This could have been fine maybe two years ago, but saying that in the era when LLMs are solving Millenium Prize Problems is incredibly obnoxious. All those opinionated "AI articles" feel like ragebait at this point.
This could have been fine maybe two years ago, but saying that in the era when LLMs are solving Millenium Prize Problems is incredibly obnoxious. All those opinionated "AI articles" feel like ragebait at this point.
GPT-6.1 (and Opus 5.5, etc.,) is a solid mid-career pro who can produce outputs very much on par with "good human standards" -- and, in some cases, far better than that.
Whatever in-house dev model they're using at OAI/Anthropic is, by all accounts, legitimately superhuman and capable of solving outstanding technical and mathematical problems with three hours of "Pro thinking."
OP's complaint is foolish, it reads like somebody who has never even used these things and has no idea what their capabilities actually are.
If the LLM makes a mistake it's probably thousands of lines of code. Good luck with that.
“Can produce”. Is doing a lot of heavy lifting there. It can, but then they also regularly write truly bafflingly bad code that no decent experienced dev would ever produce.
I’ll give you a few examples from last week. I had claude whip up a Kafka consumer. After a lot of back and forth I was pretty confident it worked and then I decided to dig into the code. I spotted some real head scratchers.
When starting up the consumer it wanted to pass a specific rebalance strategy, but then it realized that the library it was using didn’t support that strategy, so it needed to use the default.
Instead of just deleting the config option (and maybe leaving a comment), it created a function that removed that key from the map and used it like:
foo: “bar”,
baz: drop_strategy_key(
strategy: “blort”,
bit: “bang”
)
Then I started looking at the tests. They looked good but then I realized it wanted to be able to test without Kafka so what it had done was make multiple public functions that were mostly duplicates of the real functions
except that they didn’t call the Kafka library. Then it just tested those instead.Another frontier model reviewer was supposed to prevent that kind of thing but it didn’t catch it either.
I had 10 public functions that were only called in tests throughout the whole project. Essentially the tests were useless because they weren’t actually testing anything.
Clearly current frontier models are useful. And maybe they produce code that is better than the median dev by many metrics. But I don’t think they are anywhere near consistently producing better quality than a say a 70the percentile dev with 10 years experience.
And even when the quality is better than the median dev, I’m finding that the bugs are not the kind of thing that humans or LLMs are good at spotting.
And actually the article was quite interesting, though I believe it to be wrong.
yep. This is the choice quote.
Even most humans cannot produce work to the highest human standards. The fact is, most work does not need to be done to the highest human standards, it simply needs to be done to the lowest possible standard that someone would pay for, at the cheapest price.
The consumer chooses the cheapest price. Look at sweat shops, look at chinese manufacturing, look at offshoring. All of these produces an inferior product, and yet they are what gets the most profit and overwhelmingly the largest market share of consumer purchases.
yep. This is the choice quote.
Even most humans cannot produce work to the highest human standards. The fact is, most work does not need to be done to the highest human standards, it simply needs to be done to the lowest possible standard that someone would pay for, at the cheapest price.
TFA: The first point is the question of whether an LLM can do work not markedly inferior to that of a human doing the same task or not. This one is debatable. An LLM absolutely cannot produce work to the highest human standards, or even a good human standard at the moment, and it's unlikely that it ever will. Most human work, however, is not done at a good standard. At best it's mediocre, and at worst it's downright horrible. The bulk of human knowledge work done in an awful of settings, then, is unfortunately more or less interchangeable with that of an LLM (seriously, so much human work is just awful: I wish I could say otherwise). On this point, then, LLMs probably pass the criterion.
I'd recommend that you take 15->30 minutes and read the entire essay. It's a good read.Also, I'd correct your opening sentence like so:
> This is the thing: they probably AI "summarized" it.
I've seen many "summaries" that have come out of those lossy compressors... when tasked with compressing inputs of any appreciable complexity, the output is often too poor to serve as a summary. What you get sure is summary-shaped, though.
> Most of the comments in this thread are, unfortunately defending the thing that will make them less useful and less relevant...
If your claim here is that most of the comments so far are astroturfers or similar, ignore the rest of this line. If it isn't, I'd recommend that you read TFA in its entirety... or go back and read it more carefully if you already did. A sizeable section of the essay talks about what I think you're gesturing at.
Wow, this was ejected off of the front page. It's currently sitting at page 10 two hours after posting. Based on what I've seen over the years, carefully-written criticism about the products of the LLM industry rarely does do well here... for whatever reason.
100% in alignment. It almost seems like this is AI boosters looking to set the conversational agenda early to sway any dissent from happening. Real conversations are becoming harder and harder to have.
Eyuuup. Even on The Red Site (which usually has fairly sane commentary... as long as simonw stays out of the conversation thread), the first commenters on the submission about this essay have very obviously either not read the entire essay, or are pointedly ignoring the parts of it that either conflict with or directly address the claims in their comments.
As is sometimes said by idiot Twitch streamers: Fuck my chungus life, I guess.
Always these baseless accusations. I've been here almost as long as you have and find it extremely offensive, just because I'm rebuking a flawed argument with basic facts. There's no conspiracy and you're breaking the HN rules by insinuating it.
I agree!
> ...and you're breaking the HN rules by insinuating it.
It's a good thing that I'm not, then.
> ...just because I'm rebuking a flawed argument with basic facts.
cough
I wish people who felt this way would just go use the tools they hate so much before passing judgement. I think many of them used it once, in a much worse form, many years ago and not since. Judging these things by the standards of 3 months ago is unfair, let alone the standards of 3 years ago.
Many people have incredible ability to ignore evidence.
I'll include this excerpt from the essay as an attempt to shame folks who feel the urge to continue to claim that the author is critical of "AI" because they hate the tech and never ever have anything good to say about it:
> I will say that Claude Code is a noticeable step change over the Opencode-based GLM models that I tried a few months ago. Those were bad enough to not be usable for anything serious or of real complexity. Claude Code, by contrast, can do some relatively complex things: it was able to perform the bulk of my website migration from Nuxt 3 to Nuxt 4 and to the newest version of Nuxt-Content for instance, which the GLM models had utterly barfed on. It wasn't perfect: I had to do quite a bit of tinkering after the fact to make things work, but unlike the GLM attempt, Claude did actually save me some time. Claude also did pretty well with adding Schema.org markup to my website and performing a number of other stupid-but-necessary SEO-related tasks on the same website: none of these tasks are really groundbreaking, but they're the kind of relatively complex pain-in-the-ass task that you'd hope we could automate: at this point in my practice, there's little of value that I can glean from doing those tasks by hand. If it's possible to make this kind of tool accessible to people at an acceptable, sustainable cost and without burning too many resources, then, I think that'd be a good thing.
> Claude Code also does quite a bit better than Opencode tools did at CI/CD tasks, IaC in OpenTofu, building Dockerfiles and that kind of task. It's still something I'm quite iffy on, but unlike the GLM models I tried earlier, what Claude Code produces basically works, at least initially. This probably makes it a lot more immediately useful than the GLM models were, as this kind of task is a large part of what tech workers actually do day-to-day, and it's the kind of task that a software engineer might have to perform while not having a very good grasp of the underlying technology.
I recommend folks take 15->30 minutes to actually read the essay, rather than either skimming it or having some LLM "summarize" it. There's a lot of subtlety and nuance that the current generation of lossy compressors is incapable of reliably compacting.
[0] <https://deadsimpletech.com/blog/engineering-judgement-claude...>
This is not true, and never will be again. It's also, as the author says, the first point they make.
LLM's meet, and often exceed, human standards for a great many tasks and the percentage is growing every day. The statement is either ludicrously out of date, or just biased nonsense.
Regardless, given that it is the first point they make and foundational to their argument, I do not need to know more.
I'll quote you the rest of that paragraph that you -somehow- failed to read:
The first point is the question of whether an LLM can do work not markedly inferior to that of a human doing the same task or not. This one is debatable. An LLM absolutely cannot produce work to the highest human standards, or even a good human standard at the moment, and it's unlikely that it ever will. Most human work, however, is not done at a good standard. At best it's mediocre, and at worst it's downright horrible. The bulk of human knowledge work done in an awful of settings, then, is unfortunately more or less interchangeable with that of an LLM (seriously, so much human work is just awful: I wish I could say otherwise). On this point, then, LLMs probably pass the criterion.
Amusingly, you've made the same error made by this fellow over here. [0]Regardless, I'd recommend that you take 15->30 minutes and read the entire essay. It's a good read.
For analysis that justifies the claim that "[a]n LLM absolutely cannot produce work to the highest human standards, or even a good human standard at the moment, and it's unlikely that it ever will", check out [1]. In order to understand that essay, you will need to read it carefully. If you skim that essay or have a lossy compressor "summarize" it, you will not understand it.
If you're prepping a "The author just hates LLMs and has never ever used one, let alone used one seriously... they're just a hater!" retort, carefully read [2]. That essay also provides further support to their claim that LLM-based tools don't currently produce good work.
[0] <https://news.ycombinator.com/item?id=50032485>
[1] <https://deadsimpletech.com/blog/llms-on-the-boundary>
[2] <https://deadsimpletech.com/blog/engineering-judgement-claude...>
I read it, it's irrelevant.
Author: A = B. That doesn't matter though because of C.
Me: A != B. Yes, C still doesn't matter... but A != B.
C is irrelevant to both the author and myself. The point of contention is that A does not in fact equal B. Everything that is derived from that P is also not true.I do not need to carefully read an article where the entire premise flies in the face of my own lived experience. LLM's can, are, and will continue to be better than the best humans at many tasks. The number of tasks increases every day.
I'm sorry if you don't like it, or if it doesn't seem logical to you. It's just the empirically observed reality of the world you live in now.
LLM's are better at some kinds of math, some kinds of bug hunting, some kinds or transcription, some kinds of... everything. It's a simple, testable, objective, and empirical truth.
At the end of the day, the questions here are almost axiomatic: does human cognition bring anything to the table that an LLM does not, and; is there value in manually doing work that an LLM can also do?
And a secondary question: has the purportedly insane productivity boost from LLMs produced anything of actual value for end users? Or does it largely serve as an accelerant for spam and enshittification, along with a way for bosses to squeeze ever more work out of their burnt-out employees?
My take: sometimes a technology can be both incredibly powerful and a massive impediment to human development, if it gets us well and truly stuck in a local minimum.
So it's not really about trying it out, I think. It's something more emotional.