OP: [last year][1], I shared how I was using AI day-to-day as an ordinary engineer. This year, I did it again!
It's crazy realizing that Claude Code only started getting big in the last year or so. It feels so different now, with all the new models & tools we have to use. I hope you enjoy!
March 2025 was the start of the boom, IMO. Sonnet 3.7 and CC released feb 24th, Responses API + Agents SDK from OAI launched March 11. Two months after we got the first Terminal Bench, then a month later we got tau2 bench.
I guess, its debatable that o1 was the real start- but I think its hard to deny that Claude Code CLI was the first time we had what we now call a coding harness hit the mainstream. And- Sonnet 3.7 was kinda Anthropic's "o1 moment".
God. Opus 4.6 and 5.3-codex feel like ancient history.
Controversial take: AI music is more interesting than the autotuned, quantized, mass-market-optimized slop that dominates contemporary pop "music".
I walked into a paint store recently and there was some interesting music playing on the PA. It was some sort of spanish-language 80s new wave I'd never heard before. It had a nice groove. I asked the guy at the counter about it and was surprised to find out it was AI.
So... was it as good as great music? No. But it beat the hell out of muzak. And it was genuinely novel in a way that pop music isn't. I will sacrifice a lot of pride for novelty.
> Controversial take: AI music is more interesting than the autotuned, quantized, mass-market-optimized slop that dominates contemporary pop "music".
> And it was genuinely novel in a way that pop music isn't. I will sacrifice a lot of pride for novelty.
For one thing, "interesting" and "novel" are not particularly the point of art. An infinite TikTok feed of AI slop is in some sense infinitely interesting and novel.
But also, those concepts are subjective. Pop music has a ton of evolution and innovation if you break it down. Sure, not every hit is doing something clever, but plenty of them are. You might not find those features to be interesting but it takes ingenuity to stand out in an era where the barrier to entry is so low.
>Controversial take: AI music is more interesting than the autotuned, quantized, mass-market-optimized slop that dominates contemporary pop "music".
Controversial take: AI shows that people simply don't give a shit and just confirms that mass culture is a mistake and the death of everything that is good and sacred.
>Controversial take: AI shows that people simply don't give a shit and just confirms that mass culture is a mistake and the death of everything that is good and sacred.
Controversial take: It's been clear that most people have never given a shit, it just took AI for most to realize it.
I checked out from pop music roughly 30 years ago, when I moved out of my parents house. There is literally NOTHING that interests me in the pop music charts.
> Controversial take: AI music is more interesting than the autotuned, quantized, mass-market-optimized slop that dominates contemporary pop "music".
You're right, this sure was a controversial take.
Anyway, surely this dichotomy only matters if your only choices are "AI generated music" and "autotuned, quantized, mass-market-optimized slop that dominates contemporary pop "music"". However, there is a bunch of good music outside of the "pop music" zone, which isn't AI generated, so why not go with some of that instead? Nobody is forcing you to listen to pop music.
I do go with that? 99% of the time I listen to human music. My main complaint is that humans aren't making music that I like fast enough. What's wrong with listening to AI music too?
I don't know what to tell you, but me and my kid are having a blast writing ridiculous songs and plopping them with song descriptions and some of those make it to our drive playlists.
Respectfully, the primary thing you are describing here is spending time with your children. Same could be said for spending time and attention discussing children's drawings.
<< you are describing here is spending time with your children.
Note: same guy; just not anywhere near my account
You are making the argument that the type of activity does not matter as long it is 'spending time', but to say that is almost reductionist to say that that it understates how ridiculously unnuanced an opinion it is. You might as well tell me that the quality of food does not matter and generic burger should suffice.
But what if I want a burger that is custom to me and not a generic slop generated by a human to appeal to the widest possible audience[1]? What if I want something that was created just for me? Good grief? Why do I have to defend having fun and why on earth would you assume that one is mutually exclusive to the other?
The problem with AI performances is that they're too perfect, which leaves them flat. The dynamics and imperfections of human performance have yet to be replicated. That affects every human performance whether or not you notice you're noticing (but you are). AI is still in the uncanny valley here.
This sounds like CD music 40 years ago. All my friends said that it was not like a record and left an empty feeling in the room. I feel like this is not talked about any more. People listen to digital music and have forgotten there was an alternative. I suspect that in the coming years no one will think twice about AI generated music.
you can totally tell Claude to take some AI technical writing (which is, to be clear, writing where you explicitly need to not sound creative or interesting, and you need to maintain very strict "voice" [a term I only even know because claude explained it to me] so this is not a creative endeavor) and make it sound "less AI". At least working with opus 5.5, textual output already has a lot fewer of the colons and "load bearing" of a few months ago, and if a PR does have that kind of lingo I can just have Claude rewrite it "like a human" and it does great.
OP: I'm not surprised you/other people are able to get better output than me.
And I agree that "less AI" edit passes reduce a lot of the hallmarks, but I think these models truly struggle with expressing complicated ideas. Have Opus been good at that for you?
> I'm not surprised you/other people are able to get better output than me.
I think people should find this sort of result surprising by default. Like, presumably you don't know why your output isn't as good, or you'd fix it. So doesn't that lack of understanding concern you?
> So doesn't that lack of understanding concern you?
Good question! It mostly doesn’t concern me. I believe these models have a huge “latent space,” so it’s not surprising to me that other people, with the right harnesses and prompts, can get them to produce more-valuable content.
I spent a lot of time calibrating how I use these models to produce valuable, good code, right?
But as I alluded with my question above, I haven’t seen a ton of evidence that these models have human-level argumentation in that latent space. If my assumption there is wrong, then yes, I’d have to reexamine my understanding of these models.
> I spent a lot of time calibrating how I use these models to produce valuable, good code, right?
Ah, fair, there's opportunity cost to that. May as well prioritize what you actually care about, yeah. Personally I'm fine with skimming through the prose in the default style, at least as long as the LLM isn't getting stuck in its own invented jargon. It'd be different if I were publishing that output as project documentation, but I don't even really want to try that.
> I haven’t seen a ton of evidence that these models have human-level argumentation in that latent space.
Not sure what you mean by argumentation. I'd agree that they don't seem to have flashes of insight or make intuitive leaps in the same way. But they clearly can follow through the logical steps of reasoning very thoroughly, once they know what to reason about. For a technology that seemed to struggle with understanding negation a couple years back, it's seemingly miraculous.
There is no substitute for reading the code in many many cases. I spend more than 3k/mo on tokens. I find incorrect assumptions baked into the code (the ultimate spec) and missed by the agents and tests daily. This is more pronounced in data analysis and complex systems.
As a side effect having to read the code at least once inevitably makes it way shorter and usually much better.
> I still believe I use AI fairly typically, but I’ve had a notable change in background. Last October, I was an engineer on Microsoft’s Windows AI team. I am now a research engineer at Salesforce AI Research.
Priest wears robes.
I guess it’s one of those nonsensical automatic rules.
> I am now a research engineer at Salesforce AI Research. I spend more time supporting scientists & building rapid prototypes, rather than working with a large team of engineers.
I appreciate the PoV of people with different challenges, but my full-time job is not about building rapid prototypes so the guidelines are worth a bit less to me.
Nothing against the post itself, but something I find funny: the development in AI is so rapid, that everyone thinks they are ahead of everybody else. Here we have a blog post telling us "coding is solved". In Oct 2026, how many of us don't know this already?
The structure of the post seems like a comparison of snapshots between a date this year and last. On HN, it's an odd statement. In the context the author is writing in, it seems fair.
Find a lot of tech adjacent jobs on reddit and see them talk about being outskilled by new hires using AI to automate sysadmin tasks. I used to a sysadmin very early in my career and given the tools today and the problems back then, a lot of these jobs are going to be obsoleted or dramatically automated to not require FTEs. This is already started in those fields. Powershell and bash scripting is very good from these things and has been for longer than a year IMO.
We're in our own bubbles where a good part of HN population woke up to coding agents being good enough ~1 year ago, there's a group of folks that found out ~6 months before that, and then the people banging their heads on these a bit earlier than that. If you aren't using a coding agent everyday most people are just using these things to apply an instagram filter, or sort a grocery list. Muse is probably the closest thing to 'full automation' a lot of people are seeing for the first time.
Unfortunately I don't think the bubble that exists at the moment is on the promise of automating so much coding as trying to penetrate consumer use cases, which I think are still lacking and not reliable enough yet. As much as I use coding agents every day I still will not let a claw-like near a bank account or a credit card.
The problem with Muse and similar "run your life" agents is that booking a dinner reservation or signing up the kids for soccer isn't anything that I need to automate. They need to find better examples of real problems for their marketing.
This was Gemini the other day: https://imgur.com/a/7mdqwjk — this wasn't some errant load, even refresh, hard refresh, etc. got the same result for a while. An outage, AFAICT.
While trying to capture that bug, I (ironically) asked Gemini (but the Gemini-via-Google-search Gemini, since Gemini proper was an outage) for the package name of a screen recorder (gtkRecordMyDesktop); the command it returned referenced a non-existent package¹. I then found an alternative, and then hit a bug in that screen recording software that I had to then work around to get the video file there.
The site I uploaded the video to, Imgur, breaks completely — i.e., it cannot even so much as surface an error message — if your browser refuses to let it store god knows what on your disks. (So as it is, I have an extension that says "why yes, of course you can have storage, and essentially provides a black hole. But since it looks like a "success" to the Javascript, the JS doesn't crash!)
This isn't, to me, some one-off interaction. Every interaction with software these days feels like this, more and more. Just breakages everywhere that leaves me wondering if "coding is solved", where is all the bug free software?
(¹forcefully prompting the AI to actually query the package listing netted me a classic "You're absolutely right! The package name I referenced does not exist!")
Yep. Everyone is saying that AI is writing all this great software. Where is all the great software? Really, where?
My experience is the same as yours. Every single service I use. Apps, websites, doesn't matter what. Every single one seems just softly degraded. Like I can't trust that features work any more. I could report the bug, but what's the point? It's going to go to some Claude triage agent who is going to "fix" it by slopping up the app even more. Do people even use their own websites and apps any more? Apparently not - the same people declaring software solved are having their AI agents doing computer use and going on these websites for them!
Claude, please read the Bible for me. Tell me what I need to do to get into Heaven. Oh, can you dictate it to me? I'm managing an agent swarm.
You mean the three new products we launched in a month instead of a year (or two..)? Sales are good, customers are happy, we are doing well.
I know, I know, we are shit and AIs are a godsend for small shitty companies that have at least one clever person that architect their way out of a paper bag. I’m sure all ya’ll highly competent, genius academics don’t need it and find it a hindrance but the devs in my bubble really, really need it. And there are a lot of those..
They just live in denial. I don’t have any proof, but I suspect those are the same people that would nitpick your MR to death until you hint to their manager that this is not ok.
I don’t have an evidence that delusional people and nitpickers are the same people. I never said I don’t have an evidence that delusional people are delusional.
It exists, at least in terms of ROI. Some enterprises are able to do successful rollouts that allow them to significantly shrink workforce in certain departments without losing quality (no, I'm not talking about customer support).
But most of the software is indeed getting worse right now. The industry is still figuring out how to wield these new tools and what to build with them. Those that figure it out faster than others will make billions.
> Every interaction with software these days feels like this, more and more. Just breakages everywhere
I've noted the same but even before llms so I can't draw a conclusion there. I find strange issues with software I cannot explain. Not only breakages or bugs but also a strange lack of core features and UI/UX that leaves me wondering "am I dumb?".
b) you confuse technical quality with product incentives
If you see this, company doesn’t care about product. And Google doesn’t care about quality for at least a decade now, all they care is a share price and speed of delivery.
> This isn't, to me, some one-off interaction. Every interaction with software these days feels like this, more and more. Just breakages everywhere that leaves me wondering if "coding is solved", where is all the bug free software?
My annoyance is more specific than "it breaks". I hate how all this software (especially "web apps") just assumes that it will have permission to do whatever it wants and never contemplates that the user might have something to say about it.
YouTube iOS app has had comment expansion broken (Show more? More like do nothing) for about a week for me. Latest version of the app on not the latest version (but still very recent) of iOS.
> In Oct 2026, how many of us don't know this already?
The comments to this article suggest that it's still disputed.
Personally, I think it's a bit of a silly phrase, because it implies an absolute, and all anyone needs to do to disprove it is to show that coding was not solved for problem X. That's still the case -- it's just that the set of X is rapidly shrinking.
I think it's more indicative to say how much you trust LLM code. A little over a year ago, I scrutinised every line carefully, and it was buggy as hell and I had to fix most generated code by hand. Now, models write clean code with fewer bugs (far fewer) than I would. A year ago, I was doing a lot of steering during planning; now, it's rare that I don't agree with the model's first proposed solution, and even if I don't, it's most likely to fix a minor change in understanding, not to propose a substantial architectural change as it was before.
The one thing I'm not sure about is how much this is due to better prompting on my part. I doubt that this contributes much, but I still suspect that continual usage does make you more aware of what works and what does not: how much detail to provide, how to describe a problem or change, how to propose solutions while still leaving other options open.
Coding is solved in the sense that you don NEED to write code anymore. Of course you need to check and design and find better ways of doing things, but nothing of this requires you to write the code directly, you can always design, check and ask the LLM to implement a new version.
I have tons of acquaintances like that.
They vibe-code and think they've gained an advantage over those who don't know how to vibe-code
But there's nothing to know — take a 3-hour course, pay 200$ a day for Claude, and there you are, a vibe-coder.
It's just that most skilled developers find it disgusting, because the AI spits out code that doesn't work the way it should, and it all turns into the routine of "threw in a prompt, waited, reviewed, repeated." — only those who were crappy programmers are happy about this.
Always have a good chuckle at these doomerism bullshit posts. Our VPs are so busy it is impossible to find a meeting time, but suddenly they’ll develop ADHD and start vibe coding enough to replace 100 devs. Lol!
Where are the author's open source projects from before AI that make him qualified to proclaim that "coding is solved" with such certainty? He is a "researcher" of some sort at slop company Salesforce.
It’s kinda nice seeing more nuanced takes from folks as the bubble kinda deflates and both sides realize their idealized vision isn’t coming to pass. AI isn’t going to replace all the jobs, but it’s also not going away; finally, it is merely a tool in our toolchest.
What does amuse me to no end, however, is how many craftsfolk in AI circles are loudly rejecting AI imagery and video in favor of humans. The only folks who seem fine with that are the grifters looking to slop out a bunch of adverts quickly for commercial gain rather than do actual art, or folks who want “art” but don’t want to pay a human to make it for them or learn the skills themselves. Both of those camps are viewed with disdain and loathing by both sides of the AI Boom/Doom arguments, so I’ll take whatever allies I can get on those fronts.
AI music…man, I live with artisans and musicians and we have a number of “artists” we suspect are AI but whose music is pretty solid. We’re waiting for the shoe to drop, but I’m trying to look at it as a kind of turbocharged Fruity Loops/Logic as opposed to a human replacement. I hope I’m right on that, but time will tell.
The game changer I think more doomers need to experience is agents. It sounds like a recipe for disaster (a bunch of models known to hallucinate and allowed to run roughshod over a computer unattended? What could go wrong?), and it often (hilariously) is for the scammers out there giving it full access to production and fucking off to hawk their latest Masterclass on X or LinkedIn, but that first time you give it a reasonably achievable prompt and come back to a halfway decent product you can review and refine into production in 15m or less?
Absolutely magical, doubly so when it’s just 27B Qwen via Hermes and not some souped up stack in a frontier lab costing you thousands a month in tokens. If I were to evangelize AI, that’s how I’d do it. Fuck subscriptions, fuck cloud compute, fuck frontier labs and token buckets and whatever slop Sundar/Scam/Dario/Elmo et al are hawking this week; run that locally, give it a reasonable prompt that solves a real problem, and show them the results.
Then do the right thing and go for a walk with the two hours of free time the AI just freed up for you. Don’t ask for permission to take back your time.
I’ve lived through three previous financial bubbles, and their most common characteristic was that they didn’t ‘kinda deflate’. Bubbles pop, dramatically, they don’t just deflate slowly. They don’t withstand shocks and bad news that have had the market up and down like a demented bunny over the last few months.
Market volatility "over the past few months" has been very much within normal variation. Look at even a 5-year chart of VIX if you don't believe me. For that matter, what are you even counting as the last three "financial bubbles"?
With all the time and resources Google has put into their products, you can probably conclude that either a) they want it that way, or b) no one there wants it to be otherwise enough to take resources away from whatever they were doing, and put it into that.
I always find so much cognitive dissonance in these kinds of posts.
> My most successful work this year was hand-writing a tool, replacing ~4,000 lines of vibe-code with a few hundred. My system has proven more flexible, easier to test, and more generalizable than what it replaced.
> agents still need human taste and experience to produce good work.
Where do you think human taste and experience in software comes from?
> overstimulating & exhausting in a way that coding never was.
> for many of us, honing the code, finding the right design, seeing the parts mesh together — that was the meditative purpose we found in our jobs.
> That’s not the only time AI waylaid me. LLMs could not figure out how to annotate that model’s training data correctly. I spent days going back and forth with them
> LLMs are not yet good companions when brainstorming complicated things.
Why do we expect this to get better? The training data will degrade in quality if more people rely on LLMs so I'd expect models to eventually start getting worse even if they currently enjoy apparent improvements in output.
> Writing code is solved.
What a strange thing to say given all the above experiences, which I think we all share.
Why are we willing to give up so much in terms of purpose and understanding for perceived productivity gains?
This always reads like “writing english is solved” just because you collected snippets of famous works and have built a spell corrector.
Code is written to process data. The struggle was never to learn programming syntax (most grammar are only a few page long). The main struggle was that it’s hard to model a domain into an information system, identifying the components and their relationships, what is ingested, and what needs to be output.
Computers have integers and floats, but you need to implement units like distance, money, various counts,… The real world have words and sentences, but that needs encoding like ascii and unicode as well as the “string” concepts to make it work. Take HN, I’d bet that if you can model it in terms of data, writing code is not an issue.
> The struggle was never to learn programming syntax
For 85-90% of devs, yes this was the struggle. Creating and updating rows in a database from user input is all the “domain modeling” there is in most software. That’s what is solved by AI and it’s the majority of software dev.
Thank you, this is what I've been seeing. Structs and objects are easy, but the hard part is encoding the domain most accurately into given data types of the computer (or creating new data structures to solve the problem!)
If anything, I do expect the casual non-tech users to be making the most adaptable software because they have what they want in their head.
Although, part of what the dev does is figuring out the weeds, and only a part of users have the patience to sit through detail corrections. And the AI user has to verbalize their wants exactly too.
> Although, part of what the dev does is figuring out the weeds, and only a part of users have the patience to sit through detail corrections. And the AI user has to verbalize their wants exactly too.
Even with basic pseudocode and very lenient logical rules, most people won’t bother to provide a a good definition of some process, as it’s quite unnatural to be that logical. Instead, it’s mostly just playing around. When you ask the one that is supposedly 10x-ing his productivity to explain his mental model of their own projects, it’s often a wishlist instead of a real understanding of what he has built.
> I do expect the casual non-tech users to be making the most adaptable software because they have what they want in their head.
Those are the people who are most likely to give a monolithic prompt and then spend a week telling the machine 'I don't care about encryption or file permissions, just make it work.' Not to mention that most people can barely verbalize the problems they have with devices they use for 8hrs a day, let alone form a full, self consistant, project idea and then put it in writing.
if they're persistent and patient enough to see the result of what they think they want and iterate enough times they'll eventually learn. non-coders aren't dumb by default after all and I suspect they'll learn much faster than us because they won't be bogged down by the minutiae like reading docs.
Imagine you could build a piece of furniture without actually needing to know how to cut wood, drive nails etc. Imagine you could do it in 3d without knowing how to actually handle wood, you just have an image you can iterate on without actually costing you anything but time.
just because you have an idea doesn't mean it will work. kids come up with ideas all the time in their drawings, but they disobey the laws of physics and are therefore not feasible in reality. sure, the model can guide them, but eventually they would have to pick up the necessary knowledge.
The necessary knowledge needed with LLMs goes from needing to know programming languages and libraries to having a high level view of what is possible which is far easier and less boring to get
> non-coders aren't dumb by default after all and I suspect they'll learn much faster than us because they won't be bogged down by the minutiae like reading docs.
They may have a perfectly good shot at being smart, but I still don't buy the argument. The reason they struggle to become coders (assuming they tried at all), in my experience, is a lack of problem-solving and debugging skills. There is no real shortcut here. An "image" of the chair isn't enough; when the first iteration is wrong, you have to be able to explain why it's wrong.
I think there are a lot of people out there with good problem-solving skills but for whatever reason did not get into programming or they tried and they got put off with learning the languages / libraries or got stuck and didn't find a solution easily enough and gave up etc.
With a sufficiently good LLM these people will skip the tedious part of programming that most of us had the motivation / patience to go through. All the folks saying that actually writing code wasn't the bottleneck have had a LOT of years to get to that point, years a lot of people aren't interested in going through.
I'm not saying lay people will be able to join mega projects and work at FAANG. I'm saying a sufficiently courageous lay person will be able to make an app to automate whatever tedious birocratic process their niche work hasn't seen a product yet.
> All the folks saying that actually writing code wasn't the bottleneck have had a LOT of years to get to that point, years a lot of people aren't interested in going through.
The years was not spent on learning how to code. The years was spent on learning about what needed to be coded. And that would have spanned multiple domains. Even the most basic production level software have multiple concepts that need to be integrated. And for that, they need to be learned.
Using AI without the requisite knowledge is playing Russian roulette. It’s kinda wanting to log into a file, and not thinking about disk storage being finite, and rotating the log file gives you flexibility. That’s not related to coding, it’s just hard thinking about the problem and the constraints (or finding a good book or article).
> Using AI without the requisite knowledge is playing Russian roulette
Every few months that revolver has a few more empty chambers and even if it shoots them in the foot, it can bandage them quite nicely. Will a solo lay person with an agent build a high quality high reliability software business? Doubtful. Will they be able to make a tool that helps them and 10 other people or a personal website? probably.
> Will a solo lay person with an agent build a high quality high reliability software business? Doubtful. Will they be able to make a tool that helps them and 10 other people or a personal website? probably.
Just take a quick look around to see how many daily tools and utilities are actually built by experts and industries. Yes, you can put a plank on top of two concrete blocks to make a bench, but many people don’t and prefer to have good bench made, even when it’s quite easy to DIY one.
So I don’t foresee lots of people trying to make custom tools, more so to keep a subscription active.
I can absolutely believe this, actually. Considering how the access to rapid prototyping has rejuvenated my interest in programming when I am a seasoned programmer, I definitely see the value in that kind of jump-start.
And yeah, without LLMs, writing code absolutely can be the bottleneck sometimes, if only for psychological reasons. I've definitely experienced something like "coder's block" before. An anti-flow state, if you will.
(But skydhash is also right. The "unknown unknowns" are a big deal and there's only so much an LLM can do about that, at least unless the user is actively aware of the general shape of that problem and trying to learn via the LLM and fill in those knowledge gaps.)
I don't think the education barrier fully accounts for SWE salaries. It takes a particular disposition and other hard things, learned over years of doing, to actually keep do software engineering as a career.
AI changes the dispositions and difficulties, but it still favors a narrow selection of people to do the (still) difficult task of software engineering with agents.
> The training data will degrade in quality if more people rely on LLMs so I'd expect models to eventually start getting worse even if they currently enjoy apparent improvements in output.
RL is not dependent on training data. The mathocalypse didn't happen because they found better training data somewhere.
I’m not sure that’s a good argument that models will get good at novel brainstorming and research ideation. Where do you get scalable and high signal verifiable rewards for creativity and insight?
> The mathocalypse didn't happen because they found better training data somewhere.
There are reasons to suspect that OpenAI may have intentionally or unintentionally trained on chat logs from mathematicians--mathematicians who were actively using OpenAI to try to to solve the same problems themselves.[1] If those mathematicians were on the right track, and getting close to a solution, that could be classified as finding "better training data somewhere."
sorry but
given how post-training actually works, there's essentially no direct way in which literal PII-scrubbed data made its way into a production oai datamix for RL warmup purposes in 2026 to the extent this data has influence, it's indirect/at the level of process decisions.
what i am trying to say more specifically is, the folk model some people seem to still have about ai companies "directly training on user transcripts" in this naive fashion is firstly wrong, and secondarily, would be counterproductive as a strategy for continued progress
"transcript shows human trying to do an exotic thing evals don't currently cover -> build synth constructions that broadly cover its genre of problem class -> RL on the verifier set designed to cover the failure"
that's the loop...
the """flywheel""" is... diagnostics! wow!
and so this kind of process improvement driven use of data has an obvious diffuse causal influence on what happens during training, right?
but ~none of that process influence looks like "specific mathematician's specific intuitions leak into the training data".
It’s perfectly plausible that unscrupulous people at OpenAI (and that’s the culture right from the top) searched user transcripts for relevant approaches to problems they were working on, or searched them for problems to look at, and nudged models based on that.
I agree it’s unlikely this happened by accident, or entered training data (though not impossible), but it could certainly be within the context agents were using when tackling problems.
We have no evidence either way, but they had all the data they would need to do this.
saw an interview from the mathematician involved, Tristan Buckmaster, on the numbephile yt channel, and he said he thinks it was more a case of people at anthropic leaking it in person to openai. Apparently with a hint about the area they were looking at it could have been easy (ish) to get the same approach, and indeed the paper openai released for Navier–Stokes uses their approach
Rl works on one of two things - (1) a literal set of rules for a system or (2) training data. The former matches perfectly to things like (most modern) math and games that have perfect sets of rules. Even things like physics and robotic where our rules/models are extraordinarily good, rl barely adds value. Move one step farther to chemistry or a complicated physics scenario like weather, and you are back almost entirely dependant on (2) data. Now there might be a few more domains with rigorous rulesets that haven't been explored fully (id bet with some effort programming might make some strides here, maybe something like circuit layout, maybe even law if folks decided to make a lean-like law scheme...) but the vast majority of problems AI is targeting are dependant on data.
First of all ppl are just processing the new enironment. So go easy on conclusions. Its all in flux.
Secondly most the posts on HN are from individuals. It doesnt represent group dynamics and how group/institutional knowledge works. For example there might be a regulation on publishing data every quarter. On the surface it looks like compliance but in reality different internal groups will hide data, delay it, fudge it etc etc. LLMs train on public data. Only after they are allowed access internally do they discover group knowledge. We are far away from seeing that happening.
I am not OP, obviously, but I am writing to address the "Where do you think human taste and experience in software comes from?" point, hoping that I provide an alternative perspective.
As someone who wrote code for 27 years in all sorts of languages, not necessarily impressive code, but got paid for it, made a career, and changed life for the better multiple times, I don't think coding was ever an important part of the journey! Maybe it's cognitive dissonance, as you wrote above, but I believe taste comes from exploration, from trial and error, from being willing to be open and change old ways if new ways can improve how things are done!
Similar to taste in food or even music! You might think certain foods taste bad, but until you taste them, you won't be certain! Sometimes your senses will be right and the food will taste horrible; other times, you'll be fairly surprised. Similar to music—it's my life hobby—you might think certain genres or artists are bad, but until you sit down, give them a shot, and hear what they have to say, you won't know for certain if your judgment is correct.
If you meant by your comment that you need to code a lot to acquire taste and experience in software, I respectfully disagree. You are not completely wrong, though, but putting emphasis on code, if that's what you were going for, is an overstatement of code's importance. For that particular case, similar to the analogies mentioned above, it's about exploration and trying different languages, paradigms, solutions, and stacks, and unfortunately, we as humans have finite time, so we can't be trying and exploring fast enough to form judgments!
What I genuinely love about LLMs is mostly their exploratory powers. I try new stuff that I always wanted to try with actual products and form opinions about the whole experience.
Anyways, as I finished writing this, I realized, as always, I might be wrong! You might be right, taste is highly subjective! And acquiring it is a very personal thing! So I don't think right or wrong are important here!
I see what you mean, One thing I noticed is that the suggestions it gives are always generic and reproducible. If I want unique ideas I have not had good experience withh LLM's.
Fair. I'm not really expecting "unique ideas" from the LLM; I'm expecting it to help me examine my own ideas (e.g. by restating and expanding them, and sketching out an implementation plan) while simultaneously doing research. And from what I can tell, this aligns very well with the strengths of the technology.
If I'm prompting with phrases like "Any ideas?", it's because I'm exploring a domain where I know I don't know a lot, and where I expect that there is enough prior art to get a foothold.
> The training data will degrade in quality if more people rely on LLMs so I'd expect models to eventually start getting worse even if they currently enjoy apparent improvements in output.
Its not a direct analogy, but this seems to me as like saying "I already know maths and english, why do we still hire and pay teachers". Its a massive issue that just seems to be hand-waved away as "we'll figure it out later".
To compound on the problem as well, even those of us who do still write code by hand are actively discouraged from sharing it, knowing no human will ever read it and we'll never be credited for our work, it'll just go in the pockets of a few big AI companies. I suppose there will be some that take some pride in knowing their code is helping AI models advance, but thats a small pool of an already small pool, assuming most people decide to just use the AI to write the code for them. A minority of a minority.
I guess this is the chickens coming home to roost of this whole attitude towards growth now, fallout later
It's obviously getting better, by the month, and the degrading of training data leading to a collapse has been a fantasy from the start.
"Perceived productivity gain" is a baseless conclusion. At this point it's abundantly clear that the productivity gain is real and massive.
Where I see the incoherence is in that people see too much value in code review and optimizations.
I believe many of the optimizations are premature. If you did not spot the problem in your QA testing, chances are it was not that important in the first place.
If the architecture wasn't perfect, it is now extremely cheap to change it later, as you realize real deficiencies. The agent will do it for you in a matter of hours, and largely on its own.
You will have to budget a lot of QA effort for verifying the significant changes before going to production, but I don't think it's a deal breaker.
> At this point it's abundantly clear that the productivity gain is real and massive.
Oh? Where is it? Why am I not seeing it as a user if it's abundantly clear? In none of the software I've used frequently in the last year have I seen any of those “real and massive” improvements. Not saying they don't improve at all, but the rate hasn't increased since the advent of LLMs.
I'm still waiting for that shiny new era of software productivity to impact any actual users.
> I believe many of the optimizations are premature. If you did not spot the problem in your QA testing, chances are it was not that important in the first place.
QA testing can't find missed optimization opportunities unless the performance is absolutely abysmal. Even then, it can't distinguish abysmal performance due to missing easy optimization opportunities from abysmal performance that's going to require you to sacrifice functionality.
If an app performs well because it caches the result of a slow function, it's going to pass QA just fine. If optimizing that slow function would make it fast, you can drop the cache and have a simpler and more robust app — but QA can never tell you that.
So in that scenario it passed QA just fine, because the cache made it fast anyway.
Is having a simpler and more robust implementation worth the time to review, understand, and improve it? I'd put that at easily factor 5-10x the effort.
If in the future performance was found inadequate, you could still have the agent revisit the slow function.
That's what I mean with the optimization being premature.
Having a more complex and less robust implementation adds a multiplier to all of your future costs. When a system is 200k lines of code instead of 10k lines of code, every new feature you add and bug you fix takes 5–10× the effort.
I had similar concerns when I was working with GPT-5.5 earlier this year. At the time my project was perhaps ~150k LOC.
After struggling with some more complex features even after many iterations, I started to doubt, and questioned whether I'm writing myself into a dead-end.
I did not. I still made progress and things worked out just fine. Then Fable and later GPT-5.6 Sol came out, boosting productivity and complexity of features I could implement successfully.
The effort only ever went down, and my app was released in the AppStore last week, at about ~500k LOC.
I understand why people might believe that using AI and not manually cleaning up suboptimal or overly defensive implementation would eventually result in an unmaintainable mess.
Based on my personal experience and 500+ hours put into my project, I can confidently say that this is not the case. Increased effort to maintain is negligible.
If the test suite takes too long, it can easily be cleaned up later rather than spending extra time judging test cases in every new MR.
Maintenance of test cases has also become trivial, even with thousands of them. When the pipeline goes red on master I click the new chat button and run /check-pipeline, where I put some concise instructions, and the failure gets investigated and resolved in short order.
It sounds like the problem isn't really that engineer's optimizations are premature. The problem is that engineers have an opinion about the code that doesn't really matter.
What I think you're saying is that opinions that LLMs produce are good enough and malleable, so why even have an opinion?
Which is debatable - oftentimes their opinions are not good enough, and not malleable at all.
We're all still learning how to distinguish those. But in order to learn this, you have to read and understand the code they produce. LLMs can migrate a codebase to Rust, but they still suggest walking to a car wash. Every day they make decisions that humans would be fired for.
LLMs are definitely not yet capable enough to blindly trust them, and there's a pretty good chance they never will be.
> Why do we expect this to get better? The training data will degrade in quality if more people rely on LLMs so I'd expect models to eventually start getting worse even if they currently enjoy apparent improvements in output.
You are assuming that AI should be trained on speicific field to reason better but it actually doesnt need to.
> since agents write everything, it is simple to propose sweeping changes to my teammates during code review, or even write my own versions — both things unthinkable in the era of hand-written code.
That is both untrue, and should be something that occurs rarely.
Past Mid level, most dicussion in code review is centered around the conceptual level. Code is mostly use to highlight the point being made. I’ve never seen the need for someone to rewrite a patch proposed by another person. Add to it or correct misunderstandings, but never sweeping changes.
> I also now reckon with the fatigue of using these agents. Managing 6 different conversations with 6 different robots about 6 different features is overstimulating & exhausting in a way that coding never was.
6 used to be the max for me as well when I worked with Claude Code in terminal windows. Too little to hold on to and to discern the conversations. Just walls of text.
I can now go 20+ with fun because
1. I built a canvas for organizing the work spatially, so every feature has its place,
2. I condensed sessions to a WhatsApp style chat with detailed agent steps available but folded away by default, and
3. Each feature agent ends with a demo, usually a narrated video that I can watch and comment on by voice as it runs.
(My solution is available open source and free: obeya.si)
I believe that anyone who says that writing code is no longer needed, was probably not a very good coder to begin with. I have seen what the best models can do, and I've seen what people call "good", and I am not impressed.
However, I'm still split. Because this could mean that we see a jump in code quality, due to bad coders getting someone better to code for them. But we could see a decrease in code quality, due to less people "caring".
Code written by not very good coders has the possibility to be short and to the point. Even if I think the architectural decisions are wrong, the code still communicates a motive and an understanding of the problem at hand. This is all lost with LLMs.
I'm curious, what language do you code in? I use Go for a high scale backend service at this point it is better at writing code for a jira ticket than most of my team.
There is still questions about if that jira ticket should have been written but from ticket to mergable PR it's better than me.
In my experience, it's very good at following standards, so good in a structured project. Not so much in a freeform project where things get out of hand pretty quick.
I've found this to be the case as well. In a greenfield project, it tends to go off the rails pretty quick. But when it has lots of examples and a style to follow, it does better.
Most people I've seen saying coding is solved are ML engineers who write throw away code for running one-off experiments. If you can effectively "archive" code after getting the results you need, I can see why you would say coding is solved. Sustainment is a non issue.
Not to call you out specifically but I cannot believe people still use this as an argument in 2026. As if someone writing bad code while at least trying to learn how to write good code, and a prompter prompting a computer to write bad code that leads to the person learning nothing from the exercise are at all analogous.
I can virtually guarantee the bar for code can go so much lower as we rapidly deskill programmers.
Think pragmatically about the volume of software products produced in the last 20 years, and the quality of those products. Not just exceptional consumer facing products like Apple, but all of the low quality business bookkeeping crapware gluing industry together.
We actually have a fabulous opportunity to generate much higher quality code products with AI.
And even in your case, with the eager AI student -- AI code education is much better than reading docs and struggling with tools. Students can now focus on the important software topics.
I remember my intro C++ classes and how many students stumbled on pointless & transient tooling , that was completely orthogonal to their proper engineering skills.
Now students can focus on their area of expertise and produce the highest quality possible without any resource restrictions.
> I believe that anyone who says that writing code is no longer needed, was probably not a very good coder to begin with.
I’m probably the best coder in my org, which doesn’t really say much, but at least allows me to judge about the room temperature. It is solved. I don’t say that’s you, but the only people who say it isn’t “that good” are the same people who used to yak shave, rename and move things 10 times until it “feels right”.
I've had a realization recently. I was also probably the top craftsman in the teams I worked in throughout my career.
I think what people are missing is that people often become craftsman because we want to be able to keep the code adaptive and performant so we can provide business value quickly and efficiently.
The change is with models, the benefit dynamics of immaculately structured code changes dramitically. Now the real area you need to focus on is correctness and performance. Structure doesn't really matter unless it serves those two purposes, at least from the business value side.
This is exactly right. Architecture -- in fact, using any kind of abstractions at all -- is basically a crutch that humans use to make ourselves more effective in implementing and evolving code over time, usually by either compressing the problem domain so that we can pack more of it into our limited brains, or making our own mistakes visible early. It's extremely valuable for humans who need to work on complex systems -- but it might simply be unnecessary overhead for entities with significantly more working memory, just as humans won't plan out a class hierarchy for a 5-line script, much less write a PRD.
Yes, directly emitting binaries will likely become practical at some point if LLM context lengths continue to increase. We may be there already for small programs, I don't know.
As with eliminating other forms of abstraction, there's not much gain from doing so. Writing source code and then compiling it offers many useful checks, and greatly simplifies making changes. It's just something that may no longer be necessary at some point.
> I think what people are missing is that people often become craftsman because we want to be able to keep the code adaptive and performant so we can provide business value quickly and efficiently.
Nearly everyone I’ve worked with that would say this ended up being the in the bottom quartile of those shipping features and their code was often the most complex. At least in small, “fast”-moving companies.
A few people I have worked with have nailed it, but they were the ones to balance present and future weighted towards present.
I haven't tried Sonnet 5.5 myself since I find Opus 5.5 essentially unlimited in the subscription, and okayish at API rates with a $500 budget.
When I looked at a benchmark, it suggested Sonnet 5.5 wasn't much cheaper than Opus 5.5 at Low to Medium effort for similar performance. That might not be representative of real world use though.
First, you question the model. I have reams and reams of conversations with Opus 5.5 that shows it make architectural messes all the time
And then you are saying you needed to have performance tests (which many devs don't even bother with) to catch bad architecture?? lol
So what if the architectural problem is not one that would be caught with a performance test? It seems you lack imagination to think of all the ton of issues that that would be very complicated to catch with tests.
Opus 5.5 is not normally copying the whole database to update a single row for no reason, and without mentioning a workaround. It's simply not my experience, and I don't think there is anything weird about me saying so.
I don't think I commented on performance tests at all.
Maybe I lack imagination here, yes, but generally I go by experience rather than talking about imagined issues. That is precisely what many here seem to do when it comes to explaining why AI couldn't work for them, and why reviewing the code remains necessary.
> And then you are saying you needed to have performance tests (which many devs don't even bother with) to catch bad architecture?? lol
if the cost of writing code collapses, what are the barriers to including these kind of tests?
It seems like the flow and work when using LLMs has to change, spend longer on the plan, spend more time on defensive testing, have more comprehensive tests (input validation, cybersecurity, performance, data hygine) and less time on functionality
if you want to write code the old way where the architecture is human readable and the tests are an after thought, then thats on you if you then ignore the part where the human has to read it.
TLDR: either go full agent, or actually learn the code being produced, but not both.
I’ve been there, your “high standards” is just noise and annoyance for other people, focus on business value instead of jerking on variable names and “perfectly” aligned folders.
Do you also believe the same for other categories of products? For example, for a car, or for a building? Would you board a plane built by people who focus on business value at the expense of the annoying "high standards"?
Product quality is an emergent property of high standards (aka code quality).
> I believe that anyone who says that writing code is no longer needed, was probably not a very good coder to begin with. I have seen what the best models can do, and I've seen what people call "good", and I am not impressed.
I envy you.
I believe that anyone who holds this position has been extremely lucky/fortunate to work with only top-tier developers. And does not read or work with open source projects.
Last year’s models are producing code better than _at least_ 50% of the developers I’ve worked with. Today’s frontier models are better than probably 75%.
If the code that humans have been generating has been “good” (on average) or at least good enough, then the models that exist today are producing “good” code.
The problem is that in order to win you need to be in the top 1-10%, since you are competing against the same engineers. Being slightly above the average is only "good enough" to not get fired.
And the bar is already raised because every single one of those engineers now has access to the same models.
So you need to be in the top 1% of the engineers armed with LLMs. You can't just rely on LLMs getting better. AI is not the great equalizer, it is, in fact the opposite.
At this point, arguably, any code written a year ago by the coding LLMs of the time can be rewritten with those of today, but "better", whatever metric of better you optimize for. This may get better in time, with either better models or better prompts and the like.
Refactoring and clearing up tech debt is faster now than it's ever been, and assuming it'll be even better in the near future, why should anyone care about what is "good" right now?
(This is a devil's advocate kind of remark by the way, I'm very much in favor of good / quality code, but also realistic enough that nobody has or gets time to finetune code nearly as much as they should)
I guess humans have agreed, that art is a skill you must learn if you want to produce it, but engineering or software is not a skill worth learning and we are happy if computers can do that for us.
So it’s great and amazing if anyone can create their gizmos and software to help them but not if they create music or digital images, which apparently it is wrong to prompt, and not a sign of a great future.
Isn't this directly from that most people can relate to music/art but not with programming?
If you specified to someone that the AI is replacing the farting-into-mic-core music genre, they would not care. Same with programming, the average person is not exposed to this
This rings hollow to me for many reasons. When making CGI for movies quite a lot of the process has been completely automated for quite some time. So that scene in the movie is no longer art? Making video games you can use procedural generation to create a landscape, trees, rocks, etc. and most developers do at this point. Do we disregard all that when considering if the game is good or not? Or just blanket "it's bad because of that" like some people are doing with AI created stuff these days?
AI created art and photography keeps winning contests and people get all upset after the fact saying it's not real art. If it's not real art then why are the judges so impressed by it before they hear the word AI? I get the sentimental "but a soul that has experienced pain didn't create it" but if it's fun to look at and gives you joy is that not enough? And there is of course always a person in the loop creating it.
Definition of art:
The conscious use of the imagination in the production of objects intended to be contemplated or appreciated as beautiful, as in the arrangement of forms, sounds, or words.
A director imagines the movie scene and has a rough storyboard sketched, then CGI artists make it who then usually have some form of algorithm or ML help them. I'm sure most people would agree that's art even though the director only had his thoughts interpreted by many different people / machines. Why is that so different than a person asking ChatGPT or Seedance to create something they (and only they) imagined? Is it the effort involved? What if they spent 2 hours refining 1 photo to obsessively to get exactly what was in their imagination onto the screen?
I guess this is what is missing from vibed AI art. Usually it's someone with a quick prompt and nothing else. No effort or creativity from the human and that's probably what we don't like about AI art.
This is different from AI art looking all the same though
I kinda disagree with both points maybe because I've been using / seeing AI art extensively for a while. Some people put in absolutely monumental effort into their stuff, and if it all looks the same then toy story looks the same as ghibli looks the same as 1950s Disney looks the same as Wallace and grommit (all of which you can replicate fairly faithfully, combine into something that looks new, whatever else).
I think AI art is much like ai coding where people used 1 model a couple of times then wrote it off completely as garbage. Then years of very quick progress happened and all the 10 fingers on a hand and every picture looking the same disappeared.
If you can make it through the first 20 images on civit and tell me nobody is expressing themselves or their imagination in some of these pictures I guess we agree to disagree.
Most of them look like they could pass the product of a human artist, pre AI. There are no obvious tells that I can see and so I’m comfortable calling it “art”.
If we are going to say, humans should learn these skills to produce this and not just keep typing prompts into a chatbot, I agree, but I also think it applies to maths, coding, engineering and everything else. I don’t see why we need to draw an arbitrary line around supposed art.
Is the crayon drawing made by a child "art"? I believe it is. They put what was in their head onto a piece of paper. Those images are no different. Some of the things people come up with is actually really neat to look at. If they did it in Photoshop pre-AI you'd probably be impressed. But now the outlook is tainted.
I'm a pretty big fan of this one and I'm not sure how anyone could say there's no creativity being shown just because it didn't take as much effort as it would've 5 years ago:
Being able to express your imagination with more fidelity should be celebrated shouldn't it? Taking what's in your mind and showing it to other people is a really neat thing that used to be restricted to a few people. Not sure why expanding it out is so controversial. And again once you take the word AI out of it you start to win a bunch of art contests, so it can't be all that bad.
Because I’ve seen both and code created by your uncle might work and look good enough, and music and art created by your uncle is a fucking garbage that pollutes the internet.
> I guess humans have agreed, that art is a skill you must learn if you want to produce it, but engineering or software is not a skill worth learning and we are happy if computers can do that for us.
No; it's a conclusion about the product rather than the endeavour. People don't think of the software itself as "art", but as just a means to an end; so they don't mind the idea of any old fool being able to produce it.
Oddly enough, I've come to feel similarly about music. Most of what you hear in "pop music" is highly uninspired, including pre-AI; but it serves the purpose. And now we have AI that doesn't give any indication that it could compete with humans on mash-up ideas, experimental microtonal stuff, etc.; but executes "yet another four-on-the-floor, standard chord progression club anthem" very well. (Except the lyrics. Ugh.)
Over the past year, we’ve been using AI to solve everyday challenges and troubleshoot issues we encounter in our jobs. Now, we’re taking the next step by leveraging AI to build solutions, automate workflows, and improve the way we work.
$3,000/month is nuts! For $36k (1 yr of spend) you can buy a system with dual rtx pro 5000 (144GB VRAM). or spend a little more ($44k) and get two M5 Ultras w/512GB RAM. the latter, clustered, is big enough to run the biggest open weights (quantized). what you get for these systems is way more than $3k/mo of tokens, because you can run it 24/7/365. you can have pass after pass after pass of optimization, bug fixing, orchestrated subagents on backlogs, etc. way more value than paying for managed inference
I find that AI should be used more extensively for System Design. I have seen a lot of people not use AI for that and come up with frighteningly complex designs and let their agents run loose to implement it.
Retroactively speaking, I engaged with the already agreed designs and used AI to find much better ways to do the same thing.
Overall, I wish people used AI even for System Design as that seems to have a higher impact.
246 comments
[ 310 ms ] story [ 787 ms ] threadIt's crazy realizing that Claude Code only started getting big in the last year or so. It feels so different now, with all the new models & tools we have to use. I hope you enjoy!
[1]: https://ben.stolovitz.com/posts/how_use_ai_oct_2025
I guess, its debatable that o1 was the real start- but I think its hard to deny that Claude Code CLI was the first time we had what we now call a coding harness hit the mainstream. And- Sonnet 3.7 was kinda Anthropic's "o1 moment".
God. Opus 4.6 and 5.3-codex feel like ancient history.
On the CLI side Aider had a good CLI product and the only relevant benchmark back in the day.
I think I would rather shoot myself in the head
I walked into a paint store recently and there was some interesting music playing on the PA. It was some sort of spanish-language 80s new wave I'd never heard before. It had a nice groove. I asked the guy at the counter about it and was surprised to find out it was AI.
So... was it as good as great music? No. But it beat the hell out of muzak. And it was genuinely novel in a way that pop music isn't. I will sacrifice a lot of pride for novelty.
> And it was genuinely novel in a way that pop music isn't. I will sacrifice a lot of pride for novelty.
For one thing, "interesting" and "novel" are not particularly the point of art. An infinite TikTok feed of AI slop is in some sense infinitely interesting and novel.
But also, those concepts are subjective. Pop music has a ton of evolution and innovation if you break it down. Sure, not every hit is doing something clever, but plenty of them are. You might not find those features to be interesting but it takes ingenuity to stand out in an era where the barrier to entry is so low.
2. Call it slop
3. Claim victory
Controversial take: AI shows that people simply don't give a shit and just confirms that mass culture is a mistake and the death of everything that is good and sacred.
Controversial take: It's been clear that most people have never given a shit, it just took AI for most to realize it.
My experience has been that AI music mostly is that.
You're right, this sure was a controversial take.
Anyway, surely this dichotomy only matters if your only choices are "AI generated music" and "autotuned, quantized, mass-market-optimized slop that dominates contemporary pop "music"". However, there is a bunch of good music outside of the "pop music" zone, which isn't AI generated, so why not go with some of that instead? Nobody is forcing you to listen to pop music.
I don't know what to tell you, but me and my kid are having a blast writing ridiculous songs and plopping them with song descriptions and some of those make it to our drive playlists.
Note: same guy; just not anywhere near my account
You are making the argument that the type of activity does not matter as long it is 'spending time', but to say that is almost reductionist to say that that it understates how ridiculously unnuanced an opinion it is. You might as well tell me that the quality of food does not matter and generic burger should suffice.
But what if I want a burger that is custom to me and not a generic slop generated by a human to appeal to the widest possible audience[1]? What if I want something that was created just for me? Good grief? Why do I have to defend having fun and why on earth would you assume that one is mutually exclusive to the other?
Geez man. [1]https://genius.com/Bo-burnham-repeat-stuff-lyrics
And I agree that "less AI" edit passes reduce a lot of the hallmarks, but I think these models truly struggle with expressing complicated ideas. Have Opus been good at that for you?
I think people should find this sort of result surprising by default. Like, presumably you don't know why your output isn't as good, or you'd fix it. So doesn't that lack of understanding concern you?
Good question! It mostly doesn’t concern me. I believe these models have a huge “latent space,” so it’s not surprising to me that other people, with the right harnesses and prompts, can get them to produce more-valuable content.
I spent a lot of time calibrating how I use these models to produce valuable, good code, right?
But as I alluded with my question above, I haven’t seen a ton of evidence that these models have human-level argumentation in that latent space. If my assumption there is wrong, then yes, I’d have to reexamine my understanding of these models.
Ah, fair, there's opportunity cost to that. May as well prioritize what you actually care about, yeah. Personally I'm fine with skimming through the prose in the default style, at least as long as the LLM isn't getting stuck in its own invented jargon. It'd be different if I were publishing that output as project documentation, but I don't even really want to try that.
> I haven’t seen a ton of evidence that these models have human-level argumentation in that latent space.
Not sure what you mean by argumentation. I'd agree that they don't seem to have flashes of insight or make intuitive leaps in the same way. But they clearly can follow through the logical steps of reasoning very thoroughly, once they know what to reason about. For a technology that seemed to struggle with understanding negation a couple years back, it's seemingly miraculous.
As a side effect having to read the code at least once inevitably makes it way shorter and usually much better.
I'd be curious to learn how you're using them.
Dude you should buy tin. I can get you a banger box for $30k.
> I still believe I use AI fairly typically, but I’ve had a notable change in background. Last October, I was an engineer on Microsoft’s Windows AI team. I am now a research engineer at Salesforce AI Research.
Priest wears robes.
I guess it’s one of those nonsensical automatic rules.
> I am now a research engineer at Salesforce AI Research. I spend more time supporting scientists & building rapid prototypes, rather than working with a large team of engineers.
I appreciate the PoV of people with different challenges, but my full-time job is not about building rapid prototypes so the guidelines are worth a bit less to me.
We're in our own bubbles where a good part of HN population woke up to coding agents being good enough ~1 year ago, there's a group of folks that found out ~6 months before that, and then the people banging their heads on these a bit earlier than that. If you aren't using a coding agent everyday most people are just using these things to apply an instagram filter, or sort a grocery list. Muse is probably the closest thing to 'full automation' a lot of people are seeing for the first time.
Unfortunately I don't think the bubble that exists at the moment is on the promise of automating so much coding as trying to penetrate consumer use cases, which I think are still lacking and not reliable enough yet. As much as I use coding agents every day I still will not let a claw-like near a bank account or a credit card.
While trying to capture that bug, I (ironically) asked Gemini (but the Gemini-via-Google-search Gemini, since Gemini proper was an outage) for the package name of a screen recorder (gtkRecordMyDesktop); the command it returned referenced a non-existent package¹. I then found an alternative, and then hit a bug in that screen recording software that I had to then work around to get the video file there.
The site I uploaded the video to, Imgur, breaks completely — i.e., it cannot even so much as surface an error message — if your browser refuses to let it store god knows what on your disks. (So as it is, I have an extension that says "why yes, of course you can have storage, and essentially provides a black hole. But since it looks like a "success" to the Javascript, the JS doesn't crash!)
This isn't, to me, some one-off interaction. Every interaction with software these days feels like this, more and more. Just breakages everywhere that leaves me wondering if "coding is solved", where is all the bug free software?
(¹forcefully prompting the AI to actually query the package listing netted me a classic "You're absolutely right! The package name I referenced does not exist!")
My experience is the same as yours. Every single service I use. Apps, websites, doesn't matter what. Every single one seems just softly degraded. Like I can't trust that features work any more. I could report the bug, but what's the point? It's going to go to some Claude triage agent who is going to "fix" it by slopping up the app even more. Do people even use their own websites and apps any more? Apparently not - the same people declaring software solved are having their AI agents doing computer use and going on these websites for them!
Claude, please read the Bible for me. Tell me what I need to do to get into Heaven. Oh, can you dictate it to me? I'm managing an agent swarm.
Bleh.
I know, I know, we are shit and AIs are a godsend for small shitty companies that have at least one clever person that architect their way out of a paper bag. I’m sure all ya’ll highly competent, genius academics don’t need it and find it a hindrance but the devs in my bubble really, really need it. And there are a lot of those..
Just find it funny how vibes based it all is, capabilities, experiences, anti-ai and pro-ai.
That being said, I don't understand what you are referring to.
It exists, at least in terms of ROI. Some enterprises are able to do successful rollouts that allow them to significantly shrink workforce in certain departments without losing quality (no, I'm not talking about customer support).
But most of the software is indeed getting worse right now. The industry is still figuring out how to wield these new tools and what to build with them. Those that figure it out faster than others will make billions.
I've noted the same but even before llms so I can't draw a conclusion there. I find strange issues with software I cannot explain. Not only breakages or bugs but also a strange lack of core features and UI/UX that leaves me wondering "am I dumb?".
b) you confuse technical quality with product incentives
If you see this, company doesn’t care about product. And Google doesn’t care about quality for at least a decade now, all they care is a share price and speed of delivery.
My annoyance is more specific than "it breaks". I hate how all this software (especially "web apps") just assumes that it will have permission to do whatever it wants and never contemplates that the user might have something to say about it.
But don't worry. Coding is solved.
The comments to this article suggest that it's still disputed.
Personally, I think it's a bit of a silly phrase, because it implies an absolute, and all anyone needs to do to disprove it is to show that coding was not solved for problem X. That's still the case -- it's just that the set of X is rapidly shrinking.
I think it's more indicative to say how much you trust LLM code. A little over a year ago, I scrutinised every line carefully, and it was buggy as hell and I had to fix most generated code by hand. Now, models write clean code with fewer bugs (far fewer) than I would. A year ago, I was doing a lot of steering during planning; now, it's rare that I don't agree with the model's first proposed solution, and even if I don't, it's most likely to fix a minor change in understanding, not to propose a substantial architectural change as it was before.
The one thing I'm not sure about is how much this is due to better prompting on my part. I doubt that this contributes much, but I still suspect that continual usage does make you more aware of what works and what does not: how much detail to provide, how to describe a problem or change, how to propose solutions while still leaving other options open.
It's just that most skilled developers find it disgusting, because the AI spits out code that doesn't work the way it should, and it all turns into the routine of "threw in a prompt, waited, reviewed, repeated." — only those who were crappy programmers are happy about this.
And on top of that, these characters deny the fact that agents are evolving too, and meat proxies will soon be unnecessary — or maybe already are: https://x.com/tunahorse21/status/2117353858544660585
To "know" this, people would have to believe it from their personal experience.
Many people have standards and experiences that differ from yours. Yes, even if they use frontier models.
What does amuse me to no end, however, is how many craftsfolk in AI circles are loudly rejecting AI imagery and video in favor of humans. The only folks who seem fine with that are the grifters looking to slop out a bunch of adverts quickly for commercial gain rather than do actual art, or folks who want “art” but don’t want to pay a human to make it for them or learn the skills themselves. Both of those camps are viewed with disdain and loathing by both sides of the AI Boom/Doom arguments, so I’ll take whatever allies I can get on those fronts.
AI music…man, I live with artisans and musicians and we have a number of “artists” we suspect are AI but whose music is pretty solid. We’re waiting for the shoe to drop, but I’m trying to look at it as a kind of turbocharged Fruity Loops/Logic as opposed to a human replacement. I hope I’m right on that, but time will tell.
The game changer I think more doomers need to experience is agents. It sounds like a recipe for disaster (a bunch of models known to hallucinate and allowed to run roughshod over a computer unattended? What could go wrong?), and it often (hilariously) is for the scammers out there giving it full access to production and fucking off to hawk their latest Masterclass on X or LinkedIn, but that first time you give it a reasonably achievable prompt and come back to a halfway decent product you can review and refine into production in 15m or less?
Absolutely magical, doubly so when it’s just 27B Qwen via Hermes and not some souped up stack in a frontier lab costing you thousands a month in tokens. If I were to evangelize AI, that’s how I’d do it. Fuck subscriptions, fuck cloud compute, fuck frontier labs and token buckets and whatever slop Sundar/Scam/Dario/Elmo et al are hawking this week; run that locally, give it a reasonable prompt that solves a real problem, and show them the results.
Then do the right thing and go for a walk with the two hours of free time the AI just freed up for you. Don’t ask for permission to take back your time.
...
>In case you’re wondering, I’m subscribed to: > Claude Pro ($17/ mo) > GitHub Copilot Pro ($10/ mo)
Nothing wrong there at all, amirite?
Maybe AI scrapped basic arithmetic and now those numbers are totally fine
> My most successful work this year was hand-writing a tool, replacing ~4,000 lines of vibe-code with a few hundred. My system has proven more flexible, easier to test, and more generalizable than what it replaced.
> agents still need human taste and experience to produce good work.
Where do you think human taste and experience in software comes from?
> overstimulating & exhausting in a way that coding never was.
> for many of us, honing the code, finding the right design, seeing the parts mesh together — that was the meditative purpose we found in our jobs.
> That’s not the only time AI waylaid me. LLMs could not figure out how to annotate that model’s training data correctly. I spent days going back and forth with them
> LLMs are not yet good companions when brainstorming complicated things.
Why do we expect this to get better? The training data will degrade in quality if more people rely on LLMs so I'd expect models to eventually start getting worse even if they currently enjoy apparent improvements in output.
> Writing code is solved.
What a strange thing to say given all the above experiences, which I think we all share.
Why are we willing to give up so much in terms of purpose and understanding for perceived productivity gains?
This always reads like “writing english is solved” just because you collected snippets of famous works and have built a spell corrector.
Code is written to process data. The struggle was never to learn programming syntax (most grammar are only a few page long). The main struggle was that it’s hard to model a domain into an information system, identifying the components and their relationships, what is ingested, and what needs to be output.
Computers have integers and floats, but you need to implement units like distance, money, various counts,… The real world have words and sentences, but that needs encoding like ascii and unicode as well as the “string” concepts to make it work. Take HN, I’d bet that if you can model it in terms of data, writing code is not an issue.
For 85-90% of devs, yes this was the struggle. Creating and updating rows in a database from user input is all the “domain modeling” there is in most software. That’s what is solved by AI and it’s the majority of software dev.
The struggle is not where to put a semicolon. The struggle is, how do I handle validation problem X with framework Y.
If anything, I do expect the casual non-tech users to be making the most adaptable software because they have what they want in their head. Although, part of what the dev does is figuring out the weeds, and only a part of users have the patience to sit through detail corrections. And the AI user has to verbalize their wants exactly too.
Even with basic pseudocode and very lenient logical rules, most people won’t bother to provide a a good definition of some process, as it’s quite unnatural to be that logical. Instead, it’s mostly just playing around. When you ask the one that is supposedly 10x-ing his productivity to explain his mental model of their own projects, it’s often a wishlist instead of a real understanding of what he has built.
Those are the people who are most likely to give a monolithic prompt and then spend a week telling the machine 'I don't care about encryption or file permissions, just make it work.' Not to mention that most people can barely verbalize the problems they have with devices they use for 8hrs a day, let alone form a full, self consistant, project idea and then put it in writing.
Imagine you could build a piece of furniture without actually needing to know how to cut wood, drive nails etc. Imagine you could do it in 3d without knowing how to actually handle wood, you just have an image you can iterate on without actually costing you anything but time.
They may have a perfectly good shot at being smart, but I still don't buy the argument. The reason they struggle to become coders (assuming they tried at all), in my experience, is a lack of problem-solving and debugging skills. There is no real shortcut here. An "image" of the chair isn't enough; when the first iteration is wrong, you have to be able to explain why it's wrong.
With a sufficiently good LLM these people will skip the tedious part of programming that most of us had the motivation / patience to go through. All the folks saying that actually writing code wasn't the bottleneck have had a LOT of years to get to that point, years a lot of people aren't interested in going through.
I'm not saying lay people will be able to join mega projects and work at FAANG. I'm saying a sufficiently courageous lay person will be able to make an app to automate whatever tedious birocratic process their niche work hasn't seen a product yet.
The years was not spent on learning how to code. The years was spent on learning about what needed to be coded. And that would have spanned multiple domains. Even the most basic production level software have multiple concepts that need to be integrated. And for that, they need to be learned.
Using AI without the requisite knowledge is playing Russian roulette. It’s kinda wanting to log into a file, and not thinking about disk storage being finite, and rotating the log file gives you flexibility. That’s not related to coding, it’s just hard thinking about the problem and the constraints (or finding a good book or article).
Every few months that revolver has a few more empty chambers and even if it shoots them in the foot, it can bandage them quite nicely. Will a solo lay person with an agent build a high quality high reliability software business? Doubtful. Will they be able to make a tool that helps them and 10 other people or a personal website? probably.
Just take a quick look around to see how many daily tools and utilities are actually built by experts and industries. Yes, you can put a plank on top of two concrete blocks to make a bench, but many people don’t and prefer to have good bench made, even when it’s quite easy to DIY one.
So I don’t foresee lots of people trying to make custom tools, more so to keep a subscription active.
And yeah, without LLMs, writing code absolutely can be the bottleneck sometimes, if only for psychological reasons. I've definitely experienced something like "coder's block" before. An anti-flow state, if you will.
(But skydhash is also right. The "unknown unknowns" are a big deal and there's only so much an LLM can do about that, at least unless the user is actively aware of the general shape of that problem and trying to learn via the LLM and fill in those knowledge gaps.)
I see this as HN bubble. For many on here it wasn’t a struggle, but for most people I think it is. Hence the SWE salaries.
AI changes the dispositions and difficulties, but it still favors a narrow selection of people to do the (still) difficult task of software engineering with agents.
RL is not dependent on training data. The mathocalypse didn't happen because they found better training data somewhere.
There are reasons to suspect that OpenAI may have intentionally or unintentionally trained on chat logs from mathematicians--mathematicians who were actively using OpenAI to try to to solve the same problems themselves.[1] If those mathematicians were on the right track, and getting close to a solution, that could be classified as finding "better training data somewhere."
[1] https://cybernews.com/ai-news/openai-accused-stealing-anothe...
I agree it’s unlikely this happened by accident, or entered training data (though not impossible), but it could certainly be within the context agents were using when tackling problems.
We have no evidence either way, but they had all the data they would need to do this.
Secondly most the posts on HN are from individuals. It doesnt represent group dynamics and how group/institutional knowledge works. For example there might be a regulation on publishing data every quarter. On the surface it looks like compliance but in reality different internal groups will hide data, delay it, fudge it etc etc. LLMs train on public data. Only after they are allowed access internally do they discover group knowledge. We are far away from seeing that happening.
As someone who wrote code for 27 years in all sorts of languages, not necessarily impressive code, but got paid for it, made a career, and changed life for the better multiple times, I don't think coding was ever an important part of the journey! Maybe it's cognitive dissonance, as you wrote above, but I believe taste comes from exploration, from trial and error, from being willing to be open and change old ways if new ways can improve how things are done!
Similar to taste in food or even music! You might think certain foods taste bad, but until you taste them, you won't be certain! Sometimes your senses will be right and the food will taste horrible; other times, you'll be fairly surprised. Similar to music—it's my life hobby—you might think certain genres or artists are bad, but until you sit down, give them a shot, and hear what they have to say, you won't know for certain if your judgment is correct.
If you meant by your comment that you need to code a lot to acquire taste and experience in software, I respectfully disagree. You are not completely wrong, though, but putting emphasis on code, if that's what you were going for, is an overstatement of code's importance. For that particular case, similar to the analogies mentioned above, it's about exploration and trying different languages, paradigms, solutions, and stacks, and unfortunately, we as humans have finite time, so we can't be trying and exploring fast enough to form judgments!
What I genuinely love about LLMs is mostly their exploratory powers. I try new stuff that I always wanted to try with actual products and form opinions about the whole experience.
Anyways, as I finished writing this, I realized, as always, I might be wrong! You might be right, taste is highly subjective! And acquiring it is a very personal thing! So I don't think right or wrong are important here!
I actually found this to be one of their strong points. Depends on the person / thinking style / communication style, I guess.
If I'm prompting with phrases like "Any ideas?", it's because I'm exploring a domain where I know I don't know a lot, and where I expect that there is enough prior art to get a foothold.
Its not a direct analogy, but this seems to me as like saying "I already know maths and english, why do we still hire and pay teachers". Its a massive issue that just seems to be hand-waved away as "we'll figure it out later".
To compound on the problem as well, even those of us who do still write code by hand are actively discouraged from sharing it, knowing no human will ever read it and we'll never be credited for our work, it'll just go in the pockets of a few big AI companies. I suppose there will be some that take some pride in knowing their code is helping AI models advance, but thats a small pool of an already small pool, assuming most people decide to just use the AI to write the code for them. A minority of a minority.
I guess this is the chickens coming home to roost of this whole attitude towards growth now, fallout later
"Perceived productivity gain" is a baseless conclusion. At this point it's abundantly clear that the productivity gain is real and massive.
Where I see the incoherence is in that people see too much value in code review and optimizations.
I believe many of the optimizations are premature. If you did not spot the problem in your QA testing, chances are it was not that important in the first place.
If the architecture wasn't perfect, it is now extremely cheap to change it later, as you realize real deficiencies. The agent will do it for you in a matter of hours, and largely on its own.
You will have to budget a lot of QA effort for verifying the significant changes before going to production, but I don't think it's a deal breaker.
Oh? Where is it? Why am I not seeing it as a user if it's abundantly clear? In none of the software I've used frequently in the last year have I seen any of those “real and massive” improvements. Not saying they don't improve at all, but the rate hasn't increased since the advent of LLMs.
I'm still waiting for that shiny new era of software productivity to impact any actual users.
In the beginning I was using GPT-5.4 in April.
I can't speak to what other devs are building, and there's certainly no lack of people pushing out slop made in a weekend.
Companies and teams with code review are not adapting at the same speed.
I figure it will take a few additional months for the benefits to become more noticeable to end users.
As someone working on the frontier, so to say, I believe I have a better picture.
Both of those things have changed a lot, and I think a year from now you will definitely see changes (maybe not massive, but still).
QA testing can't find missed optimization opportunities unless the performance is absolutely abysmal. Even then, it can't distinguish abysmal performance due to missing easy optimization opportunities from abysmal performance that's going to require you to sacrifice functionality.
If an app performs well because it caches the result of a slow function, it's going to pass QA just fine. If optimizing that slow function would make it fast, you can drop the cache and have a simpler and more robust app — but QA can never tell you that.
Is having a simpler and more robust implementation worth the time to review, understand, and improve it? I'd put that at easily factor 5-10x the effort.
If in the future performance was found inadequate, you could still have the agent revisit the slow function.
That's what I mean with the optimization being premature.
After struggling with some more complex features even after many iterations, I started to doubt, and questioned whether I'm writing myself into a dead-end.
I did not. I still made progress and things worked out just fine. Then Fable and later GPT-5.6 Sol came out, boosting productivity and complexity of features I could implement successfully.
The effort only ever went down, and my app was released in the AppStore last week, at about ~500k LOC.
I understand why people might believe that using AI and not manually cleaning up suboptimal or overly defensive implementation would eventually result in an unmaintainable mess.
Based on my personal experience and 500+ hours put into my project, I can confidently say that this is not the case. Increased effort to maintain is negligible.
Does it work both ways? How hard should we fight premature optimizations that LLMs are introducing? Or the crazy amount useless tests?
If the test suite takes too long, it can easily be cleaned up later rather than spending extra time judging test cases in every new MR.
Maintenance of test cases has also become trivial, even with thousands of them. When the pipeline goes red on master I click the new chat button and run /check-pipeline, where I put some concise instructions, and the failure gets investigated and resolved in short order.
What I think you're saying is that opinions that LLMs produce are good enough and malleable, so why even have an opinion?
Which is debatable - oftentimes their opinions are not good enough, and not malleable at all.
We're all still learning how to distinguish those. But in order to learn this, you have to read and understand the code they produce. LLMs can migrate a codebase to Rust, but they still suggest walking to a car wash. Every day they make decisions that humans would be fired for.
LLMs are definitely not yet capable enough to blindly trust them, and there's a pretty good chance they never will be.
You are assuming that AI should be trained on speicific field to reason better but it actually doesnt need to.
That is both untrue, and should be something that occurs rarely.
Past Mid level, most dicussion in code review is centered around the conceptual level. Code is mostly use to highlight the point being made. I’ve never seen the need for someone to rewrite a patch proposed by another person. Add to it or correct misunderstandings, but never sweeping changes.
Big +1 to that.
6 used to be the max for me as well when I worked with Claude Code in terminal windows. Too little to hold on to and to discern the conversations. Just walls of text.
I can now go 20+ with fun because
1. I built a canvas for organizing the work spatially, so every feature has its place,
2. I condensed sessions to a WhatsApp style chat with detailed agent steps available but folded away by default, and
3. Each feature agent ends with a demo, usually a narrated video that I can watch and comment on by voice as it runs.
(My solution is available open source and free: obeya.si)
However, I'm still split. Because this could mean that we see a jump in code quality, due to bad coders getting someone better to code for them. But we could see a decrease in code quality, due to less people "caring".
This is very much not my experience.
There is still questions about if that jira ticket should have been written but from ticket to mergable PR it's better than me.
I can virtually guarantee the bar for code can go so much lower as we rapidly deskill programmers.
We actually have a fabulous opportunity to generate much higher quality code products with AI.
And even in your case, with the eager AI student -- AI code education is much better than reading docs and struggling with tools. Students can now focus on the important software topics.
I remember my intro C++ classes and how many students stumbled on pointless & transient tooling , that was completely orthogonal to their proper engineering skills.
Now students can focus on their area of expertise and produce the highest quality possible without any resource restrictions.
I’m probably the best coder in my org, which doesn’t really say much, but at least allows me to judge about the room temperature. It is solved. I don’t say that’s you, but the only people who say it isn’t “that good” are the same people who used to yak shave, rename and move things 10 times until it “feels right”.
I think what people are missing is that people often become craftsman because we want to be able to keep the code adaptive and performant so we can provide business value quickly and efficiently.
The change is with models, the benefit dynamics of immaculately structured code changes dramitically. Now the real area you need to focus on is correctness and performance. Structure doesn't really matter unless it serves those two purposes, at least from the business value side.
As with eliminating other forms of abstraction, there's not much gain from doing so. Writing source code and then compiling it offers many useful checks, and greatly simplifies making changes. It's just something that may no longer be necessary at some point.
Nearly everyone I’ve worked with that would say this ended up being the in the bottom quartile of those shipping features and their code was often the most complex. At least in small, “fast”-moving companies.
A few people I have worked with have nailed it, but they were the ones to balance present and future weighted towards present.
Spec was good, all tests green, app worked. If I didn't actually go in and try to understand the code I would have shipped it like this.
So here is my question to people saying it's solved : do you actually know that it is or does it simply appear so because all the tests are green?
First of all, it sounds unlikely to me that such an implementation was made with Opus 5.5 while it never mentioned the workaround.
Secondly, if the performance was indeed inadequate, that's something I would have caught during QA testing and simply have had the agent fix it.
When I looked at a benchmark, it suggested Sonnet 5.5 wasn't much cheaper than Opus 5.5 at Low to Medium effort for similar performance. That might not be representative of real world use though.
First, you question the model. I have reams and reams of conversations with Opus 5.5 that shows it make architectural messes all the time
And then you are saying you needed to have performance tests (which many devs don't even bother with) to catch bad architecture?? lol
So what if the architectural problem is not one that would be caught with a performance test? It seems you lack imagination to think of all the ton of issues that that would be very complicated to catch with tests.
I don't think I commented on performance tests at all.
Maybe I lack imagination here, yes, but generally I go by experience rather than talking about imagined issues. That is precisely what many here seem to do when it comes to explaining why AI couldn't work for them, and why reviewing the code remains necessary.
if the cost of writing code collapses, what are the barriers to including these kind of tests?
It seems like the flow and work when using LLMs has to change, spend longer on the plan, spend more time on defensive testing, have more comprehensive tests (input validation, cybersecurity, performance, data hygine) and less time on functionality
if you want to write code the old way where the architecture is human readable and the tests are an after thought, then thats on you if you then ignore the part where the human has to read it.
TLDR: either go full agent, or actually learn the code being produced, but not both.
… Yes? Of course recognizing the flaws in the code correlates with having high standards. That doesn't mean the flaws aren't there.
Product quality is an emergent property of high standards (aka code quality).
I envy you.
I believe that anyone who holds this position has been extremely lucky/fortunate to work with only top-tier developers. And does not read or work with open source projects.
Last year’s models are producing code better than _at least_ 50% of the developers I’ve worked with. Today’s frontier models are better than probably 75%. If the code that humans have been generating has been “good” (on average) or at least good enough, then the models that exist today are producing “good” code.
And the bar is already raised because every single one of those engineers now has access to the same models. So you need to be in the top 1% of the engineers armed with LLMs. You can't just rely on LLMs getting better. AI is not the great equalizer, it is, in fact the opposite.
Refactoring and clearing up tech debt is faster now than it's ever been, and assuming it'll be even better in the near future, why should anyone care about what is "good" right now?
(This is a devil's advocate kind of remark by the way, I'm very much in favor of good / quality code, but also realistic enough that nobody has or gets time to finetune code nearly as much as they should)
> my uncle can create software
Is considered great
> my uncle can create music or art
Is not considered great.
I guess humans have agreed, that art is a skill you must learn if you want to produce it, but engineering or software is not a skill worth learning and we are happy if computers can do that for us.
So it’s great and amazing if anyone can create their gizmos and software to help them but not if they create music or digital images, which apparently it is wrong to prompt, and not a sign of a great future.
If you specified to someone that the AI is replacing the farting-into-mic-core music genre, they would not care. Same with programming, the average person is not exposed to this
AI created art and photography keeps winning contests and people get all upset after the fact saying it's not real art. If it's not real art then why are the judges so impressed by it before they hear the word AI? I get the sentimental "but a soul that has experienced pain didn't create it" but if it's fun to look at and gives you joy is that not enough? And there is of course always a person in the loop creating it.
Definition of art: The conscious use of the imagination in the production of objects intended to be contemplated or appreciated as beautiful, as in the arrangement of forms, sounds, or words.
A director imagines the movie scene and has a rough storyboard sketched, then CGI artists make it who then usually have some form of algorithm or ML help them. I'm sure most people would agree that's art even though the director only had his thoughts interpreted by many different people / machines. Why is that so different than a person asking ChatGPT or Seedance to create something they (and only they) imagined? Is it the effort involved? What if they spent 2 hours refining 1 photo to obsessively to get exactly what was in their imagination onto the screen?
I guess this is what is missing from vibed AI art. Usually it's someone with a quick prompt and nothing else. No effort or creativity from the human and that's probably what we don't like about AI art.
This is different from AI art looking all the same though
I think AI art is much like ai coding where people used 1 model a couple of times then wrote it off completely as garbage. Then years of very quick progress happened and all the 10 fingers on a hand and every picture looking the same disappeared.
https://civitai.com/images
If you can make it through the first 20 images on civit and tell me nobody is expressing themselves or their imagination in some of these pictures I guess we agree to disagree.
If we are going to say, humans should learn these skills to produce this and not just keep typing prompts into a chatbot, I agree, but I also think it applies to maths, coding, engineering and everything else. I don’t see why we need to draw an arbitrary line around supposed art.
I'm a pretty big fan of this one and I'm not sure how anyone could say there's no creativity being shown just because it didn't take as much effort as it would've 5 years ago:
https://civitai.com/images/147579069
Being able to express your imagination with more fidelity should be celebrated shouldn't it? Taking what's in your mind and showing it to other people is a really neat thing that used to be restricted to a few people. Not sure why expanding it out is so controversial. And again once you take the word AI out of it you start to win a bunch of art contests, so it can't be all that bad.
Like I'm sorry that photo you sent me is dreadful, I find it hard to believe you chose it as an example of how ai "art" is good.
No; it's a conclusion about the product rather than the endeavour. People don't think of the software itself as "art", but as just a means to an end; so they don't mind the idea of any old fool being able to produce it.
Oddly enough, I've come to feel similarly about music. Most of what you hear in "pop music" is highly uninspired, including pre-AI; but it serves the purpose. And now we have AI that doesn't give any indication that it could compete with humans on mash-up ideas, experimental microtonal stuff, etc.; but executes "yet another four-on-the-floor, standard chord progression club anthem" very well. (Except the lyrics. Ugh.)
Oh and prefill is still a lot worse on local LLMs. Like 1-2 orders of magnitude worse.
Retroactively speaking, I engaged with the already agreed designs and used AI to find much better ways to do the same thing.
Overall, I wish people used AI even for System Design as that seems to have a higher impact.
...
> Last October, I was an engineer on Microsoft’s Windows AI team. I am now a research engineer at Salesforce AI Research.
That's generally not how "typical use" works.