I’m pretty sure it’s AI written. It has the common AI style of, “That’s not just X! It’s Y!”
Personally I find this annoying. I use AI for my writing but painstakingly try to maintain my own voice rather than lazily edit my prose into LinkedIn-speak.
I would like to encourage you to step back a little and try to get a broader view on this issue.
I don't want to discuss whether there is more LLM-generated content or not. There clearly is, and there is no feasible way to get rid of it , because there is simply no reliable way to distinguish what was made by humans and what wasn't. Regardless of what is claimed, it is just not possible, if only because hybrid forms exist as well. This text was written by me, but reviewed and stylistically adjusted by an LLM.
It is therefore completely pointless to get upset about LLM content and demand anything from the moderators.
All we are left with are our votes and the "reputation" of our user handles - and the awareness that we need to learn to consume content with a great deal of skepticism. We should have been doing this all along, but it seems to be something we struggle with.
On the internet, nobody knows you're a dog.
And yes, this may mean that we stop using anonymous online platforms altogether, because nothing on them can be relied upon anymore. That is a shame, but it cannot be stopped — Pandora's LLM box has been open for at least five years.
I therefore consider any discussion about banning LLM content to be futile. We are witnessing another Eternal September here: it couldn't be stopped back then, and it won't be stopped today either.
Many people in this and other threads around this topic have already stated what they are going to do, which is the inevitable conclusion: move to private chats/discords. I’m sure some amount will remain on IRC or maybe IRC will get a second wind (unlikely). Websites like HN will have to make a choice going forward because now they’re on a trajectory to complete enshittification. Your handle and karma doesn’t matter if the site is dominated by bots. It’s basically a hollow shell of what it used to be and there’s no value to it at a certain point. You’ll be replying to bots more than humans.
There’s plenty left to discuss for humans. But LLMs don’t discuss.
Thank you for your insight and sharing of your perspective. This system leads to some interesting conclusions and observations. One is, that it explains why big brand products made a significant dive in quality. My decades old bose QC25 where of superb quality at 250 € while my somewhat new Bose quiet comfort ultras priced at 350 € are of comparatively very poor quality.
It also opens the market for cheap knockoffs. If some chi-fi headphones for 60 bucks are almost as good as the big brands and the big US brands are forced for high prices despite the bad build quality by Amazon, another big seller website should emerge. Oh wait, this already happened with AliExpress and temu.
Hetzner mostly ate up the rising energy prices in germany for the last 3 years and they have big problems with their hardware supply since then. It is hard to get cloud instances in nbg and fsn. So an increase in pricing is very much expected from my side.
German electricity prices have been falling for the last 3 years. They've been below the pre-war levels for a while now.
Hardware prices, especially with the current chaos, and the huge spike in demand they've doubtless seen is more than enough to explain this price hike though.
I just tried the demo and I think, this is huge! If they manage to build a chip in 2 or 3 years, that can run something like Opus 4.6 or even Sonnet, at that speed, the disruption in the world of software development will be more than we saw in the last 3-5 years.
LLMs today are somewhat useful, but they are still too slow and expensive for a meaningful ralph loop. Being able to runs those loops (or if you want to call it "thinking") much faster, will enable a lot of stuff, that is not feasible today. Writing things like openclaw will not take weeks, but hours. Maybe even rewriting entire tools, kernels or OSes will be feasible because the LLM can run through almost endless tries.
Speed and cost wins over quality and this will also be true for LLMs.
This issue is the main reason why a big percentage of jobs in the world exist. I don't have hard numbers, but my intuition is that about 30% of all jobs are mainly "understand what side a wants and communicate this to side b, so that they understand". Or another perspective: almost all jobs that are called "knowledge work" are like this. Software development is mainly this. Side a are humans, side b is the computer. The main goal of ai seems to get into this space and make a lot of people superflous and this also (partly) explains why everyone is pouring this amount of money into ai.
Developers are - on average - terrible at this. If they weren't, TPMs, Product Managers, CTOs, none of them would need to exist.
It's not specific to software, it's the entire World of business. Most knowledge work is translation from one domain/perspective to another. Not even knowledge work, actually. I've been reading some works by Adler[0] recently, and he makes a strong case for "meaning" only having a sense to humans, and actually each human each having a completely different and isolated "meaning" to even the simplest of things like a piece of stone. If there is difference and nuance to be found when it comes to a rock, what hope have we got when it comes to deep philosophy or the design of complex machines and software?
LLMs are not very good at this right now, but if they became a lot better at, they would a) become more useful and b) the work done to get them there would tell us a lot about human communication.
> Developers are - on average - terrible at this. If they weren't, TPMs, Product Managers, CTOs, none of them would need to exist.
This is not really true, in fact products become worse the farther away from the problem a developer is kept.
Best products I worked with and on (early in my career, before getting digested by big tech) had developers working closely with the users of the software. The worst were things like banking software for branches, where developers were kept as far as possible from the actual domain (and decision making) and driven with endless sterile spec documents.
I disagree, I feel (experienced) developers are excellent at this.
It's always about translating between our own domain and the customer's, and every other new project there's a new domain to get up to speed with in enough detail to understand what to build. What other professions do that?
That's why I'm somewhat scared of AIs - they know like 80% of the domain knowledge in any domain.
The typical job of a CTO is nowhere near "finding out what business needs and translate that into pieces of software". The CTO's job is to maintain an at least remotely coherent tech stack in the grand scheme of things, to develop the technological vision of a company, to anticipate larger shifts in the global tech world and project those onto the locally used stack, constantly distilling that into the next steps to take with the local stack in order to remain competitive in the long run. And of course to communicate all of that to the developers, to set guardrails for the less experienced, to allow and even foster experimentation and improvements by the more experienced.
The typical job of a Product Manager is also not to directly perform this mapping, although the PM is much closer to that activity. PMs mostly need to enforce coherence across an entire product with regard to the ways of mapping business needs to software features that are being developed by individual developers. They still usually involve developers to do the actual mapping, and don't really do it themselves. But the Product Manager must "manage" this process, hence the name, because without anyone coordinating the work of multiple developers, those will quickly construct mappings that may work and make sense individually, but won't fit together into a coherent product.
Developers are indeed the people responsible to find out what business actually wants (which is usually not equal to what they say they want) and map that onto a technical model that can be implemented into a piece of software - or multiple pieces, if we talk about distributed systems. Sometimes they get some help by business analysts, a role very similar to a developer that puts more weight on the business side of things and less on the coding side - but in a lot of team constellations they're also single-handedly responsible for the entire process. Good developers excel at this task and find solutions that really solve the problem at hand (even if they don't exactly follow the requirements or may have to fill up gaps), fit well into an existing solution (even if that means bending some requirements again, or changing parts of the solution), are maintainable in the long run and maximize the chance for them to be extendable in the future when the requirements change. Bad developers just churn out some code that might satisfy some tests, may even roughly do what someone else specified, but fails to be maintainable, impacts other parts of the system negatively, and often fails to actually solve the problem because what business described they needed turned out to once again not be what they actually needed. The problem is that most of these negatives don't show their effects immediately, but only weeks, months or even years later.
LLMs currently are on the level of a bad developer. They can churn out code, but not much more. They fail at the more complex parts of the job, basically all the parts that make "software engineering" an engineering discipline and not just a code generation endeavour, because those parts require adversarial thinking, which is what separates experts from anyone else. The following article was quite an eye-opener for me on this particular topic: https://www.latent.space/p/adversarial-reasoning - I highly suggest anyone working with LLMs to read it.
Try it, your binary will crash at some point, like any other program. If you introduce random bit flips in the trained data, you will maybe get strange responses for some type of query.
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