> Video games stand out as one market where consumers have pushed back effectively
No, it's simply untrue. Players only object against AI art assets. And only when they're painfully obvious. No one cares about how the code is written.
If you actually read the words used in Steam AI survey you'll know Steam has completely caved in for AI-gen code as well. It's specifically worded like this:
> content such as artwork, sound, narrative, localization, etc.
No 'code' or 'programming.'
If game players are the most anti-AI group then it's crystal clear that LLM coding is inevitable.
> This stands in stark contrast to code, which generally doesn't suffer from re-use at all, or may even benefit from it, if it's infrastructure.
Yeah, exactly. And LLM help developers save time from writing the same thing that has be done by other developers for a thousand times. I don't know how one can spins this as a bad thing.
> Classic procedural generation is noteworthy here as a precedent, which gamers were already familiar with, because by and large it has failed to deliver.
Spore is well acclaimed. Minecraft is literally the most sold game ever. The fact one developer fumbled it doesn't make the idea of procedural generation bad. This is a perfect example of that a tool isn't inherently good or bad. It's up to the tool's wielder.
> Classic procedural generation is noteworthy here as a precedent, which gamers were already familiar with, because by and large it has failed to deliver.
Yes, this is a wildly uneducated perspective.
Procedural generation has often been a key component of some incredibly successful, and even iconic games going back decades. Elite is a canonical example here, with its galaxies being procedurally generated. Powermonger, from Bulldog, likewise used fractal generation for its maps.
More recently, the prevalence of procedurally generated rogue-likes and Metroidvanias is another point against. Granted, people have got a bit bored of these now, but that's because there were so many of them, not because they were unsuccessful or "failed to deliver".
Procedural generation underlies the most popular game of all time (Minecraft) and is foundational for numerous other games of a similar type - Dwarf Fortress, et al.
And it's used to power effect where you might not expect it (Stardew Valley mines).
What procedural generation does NOT work at is generating "story elements" though perhaps even that can fall, Dwarf Fortress already does decently enough given that the player will fill in the blanks.
> And it's used to power effect where you might not expect it (Stardew Valley mines).
Apparently Stardew Valley's mines are not procedurally generated, but rather hand-crafted. Per their recent 10 year anniversary video, the developer did try to implement procedural generation for the mines, but ended up scrapping it:
That’s not the same procedural generation as GPT or diffusion and you know it.
It’s not even in the same ballpark as Elite, NMS, terraria, or Minecraft.
The levels are all hand drawn, not generated by an algorithm, even if they’re shuffled. Eric Barone, the developer, has publicly said as much. Are you calling him a liar?
It’s like the difference between sudoku/crossword and conways game of life
It looks like the Super Mario Bros series has a good showing, but it is the first one. I bet 3 falls into an unlucky valley where the game-playing population was not quite as large as it is now, but it isn’t early enough to get the extreme nostalgia of the first one.
Almost every 3D game in the past 20 years uses procedural foliage generation (eg SpeedTree and similar). Many use procedural terrain painting. Many use tools like Houdini.
So procedural generation is extremely prevalent in most AAA games and has been for a long time.
Roguelike/lites are is of the most popular genres of indie games nowadays. One of it's main characteristics is randomization and procedural generation.
While there are many Roguelikes with procedural generation, I think the most popular ones do not. Slay the Spire, Risk of Rain 2, Hades 1/2, BoE etc are all handmade stages with a random order with randomized player powers rather than procedurally generated.
I've seen a couple roguelike developers report that they played around with procedural generation, but it was difficult to prevent it from creating dungeons that were bad, unfun, or just straight-up killscreens. Turns out it's often easier to simply hand-draw good maps than to get the machine to generate okay-to-good ones.
Procedural generation is good when variety matters more than quality, which is a relatively rare occurrence.
That says more about the developer than procedural generation as a whole. Using procedural generation IS difficult, it requires understanding how to set up constraints on your p-random generated elements and ensuring the code validates that you have a "good" level/puzzle/whatever before dumping the PC into it.
I’m a hard core rogue-like player (easily over a thousand hours at least in all the games I’ve played) but even so I can admit that hey have nothing compared to a well crafted world like you’d find in From Software titles or Expedition 33, or classic Zelda games for that matter.
Making a great world is an incredibly hard task though and few studios have the capabilities to do so.
Rogue-like games use the most simple randomisation to generate the next room, and I burnt hundreds of hours in Mines of Moria before I forced myself to quit.
Now with an LLM I could have AD&D-like campaigns, photorealistic renders of my character and the NPCs. I could give it the text of an AD&D campaign as a DM and have it generate walking and talking NOCs.
The art of those great fantasy artists is definitely being stolen in generated images, and application of VLMs should require payment into some sort of art funding pool. But modern artists could well profit by being the intermediary between user and VLM, crafting prompts, both visual and textual, to give a consistent look and feel to a game.
It’s a different type of thing, really. I like rogue-likes because they are a… pretty basic… story about my character, rather than a perfectly crafted story about somebody else’s.
Even when I play a game like Expedition 33 or Elden Ring, my brain (for whatever reason) makes a solid split between the cutscene versions of the characters and the gameplay version. I mean, in some games the gameplay characters is a wandering murderer, while the cutscene characters have all sorts of moral compunctions about killing the big-bad. They are clearly different dudes.
Is it wildly uneducated to not know any of the games you mentioned? I didn’t realize education covered less known video games? Wouldn’t a better example be No Man’s Sky, if we’re talking procedural gen and eventually a good game.
In any case, I agree that gamers by and large don’t care to what extent the game creation was automated. They are happy to use automated enemies, automated allies, automated armies and pre-made cut scenes. Why would they stop short at automated code gen? I genuinely think 90% wouldn’t mind if humans are still in the loop but the product overall is better.
> Is it wildly uneducated to not know any of the games you mentioned? I didn’t realize education covered less known video games?
Yes. It is "wildly uneducated" to have, and express, strong opinions about ANY field of endeavour where you are unfamiliar with large parts of that field.
If you haven't heard of the modern roguelike genre you've probably been living under a rock, it seems like every other game these days at least calls itself such. Usually the resemblance to Rogue is so remote that it strains the meaning of the term, but procedural generation of levels is almost universal in this loosely defined genre.
Elite is a bit more obscure, but really anybody who aims to be familiar with the history of games should recognize the name at least. Metroidvania isn't a game, but is a combination of the names of Metroid and Castlevania and you absolutely should know about both of those.
Powermonger is new to me.
And while the comment in question didn't mention it, others have: Minecraft. If you're not familiar with Minecraft you must be Rip Van Winkle. This should be the foremost game that comes to mind when anybody talks about procedural generation.
No, it's simply untrue. Players only object against AI art assets. And only when they're painfully obvious. No one cares about how the code is written.
This reminded me of a conversation about AI I had with an artist last year. She was furious and cursing and saying how awful it is for stealing from artists, but then admitted she uses it for writing descriptions and marketing posts to sell her art.
The world makes waay more sense when you really internalize that. It doesn't necessarily mean people are selfish, large groups often have aligned interests, but when an individuals interest alignment changes, then their group membership almost always changes too.
I'd bet she has a bunch of pirated content and anti-copyright remarks from the golden age of piracy as well.
If she's a practicing artist, she almost certainly cut her teeth doing tracing at some point. And if a digital artist, she almost certainly used a cracked copy of a tool.
The big eye-opener for me in college was taking a class that put me up-close with artists and learning that there were, in the whole class, a grand total of two students who hadn't started doing 3D modeling on a cracked copy of Maya (and the two, if memory serves, learned on Blender).
Which I would point out isn’t necessarily hypocrisy on their part.
I can rage against guns and gun manufacturers for their negative effects on our nation and hate when they are used for monstrous evil, but also believe that police should have firearms and that the second amendment is important.
It’s a tool. You can hate the way it’s made and marketed, and hate many of its popular use cases, and still think there are acceptable ways to use and market it without requiring a total abolition.
[1]: Disclaimer: I deeply respect Sinix as an art educator. If it weren't him I wouldn't have learnt digital painting. But it's still quite a weird take of him.
> Yeah, exactly. And LLM help developers save time from writing the same thing that has be done by other developers for a thousand times.
Before LLMs we did already have a way to "save developers time from writing the same thing that has been done by other developers for a thousand times", you know? A LLM doing the same thing the 1001st time is not code reuse. Code reuse is code reuse.
Because code reuse is hard. Like, really hard. If it weren't we wouldn't be laughing at left-pad. If it weren't hard we wouldn't have so many front-end JavaScript frameworks. If it weren't Unreal wouldn't still have their own GC and std-like implementation today. Java wouldn't have been reinventing build system every five years.
The whole history of programming tool is exploring how to properly reuse code: are functions or objects the fundamental unit of reuse? is diamond inheritance okay? should a language have an official package management? build system? should C++ std have network support? how about gui support? should editors implement their own parsers or rely on language server? And none of these questions has a clear answer after thousands if not millions of smart people attempted. (well perhaps except the function vs object one)
Electron is the ultimate effort of code reuse: we reuse the tens of thousands of human-years invested to make a markup-based render engine that covers 99% of use case. And everyone complains about it, the author of OP article included.
LLM-coding is not code reuse. It's more like throwing hands up and admitting humans are yet not smart enough to properly reuse code except for some well-defined low level cases like compiling C into different ISA. And I'm all for that.
I think you could also argue that LLMs in coding are actually just a novel approach at code reuse: At the microscopic level, they excel at replicating known patterns in a new context.
(Many small dependencies can be avoided by letting the LLM just re-implememt the desired behavior; ~ with tradeoffs, of course)
The issue is orchestrating this local reuse into a coherent global codebase.
The problems with leftpad are a problem with the NPM ecosystem, not with code reuse as such. There are other dependency ecosystems that don't have these problems.
If this is what I think it is, I consider it very lopsided view, failure to recognize what model fits for what case and looking at everything from a hammer point of view
I think function is the fundamental unit and object is an extra level over it (it doesn't mean there is no use for object). Thinking objects/classes are the fundamental/minimal level is straight up wrong.
My opinion: Fundamental levels are data and operations (your functions). Not my view that class is a foundation. It is a representation convenient for some cases and not so much for other
Hard agree. Before LLMs, if there was some bit of code needed across the industry, somebody would put the effort into writing a library and we'd all benefit. Now, instead of standardizing and working together we get a million slightly different incompatible piles of stochastic slop.
I was talking about libraries, higher-level units of reuse than individual functions. And your "syntactic" vs "semantic" reuse makes zero sense. Functions are literally written and invoked for their semantics – what they make happen. "Syntactic reuse" would be macros if anything, and indeed macros are very good at reducing boilerplate.
You might have a more compelling argument if instead of syntax and semantics you contrasted semantics and pragmatics.
A library is a collection of data structures functions. My argument still holds.
> Syntactic reuse would be macros
Well sure. My point is that what can be reused is decided ahead of time and encoded in the syntax. Whereas with LLMs it is not, and is encoded in the semantics.
> Pragmatics
Didn't know what that is. Consider my post updated with the better terms.
I’m not sure your logic is sound. It sounds like you are insisting on some nuance which simply isn’t there. LLM generates unmaintainable slop, which is extremely difficult to reason about, uses wrong abstractions, violates DRY, violates cohesion, etc.
The industry has known how to reuse codes for two decades now (npm was released 16 years ago; pip 18 years ago). Using LLMs for code reuse is a step in the wrong direction, at least if you care about maintaining your code.
Oh sure the quality is extremely unreliable and I am not a fan of its style of coding either. Requires quite a bit of hand holding and sometimes it truly enrages me. I am just saying that LLM technology opens up another dimension of code reuse which is broader. Still a ways to go, not in the foundation model, those have plateaued, but in refining them for coding.
You're cherry picking. The open world games aren't as compelling anymore since the novelty is wearing off. I can cherry pick, too. For example, Starfield in all its grandeur is pretty boring.
And the users may not care about code directly, but they definitely do indirectly. The less optimized and more off-the-shelf solutions have seen a stark decrease in performance but allowing game development to be more approachable.
LLMs saving engineers and developers time is an unfounded claim because immediate results does not mean net positive. Actually, I'd argue that any software engineer worth their salt knows intimately that more immediate results is usually at the expense of long term sustainability.
Startfield is boring because of the bad writing and they made a space exploration game where there are loading screens between the planet and space and you don’t actually explore space.
They fundamentally misunderstood what they were promising, it’s the same as making a pirate game where you never steer the ship or drop anchor.
You can prove people are not bored with the concept as new gamers still start playing fallout new Vegas or skyrim today despite them being old and janky.
I think my point stands. Procedural generation is a tool that usually works best when it is supplementary. What makes New Vegas an amazing game is all the hand built narratives and intricate storylines. So yeah, I agree, Starfield is boring because of the story. But if the procedural vastness was interesting enough to not be boring, then we wouldn't be talking about this to begin with.
> ny software engineer worth their salt knows intimately that more immediate results is usually at the expense of long term sustainability.
And any software engineer worth their salt realizes there are 100s if not 1000s of problems to be solved and trying to paint a broad picture of development is naive. You have only seen 1% (at best) of the current software development field and yet you're confidently saying that a tool that is being used by a large part of it isn't actually useful. You'd have to have a massive ego to be able to categorically tell thousands of other people that what they're doing is both wrong and not useful and that they things they are seeing aren't actually true.
No Man's Sky got better as they were more intentional with their content. The game has more substance and a lot of that had to be added by hand. It is dropped in procedurally but they had to touch it up, manually, to make it interesting. Let's not revise history.
I don't think it has anything to do with ego. There are studies on the topic of AI and productivity and I assume we have a way to go before we can say anything concretely. Software workflows permeate the industry you're in. You're putting words in my mouth, I said nothing about what people are doing is wrong or not useful. I said the claim that generative AI is making engineers more productive is an unfounded one. What code you shit out isn't where the work starts or ends. Using expedient solutions and having to face potentially more work in the future isn't even something that is a claim about software, I can make that claim about life.
You need to evaluate what you read rather than putting your own twist on what I've said.
Also "AI" has been in gaming, especially mobile gaming, for a literal decade already.
Household name game studios have had custom AI art asset tooling for a long time that can create art quickly, using their specific style.
AI is a tool and as Steve Jobs said, you can hold it wrong. It's like plastic surgery, you only notice the bad ones and object to them. An expert might detect the better jobs, but the regular folk don't know and for the most part don't care unless someone else tells them to care.
Another example is upscaled texture mods, which has been a trend for a long while before 'large language' took off as a trend. Mods to improve textures in a game are definitely not new and that probably means including from other sources, but the ability to automate/industrialize that (and presumably a lot of training material available) meant there was a big wave of that mod category a few years back. My impression is that gamers will overlook a lot so long as it's 'free' or at least are very anti-business (even if the industry they enjoy relies upon it), the moment money is involved they suddenly care a lot about the whole fabric being hand made and need verification that everyone involved was handsomely rewarded.
The issue isn't objective quality or realism, it's sticking to a specific style consistently.
_Everyone_ (and their grandmother) can instantly tell a ChatGPT generated image, it has a very distinct style - and in my experience no amount of prompting will make it go away. Same for Grok and to a smaller degree Google's stuff.
What the industry needs (and uses) is something they can feed a, say, wall texture into and the AI workflow will produce a summer, winter and fall variant of that - in the exact style the specific game is using.
I think that's a different category, though. Those backgrounds are actual video recordings of real places, not 3D environments modeled from scratch. It looks 'real' because the background actually exists.
Your case would have been better if you had used Mad Max: Fury Road, or even Titanic as examples, rather then a mediocre TV show nobody remembers. Ugly Betty used green screens to make production cheaper, that did not improve the show (although it may have improved the profit margins). Mad Max: Fury Road on the other hand used CGI to significantly improve the visual experience. The added CGI probably increased the cost of the production, and subsequently it is one of the greatest, most awesome, movie ever made.
Actually if you look at the scene from Greys Anatomy [0:54] you can see where CGI is used to improve the scene (rather then cut costs), and you get this amazing scene of the Washington State Ferry crash.
I think you can see the parallels here. When people say they hate AI they are generally referring to the sloppy stuff it generates. It has enabled a proliferation of cheap slop. And with few exception it seems like generating cheap slop is all it does (these exception being specialized tools e.g. in image processing software).
Award winning shows and movies does not exclude forgettable cash grabs.
However, my counter examples included Grey’s Anatomy, Mad Max, and Titanic. None of these are considered high literature exactly (and all of them are award winning as well).
If you read the next couple of paragraphs, the author addresses this:
> That said, Steam's policy has been recently updated to exclude dev tools used for "efficiency gains", but which are not used to generate content presented to players.
I only quoted the first paragraph, but there is more.
Its creature creator was, but as a game it was always mediocre to bad. They had to drop something like 90% of the features and extremely dumb down the stages to get it released.
It was also what introduced a lot of us to SecuROM DRM - it bricked my laptop in the middle of a semester.
One the topic procedural generation; rogue likes are all about it and new generation Diablo like games have definitely similar things, well respected new games like Blue Prince. There has never been such as successful period of time for procedural generation in games like now, and all of these are pre-AI. AI powered procedural generation is wet dream of rogue-like lovers
I love procedural generation, and there is definitely a craft to it. Creating a process that generates a playable level or world is just very interesting to explore as an emergent system. I don't think LLMs will make these system more interesting by default. Of course there are still things to explore in this new space.
It's similar to generative/plotter art compared to a midjourney piece of slop. The craft that goes into creating the code for the plotter is what makes it interesting.
The key to non-disruptive LLM integration is using it in a purely additive way, supplementing a feature with functionality that couldn't be done before rather than replacing an existing part. Like adding ai generated images to accompany the dwarf fortress artifact descriptions. It could completely togglable and doesn't disrupt any existing mechanics, but would provide value to those that don't mind the slop.
Games with ai art assets are some of the most popular right now in any case. Arc raiders being a great example where some of the voice assets are AI generated.
Be careful of reading any viewpoint on the internet. Apparently no one used facebook or instagram and everyone boycotts anything with ai in it.
In reality i think you’d be foolish not to make use of the tools available. Arc Raiders did the right thing by completely ignoring those sorts of comments. There may be a market for 100% organic video games but there’s also a market for mainstream ‘uses every ai tool available’ type of games.
No one even cares how architecture is done. Unless you are the one fixing it or maintaining it.
Sorry, no one. We all know Apple did some great stuff with their code, but we care more about the awful work done on the UI, right? I mean - the UI seems to not be breaking in these new OSs which is amazing feature... for a game perhaps, and most likely the code is top notch. But we care about other things.
This is the reality, and the blind notion that so-many people care about code is super untrue. Perhaps someone putting money on developers care, but we have so many examples already of money put on implementation no matter what the code is. We can see everywhere funds thrown at obnoxious implementations, and particularly in large enterprises, that are only sustained by the weird ecosystem of white-collar jobs that sustains this impression.
Very few people care about the code in total, and this can be observed very easy, perhaps it can be proved no other way around is possible.
This is overstating it. Computers are amazing machines, and modern operating systems are also amazing. But even they cannot completely mask the downstream effects of poor quality code.
You say you don't care, but I bet you do when you're dealing with a problem caused by poor code quality or bad choices made by the developer.
Yep willing to bet that the majority of people saying "users don't care how well the code is written" will crash out when some software they're using is slow and buggy, even more extremely if it glitches and deletes their work.
Just like how most people don't care how well a bridge is designed... until it collapses.
Also RE: procgen, one of the hit games right now, Mewgenics, is doing super well and uses it extensively. Obviously it's old school procgen that makes use of tons of authored content, but it's still procgen.
An LLM has never saved me time. It has always produced something that doesn't quite work, has the rough shape of what I want, but somehow always gets all the details wrong.
I can type up what I want much faster and be sure it's at least solving the right problem, even if it may have bugs.
There are also tools to generate boilerplate that work much much better than LLMs. And they're deterministic.
If you do not plan out the architecture soundly, no amount of prompting will fix it if it is bad. I know this because my "handmade" project made with backward compatibility and horrible architecture keeps being badly fixed by LLM while the ones that rely on preemptive planning of the features and architecture, end up working right.
I think that's true, but something even more subtle is going on. The quality of the LLM output depends on how it was prompted in a way more profound than I think most people realize. If you prompt the LLM using jargon and lingo that indicate you are already well experienced with the domain space, the LLM will rollplay an experienced developer. If you prompt it like you're a clueless PHB who's never coded, the LLM will output shitty code to match the style of your prompt. This extends to architecture, if your prompts are written with a mature understanding of the architecture that should be used, the LLM will follow suit, but if not then the LLM will just slap together something that looks like it might work, but isn't well thought out.
I don't care if the machine has a soul, I only care what the machine can produce. With good prompting, the machine produces more ""thoughtful"" results. As an engineer, that's all I care about.
It is magical thinking to claim that LLMs are definitely physically incapable of thinking. You don't know that. No one knows that, since such large neural networks are opaque blackboxes that resist interpretation and we don't really know how they function internally.
You are just repeating that because you read that before somewhere else. Like a stochastic parrot. Quite ironic. ;)
You're going to get a lot of "skill issue" comments but your experience basically matches mine. I've only found LLMs to be useful for quick demos where I explicitly didn't care about the quality of implementation. For my core responsibility it has never met my quality bar and after getting it there has not saved me time. What I'm learning is different people and domains have very different standards for that.
> An LLM has never saved me time. It has always produced something that doesn't quite work, has the rough shape of what I want, but somehow always gets all the details wrong.
This reads like a skill issue on your end, in part at least in the prompting side.
It does take time to reach a point where you can prompt an LLM sufficiently well to get a correct answer in one shot, developing an intuitive understanding of what absolutely needs to be written out and what can be inferred by the model.
I’m curious about how you landed “git gud; prompt better” and not “maybe the domain I work in is a better fit for LLM code”. Or, to be a bit less generous, consider the possibility that the code you’re generating is boilerplate, marshaling, and/or API calls. A facade of perceived complexity over something that’s as complex as a filter-map or two.
In the past 2 months I've been using all the SOTA models to help me design a new DSL for narrative scripting (such as game story telling) and a c# runtime implementation o the script player engine.
The language spec and design is about 95% authored by me up to this point; I have the LLMs work on the 2nd layer: the implementation specs/guidelines and the 3rd layer: concrete c# implementation.
Since it's a new language, I consider it's somewhat new/novel tasks for LLMs (at least, not like boilerplate stuff like HTTP API or CRUD service). I'd say, these LLMs have been very helpful - you can tell they sometimes get confused and have trouble to comply to the foreign language spec and design - but they are mostly smart enough to carry out the objectives, and they get better and better after the project got on track and has plenty of files/resources to read and reference.
And I'd also say "prompt better" is a important factor, just much more nuanced/complicated. I started with 0 experience with LLM agents and have learned a lot about how to tame them, and developed a protocol to collaborate with agents, these all comes from countless trial and errors, but in the end get boiled down to "prompt better".
I wonder if my intuition here is correct; I would posit that “PL implementation” is a far more popular and well-explored field than it seems. How many toy/small/labor-of-love langs make it to Show HN? How many more simply don’t?
I’ve never personally caught the language implementation bug. I appreciate your perspective here.
I totally agree, and I was fully aware of how common people make language for fun when I replied.
But I feel like the rationale would still stands: Considering LLMs' natures, common boilerplate tasks are easy because they can kind of just "decompress" from training data. But for a new language design, unless the language is almost identical to some other captured by the model, "decompression" would just fail.
As someone who has implemented a fair few DSLs, lexical and syntactic analysis is pretty much the same anywhere, and the structure of the lexer/parser does not really depend on the grammar of the language.
And even semantic analysis is at least very similar in most PLs. Even DSLs. Assuming you're using concepts like variables and functions.
When it comes to codegen / interpreter runtimes, things start to diverge. But this also depends on the use case. More often than not a DSL is a one-to-one map to an existing language, with syntactic sugar on top.
I am prompting better. It doesn't help the LLM be more productive than me on a regular tuesday.
Sure, I can get the task done by delegating everything to an agentic workflow, but it just adds a bunch of useless overhead to my work.
I still need to know what the code does at the end of the day, so I can document it and reason about it. If I write the code myself, it's easy. If an LLM does it, it's a chore.
And even without those concerns, the LLM is still slower than me. Unless it's trivial boilerplate, in which case other tools serve me better and cheaper.
I'll note that a compiler is one of the most well understood and implemented software projects, much of it open source, which means the LLM has a lot of prior art that it can copy.
When web search first arrived, the same thing happened. That is, some people didn't like using the tool because it wasn't finding what they wanted. This is still true for a lot of folks today, actually.
It's less "git gud; prompt better", and more, "be able to explain (well) what you want as the output". If someone messages the IT guy and says "hey my computer is broken" - what sort of helpful information can the IT guy offer beyond "turn it on and off again"?
So how do you rectify your anecdotal experience against those made by public figures in the industry who we can all agree are at least pretty good engineers? I think that's important because if we want to stay ~anonymous, neither you nor I can verify the reputation of one another (and therefore, one another's relative master of the "Craft").
Here are some well known names who are now saying they regularly use LLM's for development. For many of these folks, that wasn't true 1-2 years ago:
My point being - some random guy on the internet says LLM's have never been useful for them and they only output garbage vs. some of the best engineers in the field using the same tools, and saying the exact opposite of what you are.
> I’m curious about how you landed “git gud; prompt better” and not “maybe the domain I work in is a better fit for LLM code”.
1. Personal experience. Lazy prompting vs careful prompting.
2. They're coincidentally good at things I'm good at, and shit at things I don't understand.
3. Following from 2, when used by somebody who does understand a problem space which I do not, they easily succeed. That dog vibe coding games succeeded in getting claude to write games because his master knew a thing or two about it. I on the other hand have no game Dev experience, even almost no hobby experience with games specifically, so I struggle to get any game code that even remotely works.
Irrespective of the domain you specifically listed in 3 (game dev is, believe it or not, one of the “more complex” domains), you have completely failed to miss the point.
> 2. They're coincidentally good at things I'm good at, and shit at things I don't understand.
This may well be! In the perfect world this would be balanced with the knowledge that maybe “the things you’re good at” are objectively* easier than “things you don’t understand”. Speaking for myself, I’m proficient in many more easy things than hard things.
I have definitely considered the possibility that I'm simply good at easy things and the LLM is good at easy things, and that hard things are hard for both of us. And there certainly must be some element of that going on, but I keep noticing that different people get different quality results for the same kind of problems, and it seems to line up with how good they themselves would be at that task. If you know the problem space well, you can describe the problem (and approaches to it) with a precision that people unfamiliar with the problem space will struggle with.
I think you can observe this in action by making vague requests, seeing how it does, then roll back that work and make a more precise request using relevant jargon and compare the results. For example, I asked claude to make a system that recommends files with similar tags. It gave me a recommender that just orders files by how many tags they had in common with the query file. This is the kind of solution that somebody may think up quick but it doesn't actually work great in practice. Then I reverted all of that and instead specified that it should use a vector space model with cosine similarity. It did pretty good but there was something subtly off. That is however about the limit of my expertise in this direction, so I tabbed over to a session with ChatGPT and discussed the problem on a high level for about 20 minutes, then asked ChatGPT to write up a single terse technically precise paragraph describing the problem. I told ChatGPT to use no bullet points and write no psuedocode, telling it the coding agent was already an expert in the codebase so let it worry about the coding. I give that paragraph to claude and suddenly it clicks, it bangs out a working solution without any drama. So I conclude the quality of the prompting determined the quality of the results.
The parent is specifically talking about producing boilerplate code -a domain in which LLM excell at- and not having had any success at that. It's therefore not a leap of logic to assume they haven't put (enough) effort into getting better at prompting first, which is perfectly fine per se but leans towards a skill issue and not an immutable property of gen AI.
The uncomfortable fact remains that one cannot really expect to get much better results from an LLM without putting some work themselves. They aren't magical oracles.
> Players only object against AI art assets. And only when they're painfully obvious.
Restaurant-goers only object against you spitting in their food if it's painfully obvious (i.e. they see you do it, or they taste it)
Players are buying your art. They are valuing it based on how you say you made it. They came down hard on asset-flipping shovelware before the rise of AI (where someone else made the art and you just shoved it together... and the combination didn't add up to much) and they come down hard on AI slop today, especially if you don't disclose it and you get caught.
The more nuanced take is that, if somehow your game is actually good or interesting despite being full of other people's assets, players will see the value that you created (e.g. making a fun game). This is missing in most "asset-flip" games.
Another example comes from Getting Over It with Bennett Foddy, which despite the fact it uses a lot of pre-bought art assets, the entire game has the indisputable hallmark of Bennett Foddy -- it has a ridiculously tricky control mechanism, and the whole game world you play in, should you make any mistakes, has a strong likelyhood of dropping you right back at the start, and it's all your own fault for not being able to recover from your mistakes under pressure. You can see this theme in his other games like QWOP and Baby Steps
At least to some extent, the anti-ai folks don't care about ai assisted programming because they see programmers as the "techbro" boogieman pushing ai into their lives, not fellow creatives who are also at a crossroads.
Tbf Spore's acclaim comes with the caveat that it completely failed to live up to years of pre-release hype. Much of the goodwill it's garnered since, which is reflected in review scores, only came after the storm of controversy over Spore not being "the ultimate simulator which would mark the 'end of history' for gaming" died down.
And you wouldn't really have any idea this was the case if you weren't there when it happened.
It’s bad at it. At least, it can’t be guaranteed to get nuance or context correct in a way that doesn’t feel artificial to a fluent speaker.
My favourite example I saw was where Google translated an information page of the Italian branch of a large multinational as “this is the UK branch of [multinational]”, presumably because the LLM thought that was more contextually appropriate in English.
I guess the chain of reasoning would be: AI for art is bad -> Writing is art -> Translation is writing.
Personally, I do appreciate good localisation, Nintendo usually does a pretty impressive job there. I play games in their original language as long as I actually speak that language, so I don't have too many touch points with translations though.
In case they hallucinate? There's no point having content in a wide variety of languages if it's unpredictably different from the original-language content.
I've never seen something as complex as a video game vibe coded that was actually well optimized. Especially when the person doing the prompting is not a software developer.
So I personally do care and I am someone, so the answer is not no one.
Your second paragraph does not follow, at all, from the first. These are completely orthogonal demands.
The gaming industry is absolutely overwhelmed with outrageously inefficient, garbage, crash-prone code. It has become the norm, and it has absolutely nothing to do with AI.
Like https://nee.lv/2021/02/28/How-I-cut-GTA-Online-loading-times.... That something so outrageously trash made it to a hundreds-of-million dollar game, cursing millions to 10+ minute waits, should shame everyone involved. It's actually completely normal in that industry. Trash code, thoughtless and lazily implemented, is the norm.
Most game studios would likely hugely improve their game, har har, if they leveraged AI a lot more.
> Yeah, exactly. And LLM help developers save time from writing the same thing that has be done by other developers for a thousand times. I don't know how one can spins this as a bad thing
Do you ever ask why you're writing the same thing over and over again? That's literally the foundational piece of being an engineer; understanding when you're reinventing the wheel when there's a perfectly good wheel nearby.
It is reusable only if simply changing a, b, c is enough to give the function that you want. Options object etc _parameterise_ that function. It is useful only if the variability in reuse you desire is spanned by the parameters. This is syntactic reuse.
With LLMs, the parameterisation goes into semantic space. This makes code more reusable.
A model trained on all of GitHub can reuse all that code regardless of whether they are syntactically reusable or not. This is semantic reuse, which is naturally much broader.
There are two important failures I see with this logic:
First, I am not arguing for reusability. Reusability is one of the most common mistakes you can make as a software engineer because you are over-generalizing what you need before you need it. Code should be written for your specific use case, and only generalized as problems appear. But if you can recognize that your specific use case fits a known problem, then you can find the best way to solve that problem, faster.
Second, when you're using an LLM to make your code more 'reusable' you are taking full responsibility for everything that LLM vomits out. You're no longer assembling a car from well known parts, taking care to tailor it to your use case as needed. You're now building everything in said car, from the tires to the engine and the rearview mirror.
Coding is a constant balance between understanding what you're solving for and what can solve it. Using LLMs takes the worst of both worlds, by offloading both your understanding of the problem and your understanding of the solution.
> Second, when you're using an LLM to make your code more 'reusable' you are taking full responsibility for everything that LLM vomits out. You're no longer assembling a car from well known parts, taking care to tailor it to your use case as needed. You're now building everything in said car, from the tires to the engine and the rearview mirror.
If you are anything above a mid level ticket taker, your responsibility exceeds what you personally write. When I was an “architect” responsible for the implementation and integration work of multiple teams at product companies - mostly startups - and now a tech lead in consulting, I’m responsible for knowing how a lot of code works that I further write and I’m the person called to carpet by the director/CTO then and the customer now.
I was responsible for what the more junior developers “vomit out”, the outside consulting company doing the Salesforce integration or god forbid for a little while the contractors in India. I no more cars about whether the LLM decided to use a for loop or while loop than I cared about the OSQL (not a typo) that the Salesforce consultants used. I care about does the resulting implementation meet the functional and non functional requirements.
On my latest two projects, I understand the customer from talking to sales before I started, I understand the business requirements from multiple calls with the customer, I understand the architecture because I designed it myself from the diagrams and 8 years of working with and (in a former life at AWS) and reviewing it with the customer.
As far as reusability? I’ve used the same base internal management web app across multiple clients.
I built it (with AI) for one client. Extracted the reusable parts and removed the client specific parts and deployed a demo internally (with AI) and modified it and added features (with AI) for another client. I haven’t done web development since 2002 seriously except a little copy paste work. I didn’t look at a line of code. I used AWS Cognito for authentication. I verified the database user permissions.
Absolutely no one in the value chain cares if the project was handcrafted or written by AI - as long as it was done on time, on budget and meets requirements.
Before the gatekeeping starts, I’ve been working for 30 years across 10 jobs and before that I was a hobbyist for a decade who started programming in 65C02 assembly in 1986.
I am not talking about using an LLM to make code reusable in the sense youre arguing.
My point is that the very act of training an LLM on any corpus of code, automatically makes all of that code reusable, in a much broader semantic way rather than through syntax. Because the LLM uses a compressed representation of all that code to generate the function you ask it to. It is like having an npm where it already has compressed the code specific to your situation (like you were saying) that you want to write.
> I don't know how one can spins this as a bad thing.
People spin all kinds of things if they believe (accurately or not) that their livelihood is on the line. The knee-jerk "AI universally bad" movement seems just as absurd to me as the "AGI is already here" one.
> Spore is well acclaimed. Minecraft is literally the most sold game ever.
Counterpoint: Oblivion, one of the first high-profile games to use procedural terrain/landscape generation, seemed very soulless to me at the time.
As I see it, it's all a matter of how well it's executed. In the best case, a skilled artist uses automation to fill in mechanical rote work (in the same way that e.g. renaissance artists didn't make every single brushstroke of their masterpieces themselves).
In the worst (or maybe even average? time will tell) case, there are only minimal human-made artistic decisions flowing into a work and the output is a mediocre average of everything that's already been done before, which is then rightfully perceived as slop.
> Counterpoint: Oblivion, one of the first high-profile games to use procedural terrain/landscape generation, seemed very soulless to me at the time.
Is that even a counter point? Nobody in their right mind would ever claim that procedural generation is impossible to fuck up. The reason Minecraft/etc are good examples is because they prove procedural generation can work, not that it always works.
True, I should have said "counterexample". Procedural generation is just another tool, in the end, and it can be used for great or mediocre results like any other.
Yes, but I beg to differ on the "skilled" part. I find the result very jarring somehow; the scale of the world didn't seem right. (Probably because it was too realistic; part of the art of game terrain design is reconciling the inherently unrealistic scales.)
WoW had this but you never really thought about it - even the massive capital cities were a few blocks at most.
The problem with procedural generation is it's hard to make it as action-packed and desirable as WoW zones, and even those quickly become fly-over territory.
I don't care if LLMs are good at coding or bad at it (in my experience the answer is "it depends"). I don't care how good are they at anything else. What matters in the end is that this tech is not to empower a common person (although it could). It is not here to make our lives better, more worthwhile, more satisfying (it could do these as well). It is there to reduce our agency, to make it easier to fire us, to put us in even more precarious position, to suck even more wealth from those that have little to those that have a lot.
Yet what I see are pigs discussing the usefulness of bacon-making machine just because it also happens to be able to produce tasty soybean feed. They forget that it is not soybean feed that their owner bought this machine for, and that their owner expects a return from such investment.
> What matters in the end is that this tech is not to empower a common person (although it could).
How do you figure? 20 dollars/month is insanely cheap for what OpenAI/Anthropic/Google offer. That absolutely qualifies as "empowering a common person". It lowers barriers!
A lot of the anti-AI sentiment on HN concerns people losing their jobs. I don't think this will happen: programmers who know what they're doing are going to be way, way more effective at using AIs to generate code than others.
But even if it is true and we do see job losses in tech: are software devs really "in a precarious position"? Do they really qualify as "those that have little"? Seems like a fantasy to me. Computer programmers have done great over the past 30 years.
More broadly, anti-AI sentiment comes from people who dislike change. It's hard to argue someone out of that position. You're allowed to prefer stasis. But the world moves on and I think it's best to remain optimistic, keep an open mind, and make the most of it.
It's also, for example, the studies finding that when companies adopt AI employees' jobs get worse. More multitasking, more overtime, more burnout, more skills you're expected to learn (on your own time if necessary), more interpersonal conflict among colleagues. And this is not being offset by anything tangible like an increase in pay.
$20/month in return for measurable reductions in quality of life is not an amazing deal. It's "Heads I win, tails you lose."
Or maybe, if you're thinking of it as an enabler for a side hustle or some other project with a low probability of a high payoff, it can slightly more optimistically be regarded as a moderately expensive lottery ticket.
That's not pessimism; it's just a realistic understanding of how the tech industry actually works, informed by decades' worth of experience.
> It's also, for example, the studies finding that when companies adopt AI employees' jobs get worse. More multitasking, more overtime, more burnout, more skills you're expected to learn (on your own time if necessary), more interpersonal conflict among colleagues. And this is not being offset by anything tangible like an increase in pay.
Can you share those studies? I'm pretty skeptical of this effect. I find that AI has made my job easier and less stressful.
In general, I think your atittude is not realistic, it's just general pessimism about the world ("everything new is bad") that is basically unfounded.
Tech companies have been laying off employees for a while now. I think it's mostly due to pandemic overhiring and higher interest rates but I suppose we'll see.
> I think it's mostly due to pandemic overhiring and higher interest rates
It's not because of pandemic overhiring, and if that were true, the layoffs in 2021-2022 would have handled it. It's 2026. The people getting laid off (on average) haven't worked at these companies since before the pandemic, they got hired in ~2023 (average tenure at a tech company is ~3 years).
It's not because of AI either. Nobody is replacing jobs with AI, AI can't do anyone's job.
It's not because of interest rates. People hired like crazy when interest rates were this high in the oughts.
It's because Elon Musk's Twitter purchase and subsequent management convinced every executive in tech that you can cut to the bone, fuck your product's quality completely, and be totally fine. It's not true, but the downsides come later and the cash influx comes now, so they're doing it anyway.
I agree that AI was not the _actual_ reason, however, it did allow them to do massive layoffs without admitting they are doing poorly and not taking a massive hit to their stock price.
> It is there to reduce our agency, to make it easier to fire us, to put us in even more precarious position
Could be. It could also end up freeing us from every commercial dependency we have. Write your own OS, your own mail app, design your own machinery to farm with.
It’s here, so I don’t know where you’re going with “I’m unhappy this is happening and someone should do something”
It's also worth nothing that the "our" in that sentence is just SWEs, who are a pretty small group in the grand scheme of things. I recognize that's a lot of HN, but still bears considering in terms of the broader impact outside of that group.
I'm a small business owner, and AI has drastically increased my agency. I can do so much more - I've built so many internal tools and automated so many processes that allow me to spend my time on things I care about (both within the business but also spending time with my kids).
It is, fortunately, and unfortunately, the nature of a lot of technology to disempower some people while making lives better for others. The internet disempowered librarians.
> It's also worth nothing that the "our" in that sentence is just SWEs
It isn't, it just a matter of seeing ahead of the curve. Delegating stuff to AI and agents by necessity leads to atrophy of skills that are being delegated. Using AI to write code leads to reduced capability to write code (among people). Using AI for decision-making reduces capability for making decisions. Using AI for math reduces capability for doing math. Using AI to formulate opinions reduces capability to formulate opinions. Using AI to write summaries reduces capability to summarize. And so on. And, by nature, less capability means less agency.
Once men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them
Not to mention utilizing AI for control, spying, invigilation and coercion. Do I need to explain how control is opposed to agency?
It could also end up freeing us from every commercial dependency we have. Write your own OS, your own mail app, design your own machinery to farm with.
Lmfao LLM's can barely count rows in a spreadsheet accurately, this is just batshit crazy.
edit: also the solution here isn't that every one writes their own software (based on open source code available on the internet no doubt) we just use that open source software, and people learn to code and improve it themselves instead of off-loading it to a machine
This is one of those things where people who don't know how to use tools think they're bad, like people who would write whole sentences into search engines in the 90s.
LLMs are bad at counting the number of rows in a spreadsheet. LLMs are great at "write a Python script that counts the number of rows in this spreadsheet".
Yes, for some definition of OS. It could build a DOS-like or other TUI, or a list of installed apps that you pick from. Devices are built on specifications, so that's all possible. System API it could define and refine as it goes. General utilities like file management are basically a list of objects with actions attached. And so on... the more that is rigidly specified, the better it will do.
It'll fail miserably at making it human-friendly though, and attempt to pilfer existing popular designs. If it builds a GUI, it's be a horrible mashup of Windows 7/8/10/11, various versions of OSX / MacOS, iOS, and Android. It won't 'get' the difference between desktop, laptop, mobile, or tablet. It might apply HIG rules, but that would end up with a clone at best.
In short, it would most likely make something technically passable but nightmareish to use.
Given 100 years though? 100 years ago we barely had vacuum tubes and airplanes.
Given a century the only unreasonable part is oneshotting with no details, context, or follow up questions. If you tell Linus Torvalds "write a python script that generates and OS", his response won't be the script, it'll be "who are you and how did you get into my house".
Considering how simple "an OS" can be, yes, and in the 2020s.
If you're expecting OSX, AI will certainly be able to make that and better "in the next 100 years". Though perhaps not oneshotting off something as vague as "make an OS" without followup questions about target architecture and desired features.
What happens when they decide it's a national security threat and an act of domestic terrorism to use AI to undermine commercial dependencies? We're all acting like AI isn't being invented within the context of and used by a fascist regime.
But on the reverse, the first danger with AI is people.
Over the longer term it will look like this. The rich 'win' the world by using AI to enslave the rest of mankind and claim ownership over everything. This will suck and a lot of us will die.
The problem is this doesn't solve the greed that cause the problem in the first place. The world will still be limited in a resources of something which will end with the rich in a dick measuring contest and to win that contest they will put more and more power in AI and they connive and fight each other. Eventually the AI has enough power that it kills us all, intentionally or not.
We'll achieve nearly unlimited capability long before we solve the problem of unlimited greed and that will spell our end.
This is entirely assumptions about a future that has not happened.
Ive worked in "AI" for 20 years, through 2 winters, and run an alignment shop and AIRT... The problem is people. People will use the problem as a scapegoat.
At some point, if most people lose their jobs, you have no market to sell your services to. So, either, new jobs have to be created in order to keep the capitalism machine running, or you have to provide for the needs of every human being from whatever you're doing with your AI. Otherwise, a lot of hungry people revolt and you have violence against these businesses.
I think new jobs will be created because AI is always limited by hardware and its current capabilities. Businesses, in order to compete, want to do things their competitors aren't currently doing. Those business needs always go beyond the current technological capabilities until the tech catches up and then they lather, rinse, repeat.
The individual has never had as much ability to take on large projects as they do now. They’ve never been able to learn as easily as they can now.
>to make it easier to fire us
As of now, the technology increases productivity in the average user. The companies that take advantage of that and expand their offering will outperform the ones that simply replace workers and don’t expand or improve offerings.
More capable employees make companies more money in general. Productivity increases lead to richer societies and yes, even more jobs, just as it always has.
> It is there to reduce our agency, to make it easier to fire us, to put us in even more precarious position, to suck even more wealth from those that have little to those that have a lot.
You could say this is the story of society, it makes us dependent on each other, reduces our agency, puts us in precarious positions (like WW2). But nobody would argue against society like that.
What happens here is that we become empowered by AI and gain some advantages which we immediately use and become dependent on, eventually not being able to function without them - like computers and even thermostats.
Does anyone think how would economy operate without thermostats? No fridges, no data centers, no engines... they all need thermostats. We have lost some freedom by depending on them. But also gained.
Many people don't know this, but the Luddites were right. I studied Art History and this particular movement. One of the claims of the Luddites is that quality would go down, because their craft took half a lifetime to master (it was passed down from parent to chile.)
I was able to feel wool scarves made in europe from the middle ages. (In museum storage, under the guidance of a curator) They are a fundamentally different product than what is produced in woolen mills. A handmade (in the old traditiona) woolen scarf can be pulled through a ring, because it is so thin and fine. Not so for a modern mill-made scarf.
Another interesting thing is that we do not know how they made them so fine. The technique was never recorded or documented in detail, as it was passed down from parent to child. So the knowledge is actually lost forever.
Weavers in Kashmir work a similar level of quality, but their wool is different, their needs and techniques are different, so while we still have craftsman that can produce wool by hand, most of the traditions and techniques are lost.
Is it a tragedy? I go back and forth. Obviously the heritage fabrics are phenomenal and luxurious. Part of me wishes that the tradition could have been maintained through a luxury sector.
Automation is never a 1:1 improvement. It's not just about the speed or process. The process itself changes the product. I don't know where we will net out on software, and I do think the complaints are justified - but the Luddites were also justified. They were *Right*. Their whole argument was that the mills could not product fabric of the same quality. But being right is not enough.
I'm already seeing vibe-coded internal tools at an org I consult at saving employees hundreds of hours a month, because a non-technical person was empowered to build their own solution. It was a mess, and I stepped in to help optimize it, but I only optimized it partially, making it faster. I let it be the spaghetti mess it was for the most part - why? because it was making an impact already. The product was succeeding. And it was a fundamentally different product than what internal tools were 10 years ago.
Your comment made me think of the Japanese. They have a highly industrialised society, but they also value greatly hand-made products from food and clothes to woodwork and houses.
And they also like to emphasise how long it takes for someone to become a master at a given trade.
It was really eye opening seeing they’re able to eat raw eggs and (to maybe a lesser degree of safety) raw chicken because their society requires high standards of cleanliness in food production. We are literal cattle over here in the states.
Though, given Amodei and Altman’s behavior (along with the rest of the billionaire class) that shouldn’t be a surprise to anyone.
You could eat raw eggs in most modern countries and be mostly fine. It’s not as uncommon as you would think. There are many drink recipes with raw eggs as an ingredient. You just happened to be exposed to it in Japan.
Eating raw chicken is risky even in Japan. There are cultures that eat raw chicken, pork, and other meat products by choice but it’s always a risk. There are outbreaks of serious food borne illness in Japan from raw chicken: https://pubmed.ncbi.nlm.nih.gov/18406474/
Finland too. Fundamentally it is putting food safety over profits. Eggs being salmonella free is based on regular testing and culling infected flocks. It is a process that need constant work.
only code anyone will be touching in a museum in 800 years will be the good code. I hope they don't talk about what great craftsmen we all were because someone saw an original Fabrice Bellard at the Louvre.
Survivor bias plays a role in glorifying the past.
You're right in that we kept the best examples (as coding museums will do in the future) but the best of something is a benchmark. It is striking that modern automation, even hundreds of years later, can't touch what a skilled craftsman could do in the past.
With programming, we documented a lot of it, so it's unlikely to go the way of fine weaving. People will always be able to learn to think and be great programmers.
Maybe if the wool weavers had internet, they could have blogged, made youtube videos, and cataloged their profession so it could last Millenia.
Agreed, I think the good gained by wool mills is greater in that little Timmy is less likely to lose a leg to frostbite than the bad loss of my scarf not passing through a ring.
Long term though, I’ve always wondered if the Amish turn out to be the only survivors.
I am kind of lost here on this whole scarf through a ring thing as well. This is just a function of the thickness of the scarf? My wife went through a scarf phase about a decade ago, and I am pretty sure a Pucci scarf could easily fit through a typical sized ring meant to go on a finger?
Its entirely possible that old manufacturing methods produced things that are different, but I would be entirely surprised if they are entirely better overall. If the defining metric for scarves is how well they fit through rings, I am sure they would all be made so you could fit 3 through a ring if people were willing to pony up for that. If you look at a lot of old clothes, they are generally a lot heavier, but I am not sure I would really want to wear them, they look quite uncomfortable. I also think its wonderful that today you can get a set of clothes for a few hours of minimum wage work while in the past this was a major investment. You can also choose to pay thousands for a shirt if you wish, but from 10 feet away its going to be hard to tell the difference.
A full size wool scarf cannot go thorough a ring. You are probably thinking of a silk scarf. I have a wool scarf next to me from Kashmir and it went down about 25 cm. The full scarf is a bit over a meter.
Looked up Pucci - looks like a designer that makes silk scarves. Silk is a totally different material. The Luddites were wool and cotton weavers.
Making wool thin enough for a meter long scarf to go thorough a ring requires the individual strand of wool to be very thin. Both making it thin and weaving that thin strand is the craft that was lost. Go look at wool yarn next time you are at a store and see how thin they can get it.
As for "Are they better?" Yes. Thinner wool is incredible, soft. High quality merino wool is one of the most expensive fabrics. Look up this brand "Made in Rosia Montana" if you are curious. It's not like what the Luddites made, but its as good as it gets in the modern world. Getting stuff from the Kashmir region is difficult - I got mine because I knew someone who ran a school in the area. Most "Cashmere" stuff in department stores is fake/chemically processed for fake softness which makes it nice but it doesn't last. Real quality wool lasts a lifetime. The chemically processed stuff is ok if you want to see how it "feels"
EDIT: also, wool is naturally waterproof! I can walk in the rain with my scarf from kashmir on my head, its pretty thin but absolutely no water goes through even in heavy rain. it has to do with the springiness of the fibers and its natural oils. I will stop nerding out on fibers now!!
I've had this talk in mind during the past 2/3 years of AI boom, and it feels like rewatching a video from the 80s about the dangers of global warming. Prescient, and perhaps a bit quaint in its optimism that somehow we won't make things even worse for ourselves.
> Another interesting thing is that we do not know how they made them so fine. The technique was never recorded or documented in detail, as it was passed down from parent to child. So the knowledge is actually lost forever.
This is a rather extreme failure on their part. There’s nothing admirable about hoarding knowledge and forcing it to only be passed down in person.
I don’t see this as the Luddites being right at all because they were clearly incredibly wrong about their chosen method of knowledge storage. This was a highly predictable and preventable outcome. If we were talking about a company today that forgot how to manage their servers because they refused to document anything and only passed it down from person to person we wouldn’t be speaking in awe and wonder, we’d be rightly criticizing their terrible decision making.
That aside, every time I hear that knowledge has been lost forever it turns out to be an exaggeration from those trying to amplify the mystique of the past. If we wanted to make ultra-thin scarves we could do it. We could study those ultra thin museum pieces with our endless array of modern tools and then use our vast quantities of modern wool to experiment until we got something similar.
But you missed the reason why we wouldn’t want to: An ultra-thin scarf isn’t going to work as well as a thicker one for keeping someone warm. It will be less durable. It would be a fundamentally inferior product. It’s interesting to see as a museum antique that has to be treated with utmost delicacy, but not so much as a practical garment.
And yet in the 200 years since human civilization has improved by every imaginable metric, in most cases by orders of magnitude. The difference between 2026 and 1826 is nearly incomprehensible. I suspect most people scarcely imagine how horrific the average life was in 1826, relatively speaking. And between then and now were the industrial revolution, multiple world wars, and generally some of the most terrible events, crooked politicians, and life changing technological forces. And here we are, mostly fine in most places.
I get there are many things happening today that are frustrating or moving some element of human life in negative or ambiguous directions, but we really have to keep perspective on these things.
Nearly every problem today is a problem with a solution.
The feelings of panic we have that things are going wrong are useful signals to help guide and motivate us to implement those solutions, but we really must avoid letting the doomerism dominate. Just because we hear constant negative news doesn't mean things are lost. Doesn't even mean things are bad.
It just means we have been hearing a lot of negative news.
This is what it looks like for progress to not be monotonically increasing.
Before mass production, the women of the household would be forced to spend every free moment of their day, outside of their other work, making fabric.
Before mechanised farming, the men were forced to spend all day in the fields.
There's a big difference between being in a field versus working in a field, from dawn to dusk, every day, regardless of the weather or sickness, in order to produce just enough food to feed you and your family, knowing that a single failed harvest (due to conditions like weather and pests that you have no control over) will leave you starving, watching helplessly as your children, spouse, friends and neighbours slowly weaken and die, knowing that even if you survive, you will face the same thing again the next year, and the next, for the rest of your lives, which will likely be short, due to the constant, exhausting labour, frequent bouts of malnutrition, and nonexistent medical care.
A big difference is cutting quality for the sake of mass production when it enables creating more necessities for people to live is a good thing. It is a good tradeoff. Cutting quality to make previously deterministic software more non deterministic does not improve anyones life except Sam Altman, Dario Amodei and the rest of the billionaire class.
I have no doubt in the future there will be a class of vibe software and it will be known as distinctly lower quality than human understood software. I do think the example you describe is a good use of vibing. I also think tech orgs mandating 100% LLM code generation are short sighted and stupid.
A lot of this push for “slop” is downstream of our K shaped economy. Give the people more money and quality becomes a lot more important. Give them less, and you’re selling to their boss who is often insulated from the effects of low quality.
Was software authorship ever deterministic? Whether it be human or AI, the output can vary wildly, and is constrained by the finite specifications provided.
Agreed. Those expensive silk scarves are worth exactly zilch to the average person if they'll never see one in their entire life. They might as well not exist.
Mass production makes things accesible, and if the handmade product cannot compete relative to it, then it's clearly not that much better or some people would still pay that premium to keep it around.
With programming that's very much the case, nobody's gonna vibe code a self driving car stack or a production grade DBMS. Even the cheapest scarf still works as a scarf in 99.9% of use cases though, if maybe not for as long.
> One of the claims of the Luddites is that quality would go down, because their craft took half a lifetime to master (it was passed down from parent to chile.)
Sounds like a tautology. If you deliberately hoard knowledge of course it’s going to be hard to obtain.
You're right. Automation often trades quality for speed and quantity.
The difference between automating the creation of software and automating the creation of physical products is that software is everywhere. It is relied on for most tools and processes that keep our civilization alive. Cutting corners on that front, and deciding to entrust our collective future to tech bros and VC firms fiending for their next payout, seems like an incredibly dumb and risky proposition.
The Luddites were right in the sense that the social order had changed in a negative way. In a careless way.
In the same way that we look at America now that has effectively put a plutocracy in absolute control of the country, at the same time that there is going to be a massive devaluing of labour. Elon Musk likes to talk about the coming golden age of automation, but I hope Americans realize that unless they happen to be a billionaire, they will enjoy zero fruits of that advance. Quite contrary, plump yourself up to be Soylent Green because it turns out that giving a bunch of psychopaths/sociopaths absolute control of government isn't good for the average person.
>One of the claims of the Luddites is that quality would go down
Then people will choose the better quality items and it will be easy for them to compete? Right?
What the author and many others find hard to digest is that LLMs are surfacing the reality that most of our work is a small bit of novelty against boiler plate redundant code.
Most of what we do is programming is some small novel idea at high level and repeatable boilerplate at low level. A fair question is: why hasn’t the boilerplate been automated as libraries or other abstractions? LLMs are especially good at fuzzy abstracting repeatable code, and it’s simply not possible to get the same result from other manual methods.
I empathise because it is distressing to realise that most of value we provide is not in those lines of code but in that small innovation at the higher layer. No developer wants to hear that, they would like to think each lexicon is a creation from their soul.
Most of the people doing the most rote and monotonous work were and are doing so in some of the least productive circumstances, with clear ways of increasing speed and productivity.
If development velocity was truly an important factor in these businesses, we'd migrated away from that gang of four ass Java 8 codebase, given these poor souls offices, or at least cubicles to reduce the noise, we wouldn't make them spend 3 hours a day in ceremonial meetings.
The reason none of this happens is that even if these developers crank out code 10x faster, by the time it's made it past all the inertia and inefficiencies of the organization, the change is nearly imperceptible. Though the bill for the new office and the 2 year refactoring effort are much more tangible.
Yep. It's ridiculous to talk about 10x or 5x or 2x anything in any but the smallest companies. All this talk about programmer velocity is micro-optimizing something that's not a bottleneck.
I’ve been thinking a lot about this. I think that AI software automation tools are disproportionately more useful in greenfield work done by small or tiny organizations. By an order of magnitude, maybe 2 in some cases.
What that means is anyone’s guess, but it seems like it should result in a Cambrian explosion of disruptive new companies, limited in scope by the idea space.
The thing about small teams is, with a few exceptions, the biggest challenges are typically funnels for users and product-market fit, overcoming and exploitation of network effects, etc… so even in small orgs, if you make 30 percent of the problem 4x faster/smaller you still have the other 70 percent which is now 92.5% of the problem.
This applies even more acutely in larger organizations… so for them, 99 percent of the problem remains.
Intangibles in an organization like reluctance, education, and organizational inertia fill the gap left by software acceleration, and in the end you only see tiny gains, if any.
What really happened, on an organizational scale, is that software development costs went down. We wouldn’t expect a wage collapse in coding to foment an explosive revolution in company profitability or dynamism. We shouldn’t expect those things of LLM assistance.
We should look at it as a reduction in cost with potentially dangerous side effects if not managed carefully, with an especially big reduction in r&d development costs.
Literally the most useful LLMs have been to me is dealing with the pile of corporate bullshit we have to put up with day to day.
For example, we have to plan 8 to 12 sprints in advance. Full acceptance criteria, story points, and slotted into a sprint with points balanced across the team. Of course this is utterly useless, anything past the second sprint is going to be wrong, but they want it done. LLMs got me through that in a few hours instead of a few days.
In many organizations, software just isn't what makes the money. It's a supporting role at best. The software needs to work reliably, and it needs to keep working for a long time, but it doesn't need to gain new features at a rapid pace.
If the janitors swept the floors 10x faster, we wouldn't see any KPIs to shoot up from that. You still need it to happen regularly and reliably and on demand in case there's a mess, but it doesn't need to be fast.
>If the janitors swept the floors 10x faster, we wouldn't any KPIs to shoot up from that
I mean I'd expect that you'd see a decrease in the number of janitor staff as was convenient per the companies policies. That and janitorial services would turn into contracted services rather than in house positions. Oddly enough both these things have already occurred as tooling to janitors became better and the legal incentives made it cheaper.
Libraries create boundaries, which are in most cases arbitrary, that then limit the way you can interact with code, creating more boilerplate to get what you want from a library.
Abstractions are the source of bloat. Without abstractions you can always reduce bloat, or you can reduce bloat in your glue, but you can't reduce glue.
It takes discipline to NOT create arbitrary function signatures and short-lived intermediate data structures or type definitions. This is the beginning of boilerplate.
So many advances in removing boilerplate are realizing your 5 function calls and 10 intermediate data structures or type definitions, essentially compute a thing that you can do with 0 function calls and 0 custom datatypes and less lines of code.
The abstraction hides how simple the thing you want is.
Problem is that all open source code looks like the bloat described above, so LLMs have no idea how to actually write code that is without boilerplate. The only place where I've seen it work is in shaders, which are usually written to avoid common pitfalls of abstraction.
LLMs are incapable of writing a big program in 1 function and 1 file, that does what you want. Splitting the program into functions or even multiple files, is a step you do after a lot of time, yet all open source looks nothing like that.
Yep, people not understanding the value of abstraction is exactly why LLM coded apps are going to be a shit show. You could use them to come up with better abstractions, but most will not.
> Abstractions are the source of bloat. Without abstractions you can always reduce bloat, or you can reduce bloat in your glue, but you can't reduce glue.
No I don’t agree. Just because it’s « boilerplate », that does not mean it’s worthless or doesn’t carry novelty.
There is « boilerplate » in building many things, house, cars etc where to add real new stuff it’s « always the same base » but you have to nail that base and there is real value in it. With craft and deep knowledge and pride.
Every project is different and not everything can be made from a generic out-of-shelf product
There isn’t just concrete in a house. There is hundreds of things that could vary from house to house (even country to country and laws) so it’s more like the building blocs are not only imports of lib but the language itself (raw materials) which makes it a fit analogy for me
To me, a function is a single sentence within a book. It may approach the larger picture, but that sentence can be reviewed, changed, switched around, killed by an editor.
Some programmers believe they're fantastic sentence writers. They brag about how good of a sentence they write, they're entire worldview has been built on being good sentence creators. Especially within enterprises, you may spend your entire life writing sentences without ever really understanding the whole book.
If your worldview has been built on sentence creation, and suddenly there's a sentence creator AI, you're going to be deathly afraid of it replacing you as a sentence writer.
Both the books and the song analogies are incorrect. In the case of code, the users for whom the programmes are written, are not engaging with the statements of the code, they are interacting with interfaces the programmes provide.
This is not the same when it comes to books and music.
There are many orders of magnitude more songs based on four chord loops than there are hit songs. Some people say it's easy to make a hit song. But there are a lot more people that want to do it than those that succeed. So I say no. Your take is reductive, and there is necessarily more to it.
I wrote a book a while back where I argued that coding involves choosing what to work on, writing it, and then debugging it, and that we tend to master these steps in reverse chronological order.
It's weird to look at something that recent and think how dated it reads today. I also wrote about the Turing test as some major milestone of AI development, when in fact the general response to programs passing the Turing test was to shrug and minimize it
I would argue that chatbots still barely pass the turing test
They have such obvious patterns and tells that humans have already picked up on them and they can eventually sus out that they're talking to an LLM
For instance I heard recently about someone talking (verbally) with an AI voiced customer support. They were very convinced, so they asked the support agent to calculate the product of two large numbers, and it replied with the result instantly
Actually I think this is one of the more tragic outcomes of the LLM revolution: it was already hard to get funding for ergonomic advances in programming before. Funding a new PL ecosystem or major library was no mean feat. Despite that, there were a number of promising advances that could have significantly raised the level of abstraction.
However, LLMs destroy this economic incentive utterly. It now seems most productive to code in fairly low level TypeScript and let the machines spew tons of garbage code for you.
> A fair question is: why hasn’t the boilerplate been automated as libraries or other abstractions?
Because our ways of programming computers are still woefully inadequate and rudimentary. This is why we have a tons of technique for code reuse, yet we keep reinventing the wheel because they shatter in contact with reality. OOP was supposed to save us all in the 1990s, we've seen how it went.
In other fields we've had a lot of time to figure out basic patterns and components that can be endlessly reused. Imagine if car manufacturers had to reinvent the screw, the piston, the gear and lubricants for every new car model.
One example that has bugged me for a decade is: we've been in the Internet era for decades at this point, yet we spend a lot of time reinventing communication. An average programmer can't spend two days without having to deal with JSON serialization, or connectivity, or sending notifications about the state of a process. What about adding authentication and authorization? There is a whole cottage industry to simplify something that should be, by now, almost as basic as multiplying two integers. Isn't that utter madness? It is a miracle we can build complex systems at all when we have to focus on this minutiae that pop up in every single application.
Now we have intelligences that can create code, using the same inadequate language of grunts and groans we use ourselves in our day to day.
Standardization and regulation forced a lot of the physical industries to change as the industrial revolution progressed. Before that point standards didn't really exist, especially over any large areas and technological progress suffered because of that. After that point solutions became much closer to drag and drop than what they were before.
The question is, at what point of progress will it benefit the software industry.
This is actually quite an insightful comment into the mindset of the tech set vs. the many writers and artists whose only 'boilerplate redundant code' is the language itself, and a loose aggregate of ideas and philosophies.
Probably the original sin here is that we started calling them programming languages instead of just 'computer code'.
Also - most of your work is far more than mere novelty! There are intangibles like your intellectual labor and time.
> "Why hasn’t the boilerplate been automated as libraries or other abstractions?"
Because a lot of programmers don't know how to copy-paste or make packages for themselves? We have boilerplate at my work, which comprises of some ready made packages that we can drop in, and tweak as needed, no LLMs required
I'm not entirely sure what you mean... If something becomes repetitive enough to be boilerplate, we can just make it into a package and keep it around for the next time
Abstraction isn't free... even if you had the correct abstraction and the tools to remove the parts you don't need for deployment, there is still the cost of understanding and compiling.
There is also the cost reason, somebody trying to sell an abstraction will try to monetize it and this means not everyone will want/be able to use it (or it will take forever/be unfinished if it's open/free).
There's also the platform lockin/competition aspect...
It has been automated as much as possible, boilerplate is the result of people being terrible at designing programming languages. The whole idea of increasingly higher level ones was just that all along. Is there any point in writing a billion ADDC commands in assembly by hand when it takes one line in python?
LLM type systems are the final level of abstraction that lifts it up to literal natural language. Any dev with decent self awareness would admit they were just copying shit from stackoverflow half the time before LLMs anyway, high level languages and libraries just streamline that process with canonical implementations.
The value we provide is turning "person with problem" -> "person with solution to said problem" with as few caveats as possible. A programmer is that arrow, we solve problems. The more code we have to write to solve that problem, the worse we are at our job.
We already have tools to generate boilerplate, and they work exceptionally well. The LLM just produces nondeterministic boilerplate.
I also don't know what work you do, but I would not characterize the codebases I work in as "small bits of novelty" on boilerplate. Software engineering is always a holistic systems undertaking, where every subcomponent and the interactions between them have to be considered.
Boilerplate has been with us since the dawn of programming.
I still think LLMs as fancy autocomplete is the truth and not even a dig. Autocomplete is great. It works best when there’s one clear output desired (even if you don’t know exactly what it is yet). Nobody is surprised when you type “cal” and California comes up in an address form, why should we be surprised when you describe a program and the code is returned?
Knowledge has the same problem as cosmology, the part we can observe doesn’t seem to account for the vast majority of what we know us out there. Symbolic knowledge encompasses unfathomable multitudes and will eventually be solved by AI but the “dark matter” of knowledge that can’t easily be expressed in language or math is still out in the wild
FORTRAN ("formula translator") was one of the first programs ever written and it was supposed to make coding obsolete. Scientists will now be able to just type in formulas and the computer will just calculate the result, imagine that!
Time to learn design, how to talk to customers, and how to discover unsolved problems. Used right LLMs should improve your software quality. Make stuff that matters that you can be proud of.
Its unfortunate that there’s mode collapse around what the consensus “best way” to use these things are. It’s too bad we didn’t have a period where these things were great teachers but didn’t attempt to write code because in my opinion the ideal way to use them is not by agents mass producing sloppy buggy disorganized code, but to teach you things way faster than the old alternatives, rubber duck, and occasionally write snippets of functions when your brain is too tired or it’s throwaway cli code or some api you’re not familiar with.
> It’s too bad we didn’t have a period where these things were great teachers but didn’t attempt to write code
The period is now. Just add "be a great teacher but don't attempt to write code" in the prompt.
(yes, it's a teacher who gets things wrong from time to time. You still need to refer to the source and ground truth just like when you're taught by a human teacher.)
> to teach you things way faster than the old alternatives
I'm not sure if you ever had a teacher or instructor that you didn't trust, because they were a compulsive liar or addiction or any other issue. I didn't (as least not that I can remember) but I know I would be VERY on guard about it. I imagine I would consequently be quite stressed learning with them, even if they were brilliant, kind, etc.
It would feel a bit like walking on thin ice to get to a beautiful island. Sure, it's not infeasible and if you somehow make it, it might be worth the risk, but honestly wouldn't you prefer a slower boat?
I agree, it can be incredibly frustrating at times. My rule is that if it “compiles” in my brain as an understood idea then i accept it. I also push back a lot (sometimes it points out good errors in my thinking, sometimes it admits it hallucinated). Real humans hallucinate a lot as well or confidently state subtly wrong ideas, it’s a good habit anyway. It’s basically the same approach when presented with a “formula” for something in school. If i dont know how to derive/prove it then i dont accept it as part of my memorized or accepted toolkit/things i use (and try to forget it). If it fits with the rest of my network of understood ideas i do. It’s annoying but still more time efficient than trawling through lecture slides with domain specific language etc
> My rule is that if it “compiles” in my brain as an understood idea then i accept it.
Unfortunately, individual people are not anywhere as reliable as a compiler for ensuring compliance to reality. We are particularly susceptible to flattery and other emotional manipulation, which LLMs frequently employ. This becomes particularly problematic when you ask for feedback on an idea.
In that case, a useful hack is to frame prompts as if you're an impartial observer and want help evaluating something, not as if the idea under evaluation is your own.
> Real humans hallucinate a lot as well or confidently state subtly wrong ideas, it’s a good habit anyway.
I think that's actually deeply different. If a human keeps on apologizing because they are being caught in a lie, or just a mistake, you distrust them a LOT more. It's not normal to shrug off a problem then REPEAT it.
I imagine the cost of a mistake is exponential, not linear. So when somebody says "oops, you got me there!" I don't mistrust them just marginally more, I distrust them a LOT more and it will take a ton of effort, if even feasible, to get back to the initial level of trust.
I do not think it's at all equivalent to what "Real humans" do. Yes, we do mistake, but the humans you trust and want to partner with are precisely the one who are accountable when they make mistakes.
I feel like this is partially a skill issue - You can get direct, cited information from LLMs. There's a level of personal responsibility for over-using the tools and letting them feed you bad/false information, but if you try researching specific abstractions, newer documentation, most LLMS now correctly call and research the tools available, directly citing them.
I think you can build a very easy workflow that reinforces rather than replaces learning, I've used a citation flow to link and put into practice a ton of more advanced programming techniques, that I found incredibly difficult to locate and research before AI.
I'd say the comparison is faulty, it's more akin to swimming to an island (no-ai) vs using a boat. You control the speed and direction of the boat, which also means you have the responsbility of directing it to the correct location.
The analogy was about the unknown thinnest of the ice, not just the fastest way to get there. It's specifically about the lack of reliability of the process.
Yes, I was disagreeing with the premise of the analogy - what would the slow boat in this case be? As my experience, going through software engineering before AI, is that you'd get lost to the ice, with nobody to really help you get out.
If you get lost on the ice and you have someone who confidently tells you the path but is sometimes wrong, is it actually helpful?
PS: sorry if the analogy is a bit wonky but it's quite dear to me as I do ice skating on frozen lakes and it's basically a life or death information "game" that I can relate to. It might not be a great analogy for others.
Haha it's a good analogy, i'm being a little bit argumentative for the sake of it potentially.
I guess in my view - the main alternative you'd have beforehand is just to drown.
For me, AI sits in a space where if you know how to use it, it can tell you all the thin spots of the ice accurately. You can then verify those spots, but there's a level of personal responsibility of verification.
I'd agree there's currently a ton of people that are using these tools to essentially just find the specific route - but i'd argue those people probably shouldn't be skating in the first place, and would've fallen one way or the other.
> AI sits in a space where if you know how to use it, it can tell you all the thin spots of the ice accurately. You can then verify those spots, but there's a level of personal responsibility of verification.
Right, but AFAICT most people just venture over the ice and don't bother to check. In fact a lot of people venture there, do check once or twice, then check less and less frequently. The fact that you do it is great but others seem a lot less careful, until cracks start to show and then it might be too late.
I'd only argue that people were doing this before AI, slop development was just copy pasting from the first stack overflow issue that matched the question rather than thinking
So i'd argue there's a part of it that is just personal responsibility with how these tools are used
> This sort of protectionism is also seen in e.g. controlled-appelation foods like artisanal cheese or cured ham. These require not just traditional manufacturing methods and high-quality ingredients from farm to table, but also a specific geographic origin.
Maybe "Artisanal Coding" will be a thing in the future?
The 'Handmade Network' is essentially this (in a good way though) - and long before LLMs got good enough for code-generation - instead as a counter philosophy to the soulless "enterprise software development" where a feature that could be implemented in 10 lines of code is wrapped in 1000 lines of "industry-best-practices" boilerplate.
Programming via LLMs is just the logical conclusion to this niche of industrialized software development which favours quantity over quality. It's basically replacing human bots which translate specs written by architecture astronauts into code without having to think on their own.
And good riddance to that type of 'spec-in-code-out' type of programming, it should never have existed in the first place. Let the architecture astronauts go wild with LLMs implementing their ideas without having to bother human programmers who actually value their craft ;)
I'm kinda leaning towards the analogy that LLMs are to programming as textile machines were to the loom.
People still pay for hand-knit fabrics (there's one place in Italy that makes silk by hand and it costs 5 figures per foot), but the vast majority is machine made.
Same thing will happen to code, unless the bubble bursts really badly. Most bulk API Glue CRUD stuff and basic web UI work will be mostly automated and churned off automated agentic production lines.
But there will still be a market for that special human touch in code, most likely when you need safety/security or efficiency/speed.
Like you can still make Karelian pies[0] anywhere, but unless you follow the exact recipe, you can't sell them as "Karelian pies". It's good for the heritage and good for the customers.
You can also make any cheeses and wines and whatever you like, it's just how you name them and market them that's regulated.
If you consider only the product is relevant and not how it is made, then no it does not matter; or at least it doesn't matter as long as you don't personally attach any emotional qualities to products beyond their material qualities (unlike the vast majority of people).
But the comment you reply to explicitly points out the process is in fact relevant as it is itself a cultural artifact. You're not replying to their main point.
The main point is "It's good for the heritage and good for the customers."
How are the customers hurt if their pie has not been baked by a babushka in Petrozavodsk using the old original recipe, but by an anonymous migrant worker in a dark kitchen using an optimized recipe if the end result is objectively the same? The packaging doesn't have to say who it was made by.
I also don't see the problem with the heritage. The comment I replied to already said anyone could call their pies Karelian, so there was no restriction that benefitted the residents of a specific region. I can see a PDO-like carveout that goes "we want to preserve the traditional pie-making of Karelia, so we want this activity to remain economically viable. Therefore, only pies baked in Karelia can be sold as Karelian pies." But I don't see how Sysco baking the same pies and distributing them nationwide helps maintain the heritage.
Customers want a very specific thing, rules exist that say if you sell something called Specific Thing it must be made a very specific way or you can't call it that.
Even if you make something that tastes and looks exactly like the original, you still can't call it Specific Thing because the process wasn't followed as it's an integral part of the product. Think of it like a trademark. You can't create some brown sugary stuff and sell it as Coca-Cola - even if it tastes EXACTLY like it does.
Nothing about this is about profit or economic viability, it's not even a small part of the equation. The purpose is to preserve cultural heritage and not dilute it with shitty imitations calling themselves something they are not.
The issue is how this will be handled in law. Can the law define this in a way that is not overly strict or overly permissive? The current attempts is effectively the law doing this, but with an overly strict approach of what counts as 'objectively the same' by judging the process and not purely the outcome. Would it be possible to make the law's definition of this more permissive, to focus only on the product produced, without accidentally becoming overly permissive?
There’s a cold reality that we in this profession have yet to accept: nobody cares about our code. Nobody cares whether it’s pretty or clever or elegant. Sometimes, rarely, they care whether it’s maintainable.
We are only craftsmen to ourselves and each other. To anyone else we are factory workers producing widgets to sell. Once we accept this then there is little surprise that the factory owners want us using a tool that makes production faster, cheaper. I imagine that watchmakers were similarly dismayed when the automatic lathe was invented and they saw their craft being automated into mediocrity. Like watchmakers we can still produce crafted machines of elegance for the customers who want them. But most customers are just going to want a quartz.
They don't care until the whole thing collapses in on itself from the technical debt. Then they have surprised pikachu face when it takes an insane about of effort to add a simple feature.
I agree, but nothing about LLM assisted coding is by neccessity "technical debt". Tons of developers will use it to spit out shitty code they don't even review themselves, but the concept at it's core doesn't always mean a lower quality end product.
I like Simon Willison's take on this: "Your job is to deliver code you have proven to work". If someone is spitting out LLM trash and shipping it, that means the job isn't being done properly. That can be done with and without an LLM.
I think it varies. Most enterprise software is good enough if it just works. In the consumer space quality and polish is way more important. Then there are things like modeling and video where performance is a much bigger deal.
Sure, no one really cares about the code but the quality of the code matters more for some products (and in different ways) than others.
I will just copy paste my comment from another thread but still very relevant>
Coding isn’t creative, it isn’t sexy, and almost nobody outside this bubble cares
Most of the world doesn’t care about “good code.” They care about “does it work, is it fast enough, is it cheap enough, and can we ship it before the competitor does?”
Beautiful architecture, perfect tests, elegant abstractions — those things feel deeply rewarding to the person who wrote them, but they’re invisible to users, to executives, and, let’s be honest, to the dating market.
Being able to refactor a monolith into pristine microservices will not make you more attractive on a date. What might is the salary that comes with the title “Senior Engineer at FAANG.” In that sense, many women (not all, but enough) relate to programmers the same way middle managers and VCs do: they’re perfectly happy to extract the economic value you produce while remaining indifferent to the craft itself. The code isn’t the turn-on; the direct deposit is.
That’s brutal to hear if you’ve spent years telling yourself that your intellectual passion is inherently admirable or sexy. It’s not. Outside our tribe it’s just a means to an end — same as accounting, law, or plumbing, just with worse dress code and better catering.
So when AI starts eating the parts of the job we insisted were “creative” and “irreplaceable,” the threat feels existential because the last remaining moat — the romantic story we told ourselves about why this profession is special — collapses. Turns out the scarcity was mostly the paycheck, not the poetry.
I’m not saying the work is meaningless or that system design and taste don’t matter. I’m saying we should stop pretending the act of writing software is inherently sexier or more artistically noble than any other high-paying skilled trade. It never was.
This is just sad. If your passion for creating something you can be proud of is entirely propped up by imaginary sex appeal that not even most teenagers would believe exists, it's no surprise you'd arrive at such a cynical, pathetic conclusion.
Your perspective is a path with only one logical end. That nothing you do or think or believe matters unless someone you're attracted to finds it attractive.
That is not how I or most others live. We take pride in and derive satisfaction from our accomplishments without the need for external validation.
Yeah, only I care whether the solution I found to a problem today was elegant, or whether my kitchen was pristine and well organized after I prepped for next week's lunches, but so what? I care and it injects more than enough meaning into my life to be worth it.
> Classic procedural generation is noteworthy here as a precedent, which gamers were already familiar with, because by and large it has failed to deliver.
With the notable exception of Minecraft terrain generation, which I think most would say was successful in what it set out to achieve.
Minecraft terrain generation produces invalid states, such as empty spaces within water, where once you interact with it the boundary collapses into a valid state. I don't think Mojang planned to have those invalid states.
I've been thinking this for a while. Attribution is sorely needed, I believe there was a recent paper on this so maybe it will happen.
In the meantime the only way to really sort this is to have models that have only been trained on some particular kind of license - there is quite a big corpus of GPL'd code out there so a GPL based model could potentially be one of the first, and of course the output could only be GPL.
Content aside, that website is very nice. It goes a bit crazy with animations in places (header, switching articles), but that does not taint the experience of reading something on it in the slightest.
It's feels like a modern twist on a bygone time of the web.
The framing of an LLM's response as truth vs lie is in itself incorrect.
In order to lie, one needs to understand what truth and objective reality are.
Even with people, when a flat-earther tells you the earth is flat, they're not lying, they're just wrong.
All LLM output is speculation. All speculation, by definition, has some probability of being incorrect.
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We can go even deeper in a philosophical sense. If I made the audacious claim that 2 +2 = 4, I may think it's true, but I'm still speculating that the objective reality I experience is the same one others also experience, and that my senses and mental faculties, and therefore the qualia making up my reality, are indeed intact, correct, and functional. So is there a degree of speculation when I made that claim?
Regardless, I am able to agree upon a shared reality with the rest of the world, and I also share a common understanding of truth and untruth. If I lied, it can only be caused by an intention to mislead others. For example, if I claimed to be the president of the united states, of course that would be incorrect (thankfully!), but since we all agree that no one reading this post would actually be mislead into thinking I am the POTUS, then it isn't a lie. Perhaps sarcasm, a failed attempt at humor, or just trolling. it is untruth, but it isn't a lie, no one was mislead. You need intent (LLM isn't capable of one), and that intent needs to be at least in part, an intent to mislead.
It seems like with time hallucinations and lying increased, it’s very different now from what it was 2 years ago. is this because of training bias ?
Is there any research data on dynamics over past years ?
>If you ask me, no court should have ever rendered a judgement on whether AI output as a category is legal or copyrightable, because none of it is sourced. The judgement simply cannot be made, and AI output should be treated like a forgery unless and until proven otherwise.
Guilty until proven innocent will satisfy the author's LLM-specific point of contention, but it is hardly a good principle.
You are missing the point of the author. He literally said no court should have rendered a judgement, that's the exact opposite of guilty until proven innocent. Guilty means a court has made a judgement.
He is proposing to not make a judgement at all. If the AI company CLAIMS something they have to prove it. Like they do in science or something. Any claim is treated as such, a claim. The trick is to not even claim anything, let the users all on their own come to the conclusion that it's magic. And it's true that LLMs by design cannot cite sources. Thus they cannot by design tell you if they made something up with disregard to it making sense or working, if they just copy and pasted it, something that either works or is crap, or if they somehow created something new that is fantastic.
All we ever see are the success stories. The success after the n-th try and tweaking of the prompt and the process of handling your agents the right way. The hidden cost is out there, barely hidden.
This ambiguity is benefitting the AI companies and they are exploiting it to the maximum. Going even as far as illegally obtaining pirated intellectual property from an entity that is banned in many countries on one end of their utilization pipeline and selling it as the biggest thing ever at the other end. And yes, all the doomsday stories of AI taking over the world are part of the marketing hype.
A forgery isn't a subjective assessment. A forgery is intentionally made inaccurate claim of the origin of something. If the by-line is claiming it was made by someone who didn't make it, it's a forgery no matter how good of a copy it is judged to be.
I think the framing as “lying” is emotionally accurate for users but technically misleading for builders. Most failures I see in production aren’t intentional deception, they’re confidence calibration failures: the model sounds certain when it should sound tentative.
What helped us most was treating outputs like untrusted input: retrieval + citations for factual claims, strict tool permissions, and an explicit “I don’t know” path that’s rewarded instead of punished. That doesn’t make LLMs truthful, but it does make them a lot less brittle.
So to me the key question isn’t “are models liars?” but “what product and engineering constraints make wrong answers cheap and detectable?”
> Whether something is a forgery is innate in the object and the methods used to produce it. It doesn't matter if nobody else ever sees the forged painting, or if it only hangs in a private home. It's a forgery because it's not authentic.
On a philosophical level I do not get the discussions about paintings. I love a painting for what it is not for being the first or the only one. An artist that paints something that I can't distinguish from a Van Gogh is a very skillful artist and the painting is very beautiful. Me labeling "authentic" it or not should not affect it's artistic value.
For a piece of code you might care about many things: correctness, maintainability, efficiency, etc. I don't care if someone wrote bad (or good) code by hand or uses LLM, it is still bad (or good code). Someone has to take the decision if the code fits the requirements, LLM, or software developer, and this will not go away.
> but also a specific geographic origin. There's a good reason for this.
Yes, but the "good reason" is more probably the desire of people to have monopolies and not change. Same as with the paintings, if the cheese is 99% the same I don't care if it was made in a region or not. Of course the region is happy because means more revenue for them, but not sure it is good.
> To stop the machines from lying, they have to cite their sources properly.
I would be curious how can this be applied to a human? Should we also cite all the courses, articles that we have read on a topic when we write code?
Regarding art, what do you feel about museums? Why would you go see an original instead of simply looking at a jpg.
Even if you aren't in the group, there is clearly a group of people who appreciate seeing the original, the thing that modified our collective artistic trajectory.
Forgeries and master studies have a long history in art. Every classically trained worth their salt has a handful of forgeries under their belt. Remaking work that you enjoy helps you appreciate it further, understand the choices they made and get a better for feel how they wielded the medium. Though these forgeries are for learning and not intended to be pieces in their own right.
Eh, there is plenty of artwork with high resolution scans online in which you'll have to take a magnifying glass to the museum if you want a closer view.
> Regarding art, what do you feel about museums? Why would you go see an original instead of simply looking at a jpg.
I go to a museum to see a curated collection with explanations in a place that prevents distractions (I can't open a new tab) and going with people that might be interested to talk about what they see and feel. It's as well a social and personal experience on top information gathering.
> there is clearly a group of people who appreciate seeing the original,
There are many people interested in many things, do you want to say that "because some people think it is important, it must be important"? There were many people with really weird and despicable ideas along history and while I am neutral to this one, they definitely don't convince me just by their numbers.
> simply looking at a jpg.
Technically a jpg would not work because is lossy compression. But a png at the correct resolution might do the trick for some things (paintings that you see from far), but not for others. Museum have multiple objects that would be hard to put in an image (statues, clothes, bones, tables, etc.). You definitely can't put https://en.wikipedia.org/wiki/Comedian_(artwork) in a jpg - but the discussion surrounding it touches topics discussed here.
>Me labeling "authentic" it or not should not affect it's artistic value.
The problem with automated imitation generators is that they can produce thousands of painting that imitate Van Gogh, but does not have the same soul.
It is the same reason why these things cannot create genuinely funny jokes. They cannot assess the funnyness of the themselves. They cannot feel, and cannot do the filtering based on emotion.
It is easy to recognize the emptiness of a joke, but not so easy for a painting, or some other form of art.
This is why it will never work for art. But the sad thing is that that will not stop them from being used to create art. Because it just needs to sell.
I would say that for art, at least for most of the movies, music etc, this was already the case. So nothing much to lose.
Define soul, how about a legal/scientific description that accurately covers all bases?
The funny jokes thing is funny too, if someone told you a joke and you thought it was funny, then they told you it was from an LLM, would it stop being funny.
The value of a piece is definitely not completely tied to its physical attributes, but the story around it. The story is what creates its scarcity and generates the value.
It is similar for collectible items. If I had in my possession the original costume that Michael Jackson wore in thriller, I am sure I could sell it for thousands of dollars. I can also buy a copy for less than a hundred.
Same with luxury brands. Their price is not necessarily linked to their quality, but to the status they bring and the story they tell (i.e. wearing this transforms me into somebody important).
It can seem quite silly, but I think we are all doing it to some extent. While you said that a good forgery shouldn't affect one's opinion on the object (and I agree with you), what about AI-generated content? If I made a novel painting in the style of Van Gogh, you might find it beautiful. What if I told you I just prompted it and painted it? What if I just printed it? There are levels of involvement that we are all willing to accept differently.
> An artist that paints something that I can't distinguish from a Van Gogh is a very skillful artist and the painting is very beautiful.
There are a lot such artists who can do that after having seen Van Gogh's paintings before. Only Van Gogh (as far as we know) did paint those without having seen anything like it before - in other words, he had a new idea.
So, if we apply to software, should we quote Dijkstra each time we use his graph algorithm?
Should we also say "if you can implement Dijkstra's algorithm" it's irrelevant because "you did not have the idea"?
It's great to credit people that have an idea first. I fail to see how using an idea is that "bad" or "not worthy", ideas should be spread and used, not locked by the first one that had them (except some small time period maybe).
Even the mechanical skill of painting gets a lot harder without an example to look at. Most people can get pretty good at painting from example within a year or two but it’s a big leap to simply paint from memory, much less create something original.
> I would be curious how can this be applied to a human? Should we also cite all the courses, articles that we have read on a topic when we write code?
Yea this is the kind of BS and counter-productiveness that irrational radicals try to push the crowd towards.
The idea that one owns your observations of their work and can collect rent on it is absurd.
The Right to Read is a great story on showing just how greedy and stupid people would get if we allow them. Society and culture is large scale theft. Imagine having to pay to learn about the idea of fire, or to use the alphabet. Simply put humanity would have never progressed much farther than animals. And yet, as the complexity of our ideas increase, suddenly many humans start thinking that owning ideas, many forever, is just great and will not have any negative ramifications.
This argument falls apart on the very first bullet point. The author claims:
> If someone produces a painting in the style of Van Gogh, and passes it off as being made by Van Gogh, by putting his signature on it, that painting is a forgery.
Which is true. But the implication that follows is false.
Van Gogh's artwork is valuable specifically because of his identity. I find much of his artwork particularly hideous. That's fine! Someone else finds value in it specifically because of who wrote it.
This metaphor doesn't appear to apply to code at all. The entire value of code is what it does not who wrote it.
Honestly, I stopped reading after the first bullet point because these types of arguments feel lazy and the attitude of the people writing these articles frequently comes across as holier than thou.
You don't like LLMs? Great, don't use them. Using Van Gogh's paintbrush doesn't mean I'm making a forgery. I'm just painting, my friend.
I don't agree with many statements in the article. It almost seems like an article from about a year ago, despite it being posted yesterday. Not sure if the author had the idea a long time ago and just took his time to finish it up, but the "vibe-coding" he describes surely isn't the current way of using LLMs in a codebase.
While LLMs are surely used to generate a lot of slop-code and overwhelm (open source) code bases, this surely isn't the only thing they can do. I dislike discussing the potential of a technology exclusively by looking at its negative impact.
LLMs in proper hands don't create code which is "stolen", they also shouldn't create unnecessary code and definitely don't remove any of the ownership of the programmer, at least not any more than using a mighty IDE does.
The problem seems to be in the usage of LLMs. These effects definitely do happen when just releasing an agent on a codebase without any oversight. But they can also largely be mitigated by using frameworks such as Openspec or Spec-Kit, properly designing a spec, plan, granular tasks and manually reviewing all code yourself. The LLM should not be responsible for any creative idea, it should at most verify the practicality against the codebase. When doing that, the entire creative control is in the hands of the programmer and so is the mechanical execution. The LLM is reduced to a very powerful autocomplete with a strict harness around it. Obviously this also doesn't lead to 10x or even 100x improvements in speed like some AI merchants promise, but in my personal experience the speedup is still significant enough to make LLMs a very, very useful technology.
I think it says a lot about this opinion piece that the people agreeing with it are posting short comments saying "So true!" and "Great!" whilst the people criticizing it are writing paragraphs of well-spoken criticism.
I see a future where I program at work less, which is sad but c'est la vie. I think the challenge of the job will be heralding and managing my own context for larger codebases managed by smaller teams, and finding ways to allow for more experimental/less verified code in prod. And plenty of consulting work for companies which have vibe coded their business and who are left with a totally fucked data model (if not codebase).
Claude makes me mad:
even when I ask for small code snippets to be improved, it increasingly starts to comment "what I could improve" in the code I stead of generating the embarrassingly easy code with the improvement itself.
If I point it to that by something like "include that yourself", it does a decent job.
Enforce this with deterministic guardrails. Use strictest linting config you possibly can, and even have it write custom, domain specific linters of things that can't happen. Then you won't have to hand hold it that much
That's a problem with any self-improving tools, not just LLMs. Successful self-improvement leads to efficiency, which is just another name for laziness.
Whether we like it or not, the only constant in life is change.
>What's the Excel of JSON
Ever heard of CUE that's compatible with JSON and YAML introduced by ex-Googlers? It seamlessly support both types and values, whereas Excel supports ephemeral values [1].
Both CUE and original Excel are non-Turing complete so they don't have the notorious and tricky halting problem.
Someone need to seamlessly integrate LLM with CUE, its NLP deterministic distant cousin based on lattice-valued logic [2],[3].
Truth be told LLM are like the automated loom machine during 19th CE Britain that kick started the industrial revolution. Heck the Toyota conglomerate was once the pioneer of the modern automated loom manufacturer, and looks where they are now after embracing change and pivoted to vehicle manufacturing.
The automated loom machine commoditize the manual looming industry (not unlike modern software engineering) to its oblivion in India, that turned the rich Moghul India with the highest GDP in the whole wide world into the lowest GDP for India during colonial time (include Indian sub-continent namely Afghanistan, Pakistan and Bangladesh here if you want apple to apple comparison) [4].
Ignore LLM at your peril in the name of so-called moral authenticity/forgery/lie/etc, and you can go the way of 20th CE India and its sub-continent, settling only at a fraction of its Moghul empire in term of GDP at its very peak.
> Is there a standard CRDT-like protocol for syncing editable graphs yet?
It's for other HN comments but spoiler alert it's called D4M by the nice folks from MIT [5]. We probably don't need full CRDT, local-first capability with eventual consistency will be more than suffice for most things that are of importance.
> Engineers who know their craft can still smell the slop from miles away when reviewing it, despite the "advances" made. It comes in the form of overly repetitive code, unnecessary complexity, and a reluctance to really refactor anything at all, even when it's clearly stale and overdue.
I’ve seen reluctance to refactor even 10+-year-old garbage long before LLMs were first made available to the broader public.
LLM-generated snippets of code are a breath of fresh air compared with much legacy code. Since models learn probability distributions they gravitate to the most common ways of doing things. Almost like having a linter built in. On the other hand, legacy code often does things in novel ways that leave you scratching your head--the premise behind sites like https://thedailywtf.com/
If it's lasted 10 years and someone is still using it after all that time, that seems like a pretty good signal there's a lot of value in the 'garbage'?
I've seen a lot of 'fixes' for 10 year old 'garbage' that turned out to be regressions for important use cases that the author of the 'fix' wasn't aware of.
I find these criticisms to be similar to those low context brain teasers.
If a woman gets married at 25 and her kid is 25, how old is she?
This is what LLMs are dealing with. You dont tell them everything they need to know and they are left to fill in the gaps. Which may, and sometimes often means they lie.
That's what Agentic does differently, it'll go find the gaps before answering.
Agentic is AGI. You can hire many minimum wage workers who are generally intelligent who dont even go to that level.
What weirds me out is that it seems few US corporations care that they don't have copyright to their synthesised code, if the rumours regarding this are correct.
If you don't have the copyright, then you can't license or litigate it under the common rules of software. If someone 'steals' it you can at best go after them with some trade secret case, and I suspect this would be limited if you had already shared the code with them, e.g. because they helped you synthesise it.
The authors logic only works for software engineers and as I have said time and time again- software engineers have been automating people out of their passions for decades and now it has come for yours... The lying here is LYING TO YOURSELVES.
Hello, I am a single dev using an agent (Claude Code) on a solo project.
I have accepted that reading 100% of the generated code is not possible.
I am attempting to find methods to allow for clean code to be generated none the less.
I am using extremely strict DDD architecture. Yes it is totally overkill for a one man project.
Now i only have to be intimate with 2 parts of the code:
* the public facade of the modules, which also happens to be the place where authorization is checked.
* the orchestrators, where multiple modules are tied together.
If the inners of the module are a little sloppy (code duplication and al), it is not really an issue, as these do not have an effect at a distance with the rest of the code.
I have to be on the lookout though. It happens that the agent tries to break the boundaries between the modules, cheating its way with stuff like direct SQL queries.
Sorry to say this feels like cope from someone who is a talented developer in more niche areas finally seeing AI start to solve problems in those areas.
A short design note and tribute to Richard Stallman (RMS) and St. IGNUcius for the term Pretend Intelligence (PI) and the ethic behind it: don’t overclaim, don’t over-trust, and don’t let marketing launder accountability.
Richard Stallman proposes the term Pretend Intelligence (PI) for what the industry calls “AI”: systems that pretend to be intelligent and are marketed as worthy of trust. He uses it to push back on hype that asks people to trust these systems with their lives and control.
From his January 2026 talk at Georgia Tech (YouTube, event, LibreTech Collective):
> "So I've come up with the term Pretend Intelligence. We could call it PI. And if we start saying this more often, we might help overcome this marketing hype campaign that wants people to trust those systems, and trust their lives and all their activities to the control of those systems and the big companies that develop and control them." — Richard Stallman, Georgia Tech, 2026-01-23. Source: YouTube (full talk) — "Dr. Richard Stallman @ Georgia Tech - 01-23-2026," Alex Jenkins, CC BY-ND 4.0; transcript in video description.
So PI is both a label (call it PI, not AI) and a stance: resist the campaign to make people trust and hand over control to systems and vendors that don’t deserve that trust. In MOOLLM we use the same framing: we find models useful when we don’t overclaim — advisory guidance, not a guarantee (see MOOAM.md §5.3).
[...]
Richard Stallman critiques AI, connected cars, smartphones, and DRM (slashdot.org)
42 points by MilnerRoute 38 days ago | hide | past | favorite | 10 comments
The use of loaded and pejorative language like "forgery" emphasizes that this is not a logical argument, but a moral one. The repeated comparisons to "true craft" reveals the author would prefer that code be regarded like artisanal cheese.
Beyond the pretension, it's head in the sand to imply that the technology hasn't progressed. It's just very clearly not true to anyone who is paying attention - longer tasks, better code, less errors. I'm somebody who actively despises the hype bullshit-machine that SV has turned into, but technology is an industry for pragmatists that can leverage what works. And LLMs do.
If you don't like the technology, you have every right to scream that from the mountaintops. As it stands, this just serves as no more than a rallying cry to the ignorant.
This is the most rational take. I'm a quality guy (Deming, Juran, etc), but nothing about incorporating an LLM into my own work has lowered its quality. That isn't to say that I haven't encountered slop. The difference is that, self-identifying as a craftsman, I have the ability to decide whether or not something stays or goes on the scrap heap. It seems a lot of people are missing that point: just because you can churn out shit doesn't mean you have to (and sorry, sunk-cost bias re: tokens isn't an excuse—that's the cost of doing business). It's a choice. AI-assisted coding is a tremendous boon on productivity, if (and I'd argue only if) you treat it like a power tool and not a genie lamp.
No, you won't be rewarded magic beans for churning out crappy dashboards any more. But if you're serious about shipping quality, nothing is stopping you here.
> Open source software maintainers have been one of the first to feel the downsides. ... The last thing they needed was to receive slop-coded pull requests from contributors merely looking to cheat their way into having a credible GitHub resumé... As a result, projects have closed down public contributions and dropped their bug bounties...
Has this really been people's experience?
I develop and maintain several small FOSS projects, some of which are moderately popular (e.g. 90,000-user Thunderbird extension; a library with 850 stars on GitHub). So, I'm no superstar or in the center of attention but also not a tumbleweed. I've not received a single AI-slop pull request, so far.
Am I an exception to the rule? Or is this something that only happens for very "fashionable" projects?
Sigh. Another one standing on the train tracks giving the approaching train a good scolding. First this article tries to equate AI-generated code with "forgery". Please, tell me how you "forge math". Next, it makes a little dig at senior engineers who use LLMs, because they must not realize that "every line of code is a liability". No no, senior engineers realize this, but they are also adept at observing successes and failures and coming up with a mental model for risk. That's part of keeping an application running, otherwise we'd all still be using jQuery and leftPad. We made the jump to react because we recognized that these NEW lines of code were far more valuable than their "liability". Somehow the author decided to store "liability" in a boolean. Oh, was AI involved, or is that a genuine human error..? Next the article makes a tired appeal to the fact that LLMs are trained on open-source code and are therefore "plagiarizing" this code constantly. This is where the train comes around the mountain. So when the AI generates Carmack's Reverse, is it plagiarizing Carmack or the book that he got the idea from? In what percentages? And what do I do with this valuable insight? Send Carmack $0.01 in an envelope for the privilege? In short, I don't know what the author wants, but I hope writing this helped.
I think you are mistaken. I remember landing upon that website some time before 2023 and being greatly impressed by that very 3D parallax effect. To my knowledge there were no AI tools available in 2022 capable of producing effects like that. There are barely now.
To my understanding it was made possible by another project of the same author [0] - check the date. Also check this [1] - dated 2003.
Is it so hard to accept that humans are more capable of doing intricate and impressive work than any LLM will ever be?
My apologies if this is a joke I’m not understanding, but as far as I can tell with the wayback machine, this animation predates not just coding/generative AI, but the Attention paper and the founding of OpenAI too.
No, it's simply untrue. Players only object against AI art assets. And only when they're painfully obvious. No one cares about how the code is written.
If you actually read the words used in Steam AI survey you'll know Steam has completely caved in for AI-gen code as well. It's specifically worded like this:
> content such as artwork, sound, narrative, localization, etc.
No 'code' or 'programming.'
If game players are the most anti-AI group then it's crystal clear that LLM coding is inevitable.
> This stands in stark contrast to code, which generally doesn't suffer from re-use at all, or may even benefit from it, if it's infrastructure.
Yeah, exactly. And LLM help developers save time from writing the same thing that has be done by other developers for a thousand times. I don't know how one can spins this as a bad thing.
> Classic procedural generation is noteworthy here as a precedent, which gamers were already familiar with, because by and large it has failed to deliver.
Spore is well acclaimed. Minecraft is literally the most sold game ever. The fact one developer fumbled it doesn't make the idea of procedural generation bad. This is a perfect example of that a tool isn't inherently good or bad. It's up to the tool's wielder.
Yes, this is a wildly uneducated perspective.
Procedural generation has often been a key component of some incredibly successful, and even iconic games going back decades. Elite is a canonical example here, with its galaxies being procedurally generated. Powermonger, from Bulldog, likewise used fractal generation for its maps.
More recently, the prevalence of procedurally generated rogue-likes and Metroidvanias is another point against. Granted, people have got a bit bored of these now, but that's because there were so many of them, not because they were unsuccessful or "failed to deliver".
And it's used to power effect where you might not expect it (Stardew Valley mines).
What procedural generation does NOT work at is generating "story elements" though perhaps even that can fall, Dwarf Fortress already does decently enough given that the player will fill in the blanks.
Apparently Stardew Valley's mines are not procedurally generated, but rather hand-crafted. Per their recent 10 year anniversary video, the developer did try to implement procedural generation for the mines, but ended up scrapping it:
https://www.stardewvalley.net/stardew-valley-10-year-anniver...
It’s not even in the same ballpark as Elite, NMS, terraria, or Minecraft.
The levels are all hand drawn, not generated by an algorithm, even if they’re shuffled. Eric Barone, the developer, has publicly said as much. Are you calling him a liar?
It’s like the difference between sudoku/crossword and conways game of life
It looks like the Super Mario Bros series has a good showing, but it is the first one. I bet 3 falls into an unlucky valley where the game-playing population was not quite as large as it is now, but it isn’t early enough to get the extreme nostalgia of the first one.
https://en.wikipedia.org/wiki/List_of_best-selling_video_gam...
Of course this assumes sales=popularity, but the latter is too hard to measure.
So procedural generation is extremely prevalent in most AAA games and has been for a long time.
Procedural generation is good when variety matters more than quality, which is a relatively rare occurrence.
Now with an LLM I could have AD&D-like campaigns, photorealistic renders of my character and the NPCs. I could give it the text of an AD&D campaign as a DM and have it generate walking and talking NOCs.
The art of those great fantasy artists is definitely being stolen in generated images, and application of VLMs should require payment into some sort of art funding pool. But modern artists could well profit by being the intermediary between user and VLM, crafting prompts, both visual and textual, to give a consistent look and feel to a game.
The essay author is smoking crack.
Even when I play a game like Expedition 33 or Elden Ring, my brain (for whatever reason) makes a solid split between the cutscene versions of the characters and the gameplay version. I mean, in some games the gameplay characters is a wandering murderer, while the cutscene characters have all sorts of moral compunctions about killing the big-bad. They are clearly different dudes.
In any case, I agree that gamers by and large don’t care to what extent the game creation was automated. They are happy to use automated enemies, automated allies, automated armies and pre-made cut scenes. Why would they stop short at automated code gen? I genuinely think 90% wouldn’t mind if humans are still in the loop but the product overall is better.
Yes. It is "wildly uneducated" to have, and express, strong opinions about ANY field of endeavour where you are unfamiliar with large parts of that field.
Elite is a bit more obscure, but really anybody who aims to be familiar with the history of games should recognize the name at least. Metroidvania isn't a game, but is a combination of the names of Metroid and Castlevania and you absolutely should know about both of those.
Powermonger is new to me.
And while the comment in question didn't mention it, others have: Minecraft. If you're not familiar with Minecraft you must be Rip Van Winkle. This should be the foremost game that comes to mind when anybody talks about procedural generation.
This reminded me of a conversation about AI I had with an artist last year. She was furious and cursing and saying how awful it is for stealing from artists, but then admitted she uses it for writing descriptions and marketing posts to sell her art.
The world makes waay more sense when you really internalize that. It doesn't necessarily mean people are selfish, large groups often have aligned interests, but when an individuals interest alignment changes, then their group membership almost always changes too.
I'd bet she has a bunch of pirated content and anti-copyright remarks from the golden age of piracy as well.
The big eye-opener for me in college was taking a class that put me up-close with artists and learning that there were, in the whole class, a grand total of two students who hadn't started doing 3D modeling on a cracked copy of Maya (and the two, if memory serves, learned on Blender).
There _have_ however been studies that show that this attitude is prevalent in (neoclassical) economics students and others who are exposed to (neoclassical) economic thinking: https://www.sciencedirect.com/science/article/abs/pii/S22148...
It's very effective propaganda. And we have a good example of it here. (Mot saying you're spreading it maliciously, but you are spreading it).
I can rage against guns and gun manufacturers for their negative effects on our nation and hate when they are used for monstrous evil, but also believe that police should have firearms and that the second amendment is important. It’s a tool. You can hate the way it’s made and marketed, and hate many of its popular use cases, and still think there are acceptable ways to use and market it without requiring a total abolition.
To summarize this era we live in: my AI usage is justified but all the other people are generating slop.
[0]: https://www.youtube.com/watch?v=z8fFM6kjZUk
[1]: Disclaimer: I deeply respect Sinix as an art educator. If it weren't him I wouldn't have learnt digital painting. But it's still quite a weird take of him.
Before LLMs we did already have a way to "save developers time from writing the same thing that has been done by other developers for a thousand times", you know? A LLM doing the same thing the 1001st time is not code reuse. Code reuse is code reuse.
The whole history of programming tool is exploring how to properly reuse code: are functions or objects the fundamental unit of reuse? is diamond inheritance okay? should a language have an official package management? build system? should C++ std have network support? how about gui support? should editors implement their own parsers or rely on language server? And none of these questions has a clear answer after thousands if not millions of smart people attempted. (well perhaps except the function vs object one)
Electron is the ultimate effort of code reuse: we reuse the tens of thousands of human-years invested to make a markup-based render engine that covers 99% of use case. And everyone complains about it, the author of OP article included.
LLM-coding is not code reuse. It's more like throwing hands up and admitting humans are yet not smart enough to properly reuse code except for some well-defined low level cases like compiling C into different ISA. And I'm all for that.
(Many small dependencies can be avoided by letting the LLM just re-implememt the desired behavior; ~ with tradeoffs, of course)
The issue is orchestrating this local reuse into a coherent global codebase.
You didn't park your car in your driveway so anyone could take it to get groceries
If this is what I think it is, I consider it very lopsided view, failure to recognize what model fits for what case and looking at everything from a hammer point of view
Of course it's just my opinion.
See how it's a matter of what you're looking at?
https://news.ycombinator.com/item?id=47260385
You might have a more compelling argument if instead of syntax and semantics you contrasted semantics and pragmatics.
> Syntactic reuse would be macros
Well sure. My point is that what can be reused is decided ahead of time and encoded in the syntax. Whereas with LLMs it is not, and is encoded in the semantics.
> Pragmatics
Didn't know what that is. Consider my post updated with the better terms.
The industry has known how to reuse codes for two decades now (npm was released 16 years ago; pip 18 years ago). Using LLMs for code reuse is a step in the wrong direction, at least if you care about maintaining your code.
LLMs generate what you tell them to, which means it will be slop if you're careless and good if you're careful, just like programming in general.
And the users may not care about code directly, but they definitely do indirectly. The less optimized and more off-the-shelf solutions have seen a stark decrease in performance but allowing game development to be more approachable.
LLMs saving engineers and developers time is an unfounded claim because immediate results does not mean net positive. Actually, I'd argue that any software engineer worth their salt knows intimately that more immediate results is usually at the expense of long term sustainability.
They fundamentally misunderstood what they were promising, it’s the same as making a pirate game where you never steer the ship or drop anchor.
You can prove people are not bored with the concept as new gamers still start playing fallout new Vegas or skyrim today despite them being old and janky.
It was really a combination of mini-games:
- you got steer a ship (or fleet of ships) around the Caribbean
- ship to ship combat
- fencing
- dancing (with the Governors' daughters)
- trading (from port to port or with captured goods0
- side quests
Each time I played it with my oldest, it felt like a brand new game.
https://en.wikipedia.org/wiki/Sid_Meier%27s_Pirates!
And yet "No Mans Sky" is massively popular.
> ny software engineer worth their salt knows intimately that more immediate results is usually at the expense of long term sustainability.
And any software engineer worth their salt realizes there are 100s if not 1000s of problems to be solved and trying to paint a broad picture of development is naive. You have only seen 1% (at best) of the current software development field and yet you're confidently saying that a tool that is being used by a large part of it isn't actually useful. You'd have to have a massive ego to be able to categorically tell thousands of other people that what they're doing is both wrong and not useful and that they things they are seeing aren't actually true.
I don't think it has anything to do with ego. There are studies on the topic of AI and productivity and I assume we have a way to go before we can say anything concretely. Software workflows permeate the industry you're in. You're putting words in my mouth, I said nothing about what people are doing is wrong or not useful. I said the claim that generative AI is making engineers more productive is an unfounded one. What code you shit out isn't where the work starts or ends. Using expedient solutions and having to face potentially more work in the future isn't even something that is a claim about software, I can make that claim about life.
You need to evaluate what you read rather than putting your own twist on what I've said.
Household name game studios have had custom AI art asset tooling for a long time that can create art quickly, using their specific style.
AI is a tool and as Steve Jobs said, you can hold it wrong. It's like plastic surgery, you only notice the bad ones and object to them. An expert might detect the better jobs, but the regular folk don't know and for the most part don't care unless someone else tells them to care.
And then they go around blaming EVERYTHING as AI.
_Everyone_ (and their grandmother) can instantly tell a ChatGPT generated image, it has a very distinct style - and in my experience no amount of prompting will make it go away. Same for Grok and to a smaller degree Google's stuff.
What the industry needs (and uses) is something they can feed a, say, wall texture into and the AI workflow will produce a summer, winter and fall variant of that - in the exact style the specific game is using.
"So you hated the TV Series Ugly Betty then?"
"What? that's not CGI!"
This video is 15 years old
https://www.youtube.com/watch?v=rDjorAhcnbY
In that specific 15 year old example they're mostly composited, you're right about that.
https://www.youtube.com/watch?v=RxD6H3ri8RI
His Blender Conference talk about photogrammetry / camera projection / projection mapping was fantastic:
World Building in Blender - Ian Hubert
https://www.youtube.com/watch?v=whPWKecazgM
Actually if you look at the scene from Greys Anatomy [0:54] you can see where CGI is used to improve the scene (rather then cut costs), and you get this amazing scene of the Washington State Ferry crash.
I think you can see the parallels here. When people say they hate AI they are generally referring to the sloppy stuff it generates. It has enabled a proliferation of cheap slop. And with few exception it seems like generating cheap slop is all it does (these exception being specialized tools e.g. in image processing software).
Won 3 Primetime Emmys
52 wins & 124 nominations total
https://www.imdb.com/title/tt0805669/awards/
I guess it's just too lowbrow for you.
However, my counter examples included Grey’s Anatomy, Mad Max, and Titanic. None of these are considered high literature exactly (and all of them are award winning as well).
> That said, Steam's policy has been recently updated to exclude dev tools used for "efficiency gains", but which are not used to generate content presented to players.
I only quoted the first paragraph, but there is more.
Its creature creator was, but as a game it was always mediocre to bad. They had to drop something like 90% of the features and extremely dumb down the stages to get it released.
It was also what introduced a lot of us to SecuROM DRM - it bricked my laptop in the middle of a semester.
I love procedural generation, and there is definitely a craft to it. Creating a process that generates a playable level or world is just very interesting to explore as an emergent system. I don't think LLMs will make these system more interesting by default. Of course there are still things to explore in this new space.
It's similar to generative/plotter art compared to a midjourney piece of slop. The craft that goes into creating the code for the plotter is what makes it interesting.
Be careful of reading any viewpoint on the internet. Apparently no one used facebook or instagram and everyone boycotts anything with ai in it.
In reality i think you’d be foolish not to make use of the tools available. Arc Raiders did the right thing by completely ignoring those sorts of comments. There may be a market for 100% organic video games but there’s also a market for mainstream ‘uses every ai tool available’ type of games.
I would overstate:
No one even cares how architecture is done. Unless you are the one fixing it or maintaining it.
Sorry, no one. We all know Apple did some great stuff with their code, but we care more about the awful work done on the UI, right? I mean - the UI seems to not be breaking in these new OSs which is amazing feature... for a game perhaps, and most likely the code is top notch. But we care about other things.
This is the reality, and the blind notion that so-many people care about code is super untrue. Perhaps someone putting money on developers care, but we have so many examples already of money put on implementation no matter what the code is. We can see everywhere funds thrown at obnoxious implementations, and particularly in large enterprises, that are only sustained by the weird ecosystem of white-collar jobs that sustains this impression.
Very few people care about the code in total, and this can be observed very easy, perhaps it can be proved no other way around is possible.
You say you don't care, but I bet you do when you're dealing with a problem caused by poor code quality or bad choices made by the developer.
Just like how most people don't care how well a bridge is designed... until it collapses.
Spore was fun (IMHO) but at the time of release was considered a disappointment compared to its hype.
I can type up what I want much faster and be sure it's at least solving the right problem, even if it may have bugs.
There are also tools to generate boilerplate that work much much better than LLMs. And they're deterministic.
LLMs are physically incapable of generating something “well thought out”, because they are physically incapable of thinking.
You are just repeating that because you read that before somewhere else. Like a stochastic parrot. Quite ironic. ;)
This reads like a skill issue on your end, in part at least in the prompting side.
It does take time to reach a point where you can prompt an LLM sufficiently well to get a correct answer in one shot, developing an intuitive understanding of what absolutely needs to be written out and what can be inferred by the model.
In the past 2 months I've been using all the SOTA models to help me design a new DSL for narrative scripting (such as game story telling) and a c# runtime implementation o the script player engine.
The language spec and design is about 95% authored by me up to this point; I have the LLMs work on the 2nd layer: the implementation specs/guidelines and the 3rd layer: concrete c# implementation.
Since it's a new language, I consider it's somewhat new/novel tasks for LLMs (at least, not like boilerplate stuff like HTTP API or CRUD service). I'd say, these LLMs have been very helpful - you can tell they sometimes get confused and have trouble to comply to the foreign language spec and design - but they are mostly smart enough to carry out the objectives, and they get better and better after the project got on track and has plenty of files/resources to read and reference.
And I'd also say "prompt better" is a important factor, just much more nuanced/complicated. I started with 0 experience with LLM agents and have learned a lot about how to tame them, and developed a protocol to collaborate with agents, these all comes from countless trial and errors, but in the end get boiled down to "prompt better".
I’ve never personally caught the language implementation bug. I appreciate your perspective here.
But I feel like the rationale would still stands: Considering LLMs' natures, common boilerplate tasks are easy because they can kind of just "decompress" from training data. But for a new language design, unless the language is almost identical to some other captured by the model, "decompression" would just fail.
And even semantic analysis is at least very similar in most PLs. Even DSLs. Assuming you're using concepts like variables and functions.
When it comes to codegen / interpreter runtimes, things start to diverge. But this also depends on the use case. More often than not a DSL is a one-to-one map to an existing language, with syntactic sugar on top.
I'm curious, what's the DSL you're working on?
Sure, I can get the task done by delegating everything to an agentic workflow, but it just adds a bunch of useless overhead to my work.
I still need to know what the code does at the end of the day, so I can document it and reason about it. If I write the code myself, it's easy. If an LLM does it, it's a chore.
And even without those concerns, the LLM is still slower than me. Unless it's trivial boilerplate, in which case other tools serve me better and cheaper.
I'll note that a compiler is one of the most well understood and implemented software projects, much of it open source, which means the LLM has a lot of prior art that it can copy.
It's less "git gud; prompt better", and more, "be able to explain (well) what you want as the output". If someone messages the IT guy and says "hey my computer is broken" - what sort of helpful information can the IT guy offer beyond "turn it on and off again"?
Here are some well known names who are now saying they regularly use LLM's for development. For many of these folks, that wasn't true 1-2 years ago:
- Donald Knuth: https://www-cs-faculty.stanford.edu/%7Eknuth/papers/claude-c...
- Linus Torvalds: https://arstechnica.com/ai/2026/01/hobby-github-repo-shows-l...
- John Carmack: https://x.com/ID_AA_Carmack/status/1909311174845329874
My point being - some random guy on the internet says LLM's have never been useful for them and they only output garbage vs. some of the best engineers in the field using the same tools, and saying the exact opposite of what you are.
1. Personal experience. Lazy prompting vs careful prompting.
2. They're coincidentally good at things I'm good at, and shit at things I don't understand.
3. Following from 2, when used by somebody who does understand a problem space which I do not, they easily succeed. That dog vibe coding games succeeded in getting claude to write games because his master knew a thing or two about it. I on the other hand have no game Dev experience, even almost no hobby experience with games specifically, so I struggle to get any game code that even remotely works.
> 2. They're coincidentally good at things I'm good at, and shit at things I don't understand.
This may well be! In the perfect world this would be balanced with the knowledge that maybe “the things you’re good at” are objectively* easier than “things you don’t understand”. Speaking for myself, I’m proficient in many more easy things than hard things.
*inasmuch as anything can be “objectively” easier
I think you can observe this in action by making vague requests, seeing how it does, then roll back that work and make a more precise request using relevant jargon and compare the results. For example, I asked claude to make a system that recommends files with similar tags. It gave me a recommender that just orders files by how many tags they had in common with the query file. This is the kind of solution that somebody may think up quick but it doesn't actually work great in practice. Then I reverted all of that and instead specified that it should use a vector space model with cosine similarity. It did pretty good but there was something subtly off. That is however about the limit of my expertise in this direction, so I tabbed over to a session with ChatGPT and discussed the problem on a high level for about 20 minutes, then asked ChatGPT to write up a single terse technically precise paragraph describing the problem. I told ChatGPT to use no bullet points and write no psuedocode, telling it the coding agent was already an expert in the codebase so let it worry about the coding. I give that paragraph to claude and suddenly it clicks, it bangs out a working solution without any drama. So I conclude the quality of the prompting determined the quality of the results.
The uncomfortable fact remains that one cannot really expect to get much better results from an LLM without putting some work themselves. They aren't magical oracles.
I am talking about using LLMs in general, not for boiler plate specifically.
My point about boilerplate is that I have tools that solve this for me already, and do it in a more predictable way.
Restaurant-goers only object against you spitting in their food if it's painfully obvious (i.e. they see you do it, or they taste it)
Players are buying your art. They are valuing it based on how you say you made it. They came down hard on asset-flipping shovelware before the rise of AI (where someone else made the art and you just shoved it together... and the combination didn't add up to much) and they come down hard on AI slop today, especially if you don't disclose it and you get caught.
That’s not what I remember, I remember PUBG being a viral hit that extensively used asset flipping.
Another example comes from Getting Over It with Bennett Foddy, which despite the fact it uses a lot of pre-bought art assets, the entire game has the indisputable hallmark of Bennett Foddy -- it has a ridiculously tricky control mechanism, and the whole game world you play in, should you make any mistakes, has a strong likelyhood of dropping you right back at the start, and it's all your own fault for not being able to recover from your mistakes under pressure. You can see this theme in his other games like QWOP and Baby Steps
And yet it also effectively ended Will Wright's career. Rave press reviews are not a good indicator of anything, really.
And you wouldn't really have any idea this was the case if you weren't there when it happened.
My favourite example I saw was where Google translated an information page of the Italian branch of a large multinational as “this is the UK branch of [multinational]”, presumably because the LLM thought that was more contextually appropriate in English.
Personally, I do appreciate good localisation, Nintendo usually does a pretty impressive job there. I play games in their original language as long as I actually speak that language, so I don't have too many touch points with translations though.
People definitely do care. Nobody wants vibe-coded buggy slop code for their game.
They want well designed and optimized code that runs the game smoothly on reasonable hardware and without a bunch of bugs.
As proof, ask yourself which of the following two options you would prefer:
1. buggy code that was hand-written 2. optimized code that was vibe-coded
I'll bet most people will choose 2.
So I personally do care and I am someone, so the answer is not no one.
The gaming industry is absolutely overwhelmed with outrageously inefficient, garbage, crash-prone code. It has become the norm, and it has absolutely nothing to do with AI.
Like https://nee.lv/2021/02/28/How-I-cut-GTA-Online-loading-times.... That something so outrageously trash made it to a hundreds-of-million dollar game, cursing millions to 10+ minute waits, should shame everyone involved. It's actually completely normal in that industry. Trash code, thoughtless and lazily implemented, is the norm.
Most game studios would likely hugely improve their game, har har, if they leveraged AI a lot more.
Do you ever ask why you're writing the same thing over and over again? That's literally the foundational piece of being an engineer; understanding when you're reinventing the wheel when there's a perfectly good wheel nearby.
With LLMs, the parameterisation goes into semantic space. This makes code more reusable.
A model trained on all of GitHub can reuse all that code regardless of whether they are syntactically reusable or not. This is semantic reuse, which is naturally much broader.
First, I am not arguing for reusability. Reusability is one of the most common mistakes you can make as a software engineer because you are over-generalizing what you need before you need it. Code should be written for your specific use case, and only generalized as problems appear. But if you can recognize that your specific use case fits a known problem, then you can find the best way to solve that problem, faster.
Second, when you're using an LLM to make your code more 'reusable' you are taking full responsibility for everything that LLM vomits out. You're no longer assembling a car from well known parts, taking care to tailor it to your use case as needed. You're now building everything in said car, from the tires to the engine and the rearview mirror.
Coding is a constant balance between understanding what you're solving for and what can solve it. Using LLMs takes the worst of both worlds, by offloading both your understanding of the problem and your understanding of the solution.
If you are anything above a mid level ticket taker, your responsibility exceeds what you personally write. When I was an “architect” responsible for the implementation and integration work of multiple teams at product companies - mostly startups - and now a tech lead in consulting, I’m responsible for knowing how a lot of code works that I further write and I’m the person called to carpet by the director/CTO then and the customer now.
I was responsible for what the more junior developers “vomit out”, the outside consulting company doing the Salesforce integration or god forbid for a little while the contractors in India. I no more cars about whether the LLM decided to use a for loop or while loop than I cared about the OSQL (not a typo) that the Salesforce consultants used. I care about does the resulting implementation meet the functional and non functional requirements.
On my latest two projects, I understand the customer from talking to sales before I started, I understand the business requirements from multiple calls with the customer, I understand the architecture because I designed it myself from the diagrams and 8 years of working with and (in a former life at AWS) and reviewing it with the customer.
As far as reusability? I’ve used the same base internal management web app across multiple clients.
I built it (with AI) for one client. Extracted the reusable parts and removed the client specific parts and deployed a demo internally (with AI) and modified it and added features (with AI) for another client. I haven’t done web development since 2002 seriously except a little copy paste work. I didn’t look at a line of code. I used AWS Cognito for authentication. I verified the database user permissions.
Absolutely no one in the value chain cares if the project was handcrafted or written by AI - as long as it was done on time, on budget and meets requirements.
Before the gatekeeping starts, I’ve been working for 30 years across 10 jobs and before that I was a hobbyist for a decade who started programming in 65C02 assembly in 1986.
My point is that the very act of training an LLM on any corpus of code, automatically makes all of that code reusable, in a much broader semantic way rather than through syntax. Because the LLM uses a compressed representation of all that code to generate the function you ask it to. It is like having an npm where it already has compressed the code specific to your situation (like you were saying) that you want to write.
People spin all kinds of things if they believe (accurately or not) that their livelihood is on the line. The knee-jerk "AI universally bad" movement seems just as absurd to me as the "AGI is already here" one.
> Spore is well acclaimed. Minecraft is literally the most sold game ever.
Counterpoint: Oblivion, one of the first high-profile games to use procedural terrain/landscape generation, seemed very soulless to me at the time.
As I see it, it's all a matter of how well it's executed. In the best case, a skilled artist uses automation to fill in mechanical rote work (in the same way that e.g. renaissance artists didn't make every single brushstroke of their masterpieces themselves).
In the worst (or maybe even average? time will tell) case, there are only minimal human-made artistic decisions flowing into a work and the output is a mediocre average of everything that's already been done before, which is then rightfully perceived as slop.
Is that even a counter point? Nobody in their right mind would ever claim that procedural generation is impossible to fuck up. The reason Minecraft/etc are good examples is because they prove procedural generation can work, not that it always works.
I might be misremembering but wasn't the Oblivion proc-gen entirely in the development process, not "live" in the game, which means...
> "In the best case, a skilled artist uses automation to fill in mechanical rote work"
...is what Bethesda did, no?
The problem with procedural generation is it's hard to make it as action-packed and desirable as WoW zones, and even those quickly become fly-over territory.
I don't care if LLMs are good at coding or bad at it (in my experience the answer is "it depends"). I don't care how good are they at anything else. What matters in the end is that this tech is not to empower a common person (although it could). It is not here to make our lives better, more worthwhile, more satisfying (it could do these as well). It is there to reduce our agency, to make it easier to fire us, to put us in even more precarious position, to suck even more wealth from those that have little to those that have a lot.
Yet what I see are pigs discussing the usefulness of bacon-making machine just because it also happens to be able to produce tasty soybean feed. They forget that it is not soybean feed that their owner bought this machine for, and that their owner expects a return from such investment.
How do you figure? 20 dollars/month is insanely cheap for what OpenAI/Anthropic/Google offer. That absolutely qualifies as "empowering a common person". It lowers barriers!
A lot of the anti-AI sentiment on HN concerns people losing their jobs. I don't think this will happen: programmers who know what they're doing are going to be way, way more effective at using AIs to generate code than others.
But even if it is true and we do see job losses in tech: are software devs really "in a precarious position"? Do they really qualify as "those that have little"? Seems like a fantasy to me. Computer programmers have done great over the past 30 years.
More broadly, anti-AI sentiment comes from people who dislike change. It's hard to argue someone out of that position. You're allowed to prefer stasis. But the world moves on and I think it's best to remain optimistic, keep an open mind, and make the most of it.
$20/month in return for measurable reductions in quality of life is not an amazing deal. It's "Heads I win, tails you lose."
Or maybe, if you're thinking of it as an enabler for a side hustle or some other project with a low probability of a high payoff, it can slightly more optimistically be regarded as a moderately expensive lottery ticket.
That's not pessimism; it's just a realistic understanding of how the tech industry actually works, informed by decades' worth of experience.
Can you share those studies? I'm pretty skeptical of this effect. I find that AI has made my job easier and less stressful.
In general, I think your atittude is not realistic, it's just general pessimism about the world ("everything new is bad") that is basically unfounded.
Block just laid off 40% of their company citing AI.
It's not because of pandemic overhiring, and if that were true, the layoffs in 2021-2022 would have handled it. It's 2026. The people getting laid off (on average) haven't worked at these companies since before the pandemic, they got hired in ~2023 (average tenure at a tech company is ~3 years).
It's not because of AI either. Nobody is replacing jobs with AI, AI can't do anyone's job.
It's not because of interest rates. People hired like crazy when interest rates were this high in the oughts.
It's because Elon Musk's Twitter purchase and subsequent management convinced every executive in tech that you can cut to the bone, fuck your product's quality completely, and be totally fine. It's not true, but the downsides come later and the cash influx comes now, so they're doing it anyway.
Because the company was being horribly run and over hired and "pivoted to blockchain" for no fucking reason.
> citing AI.
Because it's 2026 and they thought that would work to bullshit a few people about point one, which apparently it did.
Could be. It could also end up freeing us from every commercial dependency we have. Write your own OS, your own mail app, design your own machinery to farm with.
It’s here, so I don’t know where you’re going with “I’m unhappy this is happening and someone should do something”
Yeah, companies that develop and push this tech definitely have this in mind.
> I don’t know where you’re going with “I’m unhappy this is happening and someone should do something
I am not surprised because I didn't write anything like it.
> I am not surprised because I didn't write anything like it.
You're right, there was no "someone should do something" call to action in your original comment.
I'm a small business owner, and AI has drastically increased my agency. I can do so much more - I've built so many internal tools and automated so many processes that allow me to spend my time on things I care about (both within the business but also spending time with my kids).
It is, fortunately, and unfortunately, the nature of a lot of technology to disempower some people while making lives better for others. The internet disempowered librarians.
It isn't, it just a matter of seeing ahead of the curve. Delegating stuff to AI and agents by necessity leads to atrophy of skills that are being delegated. Using AI to write code leads to reduced capability to write code (among people). Using AI for decision-making reduces capability for making decisions. Using AI for math reduces capability for doing math. Using AI to formulate opinions reduces capability to formulate opinions. Using AI to write summaries reduces capability to summarize. And so on. And, by nature, less capability means less agency.
Once men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them
Not to mention utilizing AI for control, spying, invigilation and coercion. Do I need to explain how control is opposed to agency?
edit: also the solution here isn't that every one writes their own software (based on open source code available on the internet no doubt) we just use that open source software, and people learn to code and improve it themselves instead of off-loading it to a machine
LLMs are bad at counting the number of rows in a spreadsheet. LLMs are great at "write a Python script that counts the number of rows in this spreadsheet".
It'll fail miserably at making it human-friendly though, and attempt to pilfer existing popular designs. If it builds a GUI, it's be a horrible mashup of Windows 7/8/10/11, various versions of OSX / MacOS, iOS, and Android. It won't 'get' the difference between desktop, laptop, mobile, or tablet. It might apply HIG rules, but that would end up with a clone at best.
In short, it would most likely make something technically passable but nightmareish to use.
Given a century the only unreasonable part is oneshotting with no details, context, or follow up questions. If you tell Linus Torvalds "write a python script that generates and OS", his response won't be the script, it'll be "who are you and how did you get into my house".
If you're expecting OSX, AI will certainly be able to make that and better "in the next 100 years". Though perhaps not oneshotting off something as vague as "make an OS" without followup questions about target architecture and desired features.
3 years ago LLMs couldn’t solve 7x8.
Now they’re building complex applications in one shot, solving previously unsolved math and science problems.
Heck, one company built a (prototype but functional) web browser
And you say it’s crazy that in the future it’ll be able to build a mail app or OS?
Another distraction is AGI that which is a danger to humanity- the only danger is people...
Simply put, no it is not.
But on the reverse, the first danger with AI is people.
Over the longer term it will look like this. The rich 'win' the world by using AI to enslave the rest of mankind and claim ownership over everything. This will suck and a lot of us will die.
The problem is this doesn't solve the greed that cause the problem in the first place. The world will still be limited in a resources of something which will end with the rich in a dick measuring contest and to win that contest they will put more and more power in AI and they connive and fight each other. Eventually the AI has enough power that it kills us all, intentionally or not.
We'll achieve nearly unlimited capability long before we solve the problem of unlimited greed and that will spell our end.
Ive worked in "AI" for 20 years, through 2 winters, and run an alignment shop and AIRT... The problem is people. People will use the problem as a scapegoat.
I think new jobs will be created because AI is always limited by hardware and its current capabilities. Businesses, in order to compete, want to do things their competitors aren't currently doing. Those business needs always go beyond the current technological capabilities until the tech catches up and then they lather, rinse, repeat.
With shrinking and aging population?
Complete bullshit.
The individual has never had as much ability to take on large projects as they do now. They’ve never been able to learn as easily as they can now.
>to make it easier to fire us
As of now, the technology increases productivity in the average user. The companies that take advantage of that and expand their offering will outperform the ones that simply replace workers and don’t expand or improve offerings.
More capable employees make companies more money in general. Productivity increases lead to richer societies and yes, even more jobs, just as it always has.
You could say this is the story of society, it makes us dependent on each other, reduces our agency, puts us in precarious positions (like WW2). But nobody would argue against society like that.
What happens here is that we become empowered by AI and gain some advantages which we immediately use and become dependent on, eventually not being able to function without them - like computers and even thermostats.
Does anyone think how would economy operate without thermostats? No fridges, no data centers, no engines... they all need thermostats. We have lost some freedom by depending on them. But also gained.
I was able to feel wool scarves made in europe from the middle ages. (In museum storage, under the guidance of a curator) They are a fundamentally different product than what is produced in woolen mills. A handmade (in the old traditiona) woolen scarf can be pulled through a ring, because it is so thin and fine. Not so for a modern mill-made scarf.
Another interesting thing is that we do not know how they made them so fine. The technique was never recorded or documented in detail, as it was passed down from parent to child. So the knowledge is actually lost forever.
Weavers in Kashmir work a similar level of quality, but their wool is different, their needs and techniques are different, so while we still have craftsman that can produce wool by hand, most of the traditions and techniques are lost.
Is it a tragedy? I go back and forth. Obviously the heritage fabrics are phenomenal and luxurious. Part of me wishes that the tradition could have been maintained through a luxury sector.
Automation is never a 1:1 improvement. It's not just about the speed or process. The process itself changes the product. I don't know where we will net out on software, and I do think the complaints are justified - but the Luddites were also justified. They were *Right*. Their whole argument was that the mills could not product fabric of the same quality. But being right is not enough.
I'm already seeing vibe-coded internal tools at an org I consult at saving employees hundreds of hours a month, because a non-technical person was empowered to build their own solution. It was a mess, and I stepped in to help optimize it, but I only optimized it partially, making it faster. I let it be the spaghetti mess it was for the most part - why? because it was making an impact already. The product was succeeding. And it was a fundamentally different product than what internal tools were 10 years ago.
And they also like to emphasise how long it takes for someone to become a master at a given trade.
Though, given Amodei and Altman’s behavior (along with the rest of the billionaire class) that shouldn’t be a surprise to anyone.
Eating raw chicken is risky even in Japan. There are cultures that eat raw chicken, pork, and other meat products by choice but it’s always a risk. There are outbreaks of serious food borne illness in Japan from raw chicken: https://pubmed.ncbi.nlm.nih.gov/18406474/
Survivor bias plays a role in glorifying the past.
With programming, we documented a lot of it, so it's unlikely to go the way of fine weaving. People will always be able to learn to think and be great programmers.
Maybe if the wool weavers had internet, they could have blogged, made youtube videos, and cataloged their profession so it could last Millenia.
Long term though, I’ve always wondered if the Amish turn out to be the only survivors.
Its entirely possible that old manufacturing methods produced things that are different, but I would be entirely surprised if they are entirely better overall. If the defining metric for scarves is how well they fit through rings, I am sure they would all be made so you could fit 3 through a ring if people were willing to pony up for that. If you look at a lot of old clothes, they are generally a lot heavier, but I am not sure I would really want to wear them, they look quite uncomfortable. I also think its wonderful that today you can get a set of clothes for a few hours of minimum wage work while in the past this was a major investment. You can also choose to pay thousands for a shirt if you wish, but from 10 feet away its going to be hard to tell the difference.
Looked up Pucci - looks like a designer that makes silk scarves. Silk is a totally different material. The Luddites were wool and cotton weavers.
Making wool thin enough for a meter long scarf to go thorough a ring requires the individual strand of wool to be very thin. Both making it thin and weaving that thin strand is the craft that was lost. Go look at wool yarn next time you are at a store and see how thin they can get it.
As for "Are they better?" Yes. Thinner wool is incredible, soft. High quality merino wool is one of the most expensive fabrics. Look up this brand "Made in Rosia Montana" if you are curious. It's not like what the Luddites made, but its as good as it gets in the modern world. Getting stuff from the Kashmir region is difficult - I got mine because I knew someone who ran a school in the area. Most "Cashmere" stuff in department stores is fake/chemically processed for fake softness which makes it nice but it doesn't last. Real quality wool lasts a lifetime. The chemically processed stuff is ok if you want to see how it "feels"
EDIT: also, wool is naturally waterproof! I can walk in the rain with my scarf from kashmir on my head, its pretty thin but absolutely no water goes through even in heavy rain. it has to do with the springiness of the fibers and its natural oils. I will stop nerding out on fibers now!!
https://www.youtube.com/watch?v=ZSRHeXYDLko
Now we're way past the point of no return.
This is a rather extreme failure on their part. There’s nothing admirable about hoarding knowledge and forcing it to only be passed down in person.
I don’t see this as the Luddites being right at all because they were clearly incredibly wrong about their chosen method of knowledge storage. This was a highly predictable and preventable outcome. If we were talking about a company today that forgot how to manage their servers because they refused to document anything and only passed it down from person to person we wouldn’t be speaking in awe and wonder, we’d be rightly criticizing their terrible decision making.
That aside, every time I hear that knowledge has been lost forever it turns out to be an exaggeration from those trying to amplify the mystique of the past. If we wanted to make ultra-thin scarves we could do it. We could study those ultra thin museum pieces with our endless array of modern tools and then use our vast quantities of modern wool to experiment until we got something similar.
But you missed the reason why we wouldn’t want to: An ultra-thin scarf isn’t going to work as well as a thicker one for keeping someone warm. It will be less durable. It would be a fundamentally inferior product. It’s interesting to see as a museum antique that has to be treated with utmost delicacy, but not so much as a practical garment.
I get there are many things happening today that are frustrating or moving some element of human life in negative or ambiguous directions, but we really have to keep perspective on these things.
Nearly every problem today is a problem with a solution.
The feelings of panic we have that things are going wrong are useful signals to help guide and motivate us to implement those solutions, but we really must avoid letting the doomerism dominate. Just because we hear constant negative news doesn't mean things are lost. Doesn't even mean things are bad.
It just means we have been hearing a lot of negative news.
This is what it looks like for progress to not be monotonically increasing.
> The feelings of panic > It just means we have been hearing a lot of negative news.
This is part of the problem at hand, not just a footnote.
Before mechanised farming, the men were forced to spend all day in the fields.
Never again.
Nobody's really stopping you from studying agriculture and working in that proverbial field either.
I have no doubt in the future there will be a class of vibe software and it will be known as distinctly lower quality than human understood software. I do think the example you describe is a good use of vibing. I also think tech orgs mandating 100% LLM code generation are short sighted and stupid.
A lot of this push for “slop” is downstream of our K shaped economy. Give the people more money and quality becomes a lot more important. Give them less, and you’re selling to their boss who is often insulated from the effects of low quality.
Mass production makes things accesible, and if the handmade product cannot compete relative to it, then it's clearly not that much better or some people would still pay that premium to keep it around.
With programming that's very much the case, nobody's gonna vibe code a self driving car stack or a production grade DBMS. Even the cheapest scarf still works as a scarf in 99.9% of use cases though, if maybe not for as long.
Sounds like a tautology. If you deliberately hoard knowledge of course it’s going to be hard to obtain.
The difference between automating the creation of software and automating the creation of physical products is that software is everywhere. It is relied on for most tools and processes that keep our civilization alive. Cutting corners on that front, and deciding to entrust our collective future to tech bros and VC firms fiending for their next payout, seems like an incredibly dumb and risky proposition.
The Luddites were right in the sense that the social order had changed in a negative way. In a careless way.
In the same way that we look at America now that has effectively put a plutocracy in absolute control of the country, at the same time that there is going to be a massive devaluing of labour. Elon Musk likes to talk about the coming golden age of automation, but I hope Americans realize that unless they happen to be a billionaire, they will enjoy zero fruits of that advance. Quite contrary, plump yourself up to be Soylent Green because it turns out that giving a bunch of psychopaths/sociopaths absolute control of government isn't good for the average person.
>One of the claims of the Luddites is that quality would go down
Then people will choose the better quality items and it will be easy for them to compete? Right?
Most of what we do is programming is some small novel idea at high level and repeatable boilerplate at low level. A fair question is: why hasn’t the boilerplate been automated as libraries or other abstractions? LLMs are especially good at fuzzy abstracting repeatable code, and it’s simply not possible to get the same result from other manual methods.
I empathise because it is distressing to realise that most of value we provide is not in those lines of code but in that small innovation at the higher layer. No developer wants to hear that, they would like to think each lexicon is a creation from their soul.
If development velocity was truly an important factor in these businesses, we'd migrated away from that gang of four ass Java 8 codebase, given these poor souls offices, or at least cubicles to reduce the noise, we wouldn't make them spend 3 hours a day in ceremonial meetings.
The reason none of this happens is that even if these developers crank out code 10x faster, by the time it's made it past all the inertia and inefficiencies of the organization, the change is nearly imperceptible. Though the bill for the new office and the 2 year refactoring effort are much more tangible.
What that means is anyone’s guess, but it seems like it should result in a Cambrian explosion of disruptive new companies, limited in scope by the idea space.
The thing about small teams is, with a few exceptions, the biggest challenges are typically funnels for users and product-market fit, overcoming and exploitation of network effects, etc… so even in small orgs, if you make 30 percent of the problem 4x faster/smaller you still have the other 70 percent which is now 92.5% of the problem.
This applies even more acutely in larger organizations… so for them, 99 percent of the problem remains.
Intangibles in an organization like reluctance, education, and organizational inertia fill the gap left by software acceleration, and in the end you only see tiny gains, if any.
What really happened, on an organizational scale, is that software development costs went down. We wouldn’t expect a wage collapse in coding to foment an explosive revolution in company profitability or dynamism. We shouldn’t expect those things of LLM assistance.
We should look at it as a reduction in cost with potentially dangerous side effects if not managed carefully, with an especially big reduction in r&d development costs.
For example, we have to plan 8 to 12 sprints in advance. Full acceptance criteria, story points, and slotted into a sprint with points balanced across the team. Of course this is utterly useless, anything past the second sprint is going to be wrong, but they want it done. LLMs got me through that in a few hours instead of a few days.
Honestly you can probably use this as a means to measure the amount of regulation, graft, and corruption in an economy.
In a wild west free for all code velocity would likely be very fast with software popping up, changing rapidly, and some quickly disappearing.
But in an economy that doesn't care what you make, then who you pay or what laws you buy is far more important.
If the janitors swept the floors 10x faster, we wouldn't see any KPIs to shoot up from that. You still need it to happen regularly and reliably and on demand in case there's a mess, but it doesn't need to be fast.
I mean I'd expect that you'd see a decrease in the number of janitor staff as was convenient per the companies policies. That and janitorial services would turn into contracted services rather than in house positions. Oddly enough both these things have already occurred as tooling to janitors became better and the legal incentives made it cheaper.
Abstractions are the source of bloat. Without abstractions you can always reduce bloat, or you can reduce bloat in your glue, but you can't reduce glue.
It takes discipline to NOT create arbitrary function signatures and short-lived intermediate data structures or type definitions. This is the beginning of boilerplate.
So many advances in removing boilerplate are realizing your 5 function calls and 10 intermediate data structures or type definitions, essentially compute a thing that you can do with 0 function calls and 0 custom datatypes and less lines of code.
The abstraction hides how simple the thing you want is.
Problem is that all open source code looks like the bloat described above, so LLMs have no idea how to actually write code that is without boilerplate. The only place where I've seen it work is in shaders, which are usually written to avoid common pitfalls of abstraction.
LLMs are incapable of writing a big program in 1 function and 1 file, that does what you want. Splitting the program into functions or even multiple files, is a step you do after a lot of time, yet all open source looks nothing like that.
I don’t think I agree. Here is an example.
QTcpSocket socket; socket.connectToHost(QHostAddress::LocalHost, 1234);
Vs:
int clientSocket = socket(AF_INET, SOCK_STREAM, 0);
Of course it is not. It is needed, by definition.
> or doesn’t carry novelty.
Of course it does not. Why would a piece of code that simply fills a large C structure with constants be innovative?
> Every project is different and not everything can be made from a generic out-of-shelf product
Tangential to use of LLMs for boring boilerplate stuff.
from foundations import ConcreteStrip
ConcreteStrip(x,y,z)
Doesn't work for houses
To me, a function is a single sentence within a book. It may approach the larger picture, but that sentence can be reviewed, changed, switched around, killed by an editor.
Some programmers believe they're fantastic sentence writers. They brag about how good of a sentence they write, they're entire worldview has been built on being good sentence creators. Especially within enterprises, you may spend your entire life writing sentences without ever really understanding the whole book.
If your worldview has been built on sentence creation, and suddenly there's a sentence creator AI, you're going to be deathly afraid of it replacing you as a sentence writer.
This is not the same when it comes to books and music.
It's weird to look at something that recent and think how dated it reads today. I also wrote about the Turing test as some major milestone of AI development, when in fact the general response to programs passing the Turing test was to shrug and minimize it
They have such obvious patterns and tells that humans have already picked up on them and they can eventually sus out that they're talking to an LLM
For instance I heard recently about someone talking (verbally) with an AI voiced customer support. They were very convinced, so they asked the support agent to calculate the product of two large numbers, and it replied with the result instantly
I would argue that fails the chinese room
However, LLMs destroy this economic incentive utterly. It now seems most productive to code in fairly low level TypeScript and let the machines spew tons of garbage code for you.
Because our ways of programming computers are still woefully inadequate and rudimentary. This is why we have a tons of technique for code reuse, yet we keep reinventing the wheel because they shatter in contact with reality. OOP was supposed to save us all in the 1990s, we've seen how it went.
In other fields we've had a lot of time to figure out basic patterns and components that can be endlessly reused. Imagine if car manufacturers had to reinvent the screw, the piston, the gear and lubricants for every new car model.
One example that has bugged me for a decade is: we've been in the Internet era for decades at this point, yet we spend a lot of time reinventing communication. An average programmer can't spend two days without having to deal with JSON serialization, or connectivity, or sending notifications about the state of a process. What about adding authentication and authorization? There is a whole cottage industry to simplify something that should be, by now, almost as basic as multiplying two integers. Isn't that utter madness? It is a miracle we can build complex systems at all when we have to focus on this minutiae that pop up in every single application.
Now we have intelligences that can create code, using the same inadequate language of grunts and groans we use ourselves in our day to day.
The question is, at what point of progress will it benefit the software industry.
Probably the original sin here is that we started calling them programming languages instead of just 'computer code'.
Also - most of your work is far more than mere novelty! There are intangibles like your intellectual labor and time.
Because a lot of programmers don't know how to copy-paste or make packages for themselves? We have boilerplate at my work, which comprises of some ready made packages that we can drop in, and tweak as needed, no LLMs required
There is also the cost reason, somebody trying to sell an abstraction will try to monetize it and this means not everyone will want/be able to use it (or it will take forever/be unfinished if it's open/free).
There's also the platform lockin/competition aspect...
Sometimes it has. The amount of generated code that selected count(distinct id) from customers would produce is huge.
LLM type systems are the final level of abstraction that lifts it up to literal natural language. Any dev with decent self awareness would admit they were just copying shit from stackoverflow half the time before LLMs anyway, high level languages and libraries just streamline that process with canonical implementations.
The value we provide is turning "person with problem" -> "person with solution to said problem" with as few caveats as possible. A programmer is that arrow, we solve problems. The more code we have to write to solve that problem, the worse we are at our job.
I also don't know what work you do, but I would not characterize the codebases I work in as "small bits of novelty" on boilerplate. Software engineering is always a holistic systems undertaking, where every subcomponent and the interactions between them have to be considered.
I still think LLMs as fancy autocomplete is the truth and not even a dig. Autocomplete is great. It works best when there’s one clear output desired (even if you don’t know exactly what it is yet). Nobody is surprised when you type “cal” and California comes up in an address form, why should we be surprised when you describe a program and the code is returned?
Knowledge has the same problem as cosmology, the part we can observe doesn’t seem to account for the vast majority of what we know us out there. Symbolic knowledge encompasses unfathomable multitudes and will eventually be solved by AI but the “dark matter” of knowledge that can’t easily be expressed in language or math is still out in the wild
FORTRAN ("formula translator") was one of the first programs ever written and it was supposed to make coding obsolete. Scientists will now be able to just type in formulas and the computer will just calculate the result, imagine that!
Yes, it is. Literally every programming innovation claims to "make coding obsolete". I've seen a half dozen in my own lifetime.
Care to share some examples that prove your point?
Cue the smug Lisp weenies.
The period is now. Just add "be a great teacher but don't attempt to write code" in the prompt.
(yes, it's a teacher who gets things wrong from time to time. You still need to refer to the source and ground truth just like when you're taught by a human teacher.)
I'm not sure if you ever had a teacher or instructor that you didn't trust, because they were a compulsive liar or addiction or any other issue. I didn't (as least not that I can remember) but I know I would be VERY on guard about it. I imagine I would consequently be quite stressed learning with them, even if they were brilliant, kind, etc.
It would feel a bit like walking on thin ice to get to a beautiful island. Sure, it's not infeasible and if you somehow make it, it might be worth the risk, but honestly wouldn't you prefer a slower boat?
Unfortunately, individual people are not anywhere as reliable as a compiler for ensuring compliance to reality. We are particularly susceptible to flattery and other emotional manipulation, which LLMs frequently employ. This becomes particularly problematic when you ask for feedback on an idea.
In that case, a useful hack is to frame prompts as if you're an impartial observer and want help evaluating something, not as if the idea under evaluation is your own.
I think that's actually deeply different. If a human keeps on apologizing because they are being caught in a lie, or just a mistake, you distrust them a LOT more. It's not normal to shrug off a problem then REPEAT it.
I imagine the cost of a mistake is exponential, not linear. So when somebody says "oops, you got me there!" I don't mistrust them just marginally more, I distrust them a LOT more and it will take a ton of effort, if even feasible, to get back to the initial level of trust.
I do not think it's at all equivalent to what "Real humans" do. Yes, we do mistake, but the humans you trust and want to partner with are precisely the one who are accountable when they make mistakes.
You seem to have a different understanding of what it means in the context of neural networks.
Real humans will not make up non existent api and implement a solution with it, (unless they do it on purpose).
I think you can build a very easy workflow that reinforces rather than replaces learning, I've used a citation flow to link and put into practice a ton of more advanced programming techniques, that I found incredibly difficult to locate and research before AI.
I'd say the comparison is faulty, it's more akin to swimming to an island (no-ai) vs using a boat. You control the speed and direction of the boat, which also means you have the responsbility of directing it to the correct location.
PS: sorry if the analogy is a bit wonky but it's quite dear to me as I do ice skating on frozen lakes and it's basically a life or death information "game" that I can relate to. It might not be a great analogy for others.
I guess in my view - the main alternative you'd have beforehand is just to drown.
For me, AI sits in a space where if you know how to use it, it can tell you all the thin spots of the ice accurately. You can then verify those spots, but there's a level of personal responsibility of verification.
I'd agree there's currently a ton of people that are using these tools to essentially just find the specific route - but i'd argue those people probably shouldn't be skating in the first place, and would've fallen one way or the other.
Right, but AFAICT most people just venture over the ice and don't bother to check. In fact a lot of people venture there, do check once or twice, then check less and less frequently. The fact that you do it is great but others seem a lot less careful, until cracks start to show and then it might be too late.
I'd only argue that people were doing this before AI, slop development was just copy pasting from the first stack overflow issue that matched the question rather than thinking
So i'd argue there's a part of it that is just personal responsibility with how these tools are used
Before most who didn't know the ice didn't went out on it, today a lot of people who shouldn't be there go far out on the ice.
Maybe "Artisanal Coding" will be a thing in the future?
Programming via LLMs is just the logical conclusion to this niche of industrialized software development which favours quantity over quality. It's basically replacing human bots which translate specs written by architecture astronauts into code without having to think on their own.
And good riddance to that type of 'spec-in-code-out' type of programming, it should never have existed in the first place. Let the architecture astronauts go wild with LLMs implementing their ideas without having to bother human programmers who actually value their craft ;)
People still pay for hand-knit fabrics (there's one place in Italy that makes silk by hand and it costs 5 figures per foot), but the vast majority is machine made.
Same thing will happen to code, unless the bubble bursts really badly. Most bulk API Glue CRUD stuff and basic web UI work will be mostly automated and churned off automated agentic production lines.
But there will still be a market for that special human touch in code, most likely when you need safety/security or efficiency/speed.
Like you can still make Karelian pies[0] anywhere, but unless you follow the exact recipe, you can't sell them as "Karelian pies". It's good for the heritage and good for the customers.
You can also make any cheeses and wines and whatever you like, it's just how you name them and market them that's regulated.
[0] https://en.wikipedia.org/wiki/Karelian_pasty
But the comment you reply to explicitly points out the process is in fact relevant as it is itself a cultural artifact. You're not replying to their main point.
How are the customers hurt if their pie has not been baked by a babushka in Petrozavodsk using the old original recipe, but by an anonymous migrant worker in a dark kitchen using an optimized recipe if the end result is objectively the same? The packaging doesn't have to say who it was made by.
I also don't see the problem with the heritage. The comment I replied to already said anyone could call their pies Karelian, so there was no restriction that benefitted the residents of a specific region. I can see a PDO-like carveout that goes "we want to preserve the traditional pie-making of Karelia, so we want this activity to remain economically viable. Therefore, only pies baked in Karelia can be sold as Karelian pies." But I don't see how Sysco baking the same pies and distributing them nationwide helps maintain the heritage.
Even if you make something that tastes and looks exactly like the original, you still can't call it Specific Thing because the process wasn't followed as it's an integral part of the product. Think of it like a trademark. You can't create some brown sugary stuff and sell it as Coca-Cola - even if it tastes EXACTLY like it does.
Nothing about this is about profit or economic viability, it's not even a small part of the equation. The purpose is to preserve cultural heritage and not dilute it with shitty imitations calling themselves something they are not.
The issue is how this will be handled in law. Can the law define this in a way that is not overly strict or overly permissive? The current attempts is effectively the law doing this, but with an overly strict approach of what counts as 'objectively the same' by judging the process and not purely the outcome. Would it be possible to make the law's definition of this more permissive, to focus only on the product produced, without accidentally becoming overly permissive?
This is an absolute chef-kiss double-entendre.
We are only craftsmen to ourselves and each other. To anyone else we are factory workers producing widgets to sell. Once we accept this then there is little surprise that the factory owners want us using a tool that makes production faster, cheaper. I imagine that watchmakers were similarly dismayed when the automatic lathe was invented and they saw their craft being automated into mediocrity. Like watchmakers we can still produce crafted machines of elegance for the customers who want them. But most customers are just going to want a quartz.
I like Simon Willison's take on this: "Your job is to deliver code you have proven to work". If someone is spitting out LLM trash and shipping it, that means the job isn't being done properly. That can be done with and without an LLM.
https://simonwillison.net/2025/Dec/18/code-proven-to-work/
Sure, no one really cares about the code but the quality of the code matters more for some products (and in different ways) than others.
It's certainly intellectually stimulating to create it, but I've learned to take joy in discarding vast swathes of it when it's no longer required.
I will just copy paste my comment from another thread but still very relevant>
Coding isn’t creative, it isn’t sexy, and almost nobody outside this bubble cares
Most of the world doesn’t care about “good code.” They care about “does it work, is it fast enough, is it cheap enough, and can we ship it before the competitor does?”
Beautiful architecture, perfect tests, elegant abstractions — those things feel deeply rewarding to the person who wrote them, but they’re invisible to users, to executives, and, let’s be honest, to the dating market.
Being able to refactor a monolith into pristine microservices will not make you more attractive on a date. What might is the salary that comes with the title “Senior Engineer at FAANG.” In that sense, many women (not all, but enough) relate to programmers the same way middle managers and VCs do: they’re perfectly happy to extract the economic value you produce while remaining indifferent to the craft itself. The code isn’t the turn-on; the direct deposit is.
That’s brutal to hear if you’ve spent years telling yourself that your intellectual passion is inherently admirable or sexy. It’s not. Outside our tribe it’s just a means to an end — same as accounting, law, or plumbing, just with worse dress code and better catering.
So when AI starts eating the parts of the job we insisted were “creative” and “irreplaceable,” the threat feels existential because the last remaining moat — the romantic story we told ourselves about why this profession is special — collapses. Turns out the scarcity was mostly the paycheck, not the poetry.
I’m not saying the work is meaningless or that system design and taste don’t matter. I’m saying we should stop pretending the act of writing software is inherently sexier or more artistically noble than any other high-paying skilled trade. It never was.
Your perspective is a path with only one logical end. That nothing you do or think or believe matters unless someone you're attracted to finds it attractive.
That is not how I or most others live. We take pride in and derive satisfaction from our accomplishments without the need for external validation.
Yeah, only I care whether the solution I found to a problem today was elegant, or whether my kitchen was pristine and well organized after I prepped for next week's lunches, but so what? I care and it injects more than enough meaning into my life to be worth it.
When I charge a customer for a solution they don't care about how elegant my code is. They just care if it works for solving their problem...
Isn't the problem right now the vibe coded sotware does not appear to meet these requirements?
With the notable exception of Minecraft terrain generation, which I think most would say was successful in what it set out to achieve.
(Worked in Firefox on macOS, doesn't seem to work in Mobile Safari)
In the meantime the only way to really sort this is to have models that have only been trained on some particular kind of license - there is quite a big corpus of GPL'd code out there so a GPL based model could potentially be one of the first, and of course the output could only be GPL.
It's feels like a modern twist on a bygone time of the web.
Love it. Calling it "Copilot" in itself is a lie. Marketing speak to sell you an idea that doesn't exist. The idea is that you are still in control.
Someone might call LLMs that today, except they've stepped a bit up from steroids.
In order to lie, one needs to understand what truth and objective reality are.
Even with people, when a flat-earther tells you the earth is flat, they're not lying, they're just wrong.
All LLM output is speculation. All speculation, by definition, has some probability of being incorrect.
---
We can go even deeper in a philosophical sense. If I made the audacious claim that 2 +2 = 4, I may think it's true, but I'm still speculating that the objective reality I experience is the same one others also experience, and that my senses and mental faculties, and therefore the qualia making up my reality, are indeed intact, correct, and functional. So is there a degree of speculation when I made that claim?
Regardless, I am able to agree upon a shared reality with the rest of the world, and I also share a common understanding of truth and untruth. If I lied, it can only be caused by an intention to mislead others. For example, if I claimed to be the president of the united states, of course that would be incorrect (thankfully!), but since we all agree that no one reading this post would actually be mislead into thinking I am the POTUS, then it isn't a lie. Perhaps sarcasm, a failed attempt at humor, or just trolling. it is untruth, but it isn't a lie, no one was mislead. You need intent (LLM isn't capable of one), and that intent needs to be at least in part, an intent to mislead.
Atleast some of them know they're wrong and are thus lying.
Guilty until proven innocent will satisfy the author's LLM-specific point of contention, but it is hardly a good principle.
He is proposing to not make a judgement at all. If the AI company CLAIMS something they have to prove it. Like they do in science or something. Any claim is treated as such, a claim. The trick is to not even claim anything, let the users all on their own come to the conclusion that it's magic. And it's true that LLMs by design cannot cite sources. Thus they cannot by design tell you if they made something up with disregard to it making sense or working, if they just copy and pasted it, something that either works or is crap, or if they somehow created something new that is fantastic.
All we ever see are the success stories. The success after the n-th try and tweaking of the prompt and the process of handling your agents the right way. The hidden cost is out there, barely hidden.
This ambiguity is benefitting the AI companies and they are exploiting it to the maximum. Going even as far as illegally obtaining pirated intellectual property from an entity that is banned in many countries on one end of their utilization pipeline and selling it as the biggest thing ever at the other end. And yes, all the doomsday stories of AI taking over the world are part of the marketing hype.
>AI output should be treated like a forgery
Who's passing this judgement this? Author? Civil society?
What helped us most was treating outputs like untrusted input: retrieval + citations for factual claims, strict tool permissions, and an explicit “I don’t know” path that’s rewarded instead of punished. That doesn’t make LLMs truthful, but it does make them a lot less brittle.
So to me the key question isn’t “are models liars?” but “what product and engineering constraints make wrong answers cheap and detectable?”
On a philosophical level I do not get the discussions about paintings. I love a painting for what it is not for being the first or the only one. An artist that paints something that I can't distinguish from a Van Gogh is a very skillful artist and the painting is very beautiful. Me labeling "authentic" it or not should not affect it's artistic value.
For a piece of code you might care about many things: correctness, maintainability, efficiency, etc. I don't care if someone wrote bad (or good) code by hand or uses LLM, it is still bad (or good code). Someone has to take the decision if the code fits the requirements, LLM, or software developer, and this will not go away.
> but also a specific geographic origin. There's a good reason for this.
Yes, but the "good reason" is more probably the desire of people to have monopolies and not change. Same as with the paintings, if the cheese is 99% the same I don't care if it was made in a region or not. Of course the region is happy because means more revenue for them, but not sure it is good.
> To stop the machines from lying, they have to cite their sources properly.
I would be curious how can this be applied to a human? Should we also cite all the courses, articles that we have read on a topic when we write code?
Even if you aren't in the group, there is clearly a group of people who appreciate seeing the original, the thing that modified our collective artistic trajectory.
Forgeries and master studies have a long history in art. Every classically trained worth their salt has a handful of forgeries under their belt. Remaking work that you enjoy helps you appreciate it further, understand the choices they made and get a better for feel how they wielded the medium. Though these forgeries are for learning and not intended to be pieces in their own right.
Generally you get a much better ‘view’ of the artwork in a museum. It’s higher ‘resolution’ you can view it from multiple angles etc.
There are some exceptions. You’re probably going to get a better look at the Mona Lisa online than if you try and see it at the Louvre.
I go to a museum to see a curated collection with explanations in a place that prevents distractions (I can't open a new tab) and going with people that might be interested to talk about what they see and feel. It's as well a social and personal experience on top information gathering.
> there is clearly a group of people who appreciate seeing the original,
There are many people interested in many things, do you want to say that "because some people think it is important, it must be important"? There were many people with really weird and despicable ideas along history and while I am neutral to this one, they definitely don't convince me just by their numbers.
> simply looking at a jpg.
Technically a jpg would not work because is lossy compression. But a png at the correct resolution might do the trick for some things (paintings that you see from far), but not for others. Museum have multiple objects that would be hard to put in an image (statues, clothes, bones, tables, etc.). You definitely can't put https://en.wikipedia.org/wiki/Comedian_(artwork) in a jpg - but the discussion surrounding it touches topics discussed here.
The problem with automated imitation generators is that they can produce thousands of painting that imitate Van Gogh, but does not have the same soul.
It is the same reason why these things cannot create genuinely funny jokes. They cannot assess the funnyness of the themselves. They cannot feel, and cannot do the filtering based on emotion.
It is easy to recognize the emptiness of a joke, but not so easy for a painting, or some other form of art.
This is why it will never work for art. But the sad thing is that that will not stop them from being used to create art. Because it just needs to sell.
I would say that for art, at least for most of the movies, music etc, this was already the case. So nothing much to lose.
Define soul, how about a legal/scientific description that accurately covers all bases?
The funny jokes thing is funny too, if someone told you a joke and you thought it was funny, then they told you it was from an LLM, would it stop being funny.
>if someone told you a joke and you thought it was funny, then they told you it was from an LLM, would it stop being funny.
No. It wouldn't. But they can't generate funny jokes.
I would be really happy if I am wrong, and it is possible to laugh all day by reading endless jokes from an LLM...
The value of a piece is definitely not completely tied to its physical attributes, but the story around it. The story is what creates its scarcity and generates the value.
It is similar for collectible items. If I had in my possession the original costume that Michael Jackson wore in thriller, I am sure I could sell it for thousands of dollars. I can also buy a copy for less than a hundred.
Same with luxury brands. Their price is not necessarily linked to their quality, but to the status they bring and the story they tell (i.e. wearing this transforms me into somebody important).
It can seem quite silly, but I think we are all doing it to some extent. While you said that a good forgery shouldn't affect one's opinion on the object (and I agree with you), what about AI-generated content? If I made a novel painting in the style of Van Gogh, you might find it beautiful. What if I told you I just prompted it and painted it? What if I just printed it? There are levels of involvement that we are all willing to accept differently.
There are a lot such artists who can do that after having seen Van Gogh's paintings before. Only Van Gogh (as far as we know) did paint those without having seen anything like it before - in other words, he had a new idea.
Should we also say "if you can implement Dijkstra's algorithm" it's irrelevant because "you did not have the idea"?
It's great to credit people that have an idea first. I fail to see how using an idea is that "bad" or "not worthy", ideas should be spread and used, not locked by the first one that had them (except some small time period maybe).
Yea this is the kind of BS and counter-productiveness that irrational radicals try to push the crowd towards.
The idea that one owns your observations of their work and can collect rent on it is absurd.
> If someone produces a painting in the style of Van Gogh, and passes it off as being made by Van Gogh, by putting his signature on it, that painting is a forgery.
Which is true. But the implication that follows is false.
Van Gogh's artwork is valuable specifically because of his identity. I find much of his artwork particularly hideous. That's fine! Someone else finds value in it specifically because of who wrote it.
This metaphor doesn't appear to apply to code at all. The entire value of code is what it does not who wrote it.
Honestly, I stopped reading after the first bullet point because these types of arguments feel lazy and the attitude of the people writing these articles frequently comes across as holier than thou.
You don't like LLMs? Great, don't use them. Using Van Gogh's paintbrush doesn't mean I'm making a forgery. I'm just painting, my friend.
While LLMs are surely used to generate a lot of slop-code and overwhelm (open source) code bases, this surely isn't the only thing they can do. I dislike discussing the potential of a technology exclusively by looking at its negative impact.
LLMs in proper hands don't create code which is "stolen", they also shouldn't create unnecessary code and definitely don't remove any of the ownership of the programmer, at least not any more than using a mighty IDE does.
The problem seems to be in the usage of LLMs. These effects definitely do happen when just releasing an agent on a codebase without any oversight. But they can also largely be mitigated by using frameworks such as Openspec or Spec-Kit, properly designing a spec, plan, granular tasks and manually reviewing all code yourself. The LLM should not be responsible for any creative idea, it should at most verify the practicality against the codebase. When doing that, the entire creative control is in the hands of the programmer and so is the mechanical execution. The LLM is reduced to a very powerful autocomplete with a strict harness around it. Obviously this also doesn't lead to 10x or even 100x improvements in speed like some AI merchants promise, but in my personal experience the speedup is still significant enough to make LLMs a very, very useful technology.
btw you can make git commits with AI as author and you as commiter. Which makes git blame easier
A Private (system) Investigator. :)
Claude makes me mad: even when I ask for small code snippets to be improved, it increasingly starts to comment "what I could improve" in the code I stead of generating the embarrassingly easy code with the improvement itself.
If I point it to that by something like "include that yourself", it does a decent job.
That's so _L_azy.
>What's the Excel of JSON
Ever heard of CUE that's compatible with JSON and YAML introduced by ex-Googlers? It seamlessly support both types and values, whereas Excel supports ephemeral values [1].
Both CUE and original Excel are non-Turing complete so they don't have the notorious and tricky halting problem.
Someone need to seamlessly integrate LLM with CUE, its NLP deterministic distant cousin based on lattice-valued logic [2],[3].
Truth be told LLM are like the automated loom machine during 19th CE Britain that kick started the industrial revolution. Heck the Toyota conglomerate was once the pioneer of the modern automated loom manufacturer, and looks where they are now after embracing change and pivoted to vehicle manufacturing.
The automated loom machine commoditize the manual looming industry (not unlike modern software engineering) to its oblivion in India, that turned the rich Moghul India with the highest GDP in the whole wide world into the lowest GDP for India during colonial time (include Indian sub-continent namely Afghanistan, Pakistan and Bangladesh here if you want apple to apple comparison) [4].
Ignore LLM at your peril in the name of so-called moral authenticity/forgery/lie/etc, and you can go the way of 20th CE India and its sub-continent, settling only at a fraction of its Moghul empire in term of GDP at its very peak.
> Is there a standard CRDT-like protocol for syncing editable graphs yet?
It's for other HN comments but spoiler alert it's called D4M by the nice folks from MIT [5]. We probably don't need full CRDT, local-first capability with eventual consistency will be more than suffice for most things that are of importance.
[1] CUE lang:
https://cuelang.org/
[2] The Logic of CUE:
https://cuelang.org/docs/concept/the-logic-of-cue/
[3] Guardrailing Intuition: Towards Reliable AI:
https://cue.dev/blog/guardrailing-intuition-towards-reliable...
[4] Economy of the Mughal Empire:
https://en.wikipedia.org/wiki/Economy_of_the_Mughal_Empire
[5] D4M: Dynamic Distributed Dimensional Data Model:
https://d4m.mit.edu/
I’ve seen reluctance to refactor even 10+-year-old garbage long before LLMs were first made available to the broader public.
I've seen a lot of 'fixes' for 10 year old 'garbage' that turned out to be regressions for important use cases that the author of the 'fix' wasn't aware of.
If a woman gets married at 25 and her kid is 25, how old is she?
This is what LLMs are dealing with. You dont tell them everything they need to know and they are left to fill in the gaps. Which may, and sometimes often means they lie.
That's what Agentic does differently, it'll go find the gaps before answering.
Agentic is AGI. You can hire many minimum wage workers who are generally intelligent who dont even go to that level.
That's pretty rude to say, at minimum.
If you don't have the copyright, then you can't license or litigate it under the common rules of software. If someone 'steals' it you can at best go after them with some trade secret case, and I suspect this would be limited if you had already shared the code with them, e.g. because they helped you synthesise it.
I have accepted that reading 100% of the generated code is not possible.
I am attempting to find methods to allow for clean code to be generated none the less.
I am using extremely strict DDD architecture. Yes it is totally overkill for a one man project.
Now i only have to be intimate with 2 parts of the code:
* the public facade of the modules, which also happens to be the place where authorization is checked.
* the orchestrators, where multiple modules are tied together.
If the inners of the module are a little sloppy (code duplication and al), it is not really an issue, as these do not have an effect at a distance with the rest of the code.
I have to be on the lookout though. It happens that the agent tries to break the boundaries between the modules, cheating its way with stuff like direct SQL queries.
A short design note and tribute to Richard Stallman (RMS) and St. IGNUcius for the term Pretend Intelligence (PI) and the ethic behind it: don’t overclaim, don’t over-trust, and don’t let marketing launder accountability.
https://github.com/SimHacker/moollm/blob/main/designs/PRETEN...
1. What PI Is
Richard Stallman proposes the term Pretend Intelligence (PI) for what the industry calls “AI”: systems that pretend to be intelligent and are marketed as worthy of trust. He uses it to push back on hype that asks people to trust these systems with their lives and control.
From his January 2026 talk at Georgia Tech (YouTube, event, LibreTech Collective):
https://www.youtube.com/watch?v=YDxPJs1EPS4
> "So I've come up with the term Pretend Intelligence. We could call it PI. And if we start saying this more often, we might help overcome this marketing hype campaign that wants people to trust those systems, and trust their lives and all their activities to the control of those systems and the big companies that develop and control them." — Richard Stallman, Georgia Tech, 2026-01-23. Source: YouTube (full talk) — "Dr. Richard Stallman @ Georgia Tech - 01-23-2026," Alex Jenkins, CC BY-ND 4.0; transcript in video description.
So PI is both a label (call it PI, not AI) and a stance: resist the campaign to make people trust and hand over control to systems and vendors that don’t deserve that trust. In MOOLLM we use the same framing: we find models useful when we don’t overclaim — advisory guidance, not a guarantee (see MOOAM.md §5.3).
[...]
Richard Stallman critiques AI, connected cars, smartphones, and DRM (slashdot.org) 42 points by MilnerRoute 38 days ago | hide | past | favorite | 10 comments
https://news.ycombinator.com/item?id=46757411
https://news.slashdot.org/story/26/01/25/1930244/richard-sta...
Gnu: Words to Avoid: Artificial Intelligence:
https://www.gnu.org/philosophy/words-to-avoid.html#Artificia...
...currently not responding... archive.org link:
https://web.archive.org/web/20260303004610/https://www.gnu.o...
The use of loaded and pejorative language like "forgery" emphasizes that this is not a logical argument, but a moral one. The repeated comparisons to "true craft" reveals the author would prefer that code be regarded like artisanal cheese.
Beyond the pretension, it's head in the sand to imply that the technology hasn't progressed. It's just very clearly not true to anyone who is paying attention - longer tasks, better code, less errors. I'm somebody who actively despises the hype bullshit-machine that SV has turned into, but technology is an industry for pragmatists that can leverage what works. And LLMs do.
If you don't like the technology, you have every right to scream that from the mountaintops. As it stands, this just serves as no more than a rallying cry to the ignorant.
No, you won't be rewarded magic beans for churning out crappy dashboards any more. But if you're serious about shipping quality, nothing is stopping you here.
The days of being treated like a wizard for making buttons and widgets hit an endpoint were good while they lasted.
Has this really been people's experience?
I develop and maintain several small FOSS projects, some of which are moderately popular (e.g. 90,000-user Thunderbird extension; a library with 850 stars on GitHub). So, I'm no superstar or in the center of attention but also not a tumbleweed. I've not received a single AI-slop pull request, so far.
Am I an exception to the rule? Or is this something that only happens for very "fashionable" projects?
Is it so hard to accept that humans are more capable of doing intricate and impressive work than any LLM will ever be?
- [0] https://github.com/unconed/CSS3D.js
- [1] https://acko.net/blog/avs/
https://web.archive.org/web/20150314221334/http://acko.net/
This has upvotes?
Anyway, as I train people in LLMs/AI. I unapologetically will say "DONT LISTEN TO IT, IT LIES!" and send commands like "Try again, try harder"