> I’ll go against the prevailing wisdom and bet that clean code does not matter any more. No more than the exact order of items being placed in main memory matters now.
This is a really funny comment to make when the entire Western economy is propped up by computers doing multiplication of extremely large matrices, which is probably the single most obvious CompSci 101 example of when the placement of data in memory is really, really important.
This speaks more about how easy it is to buy botted vanity metrics on Spotify than anything.
The most obvious way you can tell this is inorganic is how all of the "Discovered On" are artist-specific playlists: "Eddie Dalton music", "Best of Eddie Dalton", "Eddie Dalton Hits", etc. A real artist may have some artist-specific playlists but generally their Discovered On will be more general genre playlists, like "Pop Hits" or "Hype" or "Gym Music" or whatever.
> However, code quality is becoming less and less relevant in the age of AI coding, and to ignore that is to have our heads stuck in the sand. Just because we don't like it doesn't mean it's not true.
Strongly disagree with this thesis, and in fact I'd go completely the opposite: code quality is more important than ever thanks to AI.
LLM-assisted coding is most successful in codebases with attributes strongly associated with high code quality: predictable patterns, well-named variables, use of a type system, no global mutable state, very low mutability in general, etc.
I'm using AI on a pretty shitty legacy area of a Python codebase right now (like, literally right now, Claude is running while I type this) and it's struggling for the same reason a human would struggle. What are the columns in this DataFrame? Who knows, because the dataframe is getting mutated depending on the function calls! Oh yeah and someone thought they could be "clever" and assemble function names via strings and dynamically call them to save a few lines of code, awesome! An LLM is going to struggle deciphering this disasterpiece, same as anyone.
Meanwhile for newer areas of the code with strict typing and a sensible architecture, Claude will usually just one-shot whatever I ask.
edit: I see most replies are saying basically the same thing here, which is an indicator.
> LLM-assisted coding is most successful in codebases with attributes strongly associated with high code quality: predictable patterns, well-named variables, use of a type system, no global mutable state, very low mutability in general, etc.
That's all very true, but what you're missing is that the proportion of codebases that need this is shrinking relative to the total number of codebases. There's an incredible proliferation of very small, bespoke, simple, AI-coded apps, that are nonetheless quite useful. Most are being created by people who have never written a line of code in their life, who will do no maintenance, and who will not give two craps how the code looks, any more than the average YouTuber cares about the aperture of their lens or the average forum commenter care about the style of their prose.
We don't see these apps because we're professional software engineers working on the other stuff. But we're rapidly approaching a world where more and more software is created by non-professionals.
> That's all very true, but what you're missing is that the proportion of codebases that need this is shrinking relative to the total number of codebases. There's an incredible proliferation of very small, bespoke, simple, AI-coded apps, that are nonetheless quite useful. Most are being created by people who have never written a line of code in their life, who will do no maintenance, and who will not give two craps how the code looks, any more than the average YouTuber cares about the aperture of their lens or the average forum commenter care about the style of their prose.
I agree that there will be more small, single-use utilities, but you seem to believe that this will decrease the number or importance of traditional long-lived codebases, which doesn't make sense. The fact that Jane Q. Notadeveloper can vibe code an app for tracking household chores is great, but it does not change the fact that she needs to use her operating system (a massive codebase) to open Google Chrome (a massive codebase) and go to her bank's website (a massive codebase) to transfer money to her landlord for rent (a process which involves many massive software systems interacting with each other, hopefully none of which are vibe coded).
The average YouTuber not caring about the aperture of their lens is an apt comparison: the median YouTube video has 35 views[0]. These people likely do not care about their camera or audio setup, it's true. The question is, how is that relevant to the actual professional YouTubers, MrBeast et al, who actually do care about their AV setup?
This is where I get into much more speculative land, but I think people are underestimating the degree to which AI assistant apps are going to eat much of the traditional software industry. The same way smart phones ate so many individual tools, calculators, stop watches, iPods, etc.
It takes a long time for humanity to adjust to a new technology. First, the technology needs to improve for years. Then it needs to be adopted and reach near ubiquity. And then the slower-moving parts of society need to converge and rearrange around it. For example, the web was quite ready for apps like Airbnb in the mid 90s, but the adoption+culture+infra was not.
In 5, maybe 10, certainly 15 years, I don't think as many people are going to want to learn, browse, and click through a gazillion complex websites and apps and flows when they can easily just tell their assistant to do most of it. Google already correctly realizes this as an existential threat, as do many SaaS companies.
AI assistants are already good enough to create ephemeral applications on the fly in response to certain questions. And we're in the very, very early days of people building businesses and infra meant to be consumed by LLMs.
> In 5, maybe 10, certainly 15 years, I don't think as many people are going to want to learn, browse, and click through a gazillion complex websites and apps and flows when they can easily just tell their assistant to do most of it.
And how do you think their assistant will interact with external systems? If I tell my AI assistant "pay my rent" or "book my flight" do you think it's going to ephemerally vibe code something on the banks' and airlines' servers to make this happen?
You're only thinking of the tip of the iceberg which is the last mile of client-facing software. 90%+ of software development is the rest of the iceberg, unseen beneath the surface.
I agree there will be more of this but again, that does not preclude the existence of more of the big backend systems existing.
I don't think we disagree. We still have big mainframe systems from the 70s and beyond that a powering parts of society. I don't think all current software systems are just going to die or disappear, especially not the big ones. But I do think significant double digit percentages of software engineers are working on other types of software that are at risk of becoming first- or second- or third-order casualties in a world where ephemeral AI assistant-generated software and vibe coded bespoke software becomes increasingly popular.
The thing, everything you describe may be easy for an average person in the future. But just having your single AI agent do all of that will be even easier and that seems like where things will go.
And perhaps they'll get that convenience from an application that they don't even know came into existence because they asked their agent to do something.
I agree entirely with your statement that structure makes things easier for both LLMs and humans, but I'd gently push back on the mutation. Exactly as mutation is fine for humans it also seems to be fine for LLMs in that structured mutation (we know what we can change, where we can change it and to what) works just fine.
Your example with the dataframes is completely unstructured mutation typical of a dynamic language and its sensibilities.
I know from experience that none of the modern models (even cheap ones) have issues dealing with global or near-global state and mutating it, even navigating mutexes/mutices, conds, and so on.
> "vibe coded" is NOT the bad thing you think it is.
It's not inherently bad in the same way that a first draft of a novel is not inherently bad.
But if someone asked me to read their novel and it was a first draft that they themselves had clearly not bothered reading or editing, I'd tell them to fuck off.
At least in the novel example the author had the decency to write what they're asking you to read.
These are more like sending someone who didn't ask you a question a LMGTFY link they didn't ask for and expecting them to read all the results. Just a complete lack of awareness and respect for the maintainers
> assume it is only a matter of time until they will be useful tools in the hands of even the untrained masses.
IMO this vastly overestimates how good the "untrained masses" are at thinking in a logical, mathematical way. Apparently something as basic as Calculus II has a fail rate of ~50% in most universities.
There's nothing "basic" about Calculus II. Calculus is uniquely cursed in mathematical education because everything that comes before it is more or less rooted in intuition about the real world, while calculus is built on axioms that are far more abstract and not substantiated well (not until later in your mathematical education). I expect many intelligent, resourceful people to fail it and I think it says more about the abstractions we're teaching than anything else.
But also, prompting LLMs to give good results is nowhere near as complex as calculus.
If they truly did, there wouldn't be a huge amount of humans whose role is basically "Take what users/executives say they want, and figure out what they REALLY want, then write that down for others".
Maybe I've worked for too many startups, and only consulted for larger companies, but everywhere in businesses I see so many problems that are basically "Others misunderstood what that person meant" and/or "Someone thought they wanted X, they actually wanted Y".
The multi-decade existence of roles like "business analysts" and "product owners" (and sometimes "customer success") is pretty strong evidence that this is not the case.
Most people on here don’t belong to that group of people. So ofc they can find a way to create value out of a thing that requires some tinkering and playing with.
The question is can the techniques evolve to become technologies to produce stuff with minimal effort - whilst only knowing the bare minimum. I’m not convinced personally - it’s a pipe dream and overlooks the innate skill necessary to produce stuff.
> Many men (Not all, but many) are there simply because they want to get laid. They're not looking for a relationship, they're looking for a hook-up, and they're not honest about their intentions.
In fairness, this is not at all exclusive to online dating.
My experience with OKCupid was that women must lie to get laid, moreso than men. A man can state "just want sex" on his profile and it is socially neutral. A woman who posts such a thing has social consequences.
Or starting a job; wanting to advance in the office; become an entrepreneur; wanting to go into politics; wanting to go into the clergy; wanting to become president; wanting to visit islands; wanting run casinos; wanting to run beuaty pagents...
I was really confused how this could be possible for such a seemingly simple site but it looks like it's storing + writing many new commits every time there's a new review, or new financial data, or a new show, etc.
Someone might want to tell the author to ask Claude what a database is typically used for...
json in git for reference data actually isn't terrible. having it with the code isn't great, and the repo is massively bloated in other ways, but for change tracking a source of truth, not bad except for maybe it should be canonicalized.
It's not a terrible storage mechanism but 36,625 workflow runs taking between ~1-12 minutes seems like a terrible use of runner resources. Even at many orgs, constantly actions running for very little benefit has been a challenge. Whether it's wasted dev time or wasted cpu, to say nothing of the horrible security environment that global arbitrary pr action triggers introduce, there's something wrong with Actions as a product.
Molyneux is obviously a well-known gamedev figure, but he's always been much more on the design side than programming side, as opposed to someone like Carmack or even J Blow. I wouldn't take his opinions on minutiae like coroutines as authoritative.
> Beyond the initial 2-3 minutes of novelty, what do AI generated videos really have to offer when there is no shortage of people making professional, high quality content on competing platforms?
I don't know where they got September from; Sora launched in Feb 2024[0] which was a bit before people had become tired of awful AI-generated content. There was real belief that people would be willing to spend all day scrolling a social network with infinite AI-generated content. See the similar hype with Suno AI, which started a whole "musicians are obsolete" movement before becoming mostly irrelevant.
I think Sora 2 produced quite good videos, at least of a certain type. It was very good at producing convincing low-resolution cellphone footage. Unfortunately you had to have a very creative mind to get anything interesting out of it, as the copyright and content restrictions were a big "no fun allowed" clause, which contributed to its demise. Everything on the main Sora page was the same "cute animals doing something wholesome and unexpected" video.
My "favorite" part was how the post-generation checks would self-report. e.g. It was impossible to make a video of an angry chef with a British accent because Sora would always overfit it to Gordon Ramsey, and flag its own generated video after it was created!
> In February 2024, OpenAI previewed examples of its output to the public,[1] with the first generation of Sora released publicly for ChatGPT Plus and ChatGPT Pro users in the US and Canada in December 2024[2][3] and the second generation, Sora 2, was released to select users in the US and Canada at the end of September 2025.
AFAIK both of these use cases had many millions of invested dollars dumped into them during the Blockchain hype and neither resulted in anything. It might not be an exact match for (1), but there was famously the ASX blockchain project[0] which turned out to be a total failure. For (2), IBM made "Farmer Connect"[1], which is now almost entirely scrubbed from their website, which promised to do supply chain logistics on a blockchain.
> ASX blockchain project[0] which turned out to be a total failure.
FWIW if you know anything about the ASX, you'll know that the failure was a result of the people running the ASX and not necessarily the tech behind it.
This is a really funny comment to make when the entire Western economy is propped up by computers doing multiplication of extremely large matrices, which is probably the single most obvious CompSci 101 example of when the placement of data in memory is really, really important.
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