MrScruff

Yeah exactly. After a hard day when my brain is frazzled, a workout will actually make me feel better.

I think what the parent post was saying is that there is a finite amount of useful mental function time in any one day, and once you’ve exhausted this any attempted learning will be pretty inefficient. Also some jobs will have a faster burn rate. Doing a workout is separate as it doesn’t draw on the mental energy pool.

Considering all of the great research that has come from his labs (eg. DINO, Segment Anything) I don’t think that’s fair (no pun intended).

I’m normally comparing frontier open/cheap models against frontier closed source. I use deepseek/glm regularly, they’re fine and you can get real work done with them but it’s super obvious when you switch back to opus or even sonnet. A 3B active param MoE model is not comparable.

You really need to take the benchmarks with a massive pinch of salt. I’ve been testing local LLMs since the original llama and there’s nothing I’ve tried that is in the same category as Opus.

I think this is true for projects beyond a certain complexity. I have 100% vibe coded projects with tens of thousands LOC, and haven't seen any real issues with fully automated maintenance. Will that approach work in every scenario, absolutely not, but the size and complexity of projects where it does is growing with each new model release.

That would imply that the biological physical substrate is necessary for conciousness, which I don't think you can say with any degree of certainty. It's not an assumption I would personally make. And while I'm speculating, my own view is that whatever the eventual subjective experience of what it's like to be an AI is, it will be nothing like the experience of what it's like to be a human, regardless of the fact we're training them to interact in human-like ways.

Ah, mea culpa.

My point was the "stochastic parrot" label can be both true and irrelevant. LLMs are predicting the next token based on their training data, so at that level "stochastic parrot" is accurate. But it tells us nothing about the complexity of the system that is responsible for making the prediction. One might argue humans have evolved consciousness in order to allow world modelling that enables them to make better predictions.

The difference between a 1B LLM and Claude Opus matters, because we're talking about emergent phenomena. Is a 1B LLM conscious? I don't know, perhaps a tiny amount. Maybe Opus is more conscious. Is a jumping spider conscious? Perhaps a tiny amount.

I think (rather ironically) you're reacting to the version of my comment you have in your mind rather than what I actually wrote. My point was that "stochastic parrot" is reductionist and irrelevant as most people would agree that a real life parrot has some form of inner life, even if we don't really know what form it takes. For all we know an LLM has to build a complex world model in order to predict the next token.

Incidentally, when I pasted our exchange into Claude it managed to comprehend the nature of my argument. Perhaps its attention mechanism is more finely tuned.

I can't actually figure out what you're reacting to - perhaps you could elaborate?

The reason people are confused by LLMs is that they are stochastic parrots. They do an incredibly good job of emulating human behaviours and speech patterns as that's what they've been trained on. But like an actual parrot, it's impossible to say exactly how much conciousness they actually have. I certainly would argue that a parrot is concious, although likely less so than a human.

What we do know is that conciousness is not binary and that it emerged through evolution. That doesn't entirely rule out your magic tsar bomb particle but it gives a strong indicator as to it's likeliness.

The problem with this is that the word 'hot' only has meaning to a conscious being. And while we don't know what conciousness is, it's extremely hard to argue it's not an emergent property of physics. So if your supernova simulation is complex enough to also model emergent properties like conciousness, the simulated conciousness may well regard the supernova as 'hot'.

Do you believe consciousness to be an emergent property of the laws of physics?

You can avoid the smells with a prompt. I have a benchmark involving short story writing within specific styles and the level of sophistication achievable is increasing over time, in my opinion.

This is true, but what is also true is that with each new generation of models (and not just for code generation) it becomes less and less true.

I’d probably word it differently but I agree with much of the sentiment here. I’m also reminded of the stat where 93% of drivers rated themselves as above average.

Practically, this is just about confidence values, anticipated blast radius and balancing testing vs review overhead.

It's ASI with jagged intelligence, which is probably what it will remain for a while.

It still sounds to me like remarkable automation rather than something that's expanding the frontier of human knowledge, for now at least.

Indeed. To be honest, I think everyone on HN is aware of how LLMs work at this point, it’s not actually adding a great deal to the discussion to keep going on about autocomplete or ‘stochastic parrots’.

The results being a lot better crafted by hand I would agree with, if one removes any notion of a time constraint. Sometimes the comparison point is between the LLM authored software or nothing at all.

Which is what?

I think the other aspect to this which you allude to at the end is that all of these arguments start with the assumption that all human software engineers produce high quality code that meets the requirements, but obviously that’s very much not the case in the real world. After all, 80-90% of drivers rate themselves as above average.

If one compares a single competent software engineer directing a number of agents against a random group of engineers (not necessarily working at FAANG or a YC startup), then those quality arguments are going to be significantly less compelling.

Is your argument that there is no imaginable situation where someone who was competent at software development could find use for a semi-automated tool for writing software?

That would imply that either the person in question has infinite time, or has access to all software that could ever be of utility to them, which seems unlikely.

I'm not saying it's rational or fair.

Speaking as someone who works in the industry, I haven't really heard this sentiment. Artists are predominantly hostile to diffusion models, but optimistic about LLMs and their ability to help them write tools and scripts even if they're non-technical.

Using LLMs for creative work is quite different to using diffusion models for creative work. It normally means writing tools or automation processes to enhance the creative flow, not replacing the creative input of a human.

Super cool. I loved Galways's C64 tunes as a kid, especially Wizball & Parallax. I remember trying to write my own player in assembly (yet another unfinished project).

UBI is just a massive extension of the welfare state. Governments can’t afford the current welfare spending, so where is the money going to come from? What do you think is going to happen to the markets when a large amount of the middle classes get laid off and can’t afford to pay their mortgages? What do you think is going to happen to the tech companies built on advertising to consumers when no-one has disposable income?