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Human coders are still better than LLMs

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Re: Human coders are still better than LLMs

#621

Post by vidarh »

gitaarik wrote: post #532
JoshCole wrote: post #345

Earlier quoted context omitted.

> it can't "think" of something entirety conceptionally new, because it doesn't really "think". Hierarchical optimization (fast global + slow local) is a precise, implementable notion of "thinking." Whenever I've seen this pattern implemented, humans, without being told to do so by others in some forced way, seem to converge on the use of verb think to describe the operation. I think you need to blacklist the term th…

Yes ok, it can generate new stuff, but it's dependent on human curated reward models to score the output to make it usable. So it still depends on human thinking, it's own "thinking" is not sufficient. And there won't be a point when human curated reward models are not needed anymore. LLM's will make a lot of things easier for humans, because most of the thinking the humans do have been automated into the LLM. But ul…

> And there won't be a point when human curated reward models are not needed anymore.

This doesn't follow at all. There's no reason why a model can not be made to produce reward models.

Re: Human coders are still better than LLMs

#622

Post by msgodel »

vidarh wrote: post #219
jdiff wrote: post #91

Earlier quoted context omitted.

The crazy thing is that people think that a model designed to predict sequences of tokens from a stem, no matter how advanced the model, to be much more than just "really good autocomplete." It is impressive and very unintuitive just how far that can get you, but it's not reductive to use that label. That's what it is on a fundamental level, and aligning your usage with that will allow it to be more effective.

It's trivial to demonstrate that it takes only a tiny LLM + a loop to a have a Turing complete system. The extension of that is that it is utterly crazy to think that the fact it is "a model designed to predict sequences of tokens" puts much of a limitation on what an LLM can achieve - any Turing complete system can by definition simulate any other. To the extent LLMs are limited, they are limited by training and com…

Language models with a loop absolutely aren't Turing complete. Assuming the model can even follow your instructions the output is probabilistic so in the limit you can guarantee failure. In reality though there are lots of instructions LLMs fail to follow. You don't notice it as much when you're using them normally but if you want to talk about computation you'll run into trivial failures all the time.

The last time I had this discussion with people I pointed out how LLMs consistently and completely fail at applying grammar production rules (obviously you tell them to apply to words and not single letters so you don't fight with the embedding.)

LLMs do some amazing stuff but at the end of the day:

1) They're just language models, while many things can be described with languages there are some things that idea doesn't capture. Namely languages that aren't modeled, which is the whole point of a Turing machine.

2) They're not human, and the value is always going to come from human socialization.

Re: Human coders are still better than LLMs

#623

Post by onion2k »

xhevahir wrote: post #184
UncleOxidant wrote: post #71

There's some whistling past the graveyard in these comments. "You still need humans for the social element...", "LLMs are bad at debugging", "LLMs lead you astray". And yeah, there's lots of truth in those assertions, but since I started playing with LLMs to generate code a couple of years ago they've made huge strides. I suspect that over the next couple of years the improvements won't be quite as large (Pareto Prin…

This attitude is depressingly common in lots of professional, white-collar industries I'm afraid. I just came from the /r/law subreddit and was amazed at the kneejerk dismissal there of Dario Amodei's recent comments about legal work, and of those commenters who took them seriously. It's probably as much a coping mechanism as it is complacency, but, either way, it bodes very poorly for our future efforts at mitigatin…

In most professional industries getting to the right answer is only half the problem. You also need to be able to demonstrate why that is the right answer. Your answer has to stand up to criticism. If your answer is essentially the output of a very clever random number generator you can't ever do that. Even if an LLM could output an absolutely perfect legal argument that matched what a supreme court judge would argue every time, that still wouldn't be good enough. You'd still need a person there to be accountable for making the argument and to defend the argument.

Software isn't like this. No one cares why you wrote the code in your PR. They only care about whether it's right.

This is why LLMs could be useful in one industry and a lot less useful in another.

Re: Human coders are still better than LLMs

#624

Post by msgodel »

Buttons840 wrote: post #388
Buttons840 wrote: post #379

Earlier quoted context omitted.

LLMs aren't my rubber duck, they're my wrong answer. You know that saying that the best way to get an answer online is to post a wrong answer? That's what LLMs do for me. I ask the LLM to do something simple but tedious, and then it does it spectacularly wrong, then I get pissed off enough that I have the rage-induced energy to do it myself.

I'm probably suffering undiagnosed ADHD, and will get stuck and spend minutes picking a function name and then writing a docstring. LLMs do help with this even if they get the code wrong, because I usually won't bother to fix their variables names or docstring unless needed. LLMs can reliably solve the problem of a blank-page.

Yeah, keeping me in the flow when I hit one of those silly tasks my brain just randomly says "no let's do something else" to has been the main productivity improving feature of LLMs.

Re: Human coders are still better than LLMs

#625

Post by limflick »

ben-schaaf wrote: post #485
abootstrapper wrote: post #468

Earlier quoted context omitted.

I didn’t buy the hype of any of those things, but I believe AI is a going to change everything much like the introduction of the internet. People are dismissing AI because its code is not bug free, completely dismissing the fact that it generates PRs in minutes from a poorly written text prompt. As if that’s not impressive. In fact if you put a human engineer on the receiving end of the same prompt with the same cont…

Again, there were people just as sure about crypto as you are now about AI. They dismissed criticism because they thought the technology was impressive and revolutionary. That it was science fiction come true and only going to continue to improve. It's the exact same hype-driven rhetoric. If you want to convince skeptics talk about examples, vibe code a successful business, show off your success with using AI. Tellin…

You don't have to be able to vibe code an entire business from scratch to know that the technology behind AI is significantly more impressive than VR, crypto, web3 etc. What the free version of ChatGPT can do right now, not just coding; would've been unimaginable to most people just 5 years ago.

Don't people and companies using AI lazily to put out low quality content blind you to its potential as well as the reality of what it can do right now. Look at Google's VO3, most people in the world right now won't be able to tell you that it's AI generated and not real.

Re: Human coders are still better than LLMs

#626

Post by abalashov »

seadan83 wrote: post #188
gerad wrote: post #52

Earlier quoted context omitted.

It's like chess. Humans are better for now, they won't be forever, but humans plus software is going to better than either alone for a long time.

> It's like chess. Humans are better for now, they won't be forever This is not an obviously true statement. There needs to be proof that there are no limiting factors that are computationally impossible to overcome. It's like watching a growing child, grow from 3 feet to 4 feet, and then saying "soon, this child will be the tallest person alive."

With these "AGI by 2027" claims, it's not enough to say that the child will be the tallest person alive. They are saying the child will be the tallest structure on the planet.

Re: Human coders are still better than LLMs

#627

Post by cushychicken »

UncleOxidant wrote: post #211
cushychicken wrote: post #153

Earlier quoted context omitted.

ChatGPT-4o is scary good at writing VHDL. Using it to prototype some low level controllers today, as a matter of fact!

Claude and Gemini are decent at it as well. I was surprised when I asked claude (and this was several months back) to come up with a testbench for some very old, poorly documented verilog. It did a very decent job for a first-cut testbench. It even collected common, recurring code into verilog tasks (functions) which really surprised me at the time.

Yes! It’s much better at using functional logic than I am - which I appreciate!

Re: Human coders are still better than LLMs

#628

Post by rxtexit »

xhevahir wrote: post #184
UncleOxidant wrote: post #71

There's some whistling past the graveyard in these comments. "You still need humans for the social element...", "LLMs are bad at debugging", "LLMs lead you astray". And yeah, there's lots of truth in those assertions, but since I started playing with LLMs to generate code a couple of years ago they've made huge strides. I suspect that over the next couple of years the improvements won't be quite as large (Pareto Prin…

This attitude is depressingly common in lots of professional, white-collar industries I'm afraid. I just came from the /r/law subreddit and was amazed at the kneejerk dismissal there of Dario Amodei's recent comments about legal work, and of those commenters who took them seriously. It's probably as much a coping mechanism as it is complacency, but, either way, it bodes very poorly for our future efforts at mitigatin…

IMO it is a mixture of stupidity and denial.

I am not a software engineer but I just can't imagine my job is not automated in 10 years or less.

10 years is about the time between King – Man + Woman = Queen and now.

I think what is being highly underestimated is the false sense of security people feel because the jobs they interface with are also not automated, yet.

It is not hard to picture the network of automation that once one role is automated, connected roles to that role become easier to automate. So on and so on while the models keep getting stronger at the same time.

I expect we will have a recession at some point and the jobs lost are gone forever.

Re: Human coders are still better than LLMs

#629

Post by cushychicken »

Panzer04 wrote: post #569
cushychicken wrote: post #153

Earlier quoted context omitted.

ChatGPT-4o is scary good at writing VHDL. Using it to prototype some low level controllers today, as a matter of fact!

What kind of things is it doing? I have a hard time imagining an LLM being able to do arbitrary things. It always feels like LLMs can do lots of the easy stuff, but if they can't do everything you need the skilled engineer anyway, who'd knock the easy things out in a week anyway.

What kind of things is it doing?

Wrote me:

- a SPI deserializer that sets a bit after 12 bits read in, to trigger a prefetch

- an SDC constraints file for the deserializer that correctly identified the SPI clock and bus clock as separate domains requiring their own statement

- a test bench that validated both that the prefetch bit was being set, and that it was being set at the proper time relative to the SPI clock

- a makefile with commands for build, headless test, and debug by loading the VCD into a waveform viewer

It always feels like LLMs can do lots of the easy stuff, but if they can't do everything you need the skilled engineer anyway, who'd knock the easy things out in a week anyway.

Nearly every part of the tool flow I just described, I would consider “tricky to get right”. Been doing this for ~15 years and it’s still tough to bootstrap something like this from scratch. ChatGPT-4o did this for me from zero in about 15 minutes.

I won’t lie: I love it. I can focus on the actual, bigger problems at hand, and not the tricky little details of HDLs.

People are either deluding themselves or ignorant of the capabilities of frontier models if they don’t believe LLMs offer a speedup in workflow.

I personally believe that most of the doubt and cynicism is due to:

1) a pretty big collective identity crisis among software professionals, and

2) a suspicion that LLMs make it so that anyone who is good at articulating the problem precisely no longer needs a software engineer as a translation specialist from specs to code.

I say this as an EE of ~15 years who’s always been able to articulate what I want, specifically, to a firmware counterpart, who then writes the code I need. I can turn years of practice in this skill into great prompts for an LLM, which effectively cuts out the middleman.

I really like it. It’s helped me take on a lot of projects that are just outside of my innate level of capability. It’s also helped me learn a lot of new things about these new software adjacent areas. ChatGPT is a great tutor!

Re: Human coders are still better than LLMs

#630

Post by gitaarik »

vidarh wrote: post #621
gitaarik wrote: post #532

Earlier quoted context omitted.

Yes ok, it can generate new stuff, but it's dependent on human curated reward models to score the output to make it usable. So it still depends on human thinking, it's own "thinking" is not sufficient. And there won't be a point when human curated reward models are not needed anymore. LLM's will make a lot of things easier for humans, because most of the thinking the humans do have been automated into the LLM. But ul…

> And there won't be a point when human curated reward models are not needed anymore. This doesn't follow at all. There's no reason why a model can not be made to produce reward models.

But reward models are always curated by humans. If you generate a reward model with an LLM, it will contain hallucinations that need to be corrected by humans. But that is what a reward model is for. To correct the hallucinations of LLMs.

So yeah theoretically you could generate reward models with LLMs, but they won't be any good, unless they are curated by other reward models that are ultimately curated by humans.

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