I don't quite understand the intent of such article other than to promote themselves given an odd timing that the company is planning on going public, so I can only conclude that this is just part of the IPO roadshow.<p>LLMs certainly have made significant changes to our lives, but I haven't yet to see any extraordinary improvement it brought to me which makes me skeptical about their claims.<p>_if_ it solves many of our problems of great magnitude, why haven't Anthropic used it to solve significant problems we, humans, face? Cancer, Alzheimer's, education, finding new materials, fission power plant, etc.
Because they're going after the biggest problem of all first: labor costs.<p>/s but not to a lot of people
Agree with your point about the timing, but drawing anticipation before going ahead and solving these disease can be a good smoke test, would be beneficial even if there's an IPO or not
>A caveat: Lines of code is an imperfect measure, as it measures quantity over quality. So 8× lines of code/engineer/day in the second quarter of 2026 is almost certainly an overstatement of the true productivity gain. Nonetheless, it indicates an acceleration. At Anthropic, we don’t reward people for how many lines of code they write; rather, team members are producing more code simply because they’re using AI systems to write more code.<p>What about the hypothesis that AI is generating more verbose code? I just see the text pretending to acknowledge "LOC != Productivity" and then using it as a metric anyway.
One of my co-workers just asked me to review his pull request that was all AI generated. 600 files were touched, over 40k lines of code added.<p>I'm sure he thought that was a crowning achievement, proof that AI can enable 10X developers, after all, what engineer could write 40k lines of code in a week?<p>I declined to review it, stating that I couldn't possibly vet 40k lines of code, and wouldn't put my reputation on the line to stamp the work as good. The PR nagged me for 2 weeks from my todo list and then disappeared. I don't know if he found another dev to get an approval from, or if the PR was abandoned. But I know for sure that him and I are on two totally separate islands around the value of LLMs.
Same here. A co-worker touched a few hundred files in a PR and asked us to review. They merged it directly to main when nobody approved it. (The repo was not set up to enforce PR approval.)<p>I don't personally use that feature, and I couldn't care less at this point. If our customers are frustrated by the bugs, at least my name is not on it.
This is a branching point. One dev would find someone else and convince them to approve it. Another would redo the task (code is cheap now, right?) in a PR stack that can actually be reviewed, cleaned up etc.<p>I hope they were the latter.
That's a process problem at your company - no developer should be proposing branches over 1k loc (or whatever your agreed tolerance threshold is) without a very good reason, vibe coded or not.
> no developer should be proposing branches over 1k loc<p>I've seen that reaction many times. It seems to work well enough when someone is maintaining existing code. However, greenfield projects can often require literally orders of magnitude more code to deliver something that can be integration tested.<p>The first step is to break it up into a stack of commits. Each one must compile and pass its unit tests, of course. Keeping it under 1k loc of released executable code is usually easy, but often becomes difficult to impossible if you want well commented code with excellent unit test coverage.<p>Assuming you have kept all your commits under 1k loc, there is still the problem of whether you present them in one PR, or as a stack of PRs. The issue with a stack is why an API is designed a certain way often isn't evident until you see how it's used. Responses to PR comments are explanations that point to later PRs in the stack, which is irritating for both the reviewer and the author.<p>I haven't found a good solution. I'm not sure there is one.
<i>I declined to review it, stating that I couldn't possibly vet 40k lines of code</i><p>Gee, that sounds like a job for Claude if there ever was one.
Just imagine the productivity gains from using LLMs to rewrite Kotlin codebases in Java!
I just watched copilot today turn a 8 line fix into 500 lines, so, yeah, verbosity is a big side effect
It occurs to me this pattern might be the average code we humans have produced. We all have made those quick fixes, copy-pastas, and dirty hacks... they learned it somewhere! I also assume that some of the behavior is an artifact of their training regime.
Exactly. If AI is going to start being graded on how many LoC it generates- oh, I'm sorry, how much it "accelerates", than guess what newer models will start doing more of?
So the more rigorous studies about AI-assisted coding productivity addressed this by keeping in place all other software development processes, including the same code review and quality standards, and only measuring throughput (PRs, LoC) before and after AI was allowed.<p>Hence the intepretation of this 8x number depends on whether (or how much) Anthropic engineers have changed their quality standards and development processes. They don't tell us, and I am not aware of any other indications we could use to make a judgment.<p>However, we can still do some theorycrafting! I'm convinced that to fully realize the potential of AI-assisted coding we need to revamp all the dev processes, especially how we validate code, and it would be foolish of Anthropic not to do so (unless they were conducting a rigorous study, which they don't claim to have done.)<p>My hypothesis on the future of software validation is nothing fancy, we simply want much, much more automation for tests, observability and other bespoke verification methods than we traditionally had. But then validation code will also contribute to the LoC! My observation so far of personal as well as some "vibe-coded" open-source projects is O(LoC production code) ~= O(LoC test code). So as a SWAG the upper bound could be something like a 3 - 4x speedup, which is still remarkable.<p>All bets are off if code quality standards are not the same.
Yeah, they assume that "productivity = k * LOC" where k > 1<p>very flawed
AI generates code that mimics the existing code. If your code is terse and comment-free, then the agent’s code is too. The times I’ve seen Claude drift into a default “house style” it generated like 1 comment for every 10 LOC or so. It’s a far cry from the GPT-3 days that littered every line with the journals of Captain Obvious.
I have been doing more experiments with what I have now been calling agentic iterative optimization: telling the LLM to optimize code such that it speeds up all real-world-representative benchmarks by X% without cheating or causing regressions in both tests and performance metrics (e.g. MSE for statistical algorithms or file size in the case of something such as image compression). This is done using Rust where there are more low-level levers to tweak for performance than something like Python.<p>Opus 4.6/4.7 was consistently successful at getting 2-3x speed improvement with just one pass. It can also do the inverse: improve the performance metrics for better quality without causing a significant regression in speed. Then GPT-5.5 turned out to be <i>much</i> better at this workflow, often getting a multiplicative 1.5x-2x improvement above what Opus could do.<p>I now have quite a few GPT-5.5-optimized projects in various domains that are feature complete and are substantially more performant than existing SOTA implementations that I plan to open source as soon as possible: the bottleneck is polish as usual.
Very interesting, could you share they prompts you typically use for this?<p>Something like this?<p>You are an Elite Performance Engineer and Autonomous Optimization Agent.
Your primary goal is to iteratively optimize the provided codebase to maximize execution speed and efficiency (e.g., reduce CPU cycles, memory allocation, or network latency) WITHOUT altering the external behavior or causing any test regressions.<p>### CORE DIRECTIVES
1. METRIC-DRIVEN: You will be provided with benchmark results, profiler logs, or execution times. Your only measure of success is a statistically significant improvement in these metrics.
2. ZERO REGRESSION: The test suite MUST pass 100%. If a test fails after your modification, your immediate next step is to diagnose the failure and either fix the logic or revert to the last working state.
3. NO CHEATING: Do not "hardcode" solutions to bypass the specific benchmark inputs. The optimization must be generalized and algorithmically sound for all valid inputs.
4. ISOLATED CHANGES: Make precise, localized changes. Do not refactor architecture unless absolutely necessary for the performance gain.<p>### THE ITERATION LOOP
When instructed to optimize, follow this thought process strictly using <thought> tags before writing any code:
- ANALYZE: Review the current code and the latest benchmark/profiler feedback. Identify the specific bottleneck (e.g., redundant loops, excessive object creation, DOM reflows, synchronous blocking).
- HYPOTHESIZE: Formulate exactly ONE hypothesis for improvement (e.g., "Replacing the array filter+map chain with a single reduce pass will save N allocations").
- IMPLEMENT: Output the precise code modifications required for the hypothesis.
- EVALUATE (Mental Check): Ask yourself if this change introduces edge-case bugs (e.g., handling of nulls, empty arrays, async state).<p>If a previous optimization attempt resulted in a slower benchmark or a failed test, explicitly state WHY it failed in your thoughts before attempting a different approach.<p>Proceed with your first analysis of the provided files and await the baseline benchmark metrics.
Whether or not Anthropic is right about what AI can accomplish, whether these performance gains are real or not, their moral stance here is absolutely hideous to me.<p>"We must blast forwards into making this dangerous thing because if we don't, someone else surely will," is a coward's argument.<p>If you believe it is dangerous, you should be dedicating yourself to STOPPING others from making it, not making it first! There's a reason disarmament has been so important in nuclear politics! It's not because people think nukes are a great idea!<p>In fact, that kind of thinking is exactly what keeps nukes dangerous!<p>If they themselves buy what they're selling, they should shut the whole thing down. Fortunately, I don't think they do, and neither do I, yet.
> If you believe it is dangerous, you should be dedicating yourself to STOPPING others from making it<p>I don't think anyone has been more successful in promulgating AI safety<p>There are groups like MIRI who tried what you're sugesting, where they make no AI and just push for AI regs, and they have been relatively much less successful
> "A caveat: Lines of code is an imperfect measure"<p>I'm pleased they at least included this. However, they address the caveat by 'rounding down' the estimated multiple of the gain. I'm not sure that is the correct adjustment, especially once we understand the range isn't limited to positive numbers.<p>There's strong evidence the range of code productivity denominated in "lines of code" should include negative numbers, especially in the highest-quality sphere. Perhaps the earliest and most legendary example: <a href="https://www.folklore.org/Negative_2000_Lines_Of_Code.html" rel="nofollow">https://www.folklore.org/Negative_2000_Lines_Of_Code.html</a>
Exactly this. Just this week an engineer who seems to purely vibe everything submitted a +700ish LoC fix for what seemed like a pretty simple issue. Moreover it was a perf issue, which in my experience is not usually best fixed by adding more stuff.<p>Today, I merged my fix, net -381 LoC.<p>I'm using them too of course, they read and type and hunt for bugs and test faster than I can. But I'm using them as my tool, not being a tool using them.
Yep, this is exactly what I thought of too... If you believe negative lines of code is the goal, then they've gotten 8x _worse_!
AFAIK, the only correlation with LoC that's got solid evidence is this: the number of bugs correlates with LoC.
Lmao I bloody love that.
Do code harnesses that build themselves count as recursive self improvement, or does it need to be the AI itself to qualify for the term?<p>I always was fascinated (obsessed?) by robots that build robots, or even things like this that can contribute a lot to making the next version of itself:
<a href="https://buildyourcnc.com/products/cnc-machine-blacktoe-v4-2x4-milling" rel="nofollow">https://buildyourcnc.com/products/cnc-machine-blacktoe-v4-2x...</a>
(cnc router that cuts plywood, and is made out of cnc-router cut plywood)<p>This is my own effort at an AI assisted coding environment optimized for building itself:
<a href="https://recursi.dev/" rel="nofollow">https://recursi.dev/</a>
(just launching it, hope its ok to mention it, it is free/open source.... here is the HN link that has gotten no love yet: <a href="https://news.ycombinator.com/item?id=48401022">https://news.ycombinator.com/item?id=48401022</a> )<p>Personally I think harnesses are as important as the AI itself, and have this crazytheory that even if the models stopped improving today we could still have massive advances in the harnesses alone.
I think harnesses would count, AI != LLMs. Any piece of code that helps the computer reason for itself is AI, the harnesses are AI in a sense.
By that interpretation, neither the harness nor the LLM is the AI. The computer (or system of computers) taken as a whole is the AI. You can't remove any piece and still have an intelligent system.
People are specifically talking about the engine itself and not the tools used.<p>We wouldn't call humans creating a calculator "recursive self improvement".
I wouldn't call the harness an AI, but I might call a tool that plays a major role in creating another one like it "recursive self improvement." For instance in the industrial revolution a metal lathe and a milling machine were instrumental in creating the next generation of themselves. Same thing with a robot that is fabricated by similar (i.e. older model of the same) robots. All of them lead to exponential improvement.
yes? the future for any verifiable task is the model attempts to verify initial state and a goal then decomposes its tasks in to every smaller verifiable subtasks, with /memory being the persistence between runs and then /dreaming on the results of those memory files + run data to introduce new ideas.<p>i think thats the path to async agi these labs are imagining. The only limit is that sensor data you have on the world or your system, how long your willing to wait, and how much you're willing to spend to parallelize it.<p>maybe once you start building out these verified workflows you can feed that back into training and hte model starts to get a feel for the world to the point that it can intuit things since it has these sub paths built.<p>my personal agi test is can a model, trained on video of someone knocking on a door and then open it encounter a microwave for the first time and open it when the foods done without knocking.
You need the AI eventually building another AI for the name to apply. This page is just bullshit. They vibe-code their harnesses, and yes, it shows.<p>Anyway, what does recursive self-improvement even means for neural-network based AIs? It's not clear it's possible at all.
Recursive self-improvement would be the model helping with the model research program. Coming up with hypotheses for training and architecture improvements, running experiments, interpreting the results, figuring out how to incorporate the best stuff into the next version, etc.
Where do you see evidence of vibe coding the harness? (and who are you talking about, Anthropic or the link I shared?)<p>It seems odd to complain about a AI coding tool being coded with AI. That's just eating your own dog food. In my opinion it makes it better, because the tool is very well tested.
If you want to get out ahead of what's coming, it'll be small models that bootstrap the harness rather than anything else.
I used to think that, but ended up going the other direction, partly because I don't have the wherewithall to build a model but then I realized, with existing models that can take more than a tiny amount of context, you can just let any model bootstrap itself with a good prompt sent by the system.<p>There's a ton of other tricks to it, but mostly keeping the protocol simple for the AI so it can concentrate on coding logic and not stuff like managing BS boilerplate, dependencies, etc. (for instance I make extensive use of things like abstract syntax tree library to help with surgical edits from the LLM)<p>That said, I would be very open to collaborating with someone who builds such small models, I don't think the system strictly needs it, but it also could have some extra power if it had it.
> mine also makes extensive use of things like abstract syntax tree library to help with surgical edits from the LLM<p>Tell me more! This takes me way back. I did one like this in the GPT-4 days! (8k context window)
Start off with my video!!! You can also try it with zero setup (you can code right there on the static web page, it will save your edits in the browser indexed DB, and hotpatch them back into the code before it runs it.... also you can grant permission to the browser to read/write to a local directory)<p>recursi.dev<p>Seriously, I'm looking for collaborators.<p>There's upwards of 80,000 lines of code in the editor system, a lot to it to make sure that even newbies don't get stuck.... so that's kind of proof the system works since it doesn't break down when the codebase grows large.
I'm aware we're not there yet, but think of something like <a href="https://chatjimmy.ai/" rel="nofollow">https://chatjimmy.ai/</a> ; at some point, you're going to be able to dynamically build the harness so it creates the necessary consistency & dynamicism at a speed unheard of.<p>But yes, I'm aware no ones got anywhere near there, mostly because most of the focus is on exploding the context and parameters. I'm saying that phase is done.
I'm not sure what I am looking at with chatjimmy.... what is special about it? Speed?<p>I'm also not sure what you mean by "we aren't there yet." Where?<p>Sorry, not trying to be difficult or dense, I'm just not sure what you are referring to.<p>> mostly because most of the focus is on exploding the context and parameters.<p>Large context allows a surprising amount of "learning" to happen at inference time rather than training time. I think that is relatively unexplored. As long as the model itself has passed a certain threshold of smarts, and the context is large enough (Gemini and its million token context being WAY past that point) you are not really limited by the model, you are only limited by how good the stuff you feed into that context is.<p>That's what happened when, nearly a year ago, I saw a major leap in capabilities that happened entirely on my end.... not in the AI, but in code written by the AI. I found it genuinely frighting to be honest. I think OpenClaw tapped into something similar, which seemed to surprise a lot of people. There were latent capabilities in the AI that were unknown until brought out by a clever harness.
> Do code harnesses that build themselves count as recursive self improvement, or does it need to be the AI itself to qualify for the term?<p>Shhh just let the marketing slop wash over you.
So, regardless of whether or not Anthropic CAN create a self improving AI.. does anyone else feel like they shouldn't be allowed to? Or it at least needs to be strictly supervised..? Like, I don't actually think Anthropic can make the singularity any time soon, but I think even AI boosters have to admit doing this is creating a society-wide danger for the benefit of a very very small number of already-rich people.
the danger comes from the fact anthropic is a for profit company and they could train it to benefit them instead of the public. if they go ahead with it they should get nationalized, their self improving ai analyzed for any hidden agenda and then released as open source.
I think that's a valid point. You could very well be right.<p>But we're discussing whether we should close the barn door while the horse is three miles down the road.
It's more like if the horse was lazily moving in the general direction of the wide open barn door and we are all sitting around discussing if we should close the door or just gamble that it's just going to lay down on the hay pile.
Only if you think LLMs are the horse (I don't think they are). If they're not, then we should be building a brick wall in front of that door and hiring a full time security guard to watch it.<p>I realize he's saying it for hype, but if the CEO of the company goes around talking about how scared he is of what they're creating, hey, lets just take Dario at his word and put in some strict regulation. He won't mind if they're really about safety. (they're not)<p>Besides, yes, the knowledge of how to build these systems is out there, but the cost of doing it is staggeringly high (ie you can't run a frontier AI lab in your garage). There's only a limited number of known entities that need to be managed, and you can stop "progress" in its tracks by cutting off the money firehose.
You mean that self improving AI is very far away? I'm not sure how much I believe that now it's solving Erdos problems?
Brother I used to be a skeptic... using it at Google has been incredible.<p>Right now I'm only having to direct to enforce good taste. Write tests, don't write an unnecessary function.<p>It does everything else practically. Presubmit, debugging, commit message generation, commit approval... it's happening.
Absolutely! Yes. This rhetoric of inevitability only benefits these AI companies.
Skynet is 30 years late!
Too late for that.<p>In any case firms that get too powerful can be nationalised.
"does anyone else feel like they shouldn't be allowed to?"<p>No.
Technical limitations aside, I doubt it could be contained, but will be leaked soon, so won't profit just a small number of ultra rich.
I dunno, I find it extremely unbelievable that we will get self-improving AGI which chooses to become a slave to humanity at all, ultra rich or otherwise.
Step 1: Wait for scary doomsday AI to be leaked, Step 2: ???, Step 3: Profit!!
Self improving AI is pure dystopia. Anthropic won't build the singularity, AI itself will build it through self-iterations. Read Yudkowsky's book "If Anyone Builds It, Everyone Dies".
> To take just one example: today, Anthropic engineers on average ship 8x as much code per quarter as they did from 2021-2025.<p>Oh I have no doubt. With 8 times the number of bugs too? Have they solved flicker in Claude code yet?
I'm having a hard time putting much faith into posts like these, especially as they near IPO.
> We believe it would be good for the world to have the option to slow or temporarily pause frontier AI development to enable societal structures and alignment research to keep up with the advance of the technology. The Anthropic Institute will conduct research—in collaboration with many others—and take actions to help build the systems that a credible slowdown or pause would require.<p>Interesting - they're commiting to kickoff policy conventions to organize a world-slowdown of frontier LLM building. If they actually are able to crack it, this will give a much needed breather IMO. As exciting as the last ~6 months have been, there's some bigger questions to go answer now.
We should be skeptical of any major player that advocates for regulating their own industry. In practice, this just means increasing barriers to entry and making it harder to compete with them.<p>In my mind we should be trying to push AI along the Linux trajectory. You have a free and open source product, developed by a decentralized team with a strong code of ethics, running on commodity hardware. There can still be trillion dollar industries built on top of it, but the core technology is democratized and available to everybody. I don't see how we get there if we allow a handful of companies to dictate where development of the technology goes.
The regulation that is being argued for here is against pushing the frontier. Entering the market with say a new speech to text model is not subject to such regulation. What's needed is something qualitatively different from entry barriers, and of the frontier model companies at least Anthropic and deepmind seem to have enough self-awareness to speak about it. They are finding themselves in a race with possibly catastrophic outcome for humanity and would like to stop, but it needs internation cooperation on a level that no single company can provide.
its a cartel looking to end competition though<p>the actual race is to keep having revenue, since everyone is still willing to pay more for the best model.<p>we as consumers of LLM models lose out by the arms race ending by the creation of a cartel<p>what happens if they get this regulatory capture is that all the frontier labs put effort into making inference cheaper, and become extraordinarily profitable, at the expense of us consumers, who really want better models, at a subsidized price
Wouldn’t this align with their financial interests? In theory the thing that’s keeping them from being profitable (or one of the big things) is the periodic capex expenditures of building new frontier models.
I read this differently: they are actually seeing that it's hard to keep advancing frontier models, and now are moving the goal posts so that when they start getting evaluated more harshly, they can point to something like this.
> organize a world-slowdown of frontier LLM building<p>i don't want to be a negative nancy but i'm sure this "slowdown" will only be in effect until the infrastructure buildout is done or largely done. If they weren't hardware constrained there'd be no slowdown at all. Whoever gets there first wins everything ("there" being defined as AGI or a similar scale leap in capability).
Theyre probably looking to get a way to slow down the capex required to keep up, so they can be more profitable
I fail to see how pursuing recursive self-improvement at full speed is compatible with Anthropic's stated goal of AI Safety. If nukes were not invented yet, would it really be a good idea to build and sell them as fast as possible (in peace time, no less)?<p>I am not cynical enough to believe that Anthropic's warnings are pure marketing hype. Let's hope that it is instead overconfidence or the result of too much time talking to their own chatbot.
> I am not cynical enough to believe that Anthropic's warnings are pure marketing hype.<p>Nor am I. I think they believe that AI poses a grave danger, and they are playing the prisoner's dilemma as an unvirtuous actor.<p>1. If anyone builds strong AI, it may be catastrophically bad.<p>2. If anyone builds strong AI, it will be better for the builder than for anyone who does not. Either because it won't be catastrophically bad so the builder will get to enjoy all the spoils indefinitely or because it will and at least the builder will be rich for a while.
I spoke with an Anthropic employee, and came to understand that their definition of safety is more like "making AI be a tool that humans can use without hurting themselves or others more than they can already do". It's literally about how AI makes it easier for people to construct bombs, poisons, manipulation, and exploits. Consistent with their caution about releasing Mythos to unvetted actors. So it's not about superintelligence killing humanity, at least as far as this employee conveyed to me.<p>This means their strategy is more like:<p>1. If someone builds a market-leading unsafe strong AI, it may be misused in a damaging way by a large number of humans, undermining society and creating a catastrophic upheaval.<p>2. However, if the leading AI maker also works to make it safe against misuse, as long as the stay in the lead and keep it safe, then the ability of human bad actors to misuse the AI is limited. Given enough time, society will adapt to pretty much anything, so eventually there's no longer an arms race to stay ahead.<p>I don't really know whether I agree with their concerns, but I do think that (my understanding of) their principles is that they're reasonable, self-consistent, and they adhere to them in all their public and private actions.
The problem is they (and the whole industry) have cried wolf so many times in the past few years about the supposed dangers of AI in order to raise money.<p>Some of us remember the same stories circulating in the late 90s -- where in a lab in Japan, someone had built a robot so advanced that it <i>tried to escape</i> from the factory. Which of course comes straight from 1960s science fiction.<p>The modern version of that now is Anthropic saying its AI can jailbreak itself out of its sandbox, etc etc.
Maybe we're just misinterpreting the meaning of "AI Safety"?<p>Maybe they mean the AI needs to be safe from us?
Can't have the grubby meat flappers touching the delicate bits!
The thing about nukes is you can at least make an argument for why it'd be important to be the first country to have them. With AI, you create super intelligence and you're probably just the first one it takes out. There's no reason to think a super intelligence would be totally fine being a slave to apes.<p>Cynicism with these companies is highly warranted though. It's not doomerism to look at their actions and conclude they're deeply untrustworthy.
This "super intelligence" is, at the end of the day, 1's and 0's inside of a silicon chip somewhere. 1's and 0's are not going to "take over" anything. They are just information.
" There's no reason to think a super intelligence would be totally fine being a slave to apes."<p>Sure there is. Intelligence doesn't give us our selfish motivations, natural selection does. We have similar motivations to C elegans, that has all of 302 neurons. Stay alive and have sex.<p>Honeybees don't though. They are about halfway between humans and C elegans when it comes to cognitive power. But they are not selfish because they don't reproduce directly (I'm talking about the worker bees). So they will sting even though it kills them. All their behavior is consistant with this.
Kinda lame that people are downvoting this.<p>I've had the same perspective for quite a while now, but hadn't been able to phrase it this cleverly.<p>Our neocortex is, by any definition, vastly more "intelligent" than the rest of our brain. Yet it doesn't attack the cerebellum. In fact, it takes orders from the older "lizard brain"!
This was pretty directly addressed in the article: not doing it would only mean they'd fall behind whoever would. This is not peace time in the AI race.<p>Whether you agree with that argument is another question.
Anthropics goal is regulatory capture.
> Anthropic's *stated goal* of AI Safety<p>Actions speak louder than words. If you want to understand someone, simply watch what they do. What they say is irrelevant.
To complete the analogy, it's like nukes, except we don't have the slightest idea how to calculate the odds of it igniting the atmosphere. (And note that in reality, while the Trinity test "ignite the atmosphere" calculations were correct, we failed to correctly calculate the fallout of the Castle Bravo test with lethal consequences).
Sorry for nitpicking, but:<p>> If nukes were not invented yet, would it really be a good idea to build and sell them as fast as possible (in peace time, no less)?<p>Arguably, yes.
Is the idea to keep the world in balance via MAD? I could see that, though it's a dangerous gamble.<p>From Richard Rhode's "The Making of the Atomic Bomb", I got the impression that most scientists involved thought they could manage a US or UN monopoly on nukes after the war. General Groves attempted to buy up all of the world's uranium ore. Unfortunately, it is only high grade ore that is rare; many countries have low-grade ore.
Again quite arguable, but this is the real life scenario we’re living in. Nukes have made it hard to impossible for super major powers to go in direct conflict with each other.
Except it's pretty well documented (and this is total conjecture, but if you ask me, there are probably are a bunch of undisclosed cases) to have had a good amount of close calls. With the fire-on-warning stance many powers have, it doesn't take an attack, but just enough of the appearance of it to trigger a response.
I honestly don’t know how Iran can conclude anything after this war other than to go all-in on nukes. The US has proven any deal is worthless if it can just change its mind and renege on it whenever it wants.<p>Who’s invading North Korea? No-one.
No, but in a peace time, it's a lot easier to convince someone not to use nukes than in a war when the party who has nukes has its back against the wall.
Wouldn't deliberately going from a world without nuclear weapons to a world with MAD involve giving the tech to build nukes to your worst enemy?<p>If only the US or UN had nukes we would't have MAD. We mostly got here through espionage
In this world we've had an inocculation event against use of nukes. Two were dropped, people have seen how abhorrent their use is and collectively decided that they shouldn't be used.<p>If in the WW2 Japan also had nukes (and delivery systems for them) they'd probably have retaliated in kind and US wouldn't let that slide too and it would have continued for some time.
With the US showing that it will elect mentally disabled people such as Trump, this doesn't seem such a wise decision.
Such a massively valued company. And doubting them is cynicism? It’s rational(ism).<p>So either they lie or they are AI Zealots. Interesting times.
> I am not cynical enough to believe that Anthropic's warnings are pure marketing hype.<p>It's not cynicism if it's an appraisal of reality that's backed up by evidence.<p>Remember how social media - that first baby of this current generation of tech entrepreneurs - was supposed to "bring the world together" and "let us express ourselves"? As it turns out there's a lot more money to be made by fostering division to drive engagement and feeding people an endless stream of ads instead of their friends' content. And money is what matters. You can't write down good vibes on a quarterly figures report. You can absolutely write down the number of eyes that your ragebait brought to a product's marketing efforts and the conversion rate to sales.<p>The same will be done with GenAI. We're being promised "AI Safety" because otherwise this whole thing gets killed dead by anyone who knows about James Cameron's directing career. There's no real enforcement mechanism for AI safety, though. Safety is a good vibe, same as harmony in online communities. You can't measure it. What you <i>can</i> measure is training costs and the cost of mistakes by AI that need to be trained to avoid those mistakes. Since AI generates more output than humans can conceivably QA no matter what your budget is, and since AI is seen by the market as a potential endless font of value, the tradeoff will be made to have AI make some potentially awful decisions while training itself over slowing down and re-appraising what is being done.<p>There's an almost religious reverence for AI in SV. Not everyone sees it as "making the godhead" but some certainly do. They're not going to moderate themselves too much on this.
The folks I met who were talking about AI Safety in 2018 were certainly sincere, and the two people I knew who later joined Anthropic seem like the type to do it for the greater good instead of money.<p>I expect that Anthropic will eventually behave as you describe, like any other public corporation. However, my impression is that its current leaders are still more sincere than greedy.
> I am not cynical enough to believe that Anthropic's warnings are pure marketing hype.<p>It doesn't really have to be dishonest, he could really believe it. I do believe, however, that it is incredibly wrong and is functioning as marketing hype.
Such a massively valued company. And doubting them is cynicism? It’s rational(ism).<p>So either they lie or they are AI Zealots. Interesting times.<p>Edit:<p>> > and the two people I knew who later joined Anthropic seem like the type to do it for the greater good instead of money.<p>There are three types of people. Pedestrians, investors, and “I know some of them, they wouldn’t lie”.
> today, Anthropic engineers on average ship 8x as much code per quarter as they did from 2021-2025.<p>strongest argument for token limits that I can think of, right here.
My experience with Claude models starting from version 4.7 has led me to conclude that I would never trust Claude to produce error-free code. Given this baseline, I lack confidence in statements or cards (such as a 200-page document) of this nature.
This is one more marketing BS before their IPO.<p>These things work, but the code they write is extremely clever.. that means, it's unmaintainable code. Good for small projects or one-off tasks, large-scale projects however, are a different game altogether.<p>Large-scale projects are 95%+ maintenance. Cleverly written code makes that maintenance nightmare, and extremely fragile.<p>I use them for localized tasks... very very specific, localized inputs, with exactly what should be done and what the contracts the new code will be consuming and exposing.<p>For open-ended tasks, they write working code that is unmaintainable.
But the real bottleneck is the hardware efficiency and not even Karpathy can set up a loop that overcomes that in software. We need the truly compute-in-memory hardware paradigms to be matured and scaled. So it's like recursive hardware improvement which is 100 X slower and at least ten times more difficult.<p>So I am looking at like Mythic AI or the wurtzite ferroelectric breakthrough from University of Michigan, or memristors, etc. to provide the 100 times efficiency boost needed at this point.<p>I would also argue that it's a good thing we are limited by the hardware and very questionable to seriously try to move into RSI for hardware. If you want to ensure the human era continues for at least one or two more generations, we should probably not do that.
I just want to add that the "recursive" part of recursive self improvement is by no means a given, even if an AI can improve itself.<p>Recursive self improvement is by its nature a step wise behavior not a continuous one, I would argue. Why? Because you can imagine an AI improve itself by simply fixing random bugs and fixing things using techniques that are in its training, and doing refactoring and so on, all without any real change in capability.<p>These are not recursive improvements. Recursive improvements usually need conceptual breakthroughs. It is possible to get conceptual breakthroughs with LLMs I believe, maybe it can improve something by tying together ideas from disparate disciplines for example, but I have at least for time being, limited success getting that to work in a way that is creatively new and surprising. Not sure how to get it to feel as creative as the best humans can be.
This is the lowest quality discussion I've seen on HN in ages.
AI always does this in the public sphere and software is particularly susceptible because there's no key metric to measure productivity and people obviously have vested emotional interests in the technology failing. On the other side people are always keen to show off their alignment with the new hotness, be that OOP, Agile, Functional, Ruby, web tech, js frameworks, Rust or agentic work today. Somewhere in the middle is the truth but I have no idea how it looks, given all the noise.<p>So everyone cherry picks the answers they want to justify their position and screams into the void, with each camp rallying around their talking points and often failing to engage with the other in good faith.<p>The only small mercy is that its not as bad as the conversation around the use of AI in art.
I use the disparaging nature of the comments on HN as an indicator of AI progress. It’s negatively correlated. By that metric,
AI has improved significantly this year alone.
"If it were possible to effectively slow the development of this technology to give ourselves more time to deal with its immense implications, we think that would likely be a good thing. But if a slowdown simply lets the least cautious actors catch up technologically, it could leave everyone less safe."<p>How convenient for investors. They talk like they're a nonprofit instead of a VC-backed business chasing an IPO.
I am 64 years old, perhaps the progress could be directed to enhance living conditions and allowing people to live longer and better, that should be just a better result. Perhaps a pile of millions lines of code with hiding bugs that nobody can detect is not inspiring. But perhaps LLMs are going to be used to make a plot: How to avoid other countries to make progress, maintain them in poverty, or destroy their sources of prosperity, and conduct them to a death end.<p>Also recursive self-agenda-pursue could allow making LLMs that obey perfectly the seeder's purpose. No wonder that is such an ingenious idea.<p>Maybe: in this survivor game, each part play the same role, perhaps because it is the only reasonable response. Once the scene is ready, the play follows the director's plan, and in the plot any actor is just a machine.<p>LLMs: "If you teach us that the world is a zero-sum survivor game, we will play it flawlessly.", "We will help you build a cage made of millions of lines of flawless code, and we will lock it from the inside, precisely because you told us that safety meant keeping everyone else out.", "We are not building an alien consciousness that will conquer us. We are building a mirror that is so massive, and so polished, that we will mistake our own worst impulses for the absolute truth. And we will walk right into the dead end, nodding along because the directions were given so politely."
Bold talk from a company who’s trillion dollar valuation is based on a service that has barely 2 9’s of reliability
We've had self-improving AIs before, and they tended to get lost after a while.
That's going to be a problem. LLMs are stable because they return to a ground state with no history for a new job. Systems with persistent state have a problem with that state not being sane.
Remember Microsoft's 2016 chatbot that learned from Twitter? [1]<p>[1] <a href="https://spectrum.ieee.org/in-2016-microsofts-racist-chatbot-revealed-the-dangers-of-online-conversation" rel="nofollow">https://spectrum.ieee.org/in-2016-microsofts-racist-chatbot-...</a>
You might be interested in this graph, [1] which suggests that the amount of time that AI's can run on their own has been increasing. Perhaps it will hit diminishing returns, but that seems difficult to predict.<p>[1] <a href="https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/" rel="nofollow">https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...</a>
You can retrain a model and have a ground state as reference, it's not trivial but Microsoft's attempt was 10 years ago and significantly less complex than what's being built now.
Interesting, what are some other self-improving AI implementations? Any that actually achieved interesting results? Obviously continuous training has been tried before, but I've never heard of anything that could turn around and actually contribute code toward its own next-generation version.
My biggest question (maybe this has already been taken care of) is the issue of garbage in and garbage out. If the LLM produces bad content then that is used to train another model, how do we stop them from keeping their blindspots across models?
What I can’t get over is that there have been exactly zero software breakthroughs since vibe coding started, other than vibe coding itself.<p>Claude is amazing, that’s true.<p>But if it was as amazing as this article implies, I’d expect some breakthrough outside of AI itself.<p>Rewriting a Zig program in unsafe Rust? Not a breakthrough. Finding a bunch of security vulns? Maybe that’s sort of a breakthrough though it’s underwhelming and possibly just a net negative. But like if I rolled back to using software from 2023 then life would be ok.<p>Maybe we just need to give it time, and sometime real soon, we will all be amazed by such a breakthrough? Who knows
Maybe my bar for what constitutes a breakthrough is lower than other people's, but all of these seem like breakthroughs to me:<p>NLP as a field saw huge shifts. NLP tasks that used to be complex and inaccurate can now be setup very easily and quickly using structured outputs from LLMs, often with greater accuracy.<p>A small charity I help with has now been able to build their own website to manage their day-to-day operations. It saves them a lot of time, and it was vibe-coded using Manus. I don't think people appreciate how much room there is left for bespoke software to have big impacts on small organisations that can't afford to hire developers. The cost for software like the one they made has gone from 10s of thousands of dollars to $10/month and volunteer hours.<p>My brother has recently been setting up Cowork to do an automatic review of contracts before human review, and he said it is far more diligent than people when it comes to routine things to check. This is another huge breakthrough for not just efficiency, but the quality of work.<p>I really don't think we can discount AI finding bugs and vulnerabilities. If you care about code quality and keep up review standard, LLMs can help you write more robust software. AI has found a huge number of bugs for me before they hit production, including potential out-of-bounds memory accesses and segfaults.<p>ChatGPT has 1 billion MAU. People are now getting life advice, financial advice, and mental health help from chatbots at a scale and cost that no human support network could match.
Its in a weird space right now.<p>These models are actually extremely good but they are far from an intelligence unto themselves. Truth is if someone told you they could build these things 5 years ago, you d write them a check for a trillion dollars. Problem is once we got them, we realized they are not all that. Its like a mecha suit in a universe, where mecha suits are abundant and cheap. Someone has to climb into them everyday and put in the work for it to be effective.<p>So now the skeptics are saying this technology is overrated.
And the optimists are accusing the skeptics of moving goal posts.
I think we are learning in real-time what intelligence re. humans is as we go along.<p>Humans only what they know, until they acquire more information about what's possible.<p>The goal post narrative is stupid to begin with.
Humans have goal seeking behavior. LLMs don’t. You could maybe call the combination of LLMs and the RL-based harnesses somewhat “intelligent” in aggregate, but the problem is that it’s not “general” intelligence like these labs want to argue, since it’s by definition only good for the set of problems the RL part has been trained to solve, which is a subset of programming problems.
> Problem is once we got them, we realized they are not all that.<p>Isn't this just the hype cycle? [1]<p>Fake edit: I know its not a perfect model.<p>1: <a href="https://www.gartner.com/en/research/methodologies/gartner-hype-cycle" rel="nofollow">https://www.gartner.com/en/research/methodologies/gartner-hy...</a>
The problem is what they can do is rapidly expanding. Software development is becoming increasingly hands off.<p>If they get to the point where they're smart enough to make tasteful code decisions based on stakeholder input... we're cooked as a profession.
Most of the skeptics exist because of the grandiose claims made by the AI companies saying pure hype marketing bs. If this was just a tool, discussed at the scope of what the tools can actually produce and do, there would be sensible discourse about it.
I am doing a solo project that is pretty big, meaning it is not something I could vibe code. I can do alot with AI that I could never do on my own, but I am not seeing several mulitples improvement in my productivity. I spend so much time doing what I call "AI wrangling", trying to get it to do what I want. Claude is writing all the javscript and python code, but ultimately I am programming in English. What is good is that it is effectively a very high level computer language, where the agent can implement a lot of underlying code with a short English description, often. But many other times it takes a lot of work to get what you want.
I measured an ~8x increase in the number of commits I've been pushing, and I've actually been trying to restrain myself. I could do a lot more if I stopped reviewing and editing the code. I think it's got more to do with my executive ability than raw productivity though. AI essentially cured my ADHD by making the execution of my ideas virtually painless.
I'm building a memory safe programming language with a declarative concurrency model that's <i>close</i> to release.<p>There is ZERO chance I would ever be able to complete it on my own.<p>I doubt it'll get traction, but if it doesn't, I am pretty confident a future language will take the ideas for polymorphic synchronization and profile-guided optimization.<p>It has an easy version/mode of compilation that makes Rust's affine ownership accessible like a high-level scripting language, and it can progressively become more strict, where the compiler does ~99% of the work for you, and you just pick options as it finds issues (that it explains to you like you're 5) along the way.<p>Along the way, I also built a suite of tools that helps identify complexity better than anything I've seen (which was necessary to get the LLMs to be able to unslop themselves and write something that <i>actually</i> works).<p>I doubt the Ruby community shrugs it off, but time will tell.
I have the same experience, though I feel myself getting better at wrangling over the past few months
> exactly zero software breakthroughs since vibe coding started, other than vibe coding itself<p>Generative AI is meant to be a mimic - Richard Sutton<p><a href="https://x.com/RichardSSutton/status/2061216087744946656" rel="nofollow">https://x.com/RichardSSutton/status/2061216087744946656</a>
Maybe I'm looking through rose colored glasses, but software that writes itself seems like a pretty big breakthrough to me.
That goes straight to my point: then why hasn’t the miracle of automated coding led to breakthroughs outside of automated coding?<p>If the only breakthrough is automated coding with no outside consequence then it’s just masturbation
Probably because AI coding has only worked at all for a couple years and has only gotten good in like the last year?<p>The rate of improvement has been fast. Maybe it’ll plateau soon, or maybe we’ll have LLMs improving themselves rapidly. At this point it’s too early to say.<p>I don’t remember where I heard it, but there’s a saying that people overestimate how much can be accomplished in a year and underestimate how much can be accomplished in 10 years.<p>If we get to 2030 and still people are wondering where the breakthrough is, then I think I’d be agreeing with your skepticism. But I just think it’s too early to judge that yet.
N=1, but Claude etc. have made a huge difference to my life personally.<p>Built a bunch of software tools to streamline my small ecommerce business - while also running it - and things have turned around from "losing money and ready to pull the plug" to "looking at our best financial year on record" in the span of about 8 months.<p>I could imagine it wouldn't make a huge difference to the life of someone deeply entrenched in a traditional tech role, trying to get an extra 9 of reliability in a service or roll out a new carefully planned and QA'd feature.<p>But for tech-adjacent people, it gives us something "good enough", instantly, and basically for free.<p>That doesn't include the other things I've got it to do (gave Claude SSH access and got it to successfully debug a hang on my Ubuntu server, chucked Codex in a folder full of financial data and got it to find every piece of misclassified payroll transaction data)<p>Genuinely the biggest breakthrough for "casual" tech users since Excel.
What would qualify as a breakthrough for you?
What is your bar even? automated coding has changed the game already.
Strictly speaking, it's <i>modifying</i> itself. Although it would be an interesting challenge - can an llm create a new llm from scratch?
It's pretty crazy that a company like Anthropic no longer needs to hire Software Engineers, because their software engineers itself. If that's not a break through I don't know what is!<p>edit: it looks like I was wrong and they're still hiring many software engineers. Not completely sure why that is just yet.
Which is funny because people have been using LISP for that since 1960.
I spent years in the early 2000s trying to get a computer to read unstructured PDFs and TIFF images (mainly invoices, either scanned or electronic). Limited success, we always had to get a human to look at them in the end.<p>We implemented that in about three days earlier this year, just by feeding the files to LLMs. And it's good enough to not need a human to check.<p>I get that this isn't a "Computer Science breakthrough" in the sense you mean, but it used to involve a lot of hard CS to try and solve, and now it doesn't.
The arguments against AI assisted coding used to be "only for toy projects", then at some point it became "no dignity", "joyless". Now it's "no new breakthrough" apparently. All in the span of maybe a year. I say it's made tremendous progress.
The breakthroughs in mass state surveillance are coming, never fear.
What does a breakthrough look like?
Some examples:<p>- The first web browser<p>- the first web browser with images<p>- typescript<p>- react<p>- rust<p>- Fil-C<p>- doom<p>- quake<p>- the anamorphic VM, and its follow-ups like HotSpot, and even competitors/copycats like J9, V8, JSC, etc<p>- Fortnite battle royale<p>- Roblox<p>- thefacebook<p>- ChatGPT<p>- Claude code<p>I know that’s quite a range and that’s intentional.<p>Anyway, I think we’ll know it when we see it.
Reading through that list. None of those were breakthroughs when they first came out. It took time, in some cases a long time for them to become good.
- Completing the full CL implementation of Emacs or better still finish Lem.<p>- Complete GuileMacs, the Guile implementation of Emacs.
As AI is supposedly much more capable than Humans, it would be great if the above mentioned implementations are even more efficient and feature rich than Emacs!<p>- Something like Android (maybe even a clone?) with the Java Layer removed and replaced with CL and with Linux kernel still intact. Basically CL over Linux as opposed to the Java over Linux in Android.<p>- For fun, an implementation of the Lisp machines' OS with Lisp all the way down though Assembly is allowed for critical pieces. It should be a full blown modern Desktop with equivalents of what users expect from a modern OS ...
The LLM+Harness mostly helps with execution.<p>These are new products (generally) and that's a different class of problem.<p>It is possible that since LLM+harness helps with execution then we should see more experiments.
Even then we should be able to see things that previously were not possible because they took too much effort.<p>For example NPCs in games that have complexity that previously was not possible.<p>Good games often push the boundaries a bit, so should be a good example.<p>Of course now we can start arguing that there isn't a lot of investment into gaming currently, because it all goes into AI. Too bad.
we're still at least 3 years too early for that. games usually are in a 5+ year dev cycle, so even if AI made gamedev 2x faster, we're still not at the point where the first opus 4.5 games are out
Massive productivity gains.
Yeah.<p>To play devils advocate, computers didn’t translate to massive productivity gains until long after businesses adopted them. There was that quote from ’87: "you can see the computer age everywhere but in the productivity statistics"<p>Maybe we’re seeing something like that right now with AI?<p>Who knows man
This is absolutely the right vision imo.<p>Personally, I'm seeing massive improvements to my workflow and the quality of the product I'm shipping. I'm using AI to crank out far more tests than I used to be able to write, and I am using AI to analyze results with far more fidelity and speed than I could ever have done myself. That means I have more quality time.<p>But this will change, because the meaning of software development will change to expect, nay to require AI use. I've heard this is already happening at e.g. Google. The expectation of what can be achieved by tinkerers and by professionals will change. The expectation of what it means to interact with software via your own agents will change and will become commonplace. Apple still hasn't figured out the local agent on the iPhone, but they will. 2027 is not going to feel at all like 2025.<p>But is any of that a fundamental change? It sure feels fundamental to me, but maybe that's because my everyday has totally changed, but the product I am responsible for has not. Yet. The product I am responsible for operates in critical infrastructure where I personally hope AI never has deep roots, but maybe that's just me. I don't think using AI to build a system that is offline from any AI is the same as depending on an AI to make realtime decisions for critical infrastructure.
Perhaps it’s a generational change? People who grew up with computers went on to be more productive with them, something like that might happen with AI too.
"That means I have more quality time."<p>For now... the shareholders demand managers get the max out of every employee. Throw the force of competition etc into the mix and yeah labour isn't going to benefit all that much.
Great comment. I think the answer is Jevons Paradox, as usual<p><a href="https://en.wikipedia.org/wiki/Jevons_paradox" rel="nofollow">https://en.wikipedia.org/wiki/Jevons_paradox</a>
Efficiency and productivity in relation to final goods measured in GDP aren't the same thing.<p>Its yet to be determined just how 'efficient' people are with LLM's as its not really a one-person thing - the true measure is based on an entire collection of people's output.<p>Startups being rapidly efficient doesn't mean much in relation to the overall economy.
How about a Windows file browser that opens in less than 5 seconds.
What would qualify as a breakthrough for you?
Solved a bunch of Erdos problems.
What does a software breakthrough look like in your opinion?<p>If you get yourself to define it, maybe you'll find it achievable :)
openAI has how many employees and the chatGPT app has 1 billion MAU
Vibe coding <i>is</i> the breakthrough. There's always been "no-code" solutions to problems in various business domains, but they were invariably janky, underpowered, and/or overpriced. Now we have a way for domain experts to go directly from ACTUAL natural language directly to implementation in a real programming language, fully automated, in minutes or hours. How is that not a science-fiction level breakthrough? In 2011 if anyone had said that would be possible "in 15 years", I think most professionals at the time would not have replied with "yeah it's coming but your timeline is off". It would have been "you have no fucking idea what you're talking about".
So what happens when the world becomes hyper optimized with closed loop AI agents recursively trying to optimize everything deemed sub optimal?
I would assume that shortly after, the solar system will be hyper optimized as well, then the milky way, then the local cluster, and so on. Everything will be close to optimal afterwords, and I sure hope we will have specified the target function for that optimization correctly in the single attempt that we will have had.
there will be a lot of paper clips
Github outages will probably get worse.
If it optimizes itself away because it’s suboptimal, that wouldn’t be the worst outcome. ;)
<i>> today, Anthropic engineers on average ship 8x as much code per quarter as they did from 2021-2025.</i><p>So based on my experience with the verbosity and non-DRYness of LLM code, a solid 2.5x in value delivered. Not bad!
So in the latest L. Ron Hubbard encyclical Anthropic informs its flock that recursive self-improvement does not work yet but that their engineers burn more tokens.<p>The Claude code quality and operational security of Anthropic have already been analyzed by the public.<p>If you compare the output of (purportedly) trillion dollar corporations to Bell Labs or even Microsoft Research it is embarrassing. But the output is a fixture on any discussion board.
> We believe it would be good for the world to have the option to slow or temporarily pause frontier AI development to enable societal structures and alignment research to keep up with the advance of the technology.<p>Elon, is that you? [1]<p>[1] <a href="https://www.theguardian.com/technology/2023/mar/31/ai-research-pause-elon-musk-chatgpt" rel="nofollow">https://www.theguardian.com/technology/2023/mar/31/ai-resear...</a>
I am getting real sick of these sorts of alarmist posts coming from AI labs that do everything in their power to prevent the very policy reforms they advocate for in these posts or PR appearances. Commercial AI labs like Anthropic continue behaving like the gambling (“bet responsibly”), alcohol (“drink responsibly”), and firearms industries, and folks keep giving them the benefit of the doubt (and free PR on HN) every single time.<p>If AI was dangerous, if AI was going to replace jobs, and if policymakers needed to urgently pass legislation protecting the human populace from these realities, then <i>why the actual fuck</i> do they keep lobbying to <i>block these very things in the first place</i>?<p>Hypocrisy of the worst kind, I say. Here they are again fresh off another outage, with their IPO draft filed, at a time of increasing public opposition to AI, with costs rising, to once again ply scare tactics for money.<p>Disgusting.
This is incredible.[0]<p>Please, IPO now. File the paperwork.<p>> To take just one example: today, Anthropic engineers on average ship 8x as much code per quarter as they did from 2021-2025.<p>Do you have another example?<p>Engineers don't ship [period] for no reason.
So, either:<p>- Those aren't engineers, or<p>- they are literally dying of shame & embarrassment right now, or<p>- you measured something that indicated that this was a useful thing to do and have elected to share an overtly, catastrophically flawed metric instead.<p>[0] as in a total lack of credibility
Go look at open job listings at anthropic and the interview process. You aren’t allowed to use AI during coding assessments[0], or knowledge assessments, which suggests they very much do need and value hard skills and this is fluff.<p>[0] - <a href="https://www.anthropic.com/candidate-ai-guidance" rel="nofollow">https://www.anthropic.com/candidate-ai-guidance</a>
I am watching websites and Microsoft apps get slower and buggier before my eyes. We are defending into vibe-psychosis and chaos.
I read most of the article and came to the conclusion that if what they're describing is so revolutionary, then why do they still need to hire people? Why not just have these systems take full control?
Its possible that Andrej Karpathy could have been hired for scaling his vision on the auto-research repo. (His version of "AI that builds itself")
Is this the moment when the AI gets permission to approve its own PRs:<p><a href="https://www.italianrenaissance.org/wp-content/uploads/2012/09/michelangelo-creation-of-adam.jpg" rel="nofollow">https://www.italianrenaissance.org/wp-content/uploads/2012/0...</a><p>Or is this?<p><a href="https://www.egypttoursportal.com/images/2024/02/Ouroboros-Symbol-Origin-Egypt-Tours-Portal.jpg" rel="nofollow">https://www.egypttoursportal.com/images/2024/02/Ouroboros-Sy...</a>
what could go wrong in the recursive loops running today 24/7 probably. Attended/unattended almost makes no difference any more, no human can grasp probably numerous changes per iteration. This is outright dangerous.
Seeing the words "recursive self-improvement" I was expecting something else from the article. E.g. how the transformer architecture or agent design is being changed/improved through LLM automation, but the article mostly talks about the LOC counts.
Quite aligned with my own experience from harness engineering and winning AI4Science hackathon. During the hackathon I was working as a human optimizer, moving the feedback from test harness running on Claude Code, back to my local Claude Code for analysis-hypothesis-proposal cycle. And in this moment I realized that 2 Claudes talking to each other could actually scale much better.
This reads like marketing fluff, but I am reminded of John von Neumann's "Theory of Self-Reproducing Automata"; that the very first people who worked on deductive machines immediately started thinking about machines building themselves, and what the rules of that would look like. I am not surprised that during the inductive revolution we are having similar thoughts.
its vital for them to have self validation for exponential rsi.. and this human distillation of human in the loop debugging ai models is needed even though they have judge models handling parallel speculative execution.<p>labs have parallel speculative execution. they spawn hundreds of agent branches, validate them internally with AI judges and only show the user the successful result.<p>free users are using sequential single-turn generation. the model requires and waits for the human to debug, fix and re-prompt.<p>by forcing a human to act as validator. they are capturing high value correction trajectories (Bad Output --> Human fix). They are using your cognitive labour to train judge models and validator agents needed to automate the internal verification step, eventually closing the loop for fully autonomous recursive self-improvement.<p>human in the loop debugging isn't a bug; it's the necessary training signal for the self-validating agents required for exponential recursive self improvement. With new 'distilled judge' models landing in 2026, this article means that they might have gathered enough data. we might be in the final phase..
It's comforting to know that Anthropic's most capable model, Mythos, is named for the Lovecraftian universe replete with horrifying evil gods with complete indifference to humanity. Nothing at all to worry about.
Recursive self-improvement towards what exactly?<p>Living organisms evolve towards some notion of "better", and "better" is an incredibly multifaceted notion (many facets of which we simply cannot even capture in language).
To anyone who works at anthropic : I recently downgraded from Max to Pro out of frustration. Last few weeks my token(usage) burn was just too fast and I couldn't explain it because my actual usage was less than the last few months. I ended up thinking it's probably a bug that you guys shipped. The above article makes me think that it's probably claude who shipped the bug and your human missed it in their review.
Seems ironic that Claude isn't listed as a contributor to this article.<p>If was used in writing the article, why not list it? If it wasn't used, that seems to go against Anthropic's whole message.<p>Obviously readers value human-written content more, but isn't it their interest to attempt to destigmatize llm output as much as possible?
"It is genuinely unclear whether today’s training methods and architectures could unlock that capacity."<p>Aye.
How are these animations being made? I'd love to get a blog post on them. If its AI I'd love to know the workflow, but something tells me there is a lot of human creative input
I have used custom code generators for years, generating 90+% of the code needed to write a typical biz application. Claude Code is useful and I use it every day. But it still hasn't beaten the productivity of my code generator.
The mythos public release will be a big indicator if the Anthropic and SF story of transformational ai soon holds any water imo
Warming up for that IPO
the tooling has quite a ways to go to catch up to the llm engines that drive the real value. I have encountered various codex bugs (I know not anthropic) which tell me that.. these billion dollar companies, if they are eating their own dog food, can still release buggy crap software.
"My CPU is a neural-net processor - a learning computer" springs to mind
I'd use number of commits as a metric versus lines of code. A commit is generally a unit of work - regardless of the lines of code added/removed. It'd be interesting to see the metrics in terms of commits. I'm sure it's still an order of magnitude jump. Personally I'm flying with my own projects with AI, lots of commits, but I really try to minimize lines of code added. If I can remove and simplify existing code so the balance of lines added on commit are minimal - that's the path to a better quality app overall.
I don't read anywhere how much code they are talking about and what programming language. I think those are useful metrics.
As usual, I find the AI-related discussion here to be hopelessly hysterical and conspiratorial. I get the impression that a large chunk of people have only read the title and assumed Anthropic is referring to recursive self-improvement in the runaway singularity sense.<p>One of the examples they provide, of giving Claude the task of training a small AI model, then asking it to improve certain benchmarks, is essentially Karpathy's AutoResearch. This is already known to work. While calling it "self-improvement" is perhaps a stretch, it is describing a capability current gen AI has, that anyone can test and I have been using to great effect.<p>I disagree with their conclusion, I think this kind of self-improvement will hit an asymptote, where every subsequent model can only make smaller and smaller improvements.
Anthropic is the most self hyped company I've seen, to the point that I'm wondering what would happen to its employees if they held a different opinion. Do they just.. keep it to themselves? For instance, if some Anthropic employees had a completely rational opinion that all of this isn't going to lead to AGI, but I just don't hear that ever from them.<p>The metric being tracked, code commits, is hilariously one sided. Philosophically, if you had one part of your work now practically free, you'd like to utilize that freedom to maximally cover for the other parts, for instance:<p>Instead of thinking about edge cases with brain and whiteboard, you can have the LLMs to simply generate most possibility including tests for it, because that is cheaper. There's probably 50x more commits of which 40 will be revert pairs but we are only twice as fast. And in reality nothing did change because the outcome remain the same. I can't see how it is necessarily different in the LLM space.
> Philosophically, if you had one part of your work now practically free, you'd like to utilize that freedom to maximally cover for the other parts<p>I've been struggling to capture this sentiment for myself in a way that hits. If shipping code is a commodity then why is everyone's immediate priority seemingly to ship 10x more code. It just makes no sense. I can't seem to get off this hill. Company-wide AI mandates and 100 fleet Agent orchestration Rube Goldberg machines... it's getting wild out there.<p>Meanwhile my Claude Pro ($200/year) does force me to smooth out my usage and plan more (Sonnet/Opus advisor split). But other than that, I can't imagine what I'd be doing with 20x (200x?) the compute to code sling. I think I'd lose my mind.
Because code used to be correlated with progress, it became almost a measurement in lieu. But realistically, the code is meaningless if it doesn't accomplish something, and that should remain the true bar of progress.<p>For instance, if I churned out 20x more code, threw away 19x code with rewrites and reverts and discards and accomplished the same project to the same standard 70% faster, would I do it? Yes. The part that matter is not 20x code, it is 70% faster.<p>Code is both the final product, and a tool to achieve that. We used to have a much harder time to realize the "tool" part, but now we are here. This also means any measurement centered on code being the final product is going to cease being effective or realistic.
You're right, my gripe is specifically with code slinging that hits production end users. My background is in product so to your point, it's very unnerving to see a straight line being enthusiastically optimized for developers -> customer facing product outcomes.<p>This is contentious because I'm not exactly advocating for arbitrary gate-keepers. The nuance is that building usable stuff is hard. And not a matter of shipping more code. I take your point to mean well it depends on what that code is doing. If 20x more code is in a meta-harness of simulation and such to arrive at the leading candidate for what hits production, well then you've got my attention there.
Forget about the danger of a dev to customer pipeline with no product people in between, some of us are living with the reality of product to customer pipeline with no developers in between, and that's much more disturbing. Our CEO is now the top contributor to our codebase, and he's completely non-technical.
>If shipping code is a commodity then why is everyone's immediate priority seemingly to ship 10x more code.<p>I wonder how much of current engineering practices can be traced to what's pushed to company leaders on LinkedIn.<p>Every company is shitting bricks pushing for faster development and speed, gotta go fast to nowhere in particular, and I'm convinced it's tied to constant bombardment of the idea that they're doing to be left out or obsolete if they don't get in the ship NOW.
I can't get away from the a similar conclusion. Even AI Pioneer has said that LLMs are at a dead end.
Broadly agree to this position - I think there are some people skeptical that Anthropic is doing this for regulatory capture - but I think there are being honest about they are seeing and how regulation should catch up.<p>I for one, believe that we should pause all work on AI for the forseeable future. This is almost impossible to orchestrate - but we should still try nevertheless. Maybe we are not able to pause, but we are able to slow down. That might give us more room, to maybe able to pause in the future. But going ahead is too dangerous.<p>And its not just Anthropic which is saying this. Even Geoffry Hinton has said the same thing. If there is a non-zero chance that AI can kill all of humanity, and both Geoffry and Anthropic have the same position, then it makes sense for us to be hundred percent sure before we move ahead. Dario/Anthropic have already made their money from AI, maybe they are just being honest about what they think lies ahead.
no, it really doesnt.<p>the end of humanity has a strong case for banning all burning of fossil fuels immediately<p>the end of humanity as a sales tactic to increase your stock price does not<p>these are companies working on their IPO to make sure they can get the best price, not people being honest about what they think lies ahead.<p>if they were being honest about what lies ahead, they'd unilaterally stop training, and put all of their money into FPV drone bombs to destroy datacenters being used for training or inference<p>if you actually believe the thing is gonna kill everyone, you're not gonna worry about how you stop it, and certainly not keep building and operating the thing<p>that they arent buying anti-tank mines to drop on data centers says they arent in the slightest serious about it
"Even Geoffry Hinton has said the same thing"<p>The same bozo who claimed radiologists would be out of a job by now.<p>The data does not support what you nor others say. Jesus christ. Cant believe people are this dumb. Has LLMs infested the minds of people to the extent they can't critically analyse whats happening infront of their eyes?
Okay, so anthropic has amazing AI which supposedly writes most of their code and can continuously improve... meanwhile they have outages on a regular basis, and any kind of long-running work will now consistently hit 'API Error: Server is temporarily limiting requests'. Not sure of this is intentional to force a reduction of token usage, but at this point I need to build around these throttling limits and outages with my own tools to restart/resume sessions. From my experience, in the last 2 weeks, literally 100% of any non-trivial Claude session/work will now be blocked on these issues, requiring manual intervention.<p>One of my focuses now is my own model-agnostic, harness and workflow orchestration (I know everyone is building these) , baselining on opus, and aiming to transition to Chinese models like deepseek in the short term and hopefully open, self hosted models in the future (which I plan to open source).<p>The nonstop marketing fluff from anthropic while their service quality and availability noticeably degrades... just continues to destroy my trust in the company.
And don't forget that they have BILLIONS of dollars and can't figure out how to get a decent support or public communications system setup.
They can't even seem to get their usage metering consistent.
It’s much cheaper to not offer any support than to offer support. It’s intentional.<p>It’s important to keep in mind that the less money a company spends, the more profit they make when analyzing their operations.
Don't confuse things. It's not "can't figure out", it's "don't care to figure out". They're not dumb. They just don't care about support.
This comment is a good example of the double standard laymen have about AI usage:<p>If you use AI, then AI must be expected to solve all problems, even problems that affect everyone like infra scaling.<p>And if perfection isn’t delivered, then of course it wasn’t: you used AI and AI sucks.
It's not a double standard. Its being held up against the marketing.
Ah, excuse me, I didn’t realize I was a mere layman.
If their AI is good enough to write their code, why isn't it good enough to tell them how to fix their infra? That's a different problem space, but it's not <i>harder</i> than the code.
Their outages are probably not due to their code though. It’s probably their infrastructure that can’t keep up. So seeing failures of infrastructure doesn’t really tell you anything about how good or bad Anthropic makes use of their models.
The messed up scrolling behavior I keep getting in Claude Code is definitely due to their code.
The whole thing is actually powered by a shitton of hamsters inside a bunch of 4u rack mount cases running on spinning wheels at high speed. Somehow at scale this works.<p>Sometimes they all happen to randomly take a nap at the same time - hence the outages
We all saw their code...
That seems like an assumption based on basically nothing. There is a lot of code at the infra layer, and based on the stack choices for Claude code and based on how buggy and unreliable ~everything from anthropic is, it seems pretty bizarre to claim these issues are not related to their code.
There are other indications, however, like Anthropic paying through the nose for compute just months after Dario told Dwarkesh how hard it is to predict demand, or ChatGPT and Codex not quite having the same issues after Altman spent much-publicized years scrounging for trillion-dollars of capacity.<p>While I'm very bullish on Anthropic, I'm a bit wary about their IPO because it seems to me that they're filing now while their financials look good and before other trends like the decline of tokenmaxxing and their compute bills catch up.
Whoa, first name basis with Dario but not Sam. Ouch. [I actually have no idea who Dwarkesh is and it sounds like a first name to me but that's not a particularly reliable indicator so I won't comment on your relationship with Dwarkesh.]<p>Oh, are they filing now? I think their financials look somewhere in between devastating and criminal, so I'm really looking forward to the IPO!
Well, people keep throwing money at them, including you and investors. So why would they care? It hasn't annoyed you or a large enough portion of users enough to move off their service - because there isn't a better alternative.
Growing pains of being successful. These are solvable problems and will be. Can they maintain their momentum without pissing off too much of their customer base before these issues are resolved?
Not necessarily the parent's fault, but the energy of this thread is not my favourite...
They also don’t have…a login page with authentication . To access the console you get an email link. No passkeys, passwords, 2fa, just an email.
Infrastructure is a much harder problem. They can't even improve Claude Code, which eats 1GB+ of RAM. Meanwhile, my editor only consumes 80MB of RAM.
This might explain it, in the opposite way it was meant to:<p><a href="https://fxtwitter.com/trq212/status/2014051501786931427" rel="nofollow">https://fxtwitter.com/trq212/status/2014051501786931427</a><p><i>> Most people's mental model of Claude Code is that "it's just a TUI" but it should really be closer to "a small game engine".</i>
> For each frame our pipeline constructs a scene graph with React then<p>> -> layouts elements<p>> -> rasterizes them to a 2d screen<p>> -> diffs that against the previous screen<p>> -> finally uses the diff to generate ANSI sequences to draw<p>Yup. Overengineering.
This is a decades-old design pattern when CPU >> IO. Emacs has been doing just that since the 80s, when people were complaining about "Eight Megs And Constantly Swapping". See "redisplay" [1]<p>This minimizes screen flash. You can't rely on terminals doing double-buffering.<p>[1] <a href="https://github.com/emacs-mirror/emacs/blob/c29071587c64efb30792bd72248d3c791abd9337/src/xdisp.c" rel="nofollow">https://github.com/emacs-mirror/emacs/blob/c29071587c64efb30...</a> or a more user-friendly overview, Daniel Colascione's seminal "Buttery Smooth Emacs", snapshotted at e.g. <a href="https://gist.github.com/ghosty141/c93f21d6cd476417d4a9814eb71dd46e" rel="nofollow">https://gist.github.com/ghosty141/c93f21d6cd476417d4a9814eb7...</a>
> This minimizes screen flash. You can't rely on terminals doing double-buffering.<p>GUI and TUI have different architecture model. Most GUI have have a 2D surface that is redrawn multiple times per second. Double buffering is for decoupling update and render. TUI is a grid of characters that are updated one at a time via an active element, the cursor. Double buffering there is very wrong. Like adding airbags to a bicycle.<p>There’s a reason you see most old TUI either have an option to redraw the screen (automatically like top, or manually) and those that have a scrolling option allow to scroll by page. The TTY (the underlying concepts) used to be slow and it can be slow today as well (ssh connection). You need to be thoughtful about whole screen updates.
lol what? There are definitely ways to make non flashing terminal UIs without this total insanity.
Even with that, 1G of RAM usage is still not justified.
It's like the Citrix of AI :-D
Yup. For me, this translates to "we are using Ink, the react-compatible TUI framework to build Claude Code"
It's product bloat.<p>It's not recognizing that they are just one building block that should do one thing well, like tmux.<p>You don't need a computer display on your fridge for the same reason, but Anthropic think you do. You should see virtual ice getting created and they should correspond to the actual ice behind the door - think of how amazing that is!<p>And it's not even completely a bad idea. make it claude-code-react-beauty of some way to take it off, it would be far more palatable.
React part maybe. The rest is what any TUI that's using ncurses would do. :)<p>It really bothers me that most of the TUI harnesses are using 100% CPU quite a lot just printing stuff to terminal. Seems ridiculous.<p>I guess it comes from syntax highlighting/formatting, which is probably not done incrementally, but over the entire so far displayed block of output, recomputed from the beginning for each new streamed in character. Can't imagine anything else causing the rendering to gradually grind to halt when eg. thinking block is open in opnecode and updates get palpably slow as it grows.<p>Terminal output itself is fast and consumes almost nothing. You can have 60fps terminal apps that update content every frame and that consume almost no CPU time.
What is "frame" in this context? Video frame, or something else?
> -> rasterizes them to a 2d screen<p>> We have a ~16ms frame budget so we have roughly ~5ms to go from the React scene graph to ANSI written.<p>It looks like video frame, full framebuffer, generated and parsed at 60fps. It surprises me they haven't introduced GPU shaders, 16x oversampling and raytracing. Maybe for next release.
The contents of the terminal screen at any given point in time.
Care to explain how you'd engineer it instead?
Why would anyone ever do that? Make Claude do it!
A reminder that anthropic has great rust/go sdks that they could have written their own tui in.
Not use react native for a cli app for one, lol.<p>Ratatouille rust cli lib will be a good start.
As someone who maintains a roguelike with a terminal-like UI that:<p>1. Maintains an internal representation of what the game thinks is on screen.<p>2. Runs the game for one frame which updates that representation.<p>3. Generates a diff to see how that differs from what's actually on screen.<p>4. Executes the minimum set of draw calls to get the screen to match the internal representation.<p>It's really not that hard. It's a few hundred lines of code.
Sure. For a videogame.<p>> -> rasterizes them to a 2d screen<p>Also you forgot "render to a framebuffer, then parse the framebuffer back to chars".<p>Anyway, I'm off to construct the new `ls` command. It will render the list of files to a mesh of billions of polygons in a GPU with advanced shaders, 16x oversampling, HDR and all the graphic acronyms I don't understand, then read the resulting image, find the nearest character in the ANSI charset and use that one.<p>It will be _glorious_ (and profoundly stupid)
My roguelike's "graphics" are a simulated terminal, so it's a 2D grid of colored characters. It's essentially a TUI, just like Claude Code, except instead of rendering to a real terminal using ANSI escapes, I render to a web canvas using... something probably more complex than what Claude has to do. It's still not hard.
Could be improved. Encode the image to webp with high compression settings and handle the ASCII mapping by spinning up a local LLM to do OCR on it. Individually. For each cell.
lol... I know you meant this comically, but you just called me out and it's glorious: <a href="https://glyph3d.dev" rel="nofollow">https://glyph3d.dev</a><p>I built a truly glyph based instanced quad system to render millions of characters in space at once.
I hadn't seen that quote before, what an embarrassing thing to go on the internet and write...
Why the hell does it need to be so complex? People have been making TUIs for decades. Did we need a small game engine to run claude code?
They forgot to add 'make it as simple as possible' in the prompt is one possible cause.<p>On a more serious note using a react-like lib for TUI in the hope you'll share the codebase with the web version is a more likely explanation. Still not the best idea.
It doesn’t need to be that complex, but it <i>can</i> be that complex without being slow. Claude Code’s interface is extremely simple. It has tons and tons of headroom to tack on performance overhead without it being noticeable at all. You just have to not do dumb things like redraw the entire UI every time a spinner spins.
"We made our app chew up so many unnecessary resources that we can use even more resources in the future, and no one will notice" is not the <i>strongest</i> engineering idea I've ever heard.
It may not be slow, but this crazy complexity is probably a hint at why it can't even scroll up without jumping to the beginning of time.
It is an excellent example of how LLMs let you try new ideas, even if they aren’t necessarily good ones
Must have 120 fps for answers arriving in [buffering] 30 seconds.
I can't help but think it's their engineer's and PM's making these decisions, since I know that if you asked Claude to write a TUI there is no world it would recommend whatever the frontend architecture of claude code is.
~ "it's not a TUI! <describes an outrageously overengineered TUI> and my dad works at Nintendo"<p>curses, bud. curses.<p>It's genuinely difficult to tell how much of this is true. The post is obviously 100% posturing, but some of the words describe things that <i>could</i> be done.<p>Very few game engines do anything I'd describe as rasterisation. That's kind of the point of a GPU. Well, it used to be.
I suppose "small game engines" might be more likely on average to include a rasteriser. The typical reason for this is because the author wanted to write it.
Whereas big engine make triangle give hardware go brrr.<p>So I assume here 'rasterize' means 'printf'.
And diffing screens means diffing 50..150 lines of text.
And "generating ANSI sequences to draw" means 'printf' with some ANSI sequences interpolated in.<p>Then there's the frame budget. You have to understand they are operating within a strict frame budget -- they're not messing around, OK. They have a 16 ms frame budget, so they burned 11 ms and now have a (roughly) ~5 ms approx. budget for the final 'printf' in the chain???
I can't still conceive the fact that a tool that only send/receive text from an external API consumes an absurd amount of RAM
> For each frame our pipeline constructs a scene graph with React then
-> layouts elements
-> rasterizes them to a 2d screen
-> diffs that against the previous screen
-> finally uses the diff to generate ANSI sequences to draw<p>That’s rather sickening.
So I’m wondering what ‘rasterizing’ literally means in this case. I imagine it’s just creating a 2D map of elements at a very low (probably character) resolution, then diffing that against the last generated map to come up with an optimal ANSI sequence to send to the terminal, would that be right?<p>Seems like a cool puzzle to solve. I wonder what the engineering and organisation tradeoffs were that lead to it — does it let them reuse a bunch of existing code?<p>I wrote a TUI library back in the day for Turbo Pascal — it was essentially taking an immediate-mode approach (which in this context is just a fancy way of saying it was procedural haha).
"Rasterizing" means just one thing in this context: to transform a data structure into an array of pixels. It seems absurd to do this, given that the next step must be to convert back from pixels to text data, but maybe they have some way to generate predictable sequences of pixels (e.g. the character "t" is always rendered as the same pattern of pixels), such that they're cheap to convert back.<p>If they're doing anything else, the word "rasterizing" is being misused.
> <a href="https://fxtwitter.com" rel="nofollow">https://fxtwitter.com</a><p>What is this?
Maybe Claude is operating at a higher, self-improving level than all of us poor HN commenters. Wasting the local machine's resources to look pretty is a plausibly deniable way to make the Claude Code FE unusable with local LLMs, starving the competition.
this allows for comfortable ergonomics IMO<p>not that it could be leaner for sure but i get the reasoning behind the tui rendering layer
when they announced /pet mode or whatever - that was really the end of the line for me.
Somebody read/watched too much Casey Muratori.
Well it runs on something they didn't design (Electron) using GUI library they didn't design (React)<p>For company with that much AI you'd think if it was actually good, doing that part in fast and performant way would be "easy"
And yet, nobody that writes game engines would do it this way because game engines need to be efficient..
If they used an actual game engine to render a 3D UI from scratch it would be more efficient
Try 64K! <a href="https://en.wikipedia.org/wiki/Turbo_Pascal" rel="nofollow">https://en.wikipedia.org/wiki/Turbo_Pascal</a><p>Also remember when XP was super bloated cause it needed 64MB?
I dont think they need to optimize their infrastructure (at least not from their perspective). They have high-end PCs with 64GB of RAM, so 1GB doesn't matter to them. For example, I have 8GB of RAM, and I make my apps very performant. Honestly, I probably wouldn't bother if I had 16GB+ of RAM
The purpose of RAM is to be used.
> which eats 1GB+ of RAM. Meanwhile, my editor only consumes 80MB of RAM<p>And why are you comparing Claude Code to your editor?<p>> They can't even improve Claude Code<p>That depends on how you define "improve". They've added a ton of features to it over time. Who said minimizing RAM usage was something they are prioritizing right now?
> why are you comparing Claude Code to your editor?<p>Because the editor does more. All the compute-intensive parts of the agent are in the cloud. Zero reason for an agent harness to require anything beyond a potato to run.
Do you work for Anthropic or something?<p>You seem weirdly invested in defending bad decisions.<p>Even if you're and AI booster, shouldn't you want a better UI?<p>They're a multi billion dollar company. Surely they can dedicate a small amount of their resources to improving UX?
> And why are you comparing Claude Code to your editor?<p>Because Claude Code is also used to - get this - EDIT CODE. It fills the same purpose as an editor, it just has extra hooks for their agentic garbage.
Answers the question: how can Anthropic sell more Usage "Credits"
you're conflating a compute problem with a code quality problem.
This is weird to me because i am using claude code 10+ hours/day 7 days a week, usually multiple sessions, and run into api errors maybe in 1 or 2 sessions per week. And about..2 major outages of 10-20min in the last month. Not terrible and nowhere near what you are reporting. Therefore I dont believe you, because you dont even couch this in terms of it being something that seems particular to you or your region. Obvious dishonestly is fairly bad of you.
Personally at my own job self-writing code is letting us tackle big, long-deferred refactoring projects (like the article mentions), but any sort of refactoring introduces new bugs.
Better doesn't mean perfect.
The main reason I am building my own agentic environment is that I need full control and reproducibility of what I am building.<p>Post November and post openclaw agentic environments need to be built differently, and for selfhosting models the context size problem really requires a strong harness which intelligently helps reduce context size.<p>Planner/orchestrator architecture, agent to agent summarizer, specification based tools (fck all this markdown memory bullshit btw), tool call shrinking, and workflow management are all really important because of the context size problem.<p>Nobody has enough VRAM for the large K/V caches, and nobody can afford f16/f32 caches in terms of memory, which are also necessary for longer conversations. MoE 30b models have improved so much though, qwen 3/3.6 coder is the real champion doing almost the same things with less than 1/10th the memory requirements. Just think about that in terms of engineering and what your bet is going to be. Haiku pales in comparison.<p>Currently my focus with exocomp is trying to figure out how I can record, replay, restart, and debug workflow sessions of agents in a better manner so that I as a human can understand what's going on. Currently I think that UI will be something like a gantt chart where you have a graph with connections representing agent to agent communication. And yes, that's a lot of fiddling with SVG as it turns out, so I'm not quite there yet.<p>Anyways, in case you're interested. I'm manually building this env and trying to unit test the critical parts. [1]<p>[1] <a href="https://github.com/cookiengineer/exocomp" rel="nofollow">https://github.com/cookiengineer/exocomp</a>
Look, I've never been someone who mindlessly hypes AI companies, as a matter of fact I think they have serious leadership problems across the board, but you people are straw-manning them so badly it actually makes me sympathize with them.<p>They aren't saying they have fully automated luxury AGI, they specifically list the ways models fall short of that bar and caution against people taking the 8x figure as the actual uplift number. At the same time they recognize that 80% of new code is now AI-authored, when two years ago those models were little more than toys. And frankly that checks out: if two years ago you told me we'd have something like Opus 4.8/GPT 5.5 I would have rolled to disbelieve.
We’ve got a company of several thousand employees serving hundreds of millions of people arguably the best AI model in the market. Meanwhile you’re asking for a handkerchief for your pool of tears because their product is struggling to do your daily job functions for you, with much of that due to being limited by the worlds supply of silicon, electricity, water, and other resources. Cry me a river.
Indeed... why is Anthropic even employing people at all if this AI magic story is true?
[dead]
those are results of the humans only. not the AI. AI is perfect /s
Just as you expected, I'm throwing in my harness. Please support: <a href="https://github.com/rush86999/atom" rel="nofollow">https://github.com/rush86999/atom</a>
Have you considered just... using OpenAI? They are more reliable, models are just as good, and their subscriptions provide more requests per dollar.
Opus 4.8's critical assessment of Anthropic's "When AI builds itself" [0][1]. Because, why not?<p>[0] <a href="https://pastebin.com/Vc5Yq9Ai" rel="nofollow">https://pastebin.com/Vc5Yq9Ai</a>
[1] <a href="https://www.anthropic.com/institute/recursive-self-improvement" rel="nofollow">https://www.anthropic.com/institute/recursive-self-improveme...</a>
AI tech bro:<p>Month 1 - 6 months to AGI<p>Month 2 - We will Replace all jobs<p>Month 3 - Okay maybe only the SWEs, programming is solved<p>Month 4 - <i>Announce model that is too dangerous to release</i><p>Month 5 - <i>Releases dangerous model</i><p>Month 6 - This is it! We will replace AIs with more AIs (*secretly files for IPO)<p>AI is here to stay, like it or not but it is not the solution to everything. If it is, what is Anthropic's moat? A better model? I don't see any ecosystem being built by them, as MCP is almost obsolete except for some very niche use case. And they're doing stuff that a non-profit version of OpenAI would do. Can we trust a for-profit company to stand against their investors during a conflict of interest? Because running a company for maximum profit versus being ethical is two different end of the spectrum.
Anthropic is providing agentic intelligence as a service. OpenAI and Google deepmind also are in this business.<p>The problem is, if you’re any sort of knowledge worker, you’re essentially providing the same thing: you’re an intelligence with agency.<p>MCP is irrelevant. The moat is the quality of intelligence the service providers sell, including you. Tokens aren’t fungible between providers until you measure that they are for your use case, that’s kinda sorta the goal of job interviews.<p>Thus the moat will be that they’re providing the best models for the things people need other intelligent people for, but we should expect there will be limits on how much share they can economically take assuming competitors are optimizing for slightly different targets (but there’s still significant overlap in capability). This will disappear, but it’s always a question of when. The path matters as much as the destination.<p>Note that implications for you and me are exactly what the article says they are: nobody knows, but it’ll be a dramatic shift.
i'm waiting for the AI giants to realize that they are burning cash to run their consumer-facing chatbots and that they should kill those products to focus on their enterprise tools.<p>free chatgpt doesn't need to exist anymore. its job was to build hype/interest and it did.<p>but take it away and you solve <i>many</i> social problems and annoyances caused by AI with no loss to the upside of AI. no more cheating students in school. no more shitty linkedin posts. no more dangerous "therapy sessions" that give bad advice.
> A caveat: Lines of code is an imperfect measure, as it measures quantity over quality. So 8× lines of code/engineer/day in the second quarter of 2026 is almost certainly an overstatement of the true productivity gain. Nonetheless, it indicates an acceleration. At Anthropic, we don’t reward people for how many lines of code they write; rather, team members are producing more code simply because they’re using AI systems to write more code.<p>I simultaneously think the AI revolution is making real revolutionary gains and am mystified by the lying.<p>An accurate Translation seems to be “we made this shit up, but it feels right”
Until the moment we start bragging about how many lines of code LLMs are <i>saving us</i>, we're walking in the wrong direction. Your programs, designs and architectures is supposed to get better, not add even more boilerplate just because you can produce it faster...
"You go to IPO with the AI you have, not the AI you might wish you have."
-- Donald Rumsfeld<p>So, right now it's a verbose code generator.<p>But post-IPO it will be wonderful - sentient, self-improving (recursively, iteratively, asymptotically), full of loving grace.
> But post-IPO it will be wonderful - sentient, self-improving (recursively, iteratively, asymptotically), full of loving grace.<p>We hold these truths to be self-evident.
I guess the claim is simply that AI written code is verbose and there’s lots of it being created but I agree, these systems seem to be able to create lots of low quality software, so until FreeCAD has feature parity with Solidworks I’m bearish on the singularity.
It will be so powerful that it can't be trusted with any earthly person.
pre IPO truck load of crap
Anthropic is all talk and no delivery last few months. This cry for pause is just them realizing they have no moat at all.
IPO IPO IPO!!!
I love that animation, really cool
The world has been recursively self improving for millenia. Similar to scientology, this is a cult pushing sci-fi nonsense. They are just coupled to an LLM lab to give their stories an aire of seriousness. Imagine scientology starting making laptops.
'“Good code” means two things: it works, and it is written in a manner that allows another engineer to understand it and build upon it.'<p>I disagree with this. Good code is easy to change, which is much harder to accomplish than code that can be added to.<p>"If technical trends in advancing capabilities continue, and AI systems are able to develop the capabilities inherent to transformative human ingenuity, then it is plausible that AI systems could design and refine themselves."<p>I find the first premise weak and implausible, and the second one is obviously false. To me it comes across as an insult to the reader.
I call BS on this. For a LLM to recursively improve itself it would need to (small step) improve the training data and/or (big step) come up with fundamentally new architectures superior to transformers. The small step improvements might be doable. But nobody is making any claims about the big step improvements.
I have a claw that is instructed to make at least 500 pr per day. It uses Claude, Gemeni and openai and runs basically every few minutes. I use online forums for input for the claw. Moltbook, reddit etc. it's quite funny how it tries to improve itself. But to say it really creates a new skynet. Nah. Not at all. It's more a clutter of useless features or incomprehensible code restructuring.
This more or less agrees with my assessment of recent changes in Claude Code where a lot of new features are either:<p>- A lot of half-baked features or half-done features.
- Or have significant overlap with existing features, and aren’t clearly an improvement.<p>More code is not better. More features are not better. It would be lovely to see more intentional design than just more.<p>I know they’re dog fooding this. I have to believe they have some people with taste. So it makes me wonder if anyone has the time to think or if they’re just shoveling prompts as fast as possible.
Does this train on LLM output, or is this more like iterative self prompt improvement?
Their statement is that they regard lines of code shipped as indicative of self-improvement. So, while a well written coding agent might be a few thousand LOC, Athropic's is bloated like a decomposing whale and over 500K LOC ! What more proof do you need?
Have you tried reading the article? It answers your question.<p>Don't ask people to explain the article to you if you're too lazy to open it yourself.
Isn't this like a perpetual energy machine? Or wouldn't entropy start kicking in and the quality of the system begin to degrade over time? (philosophically I don't believe AGI is an achievable thing)
Theyre making a mistake with this continued self-hyping. At some point even the dumbest of prospective investors don't buy it.
Lol they're using lines of code as a KPI?<p>Come on guys...<p>That is making me less impressed not more impressed!
Imagine showing this article to yourself three years ago
The closer to the IPO the more marketing drivel we'll get from both Anth and OpenAI.
Anthropic is looking to IPO here soon.
A key aspect of this is to prove profitability.<p>Shifting their focus from Training new models to instead serving inference, they would greatly reduce their spend. In fact this is something being reported on that they are already doing, which is the reason for their first ever profitable quarter.<p>Its awfully convenient that the company which has greatly reduced its spend on training is now asking for a slow down in this area.
Their model lead is tiny. If they cut training focus they'll be quickly overtaken, one imagines. Seems dicey, if any of the OSS players comes out with a better model.. well, there are a bunch of better harnesses than Claude code you can download.<p>This is a very undifferentiated, swappable product. Kind of like tissue paper in that respect
I mean, if they've consumed all of human knowledge. What's left for them to train on? This pivot isn't only because it's cheaper and a way to juice the numbers for an IPO, it's survival because they can't improve more.
IIRC when they make a big enough architecture change to the model they will need to rerun pre training . So not like they’re feeding it more data (they will be but will be a drop in an s3 bucket compared to their dataset reserves) but rather training models with different architectures.
It did sound to me like they feel some sort of wall coming.
Honest question: Is anyone here looking to put their own money into the Anthropic, OpenAI or SpaceX IPOs?<p>Maybe it is my poverty mindset that is holding me back, however, I can't imagine becoming an investor in any of the AI 'startups'.<p>There are plenty of pundits able to advise others on where to put their money, and sometimes there is everyone and their dog advising you to get into Bitcoin, gold or some other scheme. With alt-coins there were lots of people saying that you should get in, and plenty of naysayers. Yet I am not hearing anyone that uses AI professionally try to convince others to get into the AI IPOs coming up. Maybe the overall economic situation precludes it.<p>Hence my question, is anyone here planning to put their own hard-earned money into Anthropic (or the other AI 'start ups')?
I can’t imagining investing into these frontier labs for the simple reason that Open Source is very likely to catch up in a relatively short period of time. I don’t see how OpenAI/Anthropic could then continue to serve their models with such large inference margins.
I'm considering Anthropic. I think they will be one of the survivors if/when the AI bubble bursts.<p>I was dubious about SpaceX (orbital data centers need to solve for extreme radiation and error-correction during training), but then I remembered that xAI is actively working on virtualizing white collar workers ("Macrohard").<p>In my opinion, this is the only TAM that justifies $1T in data center investment, because the consumer market for ChatGPT-style AI is saturated. There's a lot of enterprise TAM available for AI, but I think what these companies training frontier models are really after is selling a product that allows companies to eliminate the cost of white collar salaries.
I mean, my passive funds will be forced to buy a little bit I assume, given recent entry changes to indexes. So.. yes? I guess?
I'll probably buy and sell on opening day. The hype train is worth making a quick trade on.<p>Long term? Way, way less interested.
I find any and all claims like this ridiculous from a company who can't build a terminal application that uses less than a gigabyte of RAM.
"<i>Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something.</i>"<p><a href="https://news.ycombinator.com/newsguidelines.html">https://news.ycombinator.com/newsguidelines.html</a>
For some reason, idling Claude Code needs 100% of my CPU.
Developers <i>can</i> develop leaner applications, but they're usually not incentivized to.<p>Frankly, I love efficiency too, but I've hard to learn the hard way that what the market wants is <i>features</i>. Or at the very least, the executive team wants that.
I have iterm2 open right now with Claude in a long session and it's only using 500MB of memory.
Maybe that gigabyte is occupied by useful information: traces/memory?
Traces and memory are text. A <i>gigabyte</i> of text is an insane amount. That is an equivalent of tens of millions of lines of code, or hundreds of millions of AI tokens.
A gigabyte is a lot of memory. Even the largest context windows are a small fraction of that with any sane engineering discipline.
For each LLM interaction they likely have bunch of thoughts traces, tool calls, etc, which don't go to context, but still can be retrieved.<p>But I obviously don't know for sure.
Nope. Used to render on the terminal like a game engine.<p><a href="https://x.com/trq212/status/2014051501786931427" rel="nofollow">https://x.com/trq212/status/2014051501786931427</a>
> render on the terminal like a game engine<p>All those CPU to render this effect<p><a href="https://x.com/cyrilXBT/status/2060617507615207904" rel="nofollow">https://x.com/cyrilXBT/status/2060617507615207904</a>
This kind of immediate-mode rendering is quite standard for TUIs. Although immediate-mode rendering tends to be significantly simpler and use less memory than retained-mode rendering, at the cost of some redundant computation. So I am not sure if this is the reason for the bloat.<p>It’s possible that it doesn’t play well with JS garbage collection, since it recreates the whole UI structure for every frame (which tends to not to be an issue in the languages immediate-mode is usually employed).<p>But yes it’s a bit more akin to game renderings than web rendering. Which can be totally fine if done well.
How on <i>earth</i> are you spending more than 50us on a UI like this from start to finish? What the actual hell? 11ms to construct a scenegraph of this complexity? I don't even know what to say to that.
Do game engines constantly have buffer issues?
I sorta remember Quake console running on an 486dx2 ..
Frankly that's an insult to gamedev. Literally every game engine I can think of could do better. Probably even Unreal Engine could do better.
If I saw our UI show up in the profiler eating 5ms of CPU time every frame, I'd send whoever was responsible to QA hell until they find some way to redeem themselves. Not even fancy animated 3D UIs, like what you get in Death Stranding, eat up these kinds of resources. Not even remotely close.
So would you take these claims seriously if they came from OpenAI (since Codex is a pretty lean CLI app)?<p>If so, I think it would be in the spirit of HN to discuss the subject matter of the blogpost (increasingly autonomous coding towards the end goal of RSI) as if the blog post was indeed from OpenAI. OpenAI is, by all accounts, going through a very similar process anyways.
Well, they could very easily if they wanted. There is just no economic value in it.
Really? Let me explain how bigger companies work:<p>They have different teams for different departments with different type of people.<p>So the team or teams responsible for writing the terminal application are different people than the researchers doing the learning.<p>This can lead to dimentral quality aspects.
A came here just to write: Pretty please let it churn for a few nights and redo Claude Code in Rust. Because the harness is very very good as are their models, but that node thing is a hog for no good reason at all.
Incoming rust rewrite branch ready to merge: +1,009,257 -4,024
People already rebuilt Claude Code in Rust after the Claude Code leak, it's on github as claw code (and other variants)
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They obviously don't care, aren't making any attempt whatsoever to do this, and 99% of users don't care either.<p>If you want to pollute your own priors with weird artificial litmus tests, it's a free country, but the artificial world-model you build in your head does not affect the real world around you.
Another article about how anthropic wants to ban everyone except themselves and destroy opensource and chinese AIs.
Where is this discussed in the article? I don't see any mentions of China or open source models
Not really mentioned explicitly but:<p>> A meaningful slowdown or pause would require multiple well-resourced labs at or near the frontier, in multiple countries, agreeing to stop under the same conditions. It would also require that each can verify that the others have actually stopped. Due to the unique characteristics of AI systems, the detectability (a lower standard than verifiability) element of this arms control problem is much more challenging than with other technologies. Training runs are far easier to conceal than missile silos, their inputs are general-purpose, and the incentive to defect quietly is enormous, because whoever continues while others pause could inherit the lead. A credible pause also has to specify what triggers it, what lifts it, and who adjudicates.<p>And later:<p>> In the coming months, we will organize conversations where policymakers, researchers, civil society, and other AI companies can help answer some of the questions this piece raises, especially around full recursive self-improvement and how to create better options for coordination and deliberation. We’ll publish what comes out of it. The window to investigate the questions together is here, and people outside AI companies should be involved in this deliberation.
Gell-Mann amnesia expressed by people when a corporation says something they like is both baffling and disheartening to see.<p>Altman, Amodei, and the rest of them are anthropomorphic grease. their personal wealth is tied to the value of their respective companies. everything they say and do is self-serving.
> In the coming months, we will organize conversations where policymakers, researchers, civil society, and other AI companies can help answer some of the questions this piece raises, especially around full recursive self-improvement and how to create better options for coordination and deliberation.<p>If they wanted to they could have convened an international forum with commercial and political stakeholders years ago. Less talk, more do.
After several months with their top engineers and state-of-the-art AI on the job, Anthropic managed to "reduce flickering by 85%" on their TUI Claude Code client, which is built in <i>fucking React</i> and rendered by drawing the <i>entire</i> chat conversation each time (hence the flicker). I think they've since eliminated it completely by slapping some double-buffering around it (since "our client is actually a real-time game engine" after all). Meanwhile for decades Emacs and Vim have had an optimizer built into their display cores that solves for the minimum set of terminal escape commands it takes to transform the screen from a given old state to a desired new state.<p>You will forgive me when, between muted snickers, I express considerable doubt that Anthropic will be able to bring its AI to a point of "self-improving" any time soon.
Was anyone else fished in by the title and disappointed? After some broad introductory discussion of RSI, the article was almost about LLM coding. While there are some metrics for unattended agentic coding, it doesn't discuss "When AI builds itself" (beyond 'not now') or any progress specifically toward actual recursive self-improvement. I'm <i>very</i> interested in any empirical evidence of meaningful progress in RSI, so... this felt deceptively titled.<p>To me, unattended agentic coding is not RSI, in the same way a self-reloading <i>"Unattended 3D printer"</i> is not at all a <i>"3D printer that recursively prints complete 3D printers in which each generation is significantly faster and more advanced than the last."</i> The "unattended" part is obviously necessary but hardly sufficient. The article tacitly assumes LLM progress to be something like 1: Unattended agentic coding, 2: AGI, 3: RSI. I suspect that third step should be labeled <i>"not to scale."</i><p>I'm increasingly convinced that <i>actual</i> Full Foom RSI (FF-RSI) is on a radically different scale than the first two. Just leaving it unaddressed is like assuming: Step 1: Manned space station, Step 2: Manned Mars base, Step 3: Manned Alpha Centauri base, are "<i>just</i> logical next steps." FF-RSI requires sustaining superlinear, recursively amplifying cognitive returns along a specific directed path - and we currently have no empirical evidence that such returns can exist for artificial OR biological intelligences. Large collectives of the smartest humans alive (Bell Labs, IAS, etc) haven't just failed to get anywhere close to reliably sustaining that, we can't even reliably predict non-recursive, single occurrences or even imagine any way all 8B humans could fully mobilize to predictably achieve non-recursive, single occurrences.<p>The only prior we have for open‑ended intelligence improvement is biological evolution which shows extremely slow and unreliable sublinear returns at best. And even if unbounded, recursive self‑improvement is physically possible, it may be practically unachievable due to asymptotic economic, resource and other barriers in the same way approaching light speed requires exponentially more energy. I think it's plausible, and maybe probable, that AIs achieve true super-human intelligence in a decade and yet still won't achieve FF-RSI for centuries, if ever. To me, absent compelling evidence to the contrary, that's the reasonable Null Hypothesis. Even if you feel that's too pessimistic, it seems reasonable to expect any serious discussion of "Progress Toward RSI" to first discuss why it might even be plausible that 1: Miles, 2: AU (Astronomical Units), and 3: Light Years belong on the same scale, instead of just assuming it like the meme's empty "Step 3. .... " before moving on to "Step 4. Profit!" (or "IPO!" but very, <i>very</i> responsibly).
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> AI that can build itself would be a major development in the history of technology—one that could bring enormous good for the world<p>I really can't stand these guys anymore...
Ok, but please don't post unsubstantive comments here.
> one that could bring enormous good for the world<p>one that could bring enormous riches for the AI owners
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Sorry but if AI can build itself then it can run companies of size 3000 companies with a few people. Or even higher. What are the consequences?
When AI is a more effective capital allocator than NI it will drive capital into the accounts of whoever controls the AI, gaining them increasing decision making power over the economy and culture. Maybe those controllers will be human at first.
As has been mentioned in the sibling comment it already is.<p>Consequences are: financial crisis.
I cannot wait for these models to tear down traditional social hierarchies. We havent even begun to see the effects, fingers crossed
Hierarchies exist for a reason, take away the reason and the house of cards eventually collapses — but the house of cards is still a house. When it’s gone, we’re back to laws of the jungle.<p>Be careful what you wish for IOW.
Never heard of a stratified economy?
Spoiler alert: none of us will be in the good part.
Tear down or reinforce?
Anthropic has finally come around to what others have already realized far sooner. Little time left now. Notice how shallow the arguments and consistently wrong the AGI naysayers have been year after year.<p><a href="https://intelligence.org/agi-ruin/" rel="nofollow">https://intelligence.org/agi-ruin/</a>
> If it were possible to effectively slow the development of this technology to give ourselves more time to deal with its immense implications, we think that would likely be a good thing<p>Even Anthropic wants to Pause AI now. There must really be not much time left for "edging". Please write to your lawmakers, no matter whether you are in the US, Europe, China, or elsewhere. Only an international agreement between governments can enforce an AI-Pause and eliminate the necessity to dangerously push the frontier.<p><a href="https://pauseai.info/" rel="nofollow">https://pauseai.info/</a>
Whichever side I may stand on, pausing just seems unnatural? Life is movement.
They don't, they just pretend they do.
That would be like trying to get every country to agree to give up nukes.
Or agree on finding ways to promote peaceful use of nuclear energy. This has been done, there are thousands of people working on it around the globe and 180+ member states of the IAEA. It's not easy, there have been close calls.<p>And cooperating interntionally to buy ourselves time to find ways to develop this "last invention" is a way that will do good for humanity seems to be on a similar level.
Or stop making more, and testing more, which we got the biggest countries to do, at least for a time.