’re all cooked” —- can it possibly be both? How do other folks make sense of this? I’m not asking for a side, rather understanding the range. Does the range lead you to believe X over Y?

I think it may be all summed up by Roy Amara's observation that "We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run."

I think this is the most-fitting one-liner right now.

The arguments going back and forth in these threads are truly a sight to behold. I don’t want to lean to any one side, but in 2025 I‘ve begun to respond to everyone who still argues that LLMs are only plagiarism machines, or are only better autocompletes, or are only good at remixing the past: Yes, correct!

And CPUs can only move zeros and ones.

This is likewise a very true statement. But look where having 0s and 1s shuffled around has brought us.

The ripple effects of a machine doing something very simple and near-meaningless, but doing it at high speed and again and again without getting tired, cannot be underestimated.

At the same time, here is Nobel Laureate Robert Solow, who famously, and at the time correctly, stated that "You can see the computer age everywhere but in the productivity statistics."

It took a while, but eventually, his statement became false.

Re: 2025: The Year in LLMs

#238

Post by noodletheworld »

viraptor wrote: post #153
crystal_revenge wrote: post #120

Earlier quoted context omitted.

> I don't understand why Hacker News is so dismissive about the coming of LLMs I find LLMs incredibly useful, but if you were following along the last few years the promise was for “exponential progress” with a teaser world destroying super intelligence. We objectively are not on that path. There is no “coming of LLMs”. We might get some incremental improvement, but we’re very clearly seeing sigmoid progress. I can’t…

> exponential progress First you need to define what it means. What's the metric? Otherwise it's very much something you can argue about.

> What's the metric?

Language model capability at generating text output.

The model progress this year has been a lot of:

- “We added multimodal”

- “We added a lot of non AI tooling” (ie agents)

- “We put more compute into inference” (ie thinking mode)

So yes, there is still rapid progress, but these ^ make it clear, at least to me, that next gen models are significantly harder to build.

Simultaneously we see a distinct narrowing between players (openai, deepseek, mistral, google, anthropic) in their offerings.

Thats usually a signal that the rate of progress is slowing.

Remind me what was so great about gpt 5? How about gpt4 from from gpt 3?

Do you even remember the releases? Yeah. I dont. I had to look it up.

Just another model with more or less the same capabilities.

“Mixed reception”

That is not what exponential progress looks like, by any measure.

The progress this year has been in the tooling around the models, smaller faster models with similar capabilities. Multimodal add ons that no one asked for, because its easier to add image and audio processing than improve text handling.

That may still be on a path to AGI, but it not an exponential path to it.

Re: 2025: The Year in LLMs

#239

Post by zingar »

Greduan wrote: post #227
handoflixue wrote: post #158

Earlier quoted context omitted.

> LLMs are literally technology that can only reproduce the past. Funny, I've used them to create my own personalized text editor, perfectly tailored to what I actually want. I'm pretty sure that didn't exist before. It's wild to me how many people who talk about LLM apparently haven't learned how to use them for even very basic tasks like this! No wonder you think they're not that powerful, if you don't even know ba…

Text editors in a thousand flavours has indeed already been programmed though. I don't think you understood what op meant. Curious, does it perform at the limit of the hardware? Was it programmed in a tools language (like C++, Rust, C, etc.) or in a web tech?

What is the point that you believe would be demonstrated by a new text editor running at the limit of hardware in a compiled editor? Would that point apply to every other text editor that exists already?

Re: 2025: The Year in LLMs

#240

Post by uxcolumbo »

crystal_revenge wrote: post #182
CamperBob2 wrote: post #172

Earlier quoted context omitted.

That's all very impressive, to be sure. But are you sure you're getting the point? As of 2025, LLMs are now very good at writing new code, creating new imagery, and writing original text. They continue to improve at a remarkable rate. They are helping their users create things that didn't exist before. Additionally, they are now very good at searching and utilizing web resources that didn't exist at training time. So…

I think the confusion is people's misunderstanding of what 'new code' and 'new imagery' mean. Yes, LLMs can generate a specific CRUD webapp that hasn't existed before but only based on interpolating between the history of existing CRUD webapps. I mean traditional Markov Chains can also produce 'new' text in the sense that "this exact text" hasn't been seen before, but nobody would argue that traditional Markov Chains…

How do human brains create something novel and what will it take for AIs to do the same?
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