Yeah working on a smart way to rate limit stale requests for those who don't have accounts.But the final version will allow anybody who is not a bot, to get into a vim instance without logging in. Thanks for the feedback.
I feel like we are just inching closer and closer to a world where rapid iteration of software will be by default. Like for example a trusted user makes feedback -> feedback gets curated into a ticket by an AI agent, then turned into a PR by an Agent, then reviewed by an Agent, before being deployed by an Agent. We are maybe one or two steps from the flywheel being completed. Or maybe we are already there.
I just don’t see it coming. I was full on that camp 3 months ago, but I just realize every step makes more mistakes. It leads into a deadlock and when no human has the mental model anymore.
Don’t you guys have hard business problems where AI just cant solve it or just very slowly and it’s presenting you 17 ideas till it found the right one. I’m using the most expensive models.
I think the nature of AI might block that progress and I think some companies woke up and other will wake up later.
The mistake rate is just too high. And every system you implement to reduce that rate has a mistake rate as well and increases complexity and the necessary exploration time.
I think a big bulk of people is of where the early adaptors where in December. AI can implement functional functionality on a good maintained codebase.
But it can’t write maintable code itself. It actually makes you slower, compared to assisted-writing the code, because assisted you are way more on the loop and you can stop a lot of small issues right away. And you fast iterate everything•
I’ve not opened my idea for 1 months and it became hell at a point. I’ve now deleted 30k lines and the amount of issues I’m seeing has been an eye-opening experience.
Unscalable performance issues, verbosity, straight up bugs, escape hatches against my verification layers, quindrupled types.
Now I could monitor the ai output closer, but then again I’m faster writing it myself. Because it’s one task. Ai-assisted typing isn’t slower than my brain is.
Also thinking more about it FAANG pays 300$ per line in production, so what do we really trying to achieve here, speed was never the issue.A great coder writes 10 production lines per day.
Accuracy, architecture etc is the issue. You do that by building good solid fundamental blocks that make features additions easier over time and not slower
I think it goes down in two camps. AI is improving on these issues and people countering.
I don’t know for sure, but to me it seems the last 2 years weren’t necessarily 'intelligence' improvements but post-training improvement and tool connections, also reduced censorship.
I’m know using less AI than ever and I’ve been burning 1000USD/month before Claude Code. I have a couple of really fundamental functions built that help me to solve a big chunk of specific problems I can built a lot on that. Adding functionality became easier not more complicated.
I would think for these business problems that I’m facing AI is less than 30% of the time right. For example deciding on how to setup databases for max efficiency how to write efficient queries. Everything that in the end is really moat to you compared to your vibe coded competitors.
From my personal experience I’ve seen a lot of vibe-cded companies stuck and barely adding nec functionality or features and my guess is that they don’t trust changes anymore.
So even if AI would be as good as a really good coder one thing would still be missing a person that is knowing exactly what is happening.
And I mean okay it might be writing a form real quick. But a modern form needs to do a lot of things and if you have established patterns for all kind of inputs, the implementation is mundane.
It’s like when you learn coding, type it yourself to learn. So if you can’t scale the AI only codebase at one point you have to learn it, and I argue right now most efficient way is to write in it.
And I’m also arguing that it’s really tough to get a software so good that it’s actually an asset on the market vibe-coded only. It seems like its more of a drug for wannapreneurs than it is actually building an asset.
Like it builds you a Netflix clone, but what you see is barely the code you need to write a Netflix competitor.
I know it’s not your main point, but I’m curious where $300/line comes from. I don’t think I’ve ever seen a dollar amount attached to a line of production code before.
I think this sounds like a true yet short sighted take. Keep in mind these features are immature but they exist to obtain a flywheel and corner the market. I don’t know why but people seem to consistently miss two points and their implications
- performance is continuing to increase incredibly quickly, even if you rightfully don’t trust a particular evaluation. Scaling laws like chinchilla and RL scaling laws (both training and test time)
- coding is a verifiable domain
The second one is most important. Agent quality is NOT limited by human code in the training set, this code is simply used for efficiency: it gets you to a good starting point for RL.
Claiming that things will not reach superhuman performance, INCLUDING all end to end tasks: understanding a vague business objective poorly articulated, architecting a system, building it out, testing it, maintaining it, fixing bugs, adding features, refactoring, etc. is what requires the burden of proof because we literally can predict performance (albeit it has a complicated relationship with benchmarks and real world performance).
Yes definitely, error rates are too high so far for this to be totally trusted end to end but the error rates are improving consistently, and this is what explains the METR time horizon benchmark.
Scaling laws vs combinatorial explosion, who wins? In personal experience claude does exceedingly well on mundane code (do a migration, add a field, wire up this UI) and quite poorly on code that has likely never been written (even if it is logically simple for a human). The question is whether this is a quantitative or qualitative barrier.
Of course it's still valuable. A real app has plenty of mundane code despite our field's best efforts.
Combinatorial explosion? What do you mean? Again, your experiences are true, but they are improving with each release. The error rate on tasks continues to go down, even novel tasks (as far as we can measure them). Again this is where verifiable domains come in -- whatever problems you can specify the model will improve on them, and this improvement will result in better generalization, and improvements on unseen tasks. This is what I mean by taking your observations of today, ignoring the rate of progress that got us here and the known scaling laws, and then just asserting there will be some fundamental limitation. My point is while this idea may be common, it is not at all supported by literature and the mathematics.
The space of programs is incomprehensibly massive. Searching for a program that does what you need is a particularly difficult search problem. In the general case you can't solve search, there's no free lunch. Even scaling laws must bow to NFL. But depending on the type of search problem some heuristics can do well. We know human brains have a heuristic that can program (maybe not particularly well, but passably). To evaluate these agents we can only look at it experimentally, there is no sense in which they are mathematically destined to eventually program well.
How good are these types of algorithms at generalization? Are they learning how to code; or are they learning how to code migrations, then learning how to code caches, then learning how to code a command line arg parser, etc?
Verifiable domains are interesting. It is unquestionably why agents have come first for coding. But if you've played with claude you may have experienced it short-circuiting failing tests, cheating tests with code that does not generalize, writing meaningless tests, and at long last if you turn it away from all of these it may say something like "honest answer - this feature is really difficult and we should consider a compromise."
So what do you think the difference is between humans and an agent in this respect? What makes you think this has any relevance to the problem? everything is combinatorially explosive: the combination of words that we can string into sentences and essays is also combinatorially explosive and yet LLMs and humans have no problem with it. It's just the wrong frame of thinking for what's going on. These systems are obtaining higher and higher levels of abstractions because that is the most efficient thing for them to do to gain performance. That's what reasoning looks like: compositions of higher level abstractions. What you say may be true but I don't see how this is relevant.
"There is no sense in which they are mathematically destined to eventually program well"
- Yes there is and this belies and ignorance of the literature and how things work
- Again: RL has been around forever. Scaling laws have held empirically up to the largest scales we've tested. There are known RL scaling laws for both training and test time. It's ludicrous to state there is "no sense" in this, on the contrary, the burden of proof of this is squarely on yourself because this has already been studied and indeed is the primary reason why we're able to secure the eye-popping funding: contrary to popular HN belief, a trillion dollars of CapEx spend is based on rational evidence-based decision making.
> "How good are these types of algorithms at generalization"
> Verifiable domains are interesting. It is unquestionably why agents have come first for coding. But if you've played with claude you may have experienced it short-circuiting failing tests, cheating tests with code that does not generalize, writing meaningless tests, and at long last if you turn it away from all of these it may say something like "honest answer - this feature is really difficult and we should consider a compromise."
You say this and ignore my entire argument: you are right about all of your observations, yet
- Opus 4.6 compared to Sonnet 3.x is clearly more generalizable and less prone to these mistakes
- Verifiable domain performance SCALES, we have no reason to expect that this scaling will stop and our recursive improvement loop will die off. Verifiable domains mean that we are in alphago land, we're learning by doing and not by mimicking human data or memorizing a training set.
Hey man, it sounds like you're getting frustrated. I'm not ignoring anything; let's have a reasonable discussion without calling each other ignorant. I don't dispute the value of these tools nor that they're improving. But the no free lunch theorem is inexorable so the question is where this improvement breaks down - before or beyond human performance on programming problems specifically.
What difference do I think there is between humans and an agent? They use different heuristics, clearly. Different heuristics are valuable on different search problems. It's really that simple.
To be clear, I'm not calling either superior. I use agents every day. But I have noticed that claude, a SOTA model, makes basic logic errors. Isn't that interesting? It has access to the complete compendium of human knowledge and can code all sorts of things in seconds that require my trawling through endless documentation. But sometimes it forgets that to do dirty tracking on a pure function's output, it needs to dirty-track the function's inputs.
What is unreasonable? I am saying the claims you are making are completely contradicted by the literature. I am calling you ignorant in the technical sense, not dumb or unintelligent, and I don't mean this as an insult. I am completely ignorant of many things, we all are.
I am saying you are absolutely right that Opus 4.6 is both SOTA and also colossally terrible in even surprisingly mundane contexts. But that is just not relevant to the argument you are making which is that there is some fundamental limitation. There is of course always a fundamental limitation to everything, but what we're getting at is where that fundamental limitation is and we are not yet even beginning to see it. Combinatorics here is the wrong lens to look at this, because it's not doing a search over the full combinatoric space, as is the case with us. There are plenty of efficient search "heuristics" as you call them.
> It's interesting that you mention AlphaGo. I was also very fascinated with it. There was recent research that the same algorithm cannot learn Nim: https://arstechnica.com/ai/2026/03/figuring-out-why-ais-get-.... Isn't that food for thought?
It's a long known problem with RL in a particular regime and isn't relevant to coding agents. Things like Nim are a small, adversarially structured task family and it's not representative of language / coding / real-world tasks. Nim is almost the worst possible case, the optimal optimal policy is a brittle, discontinuous function.
Alphago is pure RL from scratch, this is quite challenging, inefficient, and unstable, and why we dont do that with LLMs, we pretrain them first. RL is not used to discover invariants (aspects of the problem that don't change when surface details change) from scratch in coding agents as they are in this example. Pretraining takes care of that and RL is used for refinement, so a completely different scenario where RL is well suited.
I didn't make any claims contradicted by literature. The only thing I cited as bedrock fact, NFL, is a mathematical theorem. I'm not sure why Nim shouldn't be relevant, it's an exercise in logic.
> “AlphaZero excels at learning through association,” Zhou and Riis argue, “but fails when a problem requires a form of symbolic reasoning that cannot be implicitly learned from the correlation between game states and outcomes.”
> So what do you think the difference is between humans and an agent in this respect?
Humans learn.
Agents regurgitate training data (and quality training data is increasingly hard to come by).
Moreover, humans learn (somewhat) intangible aspects: human expectations, contracts, business requirements, laws, user case studies etc.
> Verifiable domain performance SCALES, we have no reason to expect that this scaling will stop.
Yes, yes we have reasons to expect that. And even if growth continues, a nearly flat logarithmic scale is just as useless as no growth at all.
For a year now all the amazing "breakthrough" models have been showing little progress (comparatively). To the point that all providers have been mercilessly cheating with their graphs and benchmarks.
> Where did I say that? I didn’t even mention money, just the broader resource term. A lot of business are mostly running experiments if the current set of tooling can match the marketing (or the hype). They’re not building datacenters or running AI labs. Such experiments can’t run forever.
I'm just going to ask that you read any of my other comments, this is not at all how coding agents work and seems to be the most common misunderstanding of HN users generally. It's tiring to refute it. RL in verifiable domains does not work like this.
> Humans learn.
Sigh, so do LLMs, in context.
> Moreover, humans learn (somewhat) intangible aspects: human expectations, contracts, business requirements, laws, user case studies etc.
Literally benchmarks on this all over the place, I'm sure you follow them.
> Yes, yes we have reasons to expect that. And even if growth continues, a nearly flat logarithmic scale is just as useless as no growth at all.
and yet its not logarithmic? Consider data flywheel, consistent algorithmic improvements, synthetic data [basically: rejection sampling from a teacher model with a lot of test-time compute + high temperature],
> For a year now all the amazing "breakthrough" models have been showing little progress (comparatively). To the point that all providers have been mercilessly cheating with their graphs and benchmarks.
Benchmaxxing is for sure a real thing, not to mention even honest benchmarking is very difficult to do, but considering "all of the AI companies are just faking the performance data" to be the "story" is tremendously wrong. Consider AIME performance on 2025 (uncontaminated data), the fact that companies have a _deep incentive_ to genuinely improve their models (and then of course market it as hard as possible, thats a given). People will experiment with different models, and no benchmaxxing is going to fool people for very long.
If you think Opus 4.6 compared to Sonnet 3.x is "little progress" I think we're beyond the point of logical argument.
Are you aware that LLms are still the same autocomplete just with different token decisions more data better pre and post training and settings
We have all the data now.
I don’t see where the huge gap should come from, as one person before they said they still make basic errors.
Models got better for a bunch of soft tuning. Language and abstractness is not really the same thing there are a lot of very good speakers that are terrible in logic and abstractness.
Thinking abstract sometimes makes it necessary to leave language and draw or som people even code in another coding language to get it.
We’ve seen it with the compiler project it’s nice looking but if you would want to make a competitive compiler you would be as far as starting fresh
But the issue isn't coding, it's doing the right thing. I don't see anywhere in your plan some way of staying aligned to core business strategy, forethought, etc.
The number of devs will reduce but there will still be large activities that can't be farmed out without an overall strategy
Why do you think this is a problem? Reasoning is constantly improving, it has ample access to humans to gather more business context, it has access to the same industry data and other signals that humans do, and it can get any data necessary. It has Zoom meeting notes, I mean why do people think there's somehow a fundamental limit beyond coding?
The other thing you're missing here is generalizability. Better coding performance (which is verifiable and not limited by human data quality) generalizes performance on other benchmarks. This is a long known phenomenon.
Every investment has a date where there should be a return on that investment. If there’s no date, it’s a donation of resources (or a waste depending on perspective).
You may be OK with continuing to try to make things work. But others aren’t and have decided to invest their finite resources somewhere else.
Ah ok so you didn't really read my comment, what is your counter argument? Models are just fundamentally incapable of understanding business context? They are demonstrably already capable of this to a large extent.
> Every investment has a date where there should be a return on that investment. If there’s no date, it’s a donation of resources (or a waste depending on perspective).
what are you implying here? This convo now turns into the "AI is not profitable and this is a house of cards" theme? That's ok, we can ignore every other business model like say Uber running at a loss to capture what is ultimately an absolutely insane TAM. Little ol' Uber accumuluated ~33B in losses over 14 years, and you're right they tanked and collapsed like a dying star...oh wait...hmm interesting I just looked at their market cap and it's 141 Billion.
> You may be OK with continuing to try to make things work. But others aren’t and have decided to invest their finite resources somewhere else.
I truly love that. If you want to code as a hobby that is fantastic, and we can go ahead and see in 2 years how your comment ages.
> They are demonstrably already capable of this to a large extent.
I’d very like to see such demonstration. Where someone hands over a department to an agent and let it makes decisions.
> This convo now turns into the "AI is not profitable and this is a house of cards" theme?
Where did I say that? I didn’t even mention money, just the broader resource term. A lot of business are mostly running experiments if the current set of tooling can match the marketing (or the hype). They’re not building datacenters or running AI labs. Such experiments can’t run forever.
> I’d very like to see such demonstration. Where someone hands over a department to an agent and let it makes decisions.
That's your bar for understanding business context? I thought we were talking about what you actually said which is: understanding business context. If I brainstorm about a feature it will be able to pull the compendium of knowledge for the business (reports, previous launches, infrastructure, an understanding of the problem space, industry, company strategy). That's business context.
> Where did I say that? I didn’t even mention money, just the broader resource term. A lot of business are mostly running experiments if the current set of tooling can match the marketing (or the hype). They’re not building datacenters or running AI labs. Such experiments can’t run forever.
I misunderstood you then, I wasn't sure what point you were trying to make. Is your point "companies are trying to cajole Claude to do X and it doesn't work and hasn't for the last year so they are giving up"? If so I think that is a wonderful opportunity for people that understand the nuance of these systems and the concept of timing.
You make the mistake of disregarding tacit knowledge - the stuff that isn't in reports, docs, etc, etc because it's just "how we do things" and picked up on the job.
Unless the AI is inserted into every conversation it won't discover this, or how it changes.
Even if it had access to all this documented it wouldn't then be able to account for politics, where Barry who runs analytics is secretly trying to sabotage the project so it ends up run by his team, etc.
You're missing the point though. "1 + 1" vs "one.add(1)" might both be "passable" and correct, but it's missing the forest for the trees, how do you know which one is "long-term the right choice, given what we know?", which is the engineering part of building software, and less about "coding" which tends to be the easy part.
How do you evaluate, score and/or benchmark something like that? Currently, I don't think we have any methodologies for this, probably because it's pretty subjective in the end. That's where the "creative" parts of software engineering becomes more important, and it's also way harder to verify.
While I agree we don't have any methodologies for this, it's also true that we can just "fail" more often.
Code is effectively becoming cheap, which means even bad design decisions can be overturned without prohibitive costs.
I wouldn't be surprised if in a couple of years we see several projects that approach the problem of tech debt like this:
1. Instruct AI to write tens of thousands of tests by using available information, documentation, requirements, meeting transcripts, etc. These tests MUST include performance AND availability related tests (along with other "quality attribute" concerns)
2. Have humans verify (to the best of their ability) that the tests are correct -- step likely optional
3. Ask another AI to re-implement the project while matching the tests
It sounds insane, but...not so insane if you think we will soon have models better than Opus 4.6. And given the things I've personally done with it, I find it less insane as the days go by.
I do agree with the original poster who said that software is moving in this direction, where super fast iteration happens and non-developers can get features to at least be a demo in front of them fast. I think it clearly is and am working internally to make this a reality. You submit a feature request and eventually a live demo is ready for you, deployed in isolation at some internal server, proxied appropriately if you need a URL, and ready for you to give feedback and have the AI iterate on it. Works for the kind of projects we have, and, though I get it might be trickier for much larger systems, I'm sure everyone will find a way.
For now, we still need engineers to help drive many decisions, and I think that'll still be the case.These days all I do when "coding" is talking (via TTS) with Opus 4.6 and iterating on several plans until we get the right one, and I can't wait to see how much better this workflow will be with smarter and faster models.
I'm personally trying to adapt everything in our company to have agents work with our code in the most frictionless way we can think of.
Nonetheless, I do think engineers with a product inclination are better off than those who are mostly all about coding and building systems. To me, it has never felt so magical to build a product, and I'm loving it.
> Code is effectively becoming cheap, which means even bad design decisions can be overturned without prohibitive costs.
I'm sorry, but only someone who never maintained software long-term would say something like this. The further along you are in development, the magnitude of costs related to changing that increases, maybe even exponentially.
Correct the design before you even wrote code, might be 100x cheaper (or even 1000x) than changing that design 2 years later, after you've stored TBs of data in some format because of that decision, and lots of other parts of the company/product/project depends on those choices you made earlier.
You can't just pile on code on top of code, say "code is cheap" and hope for the best, it's just not feasible to run a project long-term that way, and I think if you had the experience of maintaining something long-term, you'd realize how this sounds.
The easiest part of "software engineering" is "writing code", and today "writing code" is even easier. But the hardest parts, actually designing, thinking and maintaining, remains the same as before, although some parts are easier, others are harder.
Don't get me wrong, I'm on the "agentic coding" train as much as everyone else, probably haven't written/edited a code by myself for a year at this point, but it's important to be realistic about what it actually takes to produce "worthwhile software", not just slop out patchy and hacky code.
I've never maintained software long-term so i could be wrong, but I interpret "code is cheap" to mean that you can have coding agents refactor or rewrite the project from scratch around the design correction. I don't think 'code is cheap' ever should be interpreted to mean ship hacky code.
I think using agents to prototype code and design will be a big thing. Have the agent write out what you want, come back with what works and what doesn't, write a new spec, toss out the old code and and have a fresh agent start again. Spec-driven development is the new hotness, but we know that the best spec is code, have the agent write the spec in code, rewrite the spec in natural language, then iterate.
because it has business context and better reasoning, and can ask humans for clarification and take direction.
You don't need to benchmark this, although it's important. We have clear scaling laws on true statistical performance that is monotonically related to any notion of what performance means.
I do benchmarks for a living and can attest: benchmarks are bad, but it doesn't matter for the point I'm trying to make.
I feel like you're missing the initial context of this conversation (no pun intended):
> Like for example a trusted user makes feedback -> feedback gets curated into a ticket by an AI agent, then turned into a PR by an Agent, then reviewed by an Agent, before being deployed by an Agent.
Once you add "humans for clarifications and take direction" then yeah, things can be useful, but that's far away from the non-human-involvment-loop earlier described in this thread, which is what people are pushing back against.
Of course, involving people makes things better, that's the entire point here, and that by removing the human, you won't get as good results. Going back to benchmarks, obviously involving humans aren't possible here, so again we're back to being unable to score these processes at all.
I'm confused on the scenario here. There is human in the loop, it's the feedback part...there is business context, it is either seeded or maintained by the human and expanded by the agent. The agent can make inferences about the world, especially when embodiment + better multimodal interaction is rolled out [embodiment taking longer].
Benchmarks ==> it's absolutely not a given that humans can't be involved in the loop of performance measurement. Why would that be the case?
Almost every task that people are tackling agents on, it’s either not worth doing, can be done better with scripts and software, or require human oversight (that negates all the advantages.
I assume this is a troll because it's just so far removed from reality there's not much to say. "Almost every task" -- I'm sure you have great data to back this up. "It's not worth doing" well sure if you want to put your head in the sand and ignore even what systems today can do let alone the improvement trajectory. "can be done better with scripts and software" .... not sure if you realize this but agents write scripts and software. "or require human oversight (that negates all the advantages." it certainly does not; human oversight vs actual humans implementing the code is pretty dramatically more efficient and productive.
> It doesn't because it doesn't learn. Every time you run it, it's a new dawn with no knowledge of your business or your business context
It does learn in context. And lack of continuous learning is temporary, that is a quirk of the current stack, expect this to change rather quickly. Also still not relevant, consider that agentic systems can be hierarchical and that they have no trouble being able to grok codebases or do internal searches effectively and this will only improve.
> It doesn't have better reasoning beyond very localized decisions.
Do you have any basis for this claim? It contradicts a large amount of direct evidence and measurement and theory.
> This is just a bunch of words stringed together, isn't it?
Maybe to yourself? Chinchilla scaling laws and RL scaling laws are measured very accurately based on next token test loss (Chinchilla). This scales very predictably. It is related to downstream performance, but that relationship is noisy but clearly monotonic
It also doesn't help that every new context is a new dawn with no knowledge if things past.
> Also still not relevant, consider that agentic systems can be hierarchical and that they have no trouble being able
A bunch of Memento guys directing a bunch of other Memento guys don't make a robust system, or a system that learns, or a system that maintains and retains things like business context.
> and this will only improve.
We've heard this mantra for quite some time now.
> Do you have any basis for this claim?
Oh. Just the fact that in every single coding session even on a small 20kloc codebase I need to spend time cleaning up large amounts of duplicated code, undo quite a few wrong assumptions, and correct the agent when it goes on wild tangents and goose hunts.
> Maybe to yourself? Chinchilla scaling laws a
yap yap yap. The result is anything but your rosy description of these amazing reasoning learning systems that handle business context.
> It also doesn't help that every new context is a new dawn with no knowledge if things past.
Absolutely true that it doesn't help but: agents like Claude have access to older sessions, they can grok impressive amounts of data via tool use, they can compose agents into hierarchical systems that effectively have much larger context lengths at the expense of cost and coordination which needs improvement. Again this is a temporary and already partially solved limitation
> A bunch of Memento guys directing a bunch of other Memento guys don't make a robust system, or a system that learns, or a system that maintains and retains things like business context.
I think you are not understanding: hierarchical agents have long term memory maintained by higher level agents in the hierarchy, it's the whole point. It's annoying to reset model context, but yet you have a knowledge base of the business context persisted and it can grok it...
> We've heard this mantra for quite some time now.
yes you have, and it has held true and will continue to hold true. Have you read the literature on scaling laws? Do you follow benchmark progression? Do you know how RL works? If you do I don't think you will have this opinion.
> yap yap yap. The result is anything but your rosy description of these amazing reasoning learning systems that handle business context.
Well that's fine to call an entire body of literature "yap" but don't pretend like you have some intelligible argument, I don't see you backing up any argument you have here with any evidence, unlike the multitude of sources I have provided to you.
Do you argue things have not improved in the last year with reasoning systems? If so I would really love to hear the evidence for this.
I love it when people include links to papers that refute their words.
So, Antropic (which is heavily reliant on hype and making models appear more than they are) authors a paper which clearly states: "tokens later in context are easier to predict and there's less loss of tokens. For no reason at all we decided to give this a new name, in-context learning".
> agents like Claude have access to older sessions, they can grok impressive amounts of data via tool use
That is they rebuild the world from scratch for every new session, and can't build on what was learned or built in the last one.
Hence continuous repeating failure modes.
10 years ago I worked in a team implementing royalties for a streaming service. I can still give you a bunch of details, including references to multiple national laws, about that. Agents would exhaust their context window just re-"learning" it from scratch, every time. And they would miss a huge amount of important context and business implications.
> Have you read the literature on scaling laws?
You keep referencing this literature as it was Holy Bible. Meanwhile the one you keep referring to, Chinchilla, clearly shows the very hard limits of those laws.
> Do you argue things have not improved in the last year with reasoning systems?
> Frankly, I find your aggressiveness quite tiring
having to answer for opinions with no basis in the literature is I'm sure very tiring for you. Your aggression being met is I'm sure uncomfortable.
> I love it when people include links to papers that refute their words.
> So, Antropic (which is heavily reliant on hype and making models appear more than they are) authors a paper which clearly states: "tokens later in context are easier to predict and there's less loss of tokens. For no reason at all we decided to give this a new name, in-context learning".
well I don't really love it when people just totally misread a paper because they have an agenda to push and can't seem to accept that their opinions are contradicted by real evidence.
in-context learning is not "later tokens easier" it’s task adaptation from examples in the prompt. I'm sure you realize this. Models can learn a mapping (e.g. word --> translation) from a few examples in the prompt, apply inputs within the same forward pass. That is function learning at inference time, not just "predicting later tokens better"
I'm sure also you're happy to chalk up any contradicting evidence to a grand conspiracy of all AI companies just gaming benchmarks and that this gaming somehow completely explains progress.
> That is they rebuild the world from scratch for every new session, and can't build on what was learned or built in the last one.
That they rebuild the world from scratch (wrong, they have priors from pretraining, but I accept your point here) does not mean they can't build on what was learned or built in the last one. They have access to the full transcript, and they have access to the full codebase, the diff history, whatever knowledge base is available. It's just disingenuous to say this, and then it also assumes (1) there is no mitigation for this, which I have presented twice before and you don't seem to understand it, (2) this is a temporary limitation, continual learning is one of the most important and well funded problems right now.
> 10 years ago I worked in a team implementing royalties for a streaming service. I can still give you a bunch of details, including references to multiple national laws, about that. Agents would exhaust their context window just re-"learning" it from scratch, every time. And they would miss a huge amount of important context and business implications.
also not an accurate understanding of how agents and their context work; you can use multiple session to digest and distill information useful in other sessions and in fact Claude does this automatically with subagents. It's a problem we have _already sort of solved today_ and that will continue to improve.
> You keep referencing this literature as it was Holy Bible. Meanwhile the one you keep referring to, Chinchilla, clearly shows the very hard limits of those laws.
You keep dismissing this literature as if you have understood it and that your opinion somehow holds more weight...Can you elaborate on why you think Chinchilla shows the hard limits of the scaling laws? Perhaps you're referring to the term capturing the irreducible loss? Is that what you're saying?
> Do you argue things have not improved in the last year with reasoning systems? I don't
Then are you arguing this progress will stop? I'm just not sure I understand, you seem to contradict yourself
> having to answer for opinions with no basis in the literatu
Having only literature on your side must feel nice.
> They have access to the full transcript, and they have access to the full codebase, the diff history, whatever knowledge base is available.
Yes. And it means that they don't learn, and they alway miss important details when rebuilding the world.
That's why even the tiniest codebases are immediately filled with duplications, architecturally unsound decisions, invalid assumptions etc.
> also not an accurate understanding of how agents and their context work; you can use multiple session to digest and distill information useful in other sessions and in fact
I say: agents don't learn and have to rebuild the world from scratch
You: not an accurate understanding of how agents and their context work.... they rebuild the world from scratch every time they run.
> You keep dismissing this literature as if you have understood it
No. I'm dismissing your flawed interpretation of purely theoretical constructs.
Chinchilla doesn't project unlimited amazing scalability. If anything, it shows a very real end of scalability.
Anthropic's paper adopts a nice marketable term for a process that has little to do with learning.
Etc.
Meanwhile you do keep rejecting actual real-world behaviour of these systems.
> Then are you arguing this progress will stop? I'm just not sure I understand, you seem to contradict yourself
I didn't say that either. Your opponents don't contradict themselves if you only stop to pretend they think or say.
Your unsubstantiated belief is that improvements are on a steep linear or even exponensial progression. Because "literature" or something.
Looking past all the marketing bullshit, it could be argued that growth is at best logarithmic, and most improvments come from tooling around (harnesses, subagents etc.). While all the failure modes from a year ago are still there: misunderstanding context, inability to maintain cohesion between sessions, context pollution etc.
And providers are running into the issue of getting non-polluted trainig data.
---
At this point we're going around in circles, and I'm no interested in arguing with theorists.
I love everything about this direction except for the insane inference costs. I don’t mind the training costs, since models are commoditized as soon as they’re released. Although I do worry that if inference costs drop, the companies training the models will have no incentive to publish their weights because inference revenue is where they recuperate the training cost.
Either way… we badly need more innovation in inference price per performance, on both the software and hardware side. It would be great if software innovation unlocked inference on commodity hardware. That’s unlikely to happen, but today’s bleeding edge hardware is tomorrow’s commodity hardware so maybe it will happen in some sense.
If Taalas can pull off burning models into hardware with a two month lead time, that will be huge progress, but still wasteful because then we’ve just shifted the problem to a hardware bottleneck. I expect we’ll see something akin to gameboy cartridges that are cheap to produce and can plug into base models to augment specialization.
But I also wonder if anyone is pursuing some more insanely radical ideas, like reverting back to analog computing and leveraging voltage differentials in clever ways. It’s too big brain for me, but intuitively it feels like wasting entropy to reduce a voltage spike to 0 or 1.
> I love everything about this direction except for the insane inference costs.
If this direction holds true, ROI cost is cheaper.
Instead of employing 4 people (Customer Support, PM, Eng, Marketing), you will have 3-5 agents and the whole ticket flow might cost you ~20$
But I hope we won't go this far, because when things fail every customer will be impacted, because there will be no one who understands the system to fix it
I worry about the costs from an energy and environmental impact perspective. I love that AI tools make me more productive, but I don't like the side effects.
Environmental impact of ai is greatly overstated. Average person will make bigger positive impact on environment by reducing his meat intake by 25% compared with combined giving up flying and AI use.
Is this before or after you account for the initial training impact? Because that would need to be factored in for a good faith calculation here, much as the companies would rather we didn't.
Inference costs at least seem like the thing that is easiest to bring down, and there's plenty of demand to drive innovation. There's a lot less uncertainty here than with architectural/capability scaling. To your point, tomorrow's commodity hardware will solve this for the demands of today at some point in the future (though we'll probably have even more inference demand then).
This is the wrong way to see it. If a technology gets cheaper, people will use more and more and more of it. If inference costs drop, you can throw way more reasoning tokens and a combination of many many agents to increase accuracy or creativity and such.
No company at the moment has enough money operate with 10x the reasoning tokens of their competitors because they're bottlenecked by GPU capacity (or other physical constraints). Maybe in lab experiments but not for generally available products.
And I sense you would have to throw orders of magnitude more tokens to get meaningfully better results (If anyone has access to experiments with GPT 5 class models geared up to use marginally more tokens with good results please call me out though).
Well, how many more dogs would you need to help you write your university thesis? It's a logical fallacy to assume that more tokens would somehow help - especially that even with cursory use you would see that LLMs, once they go off the road, they are pretty much lost, and the best thing you can do with them is to give them a clear context.
I think that as a user I'm so far removed from the actual (human) creation of software that if I think about it, I don't really care either way.
Take for example this article on Hacker News: I am reading it in a custom app someone programmed, which pulls articles hosted on Hacker News which themselves are on some server somewhere and everything gets transported across wires according to a specification. For me, this isn't some impressionist painting or heartbreaking poem - the entity that created those things is so far removed from me that it might be artificial already.
And that's coming from a kid of the 90s with some knowledge in cyber security, so potentially I could look up the documentation and maybe even the source code for the things I mentioned; if I were interested.
Dive into a forest, you'll find a couple of cool trees.
Art isn't about being cool. Art is about context.
When I tell people that art cannot be unpolitical, they react strongly, because they think about the left/right divide and how divided people are, where art is supposed to be unifying.
But art is like movement, you need an origin and a destination. Without that context, it will be just another... thing. Context makes it something.
It's not that you know the artist first and then say "this art is cool because I like the artist". The art is the means by which you know the artist. The more of their works you encounter, the closer you get to understanding the artist and what they are trying to communicate.
I dont mean this as a shade but ppl who are not coders now seem to think "coding is now solved" and seem to be pushing absurd ideas like shipping software with slack messages. These ppl are often high up in the chain and have never done serious coding.
Stripe is apparently pushing gazzaliion prs now from slack but their feature velocity has not changed. so what gives?
how is that number of pr is now the primary metric of productivity and no one cares about what is being shipped or if we are shipping product faster. Its total madness right now. Everyone has lost their collective minds.
cto and ceo are now feeling insane pressure to show how they are using ai but its not evident in output. So now they've resorted to blabbering publicly about prs, lines of code ect to save face. And ofcourse ppl giving them voice and platform have their own agendas that prevent them from asking "so what exactly have you shipped stripe from million pr/day".
Its baffling to see these comments on hacknernews though. I guess you have to prove that you are not a luddite by making "ai forward" predictions and show that you "get it"
What kind of software are people building where AI can just one shot tickets? Opus 4.6 and GPT 5.4 regularly fail when dealing with complicated issues for me.
I dunno if Rust async or native platform API's which have existed for years count as new patterns, but if you throw even a small wrench in the works they really struggle. But that's expected really when you look at what the technology is - it's kind of insane we've even gotten to this point with what amounts to fancy autocomplete.
i dont see anyone sane trusting ai to this degree any time soon, outside of web dev. the chances of this strategy failing are still well above acceptable margins for most software, and in safety critical instances it will be decades before standards allow for such adoption. anyway we are paying pennies on the dollar for compute at the moment - as soon as the gravy train stops rolling, all this intelligence will be out of access for most humans. unless some more efficient generalizable architecture is identified.
> as soon as the gravy train stops rolling, all this intelligence will be out of access for most humans. unless some more efficient generalizable architecture is identified.
All Chinese labs have to do to tank the US economy is to release open-weight models that can run on relatively cheap hardware before AI companies see returns.
Maybe that's why AI companies are looking to IPO so soon, gotta cash out and leave retail investors and retirement funds holding the bag.
i was under the impression that we were approaching performance bottlenecks both with consumer GPU architecture and with this application of transformer architecture. if my impression is incorrect, then i agree it is feasible for china to tank the US economy that way (unless something else does it first)
I think it just needs to be efficient or small enough for companies to deploy their own models on their hardware or cloud, for more inference providers to come out of the woodwork and compete on price, and/or for optimized models to run locally for users.
Regarding the latter, smaller models are really good for what they are (free) now, they'll run on a laptop's iGPU with LPDDR5/DDR5, and NPUs are getting there.
Even models that can fit in unified 64GB+ memory between CPU & iGPU aren't bad. Offloading to a real GPU is faster, but with the iGPU route you can buy cheaper SODIMM memory in larger quantities, still use it as unified memory, eventually use it with NPUs, all without using too much power or buying cards with expensive GDDR.
Qwen-3.5 locally is "good enough" for more than I expected, if that trend continues, I can see small deployable models eventually being viable & worthy competition, or at least being good enough that companies can run their own instead of exfiltrating their trade secrets to the worst people on the planet in real-time.
There aren't any released open-weight models that are "good enough" yet, but Qwen-3.5 is getting really damn close to the point where more than half of my LLM usage gets routed to it.
I suspect, but don't know, some fields of inquiry will be fruitful when it comes to "good enough" small models. Especially when it comes to constrained tasks like software development. Software development models don't have to generalize to anything a chatbot can be asked or tasked with, the space it's required to generalize on is pretty small compared to literally the whole world.
If I was a betting man, I'd put my money where my mouth is, but I'm not. I am betting with my time and focus that smaller local models are worth it, and will be worth it, though.
Even in webdev it rots your codebase unchecked. Although it's incredibly useful for generating UI components, which makes me a very happy webslopper indeed.
im grateful to have never bothered learning web dev properly, it was enlightening witnessing chat gpt transform my ten second ms paint job into a functional user interface
I don't think anybody is doubting its ability to generate thousands of PR's though. And yes, it's usually in the stuff that should have been automated already regardless of AI or not.
Depends on your circle. On HN I would argue that there are still a fair number of people that would be surprised to see what heavy organizational usage of AI actually looks like. On a non programming online group, of which I am a member of several, people still think that AI agents are the same as they were in mid 2025 and they can't answer "how many R's are in the following word:". Same thing even when chatting with my business owner friends. The majority of the public has no clue of the scale of recent advancement.
these companies contribute to swathes of the west's financial infrastructure, not quite safety critical but critical enough, insane to involve automation here to this degree
Of course not all tickets are complex. Last week I had to fix a ticket which was to display the update date on a blog post next to the publish date. Perfect use case for AI to one shot.
I'm using Opus on Claude Code and even on easy tasks, if you not review the changes properly, it creates tech debts. One of the most common issues is replicating the same logic with variables with different names (which makes grep harder to detect on future changes) in multiple places and lack of following project patterns. Even having a lot of .md files instructing to do the opposite.
I still didn't find a workflow without human interaction that can be that efficient and reliable.
I suppose at that point I’m wondering if it would have just been faster for… you, (I’m assuming) the developer to make that change and deploy it? Is the AI really faster on small changes like that, if you understand the platform/code/CI/CD enough???
Maybe for a non-dev it would be nice to submit a ticket and have it auto-fixed by an agent. But in the devs case, it feels like it would be faster to just do it manually.
I am already there with a project/startup with a friend. He writes up an issue in GitHub and there is a job that automatically triggers Claude to take a crack at it and throw up a PR. He can see the change in an ephemeral environment. He hasn't merged one yet, but it will get there one day for smaller items.
I am already at the point where because it is just the two of us, the limiting factor is his own needs, not my ability to ship features.
I think Anthropic will launch backend hosting off the back of their Bun acquisition very soon. It makes sense to basically run your entire business out of Claude, and share bespoke apps built by Claude code for whatever your software needs are.
100% its going to happen - also OpenAI will do same, there were already rumors about them building internal "github" which is stepping stone for that
Also it is requirement for completing lock-in - the dream for these companies.
I think some type of tickets can be done like this but your trusted user assumption does a lot of work here. Now I don't see this getting better than that with the current architecture of LLMs, you can do all sorts of feedback mechanisms which helps but since LLMs are not conscious drift is unavoidable unless there is a human in the loop that understands and steers what's going on.
But I do think even now with certain types of crud apps, things can be largely automated. And that's a fairly large part of our profession.
In the past three weeks a couple of projects I follow have implemented AI tools with their own github accounts which have been doing exactly this. And they appear to be doing good work! Dozens of open issues iterated, tested and closed. At one point i had almost 50 notification for one projects backlog being eradicated in 24 hours. The maintainer reviewed all of it and some were not merged.
A PR tells me what changed, but not how an AI coding session got there: which prompts changed direction, which files churned repeatedly, where context started bloating, what tools were used, and where the human intervened.
I ended up building a local replay/inspection tool for Claude Code / Cursor sessions mostly because I wanted something more reviewable than screenshots or raw logs.
We dont have product managers or technical ticket writers of any sort
But us devs are still choosing how to tackle the ticket, we def don't have to as I’m solving the tickets with AI. I could automate my job away if I wanted, but I wouldn't trust the result as I give a degree of input and steering, and there’s bigger picture considerations its not good at juggling, for now
I don't know if this is the future, but if it is, why bother building one version of the software for everyone? We can have agents build the website for each user exactly the way they want. That would be the most exciting possibility to come out of AI-generated software.
> I feel like we are just inching closer and closer to a world where rapid iteration of software will be by default.
There's a lots of experimentation right now, but one thing that's guaranteed is that the data gatekeepers will slam the door shut[1] - or install a toll-booth when there's less money sloshing about, and the winners and losers are clear. At some point in the future, Atlassian and Github may not grant Anthropic access to your tickets unless you're on the relevant tier with the appropriate "NIH AI" surcharge.
1. AI does not suspend or supplant good old capitalism and the cult of profit maximization.
Without getting into some of the other things mentioned in the article,
I don't think Vim is going away. Even with all the AI code written, engineers navigate through Claude Code / Codex using Vim (ex: Vim mode in Claude Code).
I really like Vim so much that I've built a gamified way to learn it at https://vimgolf.ai that I am working on completing.
First thing is going outside. Staying inside is only going to compound depression and cause days gone by to become a blur. Just doing that is a step in the right direction, bonus points if it involves moving the body.
Second, start writing. Writing can help prevent circular thoughts and force the brain to plan how to change your lifestyle and get you to a life worth living. If you don’t write, it’s very easy to repeat patterns over and over again.
Third, don’t lose hope. Having a positive outlook and a growth mindset is going to help you navigate setback and push through obstacles. It’s easy to resort to a fixed mindset, but the saints are the sinners who keep on trying as they say.
Not the best pelican compared to gemini 3.1 pro, but I am sure with coding or excel does remarkably better given those are part of its measured benchmarks.
yep, just double checked used gpt-5.4 xhigh. Though had to select it in codex as don't have access to it on the chatgpt app or web version yet. It's possible that whatever code harness codex uses, messed with it.
Is this long Covid or depression or Major Depressive Disorder (MDD)? Because in her earlier videos she talks about becoming bed-bound again due to her emotional state after finding news her friend who had a similar condition died.
As someone who suffers from a complex autoimmune disorder which has caused dysautonomia and suspected mitochondrial dysfunction, stress flares and exacerbates symptoms. This has a physiological basis in the complex way the HPA axis/cortisol affects us at the cellular level. My primary diagnosis is sarcoidosis with small fiber neuropathy, but they don’t fully understand all the mechanisms of auto-immune fatigue and dysregulation.
Dunno, personally I don't think its that much of my business. Sure I'm curious, but that doesn't mean I have a right, or that its a nice thing™ to publicly speculate
Long Covid and ME/CFS can make you fragile. At least it has for me. I never used to get emotionally overwhelmed, now I do. I don't have it as bad as she does, but I've been bedbound because of this. And being sick for months to years, already leaves you feeling on the brink of depression.
These image gen models are getting so advanced and life like that increasingly the general public are being duped into believing AI images are actually real (ex Facebook food images or fake OF models). Don't get me wrong I will enjoy the benefits of using this model for expressing myself better than ever before, but can't help feeling there's something also very insidious about these models too.
It's more likely than not that every single person who uses the internet has viewed an AI image and taken it as real by now.
The obvious ones stand out, but there are so many that are indiscernible without spending lots of time digging through it. Even then there are ones that you can at best guess it's maybe AI gen.
People will continue to retreat into walled, trusted networks where they can have more confidence in the content they see. I can’t even be sure I’m responding to a real person right now.
Just the other day, I saw a comment on HN accuse another comment of being AI for no good reason. I personally thought the comment was fine.
I know it's an unpopular opinion, but I don't really read too deeply into whether text is AI generated or not. On social platforms like HN I tend to just skim many comments anyway so it's not like the concept of "they spent no time writing so you shouldn't spend time reading" really applies.
I know some people use apps like Grammarly to improve their language and stuff, which I can respect. But at what point do we draw the line between AI assisted text and AI generated text?
I sometimes use AI to do research into the nuance of some topics to help me formulate a response and synthesize ideas, but if I ever get to the point where I'd be asking AI generate a response to the comment then I find it better to just not respond at all.
I think it depends on the purpose of the comment. I can see why someone may get frustrated with AI text if they were say looking for advice on xyz, as you'd usually want someone with personal or senior experience. If I want career advice, AI will give me a predictable response similar to LinkedIn, but something from a successful person in the industry will have a lot more trust and substance in it.
Maybe not an actual argument for anything, but even before these image models everyone that used the internet had seen a doctored image they believed to be real. There was a reason that 'i can tell by the pixels' was a meme.
At the point now where basically any photo that isn't shared by someone I trust or a reputable news organisation is essentially unverifiable as being real or not
The positive aspect of this advance is that I've basically stopped using social media because of the creeping sense that everything is slop
people only notice when they are prompted to look for AI or scrutinize AI
a lot of these accounts mix old clips with new AI clips
or tag onto something emotional like a fake Epstein file image with your favorite politician, and pointing out its AI has people thinking you’re deflecting because you support the politician
Meanwhile the engagement farmer is completely exempt from scrutiny
Its fascinating how fast and unexpected the direction goes
I actually think this was a good thing. Manipulating images incredibly convincingly was already possible but the cost was high (many hours of highly skilled work). So many people assumed that most images they were seeing were "authentic" without much consideration. By making these fake images ubiquitous we are forcing people to quickly learn that they can't believe what they see on the internet and tracking down sources and deciding who you trust is critically important. People have always said that you can't believe what you see on the internet, but unfortunately many people have managed without major issue ignoring this advice. This wave will force them to take that advice to heart by default.
I remember telling my parents at a young age that I couldn't be sure Ronald Reagan was real, because I'd only ever seen him on TV and never in real life, and I knew things on TV could be fake.
That was the beginning of my journey into understanding what proper verification/vetting of a source is. It's been going on for a long time and there are always new things to learn. This should be taught to every child, starting early on.
I agree. Too many adults are fooled by fake news and propaganda and false contexts. And CNN and Fox are more than happy to take advantage of this.
My personal rule of thumb is if it generates outrage, it's probably fake, or at least a fake interpretation. I know that outrageous stuff actually happens pretty often, so I'll dig into things I find interesting. But most of the time it's all just garbage for clicks.
I used to also have this optimistic take, but over time I think the reality is that most people will instead just distrust unknown online sources and fall into the mental shortcuts of confirmation bias and social proof. Net effect will be even more polarization and groupthink.
> By making these fake images ubiquitous we are forcing people to quickly learn that they can't believe what they see on the internet and tracking down sources and deciding who you trust is critically important.
Has this thought process ever worked in real life? I know plenty of seniors who still believe everything that comes out of Facebook, be AI or not, and before that it was the TV, radio, newspapers, etc.
Most people choose to believe, which is why they have a hard time confronting facts.
And not just seniors. I see people of all ages who are perfectly happy to accept artificially generated images and video so long as it plays to their existing biases. My impression is that the majority of humanity is not very skeptical by default, and unwilling to learn.
Yes. People willingly accept made up text (stories) if it fits their world view, and for words we always knew that they could be untrue. Why should it be different for images/audio/video?
> By making these fake images ubiquitous we are forcing people to quickly learn
That's quite the high opinion on the self-improvement ability of your Average Joe. This kind of behavior only comes with an awareness, previously learned, and an alertness of mind. You need the population at large to be able to do this. How if not, say, teaching this at schools and waiting for the next generation to reach adulthood, would you expect this to happen?
I agree that improvement for the Average Joe will be very hard. I also think that taking more attention to teach the younger generation is vitally important. But mostly I don't see an alternative. I don't think we can protect people from fake information without giving up our freedom, and that isn't a viable alternative in my mind. So what is left but trying our hardest to teach people to think critically?
Our institutions have been trying to get our kids to think critically for a while. At least when I was in school, we didn't focus a lot on memorization (sometimes we did, like memorizing the times tables or periodic table). My teachers tried to instill in us an understanding of the concepts, something I took for granted. Many of my classmates have gone on to become lawyers, doctors, other prestigious careers.
But I feel like we live in a different time now. I hear teachers tell stories about school admin siding with parents instead of teachers, and the kids aren't learning anything. Anecdotally of course.
I think our teachers really want the kids to think critically. But parents and schools don't seem to value that anymore.
When it comes to graphic content on the internet I usually consume it's for entertainment purposes. I didn't care where it came from before and don't care today either. Low quality content exists in both categories, a bit easier to spot in AI generated, so it's actually a bonus.
I feel like there is one or two generations of people who are tech savy and not 100% gullible when it comes to online things. Older and younger generations are both completely lost imho, in a blind test you wouldn't discern a monkey from a human scrolling tiktok &co
Boomers used to tell us to never trust anything online and now they send their life savings to "Brad Pitt"
New generations gets unlimited brain rot delivered through infinite scroll, don't know what a folder is, think everything is "an app" and keep falling for the "technology will free us from work and cure cancer"
There was a sweet spot during which you could grow alongside the internet at a pace that was still manageable and when companies and scammers weren't trying so hard to robbyou from your time money and attention
Your post seems a little naive to me, a lot of people are just not interested in putting in the work or confronting their own confirmation bias, and there's an oversupply of bad actors who will deliberately generate fake imagery for either deception or exhaustion. Many people are just not on quest for truth and are more interested in the activation potential of images or allegations than in the factual reliability.
In reality: millions of boomers are scrolling FB this very minute reacting to the most obviously fake rage/surprise/love bait AI slop you've ever seen.
People already have access to every form of niche pornography they could dare to imagine (for absolutely free!), I really doubt that 'personal taste' is the part that makes OF models their money. They'll be fine.
I think you're under-estimating how much personal taste applies in that industry. Yes, there's a lot of free content but it's often low quality and/or difficult to find for a particular niche. The OF pages, and other paid sites, are curated collections of high quality stuff that can satisfy particular cravings repeatedly with minimal effort.
A big part of it also the feeling of "connection" with the creator via messages and what not, but that too can be replicated (arguably better) by AI. In fact, a lot of those messages are already being generated haha.
I was mostly hinting towards the 'connection' part of it, yes - I think that's really where the money is made more than anything else. That's the part that'll start killing the industry once some company tunes it in.
This is the dystopia of that pacified moon from "Mold of Yancy" by PKD but taken to the next level.
What's astonishing abut the present is that even PKD did not foresee the possibility of an artificial being not only being constructed from whole cloth but actually tailored to each individual.
I don't think so. Talking to people in this space, I've found out about broad camps. There are probably more:
-They simply aren't into real women/men (so you couldn't even pay a model to do what they're looking for).
-They want to play out fantasies that would be hard to coordinate even if you could pay models (I guess this is more on the video side of things, but a string of photos can put be together into a comic)
-They want to generate imagery that would be illegal
Based on this, I would guess fetish artists (as in illustrators) are more at risk than OF models. However, AI isn't free. Depending on what you're looking for, commissions might be cheaper still for quite a while...
Even ignoring the model censorship making high quality sexual imagery/videos not possible, this is a crazy take. You think OF models are making money because it's the only way to see a nude man/woman with particular characteristics on the internet?
You're completely misunderstanding what the product being sold is.
Their point is that the point of OF is that there is (supposed to be) a real human. It's a (para)social relationship that no image generator model is going to give you.
If you can make X money running one client at a time you can make ( X × N ) money if you work with N clients at a time. You have to give just enough human to keep'em hooked.
Sex work shouldn't be shunned, but it's not a normal profession either. Mental health, addiction and abuse is just as much of a problem online and in countries where prostitution is legal and normalized.
You can’t really because these powerful models are censored.
You can create lewd pictures with open models but they aren’t nearly as good or easy to use.
I’ve seen some very high quality NSFW AI video in the last few months. Those models are not far behind and the search and training space for porn is smaller than being able to generate anything
> I’ve seen some very high quality NSFW AI video in the last few months. Those models are not far behind and the search and training space for porn is smaller than being able to generate anything.
Agreed. In my opinion, the primary limitation of the porn models is actually poor labeling of the training set. The company that manages to produce a well-labeled, porn-tuned AI image model is going to absolutely clean up.
The extractive dark patterns that will emerge from a parasocial chat "AI relationship" that can generate porn images relevant to the chat on the fly will be staggering. Once that proceeds to being able to generate relevant video, all holy hell is going to break loose.
> The company that manages to produce a well-labeled, porn-tuned AI image model is going to absolutely clean up.
For anime/non-photographic content that essentially exists (Pony, then Illustrious, then probably some new-fangled thing by now that I don't even know about), thanks to the meticulously tagged booru image corpus. However, as strong as these models are on matters of anatomy and kinks, they're limited in other ways due to the hugely biased dataset and dependence on tag soup prompts rather than natural language (many find the latter a plus, not a minus, though).
I haven't heard of any proprietary/cloud-based NSFW model that would be massively better than what's available for free. There are many NSFW-friendly services, but by and large they're just frontends to models trained by other people.
Because models can be used to alter existing images, you can use open and commercial models together in content creation workflows (and also the available findings of open models, and the ability to further tube them very specific used, are quite powerful on their own), so the censorship on the commercial models has a lot less effect on what motivated people can produce than you might think.
I still think, even with that, that like most predictions of AI taking over any content industries, the short-term predictions are overblown.
Doesn't Grok allow users to create lewd content or did they roll that back?
Also, I suspect that we'll soon see the same pattern of open weights models following several months behind frontier in every modality not just text.
It's just too easy for other labs to produce synthetic training data from the frontier models and then mimic their behavior. They'll never be as good, but they will certainly be good enough.
It stands for "OnlyFans" a website originally for creators to engage directly with their audiences but quickly became a website where women sold explicit pictures of themselves to subscribers.
They still run ads trying to push the narrative that it's for comedians and musicians.
But at this point, OnlyFans is so synonymous with egirls that suggesting someone has an account is used as a way to insinuate they sell pictures of themselves.
Jaded, but if I knew there was a possibility of a bunch of incriminating footage of me (images, video, etc.) out there in the pre-AI days, I would do my absolute best to flood the internet with as many related deepfakes (including of myself) as possible.
Oh we’ve seen nothing yet of the chaos that generative ai will unleash on the world, looking at Meta platforms it’s already a multi million dollar industry of selling something or someone that doesn’t exist. And that’s just the benign stuff.
This has been true for a while with digital art, photoshop, etc. Over time, people's BS detectors get tuned. I mean, scrolling by quickly in a feed, yeah, you might miss if an image is "real" or not, but if you see a series of photos side by side of the same subject (like an OF model), you'll figure it out.
Also, using AI will not allow you to better express yourself. To use an analogy, it will not put your self-expression into any better focus, but just apply one of the stock IG filters to it.
> a series of photos side by side of the same subject
Cameras are now "enhancing" photos with AI automatically. The contents of a 'real' photo are increasingly generated. The line is blurring and it's only going to get worse.
It's shitty, but I think it's almost as bad that people are calling everything AI. And I can't even blame them, despite how infuriating it is. It's just as insidious that even mundane things literally ARE AI now. I've seen at least twice now (that I'm aware of) where some cute, harmless, otherwise non-outrageous animal video was hiding a Sora watermark. So the crazy shit is AI. The mundane shit is AI. You wonder why everyone is calling everything AI now. :P
It seems like a low level paranoia - now I find myself double checking that the youtube video I'm watching isn't some AI slop. All the creators use Getty b-rolls and increasingly AI generated stuff so much that it's not a far stretch to have the voice and script all be auto generated too.
I suppose if the AI was able to tell me a true and compelling story, I might not even mind so much. I just don't want to be spoon fed drivel for 15 minutes to find it was all complete made up BS.
https://vimgolf.ai
I proxy to neovim instances for each level. Still working out some kinks but soon to complete it
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