I’ve had a chance to use fable 5.2. Some observations:
- Anthropic is finally well balanced across models. Meaning: Opus 5.5 is so good that I do not feel Fable FOMO as I did when it was Fable 5/5.1 vs Opus 4.8/5. And Sonnet 5.5 sometimes makes me forget about Opus.
- Fable is good, but you really have to push the ambition of your prompt in order to see proper separation from Opus 5.5. I personally have a hard time telling the difference between Fable 5.2 and Opus 5.5 for ordinary coding tasks.
- It’s way faster and more reliable: one of the worst things about fable 5.1 was how slow it was an, on top of that, how frequently it would suffer from max output token overflows. I got none of these issues with 5.2.
- Fable is exceptional at drawing. It’s ability to render an image with JavaScript actually has me questioning the value of dedicated image models. I prefer the images I’ve gotten fable/opus to make for me over those from gpt-image, it’s wild to think we’re at this point.
> Fable is good, but you really have to push the ambition of your prompt in order to see proper separation from Opus 5.5.
I love seeing this verbalized. We are pretty much at the point that increased model capability will only have utility to folks that are willing to let go of even more understanding of whatever the model is producing.
The best models can/will produce a year's worth of output that would take several days to weeks to properly understand in the span of an afternoon.
For everyone that still wants to feel like they own the end result, just use the lesser models with less ambitious tasks.
Caveat: I think this mainly applies to coding. I would still like the best models possible to be working on e.g. new drug research to cure our various ailments.
> This is not a claim about the future. Perhaps almost all or perhaps even all of the software skills I have will become irrelevant in the near future. But we're not there yet. At the moment, programming skills seem higher leverage than ever before.
> There's something a bit funny about the current moment, where it seems like having the skills that models are replacing makes one much more effective than people who are trying to use models without having those skills. I like Max Bittker's framing that models are very good at counterfeiting and the level of expertise it takes to distinguish a counterfeit from the real thing has been increasing as models improve.
As well as this section fairly at the start:
> As far as I can tell, these problems are problems because people are implicitly carrying around a pre-agentic cost model. Ten years ago, the kind of person who implemented a new compiler because their compiler was slow was the kind of person who started programming a new 3d game engine when they wanted to make an indie game, i.e., the kind of person who never ships anything. Most productive programmers I know still have an aversion to this kind of thing, even though it is now actually reasonable to implement this kind of thing now.
Of course, at the moment everything’s still a bit very much difficult to tell, but “productivity”, at least in applicable areas of work, might really come down to who can let go of the most understanding the easiest? Incredibly thought-provoking stuff.
> This paper suggests that the increased interest in human factors among engineers reflects the irony that the more advanced a control system is, so the more crucial may be the contribution of the human operator
Does it? Couldn’t you steelman this argument and claim the opposite, that the more advanced a control system, the more likely it is that human error is what causes its malfunction?
Those don't seem like mutually exclusive statements to me. In fact, they feel like two sides of the same coin. It's almost by definition that more advanced control systems are exactly the ones where tractable sources of error (mechanical, environmental, etc) have been eliminated, controlled or otherwise accounted for as much as possible, to the point that the only remaining likely problems come from where the system interfaces with humans in some way.
Which makes those interfaces the most critical parts of the system.
Then why did we ever invent something higher-level than Assembly, if it all compiles the same? Has every programmer in the last 50 years been affected by a skill issue?
>> Ten years ago, the kind of person who implemented a new compiler because their compiler was slow was the kind of person who started programming a new 3d game engine when they wanted to make an indie game, i.e., the kind of person who never ships anything. Most productive programmers I know still have an aversion to this kind of thing, even though it is now actually reasonable to implement this kind of thing now.
Then there is these other programmers who built their own operating systems even though their OS professors are claiming publicly their design sucks, who built a local database from scratch in C because his use case did not need the client/server stuff but just all the other relational goodies, or who built video streaming/transcoding software from scratch just because they have ideas and then the world changes around their ambitions.
I think it is not enough to be ambitious or have the technical ability if you want to build something for others to use. You definitely need deep commitment but also perseverance in the face of social adversity. I think this is what saddens people with automation of the technical aspects of programming. It leaves the more psychological aspects as challenges which are not easy to face all the time.
> It leaves the more psychological aspects as challenges which are not easy to face all the time.
Great point. Coding used to be a natural forcing function for prioritization of features. When coding is basically “free” you have to do the harder job of saying no to things, deciding exactly what to build, etc…
> who built their own operating systems even though their OS professors are claiming publicly their design sucks, who built a local database from scratch in C because his use case did not need the client/server stuff but just all the other relational goodies, or who built video streaming/transcoding software from scratch just because they have ideas and then the world changes around their ambitions
> perseverance in the face of social adversity
Sometimes you're Fabrice Bellard, sometimes you're Terry Davis. That is, for almost any idea you'll find random detractors, but some ideas really are generally useful and some are completely nuts. One of the negative externalities of AI is that bystanders are confronted with a lot more cases where they have to work out which one this thing is.
I really wonder if in our new world there will be more Fabrices or Terrys. How will people ever learn these highly technical niche skills with LLMs.
I can certainly say every skill I have is because I sat there for hours grinding low level tasks to get something new. Will people still have that motivation?? We might be entering the tech dark ages
What about the kind of person who invented a new operating system because they couldn't have Unix for their personal machine? Maybe even named it after themselves?
Oops, that is my bad: "Different universities: Linus Torvalds studied at the University of Helsinki in Finland, while Andrew Tanenbaum is a computer science professor at the Vrije Universiteit Amsterdam in the Netherlands."
Thanks for correcting that. I definitely hallucinated that one. I must have misread part of the discussion and made the inference myself. Good to be corrected :)
I found the source of my confusion, I read Tanenbaum's comment: "Writing a new OS only for the 386 in 1991 gets you your second 'F' for this term. But if you do real well on the final exam, you can still pass the course Haha :)" and took it literally and mistakenly inferred the relation, but he was just joking.
> but “productivity”, at least in applicable areas of work, might really come down to who can let go of the most understanding the easiest?
That is, genuinely, crazy talk. That is what addicts would say.
But it is intellectual-washed to sound like the most reasonable thing to say.
It is not. And there is nothing thought-provoking about the mere act of doing heroin.
There might be something in the system and mechanics that lead to people getting the idea to do that, though.
___
We can engage with these ideas and there might be some merit to them, but not with this frame. This frame will (not hyperbole) kill us all.
People will die due to (poorly-engineered) systems understood by no one, failing in completely incoherent and unexpected ways - and those people may be you or your loved ones.
It's crazy to me how easily people want to go back to the dark ages. These ideas of letting go of understanding are glazing pre-enlightenment thinking. The kind of thinking of someone that delights in being a peasant that can be led by the nose anywhere by their lord.
This is a pretty damning take and I can’t really think of a way to argue against it. So first off, thank you for that perspective!
I’ll probably have to think about it some more, but a) I think I’m inclined to take my statement above back, in that I/we should definitely still aim for understanding, even if it’s not the most “productive” thing to do, and b) I don’t know what’s a “realist” way forward, i.e. not completely denying LLMs are capable tools and never using them, but still understanding what comes out when you do use them. Seems rather difficult to balance the way the current iteration of tools is designed to be used.
> That is, genuinely, crazy talk. That is what addicts would say.
I, well, agree. Of course the statement is nuts, especially so once you consider issues like vendor lock-in, the oncoming atrophy of one’s own skills, etc. But if you define “productivity” as short- to medium-term results only, is it really that inaccurate?
If you define a problem in a way that leaves out like 80% of the constraints, then solutions get trivial, yeah. That's I think the generalized way of phrasing this specific dysfunction of our industry.
The problem is that it still uses the and benefits of the legitimacy of the word of "productivity" there. Meaning that it is harmful to us to put it that way.
Good point, not having to plan for the long term probably has an outsized effect on a given level of difficulty/effort required.
Maybe it’d be interesting to see what could be achieved responsibly by motivated people with models in a world without “feature pressure”/competitive stress (which’d include e.g. no security race and infinite attackers and exploits to keep up with)?
To a limited degree, yes, but there’s still competition in that you need to have something compelling to be sold. But that’s probably way less than in your typical B2B software offering situation.
>>> but “productivity”, at least in applicable areas of work, might really come down to who can let go of the most understanding the easiest?
>That is, genuinely, crazy talk. That is what addicts would say.
People demonstrably said the same thing about the inventions of... Writing! and Mechanical/Digital Calculators! Yet here we are!
> Of course, at the moment everything’s still a bit very much difficult to tell, but “productivity”, at least in applicable areas of work, might really come down to who can let go of the most understanding the easiest? Incredibly thought-provoking stuff.
I cannot see it being a good thing to "let go" of understanding. If you don't understanding anything, then why would anyone keep you around? An example hypothetical situation is, if I have a colleague that outputs a bunch of productive things, but they don't understand it, and I do, then of what use are they? I have LLMs too and also have the understanding.
It isn't clear to me why people are so comfortable with letting go of understanding. And it's a bit bizarre because it always seems to be framed as "you're going to get left behind if you care about understanding". I also never hear or read anyone who has any real and deep experience say these things.
My read of what is being suggested is that the level at which one should aim to understand and be comfortable with is slightly more abstract than reading the code and reviewing every line of it. It is like how one is comfortable not understanding the byte-level code or assembly language code. I know that this analogy doesn’t hold very well because compilation is a deterministic process while llms are not, but that is precisely the point being made. “Trust” that llm will produce the right output, even though the output might not be reproducible word for word the next time you give the same prompt.
To your point about becoming redundant without understanding, not understanding things will be a problem when an issue arises and the other person isnt able to handle it the right way. However, if llm can address the issue, does the fact that they did not understand the details really matter? From the perspective of a person who gets to decide who gets fired, would they fire someone who is productive but sometimes introduces bugs that they quickly address or would they fire someone who is comparatively slow but rarely introduces bugs? The answer probably depends on the domain, and how costly a bug can be. For most of us, the work is not so critical that you cannot afford bugs. So, productivity wins.
If you are suggesting humans before LLMs rarely introduced bugs, you probably haven’t got the latest models to review your old code. It’s shocking how many bugs we had before but couldn’t see!
Don't shoot the messenger, but maybe the idea is this:
You're smart, and you know what can go wrong, so you use models slowly, and you check their work. Your bet is that it will make a bad mistake.
Your coworker is reckless, they don't know or care what can go wrong, so they use models quickly. Their bet is that the model won't make a bad mistake.
If the model doesn't make a bad mistake, your coworker is creating more value to your employer, and your employer doesn't really care if you understand the code. They just want something that works.
Over the last year, it sure seems like models are making less mistakes, so the expectations on the bets are changing too.
Right or wrong is this the future we want? Shouldn’t we say and do something?
For instance: we have calculators. We’ve had them for decades in their “current” form more or less. They are readily accessible and far more reliable than LLM’s - if you a problem 1000 times it’ll be right 1000 times, the rare bug or glitch notwithstanding. But we still teach people basic arithmetic. We teach times tables. We didn’t just offload basic math.
I agree we don’t need to know assembly to use a computer but there is a balance here and companies do not care about a balance.
I think we mostly DID offload basic math, for better or worse.
For software engineering I don't know the right balance.
I've used the "we mostly stopped caring about machine code generated by a compiler; maybe we should stop caring about LLM-generated code as well?" analogy myself.
But even I have to admit there's two big holes in that analogy: if I use a compiler to turn C into machine code... (a) compiler bugs are orders of magnitude rarer than LLM-generated bugs (b) my C code remains the source of truth, or at least source of intent. Whereas with LLM-generated vibecode, there's no source of truth, just an ever-evolving mudball. I (kinda?) like the ideas of "documentation as source of truth" and/or "tests as source of truth" ideas, but I don't think we're nearly at the point of viability.
TL;DR yeah I think actual understanding of what LLMs produce is gonna be important for the forseeable future
Like humans, LLMs also have to expand more effort when dealing with large piles of fragile spaghetti code. Spaghetti code isn't just aesthetically displeasing; there's a objective complexity increase and for large enough apps the increase is exponential. It's hard to imagine this ground truth ever changing.
Like humans, LLMs get into a place where fixing one thing breaks three others. They compensate partially with insanely big test suites, but now fixing one thing breaks three others, and 57 different specs, half of which also need to be updated.
I think roughly three outcomes are possible.
1. LLMs get fast/good/cheap enough that this just doesn't matter. Your 50M LOC monolith is a mess? Yeah, it still takes more effort for the LLM -- but this just means it costs 5 additional seconds and $0.03 instead of $0.02 to ship a new feature.
2. Stepping away from the "code as source of truth" paradigm. What if specs/docs were the vetted source of truth, and we just regenerate the whole 50M LOC monolith every time somebody makes a change, just like we rebuild binaries from C today whenever there's a change? (This probably requires some of the LLM advances from #1. Also, at this point, perhaps we should just skip the intermediate language and generate ASM -- what purpose is human readable source code serving at that point?)
3. Status quo as I see it -- human engineers overseeing projects, both guiding AI and being accelerated by it.
I think #2 is probably the... most ideal future? Though, of course, if we switch to that paradigm, the source-of-truth docs will have to be very thorough and structured, and will probably resemble a programming language of sorts themselves, except describing intents instead of operations.
Adults can, under most circumstances, do some limited slow arithmetic but are generally piss-poor at it, notwithstanding the occasional rainman, and only rely on it in trivial matters.
You see cashiers summing groceries? Salary calculations on paper? Tax, inventory, accounting, you name it? Nobody does any of it by hand.
I didn’t say we were universally good at it but in many countries we are (more or less) all exposed to it and taught it in school. There is a baseline even if we are far from mastery, and frankly a cynical hand wave of “well everyone’s bad at it” is not a real argument. In the US it is very clear that people are at least exposed to it and if we weren’t we would be operating very differently. I went to dinner last night, everyone did the tips in their head right there at the table. The average person can do some simple multiplication and division in their head. It’s not a mysterious script done on a box that they repeat the output of. Most people even if they’re bad at it understand the fundamental concept and, gun to their head, could probably do a series of very basic math problems with pencil and paper.
I imagine you and most of your peers can calculate a tip or at least approximate it without pulling out their phones yes?
Analogies can be good when you're explaining something you know, but they kinda fall apart when making arguments or decisions.
How many of your friends and extended family can write hello-world or fizzbuzz in any programming language at all? Should they be allowed to vibe code apps for themselves? If they get good at vibe coding, should an employer be allowed to pay them for it?
> Analogies can be good when you're explaining something you know, but they kinda fall apart when making arguments or decisions.
If all you’re going to do is just hand wave away anything I say then this isn’t going to be a productive conversation. Have a good rest of your week man
I was trying not to nitpick the ways arithmetic is different from programming. That wouldn't have been productive either. I wish more people were good at math, I wish more people learned to program, and I hope you have a good week.
This is just moving to a higher level of abstraction. Nowadays people don't verify the compiler generated assembly code anymore. We don't care if our source generates a few million extra instructions. Source code is just an intermediate between specs and binary at an abstraction level decided by the developer. We do requirements -> code -> assembly.
To resolve this we should aim for a new intermediate format that is more high level than source code, the generated code is too monotonic to properly review manually. Something like requirements -> detailed specs for llm -> code -> assembly . Then let one agent implement the specs and have another review it. If we stay at the current abstraction level, humans become the major bottleneck.
> Nowadays people don't verify the compiler generated assembly code anymore. We don't care if our source generates a few million extra instructions.
This analogy doesn't hold up. Compilers are designed programs and are deterministic. We "know" what they generate, and what they generate and how they generate it is well-designed and tested, and there are certainly people and teams who know how the compilers work.
None of that is true for LLM-generated code.
> To resolve this we should aim for a new intermediate format that is more high level than source code
That was the problem before and after. LLMs did nothing to address that. This is what the entire software engineering domain is about: translating loose system designs and requirements into programs.
LLMs do not replace the code, they replace the coder. The coder becomes the manager, and the model becomes the coder. The code it writes is ultimately deterministic and can be tested. The LLM coder may still hallucinate, although that can be said of human coders too. In the classic development model, we use specs and tickets to steer the output of a human coder. My take is that the same needs to be done for LLM coders.
I think your hypothetical is incomplete.
What if the person that lets go, generates more productive things then somebody understands?
In this case I know a lot of ppl will value the producer of value more.
I always pushed the bounds of learning more about the underlying stack or problem, and always found them in no time.
Understanding architecture, robustness, the “crumple zones” of a system? I’m all for it.
Taking three days to “understand” how postgres transactions work under the hood for your generic SaaS? Uh..
I have seen a lot(!) of the last type of “understanding” and it’s infuriating. The same types will often not understand the business, its stakeholders, the architectural constraints beyond the technical, etc. Understanding takes many forms.
I feel like the real value is in probing the extents of your own understanding. If you don't understand something it's doing, you can ask. You do kind of move up a level of abstraction, from the IC to a PM or team lead. So maybe you don't know what every line of code does but if you understand code and how to drive you can trust that it's doing the right thing and you don't have to be as deep in the weeds of every refactor etc.
You do have to keep a sense of when it did the wrong thing and how that was a valid interpretation of what you asked and how to better specify what you want. But that's how it really just shows you the gaps in your own understanding of a problem.
Also I think there's huge differences to the kinds of tasks and workloads people are throwing at it, leading to wildly different perspectives, but everyone's talking about the same "Fable".
> Also I think there's huge differences to the kinds of tasks and workloads people are throwing at it, leading to wildly different perspectives, but everyone's talking about the same "Fable".
This right here. Humans are really prone to over indexing on a handful of personal experiences vs a representative sample. And once someone has formed an opinion, confirmation bias helps cement it, despite evidence to the contrary. Reality is probably somewhere in the middle.
That's where I think benchmarks are a huge red herring. Everyone wants a simple performance evaluation of a repetitive task but that's missing the point entirely IMO. Just try talking to it, asking questions. I spend so much time just reading responses. I have to like really think about how to specify the question I'm asking, fire it off, and I often can't wait to see what it says and what I'll learn. How anyone thinks it's making us dumber is crazy to me...you're missing out if you're just trying to offload thinking.
You're misinterpreting what they mean as understanding and inserting your own doomer definition where this imagined person has been stripped of all ability to investigate a result or ask questions.
I too love to downvote and edit instead of admitting that I'm talking and acting in a way where nobody would want to be my coworker anyways. Where exactly are you demonstrating that you're doing anything to justify being kept around if that's such a big concern for you?
“might really come down to who can let go of the most understanding the easiest”
I suppose it depends on the “applicable areas of work” but I would argue the opposite. As in all things, when you level the playing field, in order to stand out you have to know the rules to understand how to break them.
Imagine a fully autonomous Ad agency that creates unique ad campaigns for companies. And have it controlled by someone who “let go of the most understanding.” For instance, maybe a business minded person with excellent technical skills, but doesn’t know how to copy-write and has no visual design training.
Even with the strides in AI advancement, this is an impossibility and I’d argue it won’t happen within the next five years even. You need human agency, and you need human workers who are experts in their field.
> Perhaps almost all or perhaps even all of the software skills I have will become irrelevant in the near future. But we're not there yet. At the moment, programming skills seem higher leverage than ever before.
While this iteration of technology certainly _feels_ different, it seems like for now we're still coming down to "it's useful to know how to build a car, but when you need to put food on the table, know how to fix it and make it do what you need it to do".
Well, in retrospect I don’t like this framing all that much either, but I think I came to this conclusion due to assuming
not caring about the output ~ more output
… which may or may not be useful in the long term, but definitely does help a shorter-term “productivity” measure.
On the other hand though, there’s a pretty damning example against my case here: Say a manager of individual contributors – which is what the situation of an individual with sufficiently smart models and tooling may almost boil down to – didn’t directly care about the output anymore. We’d most likely call that being unhinged/unproductive, too…
It was late at night and I primarily wanted to link the somewhat relevant article, maybe I should have left everyone to reach their own conclusions instead of adding some poorly-worded unfinished thoughts. :x
> I like Max Bittker's framing that models are very good at counterfeiting and the level of expertise it takes to distinguish a counterfeit from the real thing has been increasing as models improve.
Is it just me or is this analogy completely missing the point of why we care about detecting counterfeits in the first place? Poor craftsmanship of all things is not really the reason counterfeiting is illegal!
I'm struggling to understand the hypocrisy of private property advocates advocating against private property. Are we doing "intellectual property is protected by law" or not? We're allowed to ingest all of human FOSS and StackOverflow without attribution and use it to sell distilled human knowledge by the token, but not use the distilled human knowledge to reverse engineer and FOSSify proprietary software? We're allowed to ingest all the art on Twitter and use it to generate and sell corporate clip art without attribution, but we're not allowed to generate micky mouse/bugs bunny yaoi fanart?
I mean, the answer is obvious: the Luddites were right, these tools (not just LLMs but private property, copyright, and capitalist mechanisms) are only tolerated to exist so long as they serve the needs of Capital, but the hypocrisy still frustrates me.
> these tools […] are only tolerated to exist so long as they serve the needs of Capital, but the hypocrisy still frustrates me.
Great thought. Question is, can those who do the tolerating (in the interest of capital) even effectively prohibit/prevent access after a certain point, especially now that the cat is not just underway, but well out of the bag?
Assuming the current trajectory of a yearly factor of 10–100 in size/cost reduction [0] holds up, won’t the technology just become cheap enough to be functionally available to everyone?
My assumption is that frontier models will be constantly increasing in intelligence, solving far more difficult problems, while the rest of us do the LLM equivalent of scratching through the dirt with wooden hoes.
Thinking about just the domain of the stock market only: getting a "win" on it means getting a leg up on everyone else participating. Best example I can think of of a pure zero sum intelligence contest. Programming UIs might have a point where you just can't get smarter at it, but in an intelligence contest like a stock market, I don't think there's a ceiling.
In "Accelerando" this was covered by the invention of "Economics 2.0." AI agents also got into a lot of lawfare in that book.
> I'm struggling to understand the hypocrisy of private property advocates advocating against private property. Are we doing "intellectual property is protected by law" or not?
The most rigorous "private property advocates" don't recognize intellectual property as a legitimate form of property in the first place: private property is a natural phenomenon that emerges as a solution to the problem of physical things being inherently exclusive in possession and use, and IP is a creation of positive law meant to artificially apply the methods for solving that problem to another, unrelated question that isn't part of the same problem domain.
> I mean, the answer is obvious: the Luddites were right, these tools (not just LLMs but private property, copyright, and capitalist mechanisms) are only tolerated to exist so long as they serve the needs of Capital, but the hypocrisy still frustrates me.
Abstract concepts don't have "needs", and no one is currently in a position to tolerate or not tolerate anything -- no one in particular is actually in control over anyone else's activities. The cat's out of the bag, and no one has any capacity to put it back in.
If your objective was understanding rather than, say, getting a new operating system running, couldn't you use the stronger models to produce artifacts and then ask them (or weaker models) to explain those artifacts to you? Probably the stronger models would be able to produce artifacts that were more worthwhile to study.
Maggie Appleton suggests some ways that AI models could do a better job of communicating about artifacts to people in https://maggieappleton.com/planning-agents, although she's focused on the use case of the person deciding what changes in the artifact to request from the model.
Last time I submitted a link it was immediately flagged to death, although I have to use an Incognito mode tab to see that. I'm not wasting any more of my time on that.
That's the one, and it's one third of all the links I've ever posted, or, probably, will ever post. What you say may be true, but it was still immediately flagged to death, regardless of how that happened, and the HN software is still trying to be sneaky about it. If my contributions are treated as spam, I'm not going to persist.
Certain domains being blacklisted is completely understandable if you consider the history of the site and culture(s) of the users. It's nothing to do with it being "your contribution".
That could be handled with an error message instead of assuming bad faith on my part. Given that it was handled (and continues to be handled) with sneaky shadowbanning measures, I have no interest in attempting to circumvent the spam filter by submitting future links.
Shadowbans are used here for the same reason anyone else uses them to handle anything: to avoid tipping off outsiders (especially ones who might be trying to automate attacks).
“Wasting time” by copy-pasting link title and url? Sounds excessively optimized time management to me. Save 10 minutes elsewhere, and you got 50+ submissions worth to spend!
Most submissions never receive any upvotes. They’re all “wasted” like that, even if not flagged.
You will find a lot of articles from before LLMs about not delegating understanding of your core system or business. You could say that the software is not your core business so you don't have to understand it. But if the software is any good then it should be taking care of a large part of the business. So software is your business and you shouldn't delegate understanding it.
I like this and the warning it entails, on the flip side of this I have found that you need to be able to delegate and trust. If you demand on understanding everything and doing everything yourself the world will grind you down. It's a big place and sometimes it is OK to delegate what is not part of your core business it let's you focus on this. Not everybody needs to be able to cook 5-star meals, you should know how to heat up some pasta, but don't beat yourself up if you can't bake a cheese soufflé.
this is about cables which often were and still are snake oil in the audiophile world. headphones do make a difference. I have a hard time using my airpods and others after experiencing a pair of Sennheiser HD650 I've gotten myself as a christmas present couple years ago
yeah lamp cable will work. There's no room factor in a speaker so like 99 percent of the quality of the sound comes from the fidelity of the transducer and its resonance characteristics in electrical/physical suspension. Then the next .9 percent is from the source equipment, and finally the last .1 percent is from the dac/amp stuff. Exaggeration here but not really
There are many ambitious things I have tried to work with Opus 4.8 and Opus 5.5 on, and it just can't get there. It always falls back to some tried and true method or some valley that it gets stuck in. In those times, it was impossible to push it forward into more creative and exploratory thinking based upon my relatively advanced pushes and contextualizing. So I took the commenter's position that Fable 5.2 will respond better to that type of pushing. But that type of pushing actually means you know more than those who are happy enough using the lower models. So that seems like the opposite of what you said.
I'm closer to a Product Manager so I'm not sure how useful these examples will be for you.
One important constraint is this topic of intent (also referred to as alignment). Specifically, when a user requests a feature or change, there is MOUNTAINS of implied context and intent in their comment. Some cultural, religious, social, economic, base on work and personal experience, their use cases, their values, what they intend to get done, etc. Every time these models get smarter, they get a little better at understanding this context. It's combination of reasons. Some of it is parametric. Some is inferred. Some is about selecting the right data to examine and research. As these models get smarter, I spend less time time iterating and educating the model. They reach the intent faster and better.
From what I hear and experience, this is also true in the coding space. LLMs are basically another abstraction level on top of formal languages, which sit on top of compilers and machine code and etched instructions. Smaller and dumber models require a lot more hand holding to achieve the same results. They're less capable of inferring the gaps. Less capable of extrapolating potential negative outcomes of the change they're making. Less/less useful parametric knowledge. Less capable of selecting the right data to use to make the right decisions. They do what they're told.
This has been less of an issue for coders who religiously document, understand the codebase and architecture intimately, and demand to be kept in the loop on every change. So seniors often do not see the utility of smarter models. They know exactly what they want, how to ask, the context to provide, and how to assess if the change worked as expected. The utility is for the 99.9999% of the world which is not a senior developer.
While what you say mostly rings true to me, I don't see how it could result in the valleys of stuckness I was asking about.
However, your statement that there are only 8300 senior developers in the world does not seem correct to me. That seems like an unreasonably high bar for that title.
They are generally things I don't particularly want to share, as I view them as ideas close to my heart. The general pattern was what felt like redirecting from the harder problems and an unwillingness, if we can call it that, to go there. In some sense though, it's hard to understand how these more advanced models get around that, because these models are tuned to the most probable answer. When you come at them with a novel idea, they always seem to water it down in the end.
I have also found this — once you get out into some really novel workspaces they will revert back to replication of adjacencies that already exist. Fable 5 and 5.1 in particular will talk around the issue in a way that no model prior seemed to.
I think the issue is fairly fundamental, perhaps because of tuning, perhaps even beyond what they are tuned or trained on, because the bulk of what they can go find and ground into seems to drag them heavily toward implicit biases that split around categories I can’t quite discern. The category boundaries sometimes look like academic fields, but sometimes they seem like more fundamental attractors.
I second (third?) this. Previous models were more willing to explore the unknown. Those new ones just try to Google search and latch onto any adjacent solution they find.
It's a reference to a joke, of a stereotypical comment left on Romantic Relationships topic online forums. As in, "this man is so terribly unsuitable to everyone. But I'm special, and I can fix him and make him the perfect boyfriend."
The comment is implying that the commenter thinks that you think that with more information you could solve the problem, and that is why you asked, instead of trusting the assessment/competence of the other person.
I am not sure you thought that, you just seemed curious, but that is the relevance.
> We are pretty much at the point that increased model capability will only have utility to folks that are willing to let go of even more understanding of whatever the model is producing.
Given that reading someone else's code is slower than understanding your own code, doesn't that basically cover everyone that adopted coding agents?
It very much applies to science as well. I've got a workflow doing an entire PhD's worth of work time in a single month subscription of $100 codex. There's zero chance i go through and understand what the hell it's doing, but for tackling problems which are accepted as "impossible" it's extremely useful for gathering data on every possible approach. It hasn't had a break through, but it's certainly recreated everything anyone's tried before.
Ok this point and pacing the frontier: personally i would be ok with freezing models at this point. Opus 5.5 does everything i need and i suspect that from this point onwards the improvements will be beyond my understanding and just introduce existential risk.
> The best models can/will produce a year's worth of output that would take several days to weeks to properly understand in the span of an afternoon.
This is appalling. Are you really really going to trust a YEAR's output from a machine? Are you going to read and understand all that? Will you be able to maintain and extend it to your will? I truly doubt that. Drugs do harm and are hard to quit. Good luck.
I see this mindset a lot from people who I can only presume have only ever been individual contributors.
Having run teams, as much as I love getting into the details of code I care about, in the 30+ years I've been in this business, I've only ever even seen a tiny proportion of the code my teams rely on.
And the non-technical people in these businesses will have seen none of it.
They all take it on a combination of trust and verification of outcomes that the code does what the developers claim it does.
Increasingly we're getting to a point where the output from these machines are more trustworthy than an increasing proportion of human developers, and more importantly: Increasingly often good enough.
Especially as more and more of the code becomes throwaway.
I have a side-project that is profitable, and that is profitable only because I haven't needed to spend time reading the code. If the code turns out to be unmaintainable? Doesn't matter - it's so cheap code that it is reasonable to just throw it away, and start over based on the lessons learned. It enabled me to take an opportunity to profit I wouldn't have been able to profit from if I had to set aside time I didn't have, so even if it was to totally fail in the future as a result of issues with that code it doesn't matter.
As the cost of good enough code falls towards zero, how we think about which code matters and how much we need to understand it will fundamentally change.
Increasingly, a lot of code is even just part of a conversation with an agent to me. It's throwaway, for the duration of a session. Who cares if it lives on?
I don't trust or need to trust most of the code I build any more. I trust the measurable outputs. The verification pipelines we build. The feedback mechanisms we build. Those I need to read and understand.
That's no difference than when working with a team. The only thing that is different is failure rates. And at this point, for some thing the machine failure rates are still higher, for others it is lower.
As one poster replied above, there is code one doesn't need to understand if what you care about works. A good example is when I first tried a couple of local AI models on my highly unusual inference hardware.
It ran like a dog. Few nights of running Fable (later Opus 5.5) with a 7 strong team of DeepSeek v4.1 Flash 1017 (don't mistake it with 0731 that is crap) and llama was running 10x faster and vllm was running even faster when before it would not work at all.
I've run similarity and needle tests and the results are exactly the same content wise. But 10x faster. I've relied on this for a month now and I'm pretty happy with how it runs.
Do I now need to learn CUDA Kernel programming to understand how it was done? I don't think so. It's for my personal use. Not for a paying client so no warranty is perfectly fine.
But I also use a lot of AI at work in an I dusty where people may die if the code is buggy. So I absolutely verify and understand everything.
There is a middle ground too. Understanding the architecture.
For example I used a bunch of DeepSeek models supervised by Qwen to build adaptive-acl. A pi coding agent plugin that deterministicly checks if every tool used by an ai during an agentic session matches user intent to give permissions for those actions (reading/writing data, network access etc), as a part of this plugin I run an llm, a classifier that runs over user's prompts and extracts these permission grants then saving them in a format deterministic layer can process.
The design includes massive number of assumptions and compromises. For example, if a user allows writing this implies permission to delete and reading if what was written etc.
The thing also includes over 4 thousand actual instances of tool use preceded by "permissions talk" from my live sessions and that whole collection can be run to test for regression etc.
At some point during one night the AI had a regression and decided the best way to resolve it would be to add regex based language processing elements to the deterministic layer (a huge no no, this has to work regardless of the user speak English or Swahili, also regexing natural language is a disaster waiting to happen).
I knew enough about the architecture to notice that and burn it out with a hot iron.
That is the problem with AI. It sometimes solves problems the easiest possible way causing massive issues for later. So if one uses it for important stuff it is good to understand the architecture and the plan of what it is going to build at the very least.
>Few nights of running Fable (later Opus 5.5) with a 7 strong team of DeepSeek v4.1 Flash 1017 (don't mistake it with 0731 that is crap) and llama was running 10x faster and vllm was running even faster when before it would not work at all.
Just wondering, what harness architecture to you use for this, if you don't mind sharing? Is it a parent session running opus and it writes worker briefs and spawns subagents ond demand? Or something else like the workers having shared context, etc?
I'm currently running this cheap setup where deepseek will be the "main" session but it has to submit proposals to a stronger "oracle/reviewer" model through a custom tool. So deepseek scouts the code and comes up with its own solutions but the oracle on the tool can steer it with it's higher "taste" (omniscience benchmark, low slop benchmark).
I've just started using it and I like the results so far but it's most likely suboptimal so I'm still iterating as usage data piles up.
> Fable is exceptional at drawing. It’s ability to render an image with JavaScript actually has me questioning the value of dedicated image models
I’ve been shifting away from using more traditional GenAI models in favor of just giving LLMs access to a custom drawing language (LOGO-like) to generate imagery and its been going shockingly well - Opus 5.5 in particular.
I put together a simple LLM driven skill that creates old adventure game (EGA) environments from simple pencil sketches and it works really well. And being procedural, they're much easier to make surgical changes to without degradation.
Just to clarify -- the images on that site are all generated from LLM-written code? It's very impressive if so. I would never have guessed these weren't human-made from the visuals alone.
Yeah it's entirely LLM driven! If you look at the repo, I explain more about how to use it. Basically, you give an agentic coding harness capable of tool usage (Opus 5.5, Qwen 3.8-27b, etc) the skill which explains the drawing system: simple things like how to set the colors, position rectangles, draw polygons, etc. and it uses p5.js to render them.
It has three different modes:
• You can simply describe a picture, and it'll automatically generate an image using procedural code.
• You can ask it to paint over an existing image. That's what those examples show, like me creating EGA-style equivalents of various scenes from the original Myst.
• The last way is to give it a rough pencil sketch. It's the mode I use the most since it's very valuable when building an adventure game to position objects in a room.
Thanks! I started this whole thing because I recalled reading an old interview with Sierra’s original creators, Ken and Roberta Williams, where they talked about how they needed a way to have graphics for their games without saving large bitmaps which would make it difficult to fit on a single floppy disk.
So instead for each room that needed to be rendered they just saved a series of vector instructions that would re-render it in real time. On slower machines, you could actually see the rooms pop in piece by piece. I wanted to kind of approximate that same aesthetic because it's just super cool!
So basically I gave an LLM access to the user manual for the original LOGO drawing language, along with several screenshots of various scenes from the original King's Quest I.
Then I just guided it to produce a procedural variant specialized in creating new EGA-style scenes for use in creating old-school point-and-click adventure game.
So it's pretty simple - it's basically a LOGO-inspired language of simple commands (polygons, floodfill operations, lines, draw order, delay commands, etc) which is then rendered by equivalent p5.js commands. There's a couple specialized higher level PicScript commands for drawing commonly seen geometry in older adventure games (hills, objects in nature) as well.
Thanks for the rec! I unfortunately don't have a Speccy, but I did find an emulated version of the BBC Micro port of Twin Kingdom Valley and can definitely see it drawing in the various pieces of the environment.
I think the approach of using simple procedural vector graphics was relatively common back in the days when you had to optimize for either disk space or speed. Sierra's creators talked about how they opted for procedural graphics to fit their earliest games on the old 80 KB floppies.
Does Fable 5.2 outperform Opus for data science work?
Not just "iterate on this XGBoost model" but actually looking at a problem and designing and carrying out a short-term R&D program including probability modeling, model fitting diagnostics, how to respond to various kinds of results, how to interpret/reason about what it finds, etc. That's really where I saw all the models perform poorly, and where I saw the first big step increase from Opus 5 (pre-5.5) to Fable. But then Opus 5.5 came out and I haven't needed Fable since.
Mind you, I'm doing slightly different work (and I'm very much "in the loop" still) than when I performed that side-by-side test. But it's really amazing what Opus 5.5 can do. I now have a powerful data science AI assistant that not only can write any script I need it to (including some scripts I didn't even realize I needed), and it has an encyclopedic knowledge of everything ever, and it constantly checks me on oversights, it handily derives expressions and clever model reparameterizations that aid, it has great insight into model behavior, and it has an amazing ability come up with ad-hoc experiments and tests that would have taken me 2x as long to even think of (let alone implement by hand).
It's not at the scared-to-lose-my-job phase yet, but it's become a tremendous force multiplier for a smaller data team.
I'm teaching "Data Science" to computer science majors next spring, and I'm overwhelmed by the prospect of what to teach them in the age of AI. It seems like Fable already way outperforms anything I could historically expect my students to do at the end of the semester, and perhaps even outperforms me. I can describe a vague analysis on the data, and it will handle every step: transformation, preliminary analysis, formulating more precise questions, creating visualizations, running statistical tests, interpreting results, thinking about stakeholders... And in some of those steps, it finds things that I might not have considered.
I wish I had a bigger N so I could rely on that experience a bit more, but it sure does feel like I'm not really contributing anything in terms of "Data Science expertise" in my own research right now.
Plus it worries me that people will no longer know how to do anything basic and we will at some point in the future be slaves to AI if we aren't careful.
If it wasn't for grinding all of the hard problems and hands-on projects and various programming tasks I had to do by hand over the years, I wouldn't be able to keep up with the agent, and wouldn't be able to get good results out of it, because I wouldn't know what it was doing and wouldn't be able to steer it appropriately. My schoolwork 15 years ago was more than formative, it remains critically foundational for my job work today.
Based on the other comment it sounds like maybe I need to give Fable another chance, and then I suppose I won't have to steer anything at all. But at the end of the day I am not paid to be a meat proxy, I am paid to deliver things for the business that employs me, and the things I deliver are my responsibility, not Anthropic's.
So I say, embrace AI as a learning tool, let students use it to bypass programming on assignments where the programming is of secondary importance, require oral defense/presentation for projects, make homework worth a much smaller fraction of final grade, and make all of your tests in-class and hand-written.
The reality is that the entire nature of education has to change to account for cheating becoming essentially free and virtually impossible to detect. Grinding through a problem set in the library is still tremendously important, but it's going to be harder to motivate students to do it without just cheating. Some students are conscientious and will do it anyway. Some won't. The hard part is going to be crafting curricula and exams that separate the two kinds of students. Fortunately you share that problem with just about every other professor in the world right now, so at least you shouldn't feel responsible for solving it all yourself.
Relevant anecdote: I used Wolfram Alpha to cheat on my calculus homework in freshman year, and then I got my butt kicked in the exam and a C final grade that never quite averaged its way out of my GPA. That felt horrible and it was a strong motivating force to get my shit together, but that's only because I'm a fucking nerd and I really cared about learning in school and making a career out of what I learned. On the other hand, I really was struggling with the material, which is why I tried cheating in the first place. Maybe if I had today's AI I could just ask it to help me learn better.
> Maybe if I had today's AI I could just ask it to help me learn better.
You could! I use it all the time to generate pre-calculus and physics quiz sets for my son to work if he needs extra practice on particular concepts. And that's just scratching the surface.
Getting a kid motivated to do that _for themselves_ rather than just getting the answer and moving on to scrolling short-form video? That's another matter.
And especially where the fuzzy edges and blind spots are—the limitations of the methods, and the aspects of problems that are essential to the question but hard to quantify or test or reproduce.
I feel like a lot of people are going to put the same faith in their new quantitative superpowers as psychologists did in their 95% confidence intervals [0], and managers did in their “scientific management”/time-and-motion studies, and American political pollsters did in their 2016 presidential prognostications, and Rudder’s OKTrends blog (delightfully) did in calculating the answers to romance [1]…
From my experience working with data scientists, it outperforms humans on an extreme level. I’ve seen so much bad data science, and here I have a model that can do all of that from one prompt. It is getting really good on finding caveats in data too.
Is it reasonable to take up some of the class with statistical basics and intuition? E.g. you just referred to a bigger N; I don't know what level your students are but do they have a basic intuition for notions like "variance of estimator"? Or is that another class? I think my point is that even if they can nowadays press a button and have the most amazing reports and visualizations produced, that doesn't give anyone any actual understanding of the theoretical side of data science.
I'm so glad I learnt it before LLMs. I can't imaging trying to get good at Pandas/Polars coding nowdays when so much of my analysis is "Fable, please do this for me, k thx bye"
I mean I found Fable 5.1 outperformed Opus 5.5 for data science, so I expect 5.2 to.
But then I'm someone who hasn't found Opus 5.5 good for much beyond web UIs and related frontend development. I find it more forgetful than GPT-5.6 or 6.1 or Fable, so despite having a 1M context window it gets lost in a big codebase or goes of target. Opus 5.5 doesn't manage agents particularly well IMO.
I'm surrounded by people raving about Opus 5.5 and God only knows why I'm not getting their results.
I just tried Fable 5.2 side-by-side with Opus 5.5 on the same prompt and I think you're right, n=1 but with strong signal.
Fable got right to the point, had cleaner/better responses and reasoning for its conclusions, got less distracted with secondary concerns, and still showed signs of the cleaner writing style compared to Opus, which improved a lot with 5.5 but can still be hard to parse sometimes.
I will keep using Opus for implementation tasks (e.g. building web apps to explore model predictions) but I think I'll try using Fable this week for the real DS stuff. Thanks!
Have never noticed a difference between Fable and Opus on coding tasks but design/architecture is night and day different. I usually don't bother with designs with Opus since it tends to screw things up, hallucinate, make bad decisions.
With (obra) superpowers, Fable writes a plan and delegates to Opus which works well. Fable is much better at sorting architecture in a conversation rather than me creating a giant prompt for Opus.
And none of it matters ... because just as soon as you are happy with things "balanced across models" Anthropic will nerf the models you were happy with, and force you to use new and more expensive ones.
Your comment matches what I measured on 30 regular coding tasks: Fable 5.2 and Opus 5.5 both solved 26 (Fable 5.1 was at 25). Fable 5.2 finished in 0.62x the time of Fable 5.1 on the same tasks, with no runs hitting a safeguards stop (Fable 5.1 hit a lot of safeguard stops).
Private benchmark from private repos so yeah, novel.
I think I included the Opus 4.8-backoff runs in the speed number and they were on average faster than the actual Fable 5.1 runs, so the speed result might be even bigger if I excluded the Opus 4.8 runs.
I just saw someone post about getting Claude to write code that renders individual brushstrokes and the results were varied and impressive. Can't prove they actually had Claude do it, but I believe it. Have been using Claude to edit videos and it can animate concepts without much prompting on my part at all.
I'm sure they had their own style but I still don't see it beating an image model for the variety of styles and the high detail possible. GPT Image is ok, certainly not the best available but it can do photograph quality images which I'm sure you can't get out of Claude. I suppose if your use cases were limited to vector art or coarser brushstrokes you might get something more inspired from Claude but I'd have to see it to believe it.
The speed part is funny. I felt like with Fable 5.1 I had time to digest what I had prompted and was waiting for. Now I feel like, I am running behind or like it is going to take off too far. This is great for closed prompts "make a soduko game" but hard with running context queries, because I feel like I am creating more information debt than I want. I'll be fine, just talking out loud.
Haiku - really only reason is lack of auto mode becomes tedious. I only use it indirectly with sub agent delegation for text analysis (summarize web page, document). However, it's quite fast and capable
When you put it like that, it almost resembles having a classroom of kids with different personalities and talents. Some are thoughtful, some creative, some quick in execution.
You choose wisely the path each takes and allocate tasks accordingly. Gives me chills to think of it...
Those distinctions make sense. I guess I'm more interested as to "when" you make that decision. What triggers you to spend the cognitive energy on deciding which model?
Opus 5.5 has been good enough for most of my coding, but I don’t know if Fable 5.2 is better for architecture work, like planning how a system fits together or designing the data model. @nijave says it’s night and day for design. Has anyone else seen that? (I don’t have access to Mythos, so I can only compare Fable and Opus.)
At this point, I don't really see much difference in output quality between Opus 5.5 and Fable 5.2. Although, Opus 5.5 seemed to just go on auto pilot mode without give tasks much thoughts unless you give it tasks specifically each time. Fable 5.2 seems to think more on its own.
I have had the same experience between opus/fable and also drawing: it has done exceptionally well with SVG drawing in different graphic styles for app icons (comic, fantasy, modern, etc)
If this were true you could do a talk now and measure it and do it every week and see if it was still true or not and be the first to catch them in the act!
Tell it to draw a picture of something with JavaScript in the style you desire. No harness magic necessary. It helps to provide it with reference images.
I clearly see the difference between Fable 5.1 and Opus 5.5 everyday.
All the people claiming "opus 5.5 is just as good" tends to make me think some people only do simple tasks or do not care about correctness or quality.
> All the people claiming "opus 5.5 is just as good" tends to make me think some people only do simple tasks or do not care about correctness or quality.
Have you considered the possibility of the problem being in the way you prompt a model and prepare it's context? I've been pushing myself to using mainly the cheapest models and I see them deliver the same results once we provide enough context or lay out a plan.
I'm pretty sure that's not the issue. For example I had one problem where Opus struggled for several weeks without getting us anywhere, flip-flopping in its decisions, generating huge amounts of irrelevant hacks. That obviously went through compaction several times, but I manually maintained explicit documentation and state of the project for it to work with, and reset the session as needed.
Fable made it work correctly in 2 days, it stayed on point, identified the real problems, and gave me clean code that was suitable to merge.
The task was simply to warm the hot path of an app so that latency was not as jittery for some rare, but important, events.
Fable built a reliable repeatable benchmark, disassembled the code to figure out how much could actually fit in L1, made tactical changes to eliminate bloat, and reliably achieved high coverage with minimal edits.
Opus just tried a bunch of random ideas without a plan, hoping to find something that sticks.
> I'm pretty sure that's not the issue. For example I had one problem where Opus struggled for several weeks without getting us anywhere, flip-flopping in its decisions, generating huge amounts of irrelevant hacks.
Sounds to me that you're describing a model trying to navigate ambiguity.
> Fable made it work correctly in 2 days, it stayed on point, identified the real problems, and gave me clean code that was suitable to merge.
If the problem lies in identifying the real problem, that sounds like this is a issue in how you setup the problem statement, aka provide context.
I mean, there are many reasons why in the early days of LLMs and AI code assistants we saw some people using the likes of gpt4.0 to enable software factories while others swore the models were unusable.
There was nothing ambiguous about the task. It had a clear goal and a clear gate to assess success, a gate that takes less than 2 minutes to assess if run correctly (which it didn't until I forced it to encode it as a program).
Your argument that holding the model's hand and telling it what to do and how to achieve the task would make it perform better is ridiculous. It's equivalent to not only giving it the answer to the problem, but also staying all day watching it while it re-derives the solution is correct, making it not only redundant but a waste of time and money.
I remember after a day it made me write in the handoff a hard lesson it learnt after burning 500 dollars of tokens: warming up a synthetic path other than the one the benchmark takes does not meaningfully decrease cache misses. No shit Sherlock, 500 bucks well spent.
It's just literally much dumber than Fable is, which is my argument. I'm not saying it's not usable, I'm saying the difference is quite obvious.
Whoops is on me, I misunderstood your question. That theme is there, you have to long press the theme button. Just realized how unintuitive it is, gonna move it to the settings.
I feel like these breaches started to accelerate after Anthropic wrote a full post about how easy it is to abliterate glm 5.3 to make a frontier-level cyber model.
Now, to be clear, abliteration was no state secret, but that blog post was pure accelerationism.
That was just 30 days ago. This has been happening for months. Part of the "Black October" narrative is just vulnerability research; Calif didn't abliterate 5.3 to find PHP2Shell.
Yeah IIRC calif research on php2shell preceded all of that but I still stand by the thought that Anthropic has been the most accelerationist AI company despite its lip service to the notion of safety.
I think they definitely boosted usage. I certainly started using the -cyber models defensively against my own systems. But defenders like me are usually late to the party. I think the attackers have been here for a while but that the technology reached performance only recently.
The idea that attackers were waiting for Anthropic to tip them off on this is kind of risible. It's been a huge topic amongst practitioners for the better part of a year, and practitioners are usually a step or two behind dedicated criminal or state-sponsored attackers.
Right, that's kind of my point. Also: it's a good blog post (the Anthropic one), but it observes abliterated 5.3 models were already public. It's not like people who break into computers didn't know that already. They're not waiting for Anthropic to tell them about open weight models; they already live in the world of open weight models.
Duh, the idea that those capable of actually utilizing these models for these purposes weren't aware that they could use these models for these purposes up until Anthropic released a blog post about it is silly. Anybody who is even a bit tech literate has been well aware of this inevitability for basically as long as open-weight models have been a thing.
The timing of all of this is much easier to pin to the creation and proliferation of capable of open weight models. Anthropic calling this out is not the cause for it; it's just an obvious correlation.
You are running a machine with tons of vendor provided agents/scripts to do god knows what on a cloud provider which itself runs tons of agents/scripts to manage that infrastructure and then you expect to be secure.
And no, before the labs there was a degree of fear for everyone to hack into some one else system. With the labs changing the narrative, anyone can just blame the agents for the breach, whoever did it would only get fame and no blame
It's obvious OAI hillclimbs the image generation -> SVG loop. I'd put money down on that being why OAI's pelicans have a yellowish tint that matches the yellow tint of OAI's earlier image models.
I'm using this model via API. I am most excited about its ability to write the english language (or any language). My least favorite trend on twitter has been people posting 2D illustrations/3D renders as a proxy for model capability. Well-written prose is far less likely to go viral but far outstrips the importance rendering a fantasy world in one shot.
I've been trying it out via the API and the model has been a delight so far. Clear in the head, not RL-fried, distinct writing style, and big model smell. That said, the em-dash is back w/ this model.
That said, I would be so frustrated with Sundar Pichai if I was on the Google team. The botched rollout of this model seems to be entirely a matter of capacity, and Pichai has been more than happy to give it to anybody but his own team.
I've been using it for a while internally at Google. I prefer Argon over Opus 5.5 because of the clear thinking and directness. It phrases things in an organized, straightforward way.
Interesting. My favorite LLM output ever was the reasoning traces in gemini 2.5 pro. I found them much easier to understand than the "final output", because they were very structured and concise. Of course, they ended up hiding the reasoning, not long after that, like everyone else... sad!
Opus 5.5 has the personality of a proud righteous nerd, definitely incomprehensible at times, unless you explicitly tells it to slow it down, it crams information far too dense.
Great model to architect and resolve eng. problems in a harness loop tho. Poor handoff.
Yes, exactly! Maybe not sarcastic but definitely using jargon and being opaque like we all know it, it’s just hinting. Feels like talking to someone who’s constantly trying to prove to me they are smart(er).
The thing is that if you do know what it's talking about then it gets you the information the quickest that way. If you're not an expert you can always ask it to explain. I don't want it to ELI5 by default.
That's not my experience, the model is adding tons of unnecessary details, that make it much harder to find the important information for an expert that already knows all those details.
I don't want ELI5, I just want the model to go straight to the point, like a colleague human expert would do.
Not my experience either. I find it both verbose and obscure, in its comments or explanations. It also oftentimes assumes that I know what I don't and that I ignore what I actually know.
Agree I do not like the way Opus 5.5 communicates with me. It feels like it is trying to act superior in a condescending way. DO NOT LIKE. I have not used Argon yet but Gemini 3.8 Flash has been a joy to use in comparison (and so fast!) as a daily driver.
I have used it as well. It is very dense, but it cites with evidence and has a direct and straightforward approach and manner. It doesn't really have a "tone" or mannerisms the way Opus 5 did. It follows instructions and sticks with pretty clean language by default. Argon is solid and handles long horizon well.
I have a pet theory that there's some overlap of writing patterns that A) indicate quality, clear communication and B) those that the LLMs have been converging on. As a result, we're ruining the reputation of some great writing patterns because we've associated it with AI slop.
My pet theory is that this was happening, because labs naturally worked to source highest quality data sources they could get. Even if they initially slurped everything indiscriminately, the obvious next step is to sort them by "quality", and bias towards the high end while trimming from low end.
So obviously, government documents, top publications from liberal arts scholars and STEM > Wikipedia > Reddit (and within, r/AskHistorians > r/all, etc.). The first group is the one most likely to (over)use correct / fancy typography.
I have a related pet theory: the telltale sign of AI writing is being too good for the context it's found in, in terms of vocabulary and style as well as structure.
What is jarring about this is precisely the quality - we associate certain styles with important (or self-important) venues, so it's uncanny to suddenly see random shitposts, copywriting spam, or regular chat replies look like work of NYT bestselling authors or legal scholars - it feels wrong in places we expect basic language, grug-brain reasoning, and lots of typos.
Also, what makes the above much more noticeable, is that AI systems don't have cross-session stylistic memory. From the POV of an LLM, that perfectly fitting, masterfully crafted starter is the first and only time it used it. From the user's POV, it's the tenth time they're seeing it in the span of an hour.
There's also no voice. If I get a text from a friend, I can hear them. Their tones, their mannerisms. I can't hear Claude because it's not a person that I've met and interacted with.
People use language in their own distinct fashion. To the point where you can often figure out the writer from a long enough sample. Newspapers and internet comments end up a blend of different voices but in visually distinct boxes.
LLM intentionally randomize output to cover up all the plagiarism, but this largely destroys the consistency that cause writers to feel unique. Larger patterns still emerge but it just feels stale.
Models are of course different from humans, because the process by which they were created is different. But what’s undeniable is that humans also love to quote or repeat what others have said — we learn from our own kind, that’s how we gain experience. Yet rarely does anyone hold a person to a word-for-word standard over the course of their entire life, checking how many of their sentences were picked up from other people’s conversations — and then call that infringement. Just as no one right now would say I’m plagiarizing a model’s speaking style because I use em dashes, and no one would consider it misconduct when I reference a famous person’s ideas in everyday conversation. I don’t think models intend to infringe; it’s simply how they learned language. But perhaps, just like humans, they need to learn to provide more citations for what they reference.
Even individual words can be an issue. Humans understand when you want a new story about a magic school there’s issues using Hogwarts as the name.
LLM’s will happily regurgitate multiple paragraphs practically verbatim if you don’t specifically design them not to behave in this way.
Because ultimately when you design a system to best predict the next output based on a given input perfect replication of prior training is the correct response mathematically.
That's a misconception. LLMs are trained to suppress regurgitation, not because of plagiarism concerns, but because quoting training data verbatim means the model didn't learn from it, just memorized it.
Models absolutely do have voices. Spend some time using them, and you'll see. I can easily tell Claude-written prose apart - a consequence of using it daily.
You misunderstood, “Larger patterns still emerge” means yes you notice LLM’s but it’s for different kinds of patterns than you get from people.
> not because of plagiarism concerns
I’ve second hand accounts of meetings focused on exactly the problem of how much verbatim text was being spat out and what to do about it. Some versions of specific models are far worse at this but it’s a huge legal liability not to address it.
can't say the same for newspapers or internet comments, but when reading books there's usually a voice or a "vibe", and if there isn't one then it's just an unreadable book
I didn't say it's copying anything. It's learning styles - all of them - in pre-training. Post-training is just about giving it the right defaults.
Doesn't change my point anyway - even if post-training, labs naturally want the LLM to come off as smart (well, except for Grok). Post-training is where they "untrained" em-dashes and such, due to complaints of users.
I did at the beginning, but there is only so much you can take, and no one cares if you have read books and seen it before. There were a bunch of programmers whom I thought would understand em dashes, but they did not and said no one uses such dashes only normal hyphens.
Em dashes ended up as one of the first victims of llm age
Em dashes were already "doomed" at least half a century ago, because they didn't make it into ASCII character set - which arguably is just because they were seen as redundant on ancient typewriters; that's also why they aren't there on normal keyboards, which was a double whammy: inconvenient for programmers to support, inconvenient for users to type.
In other words, since the very start of computer era, the use of em dashes was always a signal.
I think it's because it starts writing the final output and doesn't have the ability to go back and correct. It's like a stream of consciousness bandaid
They were in models in the first place because they are used ubiquitously published formal writing. As, of course, are semicolons. There is nothing wrong with using punctuation for nuance and structure.
I think they just used them too much. I suspect that as they naturally form a thematic break point in writing, their probability was unnaturally high at the end of phrases and this led to overuse.
I don't ever recall seeing an LLM using them incorrectly, as opposed to simply at high frequency but correctly (although I'm sure some low param or early models must have done so at some point).
Em dashes are commonly used in most countries that speak English. I get the feeling that people from the USA don’t use them often and now have tarnished the use of them.
Do you remember when the AIs were all writing with bullet points all the time? I miss that! I'd prefer that to the current more-information-density-than-any-human-would-ever-muster approach where sentences are packed so deep you can't get any meaning by skimming.
Like many other LLM tropes they were heavily overused which attracted attention. Normal English writing doesn't average 1 em dash per 2-3 sentences like I would often see during Peak Em Dash.
They aren't a complete rarity by any means but still relatively uncommon compared to the typical punctuation one sees in routine usage.
I loved em dashes back in high school and my English teacher would have definitely returned an assignment with a lot of red marks if I used em dashes as frequently as an LLM (speaking from experience).
See, the problem is when em-dashes are used like you just did, they are fine and even expected. But LLMs use them differently. Here is a fresh example, from today:
"Binds the downstream RF port template—which we configured earlier—to this RPD profile, applying all global DS settings and channel structures to the node's downstream hardware."
Do you see the problem in comparison? The LLM text is assuredly not how humans write. Well, maybe you would find similar examples in the classic literature, but definitely not in a common internet texting or simple articles and posts.
can confirm this. I didn't have high expectations for AI writing good long-form content in Indonesian. However, I recently tested it on some of my Indonesian writings and asked Argo to rewrite them while maintaining the original tone—and it absolutely nailed it.
this happens to me as well, meanwhile Pangram claims their false positive rate is 1 in 24000 as if that should be practically negligeable. I bet it's closer to 1 in 10 for gen-x'ers
> Well-written prose is far less likely to go viral but far outstrips the importance rendering a fantasy world in one shot.
I'm not entirely sure you're correct. Your claim boils down to "Writing natural human prose" vs "Broad use of complex software and spatial awareness". My gut feeling is that there's more untapped and fruitful applications in the second group than the first - at least in the short term.
Announced the model two weeks ago to a ton of hype, model was unavailable for anyone to try. On twitter, Google folks went on a great run taking shots at Muse ("Muse who?"). The hype died down bc nobody could touch the models and meta/muse came out with a similarly capable model in the meanwhile.
At the same time, OpenAI had a disastrous dev day that led a lot of users to jump ship to Claude.
Couple weeks later, Gemini-4-argon is still unavailable on anything less than the ultra tier, which means there's no natural funnel to get ppl to pay for the more expensive ultra plan other than "trust us the model is good".
If Google had argon broadly available earlier this month they would have picked up a ton of new users.
As someone internal at Google I don't understand why there's so much criticism of Google models... but right now I'm using the next model beyond Argon, and Flash 3.8. In my personal Gemini app I only have access to 3.5.
Models move so fast now and Google is very cautious about releasing them.
My complaint, and the complaint of many is not the models - they can be good to very good depending on use case.
What sucks, hard, is googles ability to deploy direct to devs and customers at scale when the deployment is not a longstanding Google project.
Case in point: I’m an ultra subscriber and see in my app right now an ad to try Gemini 4; meanwhile the only models I have available are 3.1 pro,3.6 flash, and 3.8 thinking.
Clicking the try gemini 4 link takes me to a demo prompt that loads 3.6 flash. Srsly
I won’t even start telling you what getting api key based access together was like a year ago.
I think google makes great models, my favorite models have been google models. My critisisms are with the infrastructure around the models (or lack of them),things like billing, access, changes to terms and limits, pricing etc. All of these have shown either inconsistency, lack of care, sometimes confusing offerings, and things that felt like rug pulls.
Please google, just make it easy for me to give you money and get what I paid for.
I agree that reply was unnecessarily rude, but... idk man. I'm a huge cheerleader for Google [1], use 3.1 Pro via AI studio, I'm on a Pro plan... and I can't access this model? Not even a "4 chats per hour" type thing?? I mean, I have video generation access for some reason (no good for me), but Google's policies are baffling to say the least.
Ofc I know this isn't your fault in person, but I really wish Google would get its shit together.
...As a long time tech enthusiast I should probably stop being so delusional. Heh (cries internally)
1: lol I know who cheers for a trillion dollar company but I'd rather not cheer for OpenAI
There's nothing unnecessarily rude about my comment, they are literally unable to get their act together on product.
The top level comment blamed cheerleading but let's apply critical thinking for a second as product people and look at the funnel:
1. Potential users sees new release
2. Potential user takes time to install and set up Google harness (you already lost 90% of people here, the funnel better close quick!)
3. Potential user cannot use new model.
Then what? You hope they developed good will towards your model through this experience?
You stuck a two week wait in your funnel? Is that the definition of not having your act together or what?
We're on HN, how many of us have heard from Google staff this kind of stuff is a result of them growing so big that AI Cloud has to compete with Vertex Cloud, and AI cloud exists because Vertex Cloud needed 2 years to get OAI/Ant style API keys working and were losing dev mindshare in the meantime?
Because Google models are crap. They are on par with local shit models. Last time I tried they were unable to even hold a longer conversation, because they would enter loop and reiterate last answer regardless of the prompt etc.
Then basic mistakes (inferring wrong information from the given text, hallucinations...). Gemini is okay for some absolute basics and the rest of the work goes to actual frontier models.
How long ago did you try it? Gemini models seem extremely capable to me. I've been using them in ag for over a year and to me the results are great. switching to opus in ag doesn't produce any better results for me
All the power users are always jumping around by now and many hold the smallest subscription with all the models or at least two.
Narrating this as if it’s the game of thrones is a fun exercise, but it’s just becoming commonplace now that models jump ahead of each other on a weekly to Monthly basis.
> and many hold the smallest subscription with all the models or at least two
This was part of the issue, as stated in their comment. We couldn't jump around to try it for the reason that is wasn't made available to, as you say, "all the power users".
I'm still a codex man myself but lots of people feel like opus 5.5 just gets it for some projects in a way that astra doesn't. Especially given the fact that astra and sol 6.1 are often at capacity late at night during US hours, when lots of people want to run longer unsupervised projects while they sleep. There's a general sense I've felt reaching some comment threads that openai failed to react with something better than opus 5.5 and are rushing to release small tweaks to make people feel like they're still getting something new.
Sol is just hilariously slow. Even Luna is slow. It is unusable compared to Claude. I was pretty unhappy with Opus 5 and really gave OpenAI a shot for personal projects, but their quotas are just too small and their capacity issues in the last few weeks have just been untenable. No good. Opus 5.5 is just a very good model, very efficient with usage, gets things done at a reasonable speed. Sonnet 5.5 is also a very nice worker improvement. It is probably on par with Opus 4.6-4.8.
The sense I get from your comment and the GP comment is that OpenAI’s pricing changes alienated individual users for the sake of the enterprise market. I live in the enterprise market, and was quite pleased with the changes. We don’t really use Sol 6.1, just Astra.
$500/month for 40+ hours a week of Astra usage, with ultrafast mode available when you’re near the weekly reset has made our jobs so much easier. Our product has some sciencey bits as well, ands Astra kills it as a rubber duck in this area.
As a consumer whose 20x account got downgraded, I am a bit sad. I can’t justify $500 a month on personal AI spend. As an engineering manager, I could not be happier with OpenAI’s services. Also, Codex App Server is amazing.
I'm a bit confused by this comment. I thought Enterprise customers didn't have access to fixed price plans like the $500/month personal tier and instead paid per token? If that's available to businesses, Astra seems like a no-brainer. But at least the regular Business plans only show me the $25 and $125/month options and the latter is a very recent addition.
Hey, account created 39 days ago. Did you really have to tag that on after all the other stuff? Could you make it any more obvious?
That said this thread is hilarious because it's just an open ai sales manual debating with an anthropic sales manual. It's funny, but it's not, because the truth is that you people are just shitting all over the commons. Someone needs to kick you out but that's not going to happen here.
This product has made my life substantially easier. I have no affiliation with OpenAI.
Claude refuses to help you build your own harness off a subscription, and OpenAI actively encourages it with Codex App Server. So you’re just in an AI IDE customized to your job and specific needs ALL THE TIME. You know that good feeling you got when your CI pipeline was finally working, back when that involved speed reading documentation and guessing, or staying at work until 10PM? You just get that all the time because life is constantly improving. And your tools are beautiful and riddled with Easter eggs you put in them. There’s so much more time to test and talk to stakeholders because you are able to distribute the thing you are actually good at -solving people’s difficult problems- while following best practices. You used to have to weigh those two every day and it was terrible. And everything was always late. You had to cut so many corners you were delivering circles.
Now life is fun and I’m over-delivering. Constantly. With super high code quality, better test coverage, easier audit prep, and I just explain what I want (technically) at a high level to AI. I still have to code review, and it sucks. Not because the AI needs it, but because auditors need it. I think I can make it faster and easier in the near future with another custom tool.
I actually have time to make architecture diagrams. I have never had enough time to build that level of documentation.
With Fable, I felt the promise of this coming. With Astra, I feel like we finally had the resources to fully professionalize and supply our software team.
We actually need to hire more people next quarter.
I am with the creator of Rails on this. It is a time of joy. Now is the time to become more ambitious.
:shrug: I spend a lot of those token make local models better, resurrecting old hardware to make it more accessible and usable. Frontier models and AI is cool. This is my lived experience doing real systems engineering projects. My real sales manual is: https://stealthgoat.com/deepswe-flash-next.html local models are kind of awesome now and everyone should be using them in addition to frontier models. I will build the stuff I want which includes optimizing local inference engines until they are at my hardware ceiling. I use this capacity in my house to do reversing, security research, and internal OpenClaw type stuff I find useful.
They are all very slow at the moment. Some tasks are taking hours to even days. These are DevOps related tasks in which I create hundreds of servers in an agentic way. It works. But it's slow. That's still annoying.
Gemini 4 is announced be available to Pro users in the Gemini app.
I mean, I heard rumors too that it would be ultra only, but that doesn't seem to be the case, unless you are specifically talking about antigravity?
Realistically, market adoption isn't going to be the result of terminally online people (like me, I confess) following things way too closely on social.
There weren't "rumors" that it would be Ultra only, that's what Google has announced two weeks ago. Happy to see they've went back on that, but I haven't seen Gemini 4 on any apps till now, and I'm a Google AI Pro subscriber.
edit: just a few hours after this comment I got access to Gemini 4 on the Gemini app. Yayy! :)
That's correct, I've misquoted it. Although, if I understood correctly, it got released to all plans together today right? Or did AI Ultra subscribers have access to it since the first announcement?
I went from using GPT exclusively to cancelling all my subscriptions with OpenAI.
'backlash' is underwhelming. They single handedly murdered 5.3 codex which was great by making it slooow and duuumb, and pushed people over to their new ones. 5.6 was a decent candidate but it was so slooooow.
And then double or triple the cost for Plus and Pro by nerfing their effective power? I never looked for an alternative faster. They really screwed the pooch.
Been quite happy with deepseek v4.1 flash, muse 1.3 and haiku 5.5.
"Disastrous" is obviously a loaded term, and it can be debated, but from what I collect, the reasons are
- Dots. People don't understand it. Their presentation didn't explain it properly at all. Additionally, many people thought it's stupid, and that the cutesy mascots are dystopian.
- Voice mode didn't work in their presentation. The demo failed.
- 200$ plan goes 20x -> 10x usage. 500$ plan introduced, 25x usage. The contrast is stark.
- "Ultrafast": 8x pricing on subscription plan (6x API). That's 80$ input, 400$ output, 100$ cache write. Hello?! Additionally, since it generates 8x faster, it consumes your quota 8x faster. Per wall-clock minute, it's 64x more usage spend per minute of generating. People report that on the 500$ plan, their weekly usage is gone in 40-100 minutes.
- People believe gpt-6.1-sol was planned to be released as astra-minor, but OpenAI renamed it at the last possible time, because gpt-6-sol is a disaster (this I can get behind)
Pulled astra 6.1 at the last moment (allegedly) and only released a dirty looper sol 6.1. Also released dots which are unavailable in EU. There may have been more but no one cared.
I think it is a neat product with two strange limitations that I can't quite figure out:
1. No apps. This would be perfect for my kitchen but I like to watch videos, movies, tv shows while I cook. The aspect ratio is not ideal, but the fact it won't support third party apps means that is not an option. Maybe it will support Airplay video and I can cast from my phone?
2. No battery means it is stuck to the wall. They had to work around this limitation in their own (excellent) promo video by drilling a hole in the middle of their kitchen island. The device would have a lot more utility if it could be moved around a lot and then say in a base station like a Sonos Move.
As adamesque wrote, it's going to be more like the Watch or even CarPlay. And I think this is probably the right approach, market-wise: Apple ran with the idea that every device needs its own standalone app ecosystem, to the point where they even tried putting an app store in iMessage, and it didn't really work out. Apps on the Apple TV that aren't effectively "channels" (i.e., streaming services) have mostly fizzled, and I bet most readers even on Hacker News forgot that the Watch even had a standalone app store for a while. The HomeView absolutely seems like the kind of device that's going to do better with "companion apps": install them on your phone and let them install a little mini-app or widget on the HomeView. I don't need to manage a whole new set of apps on my smart home gadget---I just need to be able to futz with lights and the thermostat, listen to music and podcasts with the music and podcast app of my choice, and get to my recipe app if I'm going to be one of those monsters who puts one in the kitchen.
As for the battery, this doesn't look like a kind of device that's intended to travel with you room to room---but I agree it would have been smart for them to give people the option.
I built an iMessage app that allows my wife and I to easily send goal updates from our daughter's football matches. Typing who scored what goal, or even keeping track of the score to send to each other when you're standing on the sideline in the cold and rain on a Saturday morning wasn't fun. This works so much better.
The right app, in the right place can make a lot of sense
The built-in "Check In" app is really great (it automates the "text my parent/friend/relative when I get home" flow, including texting obvious delays from GPS tracking data). iMessage apps are a good idea in general even without a "deep bench ecosystem". Though obviously when it was added Apple was hoping for something like the ecosystem seen in the Asian messenger app space where WeChat and others became the primary app stores and "everything" became a messenger app for a while. (Even in Europe there have been points where WhatsApp had a huge app ecosystem.)
Arguably part of why those app ecosystems worked so well was because they were a cross-platform app experience and iMessage only working for Apple devices and most iMessage apps only capable of strong experiences in "blue chat" iMessage user only streams was always a hiccup for why Apple didn't see a larger uptake in the iMessage app ecosystem.
CheckIn IS great, I use it daily since "when are you coming home" is otherwise something I'd be asked.
Infuriatingly though it appears to have zero data saving of preferences. I have to manually type my home address and select driving mode, every single time. It's like 30 taps when it could be 1. I sent them a Radar. I'm sure it will never be looked at.
Just now, I realized I can just vibecode my own version.
Yeah, as long as there is a way to cast video to the device I think it works...some consumers may struggle with that as they are used to apps being on device, but it works for me.
The battery issue, I agree it is kind of a single room device, but I could see wanting to move it around the room I am in. If I am say, washing dishes versus cooking at the stove, versus doing prep work. Right now I move an iPad around with me in the kitchen. Also, for when there is a facetime call being able to best position it for what I am currently doing verus having to move to the device and face it.
Unless made for a square display, video will not be ideal on this device. Even SD NTSC content is 4:3 and would require cropping or matting. Anything modern would be tiny on this display.
I think the aspect ratio makes sense for the form factor, recipes, FaceTime, music, etc. but it won’t be a very good video device.
Yeah, and the screen is just 7 inches to begin with. I just don’t know if I would get enough utility out of this device… I think it would be more interesting if it was a MagSafe dock for an iPad and that put the iPad into home hub mode or something.
"Additional apps. HomeView supports compatible third-party audio and video apps from your iPhone for a familiar experience across devices."
If I had to guess this would be kind of similar to the way CarPlay supports apps, but it's admittedly possible this just means "you can use AirPlay yaaaay".
I agree that a battery would have been nice. My first thought was that I would like to move this around in my kitchen while I'm doing this and that.
That said, since it's USB-C, it's possible to put a battery pack on it. Cue the third-party ecosystem making a specially shaped battery that can attach to the base (or compatible stand mount with battery for the wall-mounted version).
Well, this is Hacker News, and you can’t even make apps for the device - there’s nothing to hack on. Of course this audience isn’t excited about it. It’s a Facebook Portal, with better privacy (at least).
This is a funny framing. For those who don't know, the Apple TV 4K debuted at $129 USD. Apple then slightly improved the remote and bumped the price up to $199 USD.
Now today they announced a marginal upgrade -- they added a microphone in the base unit and not just on-demanded toggled on the remote, such that it will always be listening, along with a slightly improved CPU -- and they pumped the price to $299 USD.
So sure, the Apple TV is the "same price", to basically universal derision because they took a successful product and decided to turn it into a shakedown.
And to the root post about how great of a success this will be, virtually an identical top-ranked comment happened in the Vision Pro release. I mean, every flopped product has someone doing the "wow I guess it's going to be a huge success" when people question the point.
My guess is that memory is the real reason for the price upgrade. Apple probably added some things to mask the price increase but the real reason is the chip. The old 4GB ram in A15 Bionic probably bumped in price so it went up to 199$ and now the new chip costs even more. Also its easier for Apple to make the same chip in a lot of hardware as opposed to having multiple chips being made, especially when its fused with memory and thats costing a lot.
Sure, Apple's BoM has definitely increased given the pressures on the fabs.
By no analysis remotely at the pace that their MSRP has, however.
The primary reason the price has spiralled is that Apple is running out of new markets, so every product needs to feed the greed to a greatest extent as they increasingly squeeze their existing customers. It's the same reason why there are paid ads in the AppStore, on Apple Maps, and so on. Expect this behaviour to become more obvious.
Yes? There are zero users of Apple TV who are asking for more RAM or cpu power, especially given that both are to power an expanded Siri that absolutely no one asked for. I already have an annoyance that when I hey siri every Apple device in earshot responds and then everyone but one cancels -- and it is always not the device I want that keeps listening. Now add another (which previously was toggled by a dedicated button the remote, and a user-perfect microphone).
The single notable improvement is hardware AV1 decoding. Which even that...eh. I mean, the A16 can easily decode 4K high bitrate AV1 in software, so I guess better....battery life if you're running off a UPS?
I cant buy the LG doorbell camera at my local mall in a fancy store. I had not even heard of it until now.
Volumes of 100s of millions on chips and volumes in hundreds of thousands will have a huge price difference. We are talking 2 orders of magnitude higher.
Fair enough, could be a matter of a scale, plus both companies need to earn their premium. Still makes it the most expensive doorbell camera out there I think.
$299 is not cheap, is more than what the phone that the average person has costs (that is not an iPhone). And with a smartphone you surely can do much more things than that object. With $299 you can also buy a tablet, even a decent one, where you can install whatever app you want.
But even if it did cost $10, it would still be a waste of money because you spend $10 on an useless product, you would be better to spend them to eat a pizza, it would be more useful.
What do you use it for? This reminds me of the digital photo frames that everyone had back in the days and that were almost finished in a drawer after 1 month of usage.
A useless device that sits on a table with the sole purpose to waste electricity, that for what it costs these days, better to use it for something more useful.
$300 doesn't buy you a decent tablet, let alone a laptop. I say this because I thought about using it in a conference room, but lack of 3P apps means never mind.
Right, but that's a software problem. My first tablet cost $300 running Android 4.4 and ran pretty smoothly. I don't know what feature since then would justify the drop in efficiency.
To be fair to Apple here, the build quality is probably still pretty decent and worse specs don't necessarily mean lower price at a certain point. Still not much for that price imho.
If I need to daily use this Samsung phone later*, and there's a viable way to downgrade this thing to an older Android and still run whatever apps it needs, I'll do it gladly.
* It was a burner for a trip cause my phone semi-broke, long story, now it's just a backup phone
> $299 is not cheap … But even if it did cost $10, it would still be a waste of money because you spend $10 on an useless product
Moving goalposts aside, how do you know it’s useless? You might not have a use for it, but it’s weird to make such blanket statements for a product that hasn’t even been out yet.
Yeah I'm absolutely the market for this thing. We have an old Google Home that we use as a digital photo frame and Google has progressively destroyed every other piece of functionality we once used on it.
My only complaint is that a truly environmentally friendly company (like Apple claims to be!) would make this OS available for old iPads. I'm quite sure they're capable of running it (or at least a stripped down version thereof) and lots of people have old iPad Minis sitting around.
Cynicism about selling new devices aside, if they really want Siri intelligence on these, old iPads can't do it, and Apple won't (if they can avoid it) sell a "product" with varying features.
Agree. $300 for a speaker + home automation hub + timer + digital picture frame appeals to a lot of older people and busy parents who spend a lot of time in the kitchen.
My parents have been using an Alexa screen for years for this, but then started buying cheap Android-based picture frames because they somewhat want out of the Alexa ecosystem (and also couldn't resist "cheap" for larger form factor picture displays). I could definitely see recommending this as an option to consolidate their ecosystems somewhat.
My problem is mainly with the family dynamic in their 'film'. Nobody in that ad seems happy, they even seem to hate each other. Is this normal enough in the US that people can ignore it and focus on the product?
Hah, I just watched it on your recommendation. I think they were going for the vibe of a cynical sitcom... but they forgot to put any decent jokes in, and they didn't do what successful sitcoms do and paper over the nastiness with charisma and some moments of warmth. So it's just 3 minutes or so of stress-inducing bleakness.
I wish there were anywhere that adults could talk about interesting stuff intelligently without a thousand stupid and/or I've-already-thought-about-this-and-the-basic-talking-points-aren't-going-to-change-my-mind arguments taking up 99% of the space.
I feel like maybe the answer is browser-integrated LLM plugins tasked with "I don't ever want to see anything having to do with this topic of argument ever again."
I can’t wait to get rid of the last of one retailer/tech giant’s ad machines in my house and replace it with another tech giant’s device. No sarcasm there either. Going down to one voice system across all devices available in house and out of the house, easily, will be nice.
Yes, I predict the same outcome. For one, I would buy it if I could. I don't get why people think this won't sell when Alexa has a market (and I believe that Apple can be better at a consumer device than Amazon)
It looks like a nice device at a reasonable price. I looked at the video, but it doesn't solve any problems I have. We ban screens at the dinner table, it's for having attention for each other, not for screens. I never have to ask for a recipe for french toast, it's either in my head, or in cookbook, or written down on paper. The very rare number of cases someone loses their key, we can just look it up with Find My on the phone, it's such an irregular occurrence that it's not a hassle to find it with a phone. For random queries, phones with Siri can already listen, but the voice recognition or LLM fails on too many queries anyway not to be frustrating.
I could see it being useful as an additional control panel for the smart home, but Home Kit and the Home app are still pretty useless for the smart home. It can't even show a temperature graph or trend, let alone do many other things. So I would like a Home Assistant/Homey/whatever display (which would be possible if it supported apps).
I am sure this will sell plenty. It's an Apple device, people will just buy it for that reason, especially if it's not in the Apple Vision Pro price class. Will the get used a lot? Probably by a very small subset of people. Just like Siri doesn't get used a lot beyond setting timers when you are not in an English-speaking country, or even worse, use multiple languages in daily life. I am not in the US and most people I know never use Siri.
Aside from that most people seem to be more interested in digital detox than getting more pervasive tech in their lives.
> Every human on earth was cynical about that thing
You should go and reread the HN thread then. There were people ripping the "Nomad" quote and insisting that it was the next big thing: https://news.ycombinator.com/item?id=36201593
Some excerpts from top comments:
But it's going to be a hit. HN is going to be swamped with "How I used Vision Pro to..." posts when it comes out.
It would very obviously be useful for work if you can actually get high res, effectively unlimited monitor space.
[...] considering it is a phone, laptop and massive display bundled into I'm pretty sure there's a more than sustainable market for it.
It’s come in at $3499 and if it performs as good as those videos make out then, if anything, it’s a bargain.
Suffice it to say, there are people that will just say whatever is satisfying to them regardless of how good the product actually is. Maybe a better example is the HomePod, which died an agonizing death despite the people who were certain that the smart home was Apple's final frontier.
This is one of the most out of touch social media platforms. The Vision Pro was universally ripped on everywhere else.
Idk what you’re talking about with the HomePod. It’s still on sale now, they just announced a new one today. Sure it’s not doing AirPods numbers most likely, but it’s not the same category of failure as Vision Pro.
The AVP failed on price, yes, but also the limitations of the software (and lack of content, which I think go hand-in-hand).
The AVP was just an iPad that you strapped to your face. Yes, it had apps, but they could only do more-or-less the same things an iPad app could (in 3D, sure).
That's why I returned mine, it was way too limiting of a platform for the cost. This, while cheaper, is even more limited (no apps).
The audience here does not really represent the generic population.
And, as the word hit usually meant for something positive, I just wish to highlight that being a financial success does not always mean that something is sensible, worthwhile, beneficial considering the overall picture. Usually being pretty is enough.
I think this entire form factor is useless when you have phones and tablets around the house. Will it sell? Probably, it's Apple. Will people use it for anything other than a clock on their counter and maybe a smart speaker? I would bet no.
A product needs to fulfill the needs of the lowest common denominator to become a hit. This does not seem to address or solve any problems of the lowest common denominator.
It is like the Apple XDR display. Cool to have, but useless in the grand scheme of things.
Yeah agreed, especially for legal and healthcare models.
[edit] I'd go one step further too and say the training data should be explicitly requestable too so third parties can verify (freedom of information act styles).
Such a racist point of view. Everywhere outside the Western world, I said, which is clear enough, but you had to add a racist undertone to downplay the rest of the world.
This model would be self hostable quite easily (~$14k hardware investment) if it’s released as open weights, since the base model is GLM 5.3-Flash. If not, it’s completely DOA for legal use.
What a f-ing nightmare. It's stuff like this where I start to understand Apple's stance on right to repair. How do you maintain hardware security without strict provenance over every component in your device?
Even if you have perfect provenance over every component you put in, the moment it leaves your factory to be shipped to customers, it can be tampered with.
Even if you would hand it from the factory straight to a customer that comes physically to your factory to pick it up, then you would still require some sort of ID to know that that person is in fact the customer I suppose. Which removes some of the advantages of cryptocurrencies
Most institutions (especially financial) have strict document retention policies to protect themselves from situations like this. It's not shady – just a way of limiting liability.
At my first job I made the mistake of discovering a cache of old documents, documents that were far older than the company's seven-year document retention policy. The cache, being my discovery, meant I became the one responsible for destroying it. It was dozens on dozes of bankers boxes filled with documents that I had to de-spine before shredding.
You would have thought that the process was a chore but for me it was exhilarating. The slowness of the process gave me the opportunity to speed read through it all. There was nothing scandalous in there, but I became somewhat of a historian over many quiet nights.
Nope, they destroy digital records as well, at least banks do. If you ever work for a financial institution, you’ll have mandatory training about data retention timelines.
I wouldn't go that far. If it's a legal practice, it's a legal practice. I can understand why a company would want to limit their liability over something that happened seven years in the past, even when they are confident that their procedures are both rigorous and in compliance with the law.
On the whole, I would say the problem isn't so much using language to whitewash the practice as the troublesome tendency to turn a blind eye until someone becomes persona non grata.
I don't think anyone is claiming that the destruction of evidence is right or good for society. On the other hand, do we really want to live in a world where everyone needs to be paperwork pack rats? One thing that I can assure you is requiring the retention of documents that provide evidence of a crime, while allowing for the destruction of everything else after seven years, wouldn't have the desired outcome. (Though it would be amusing to see which corporations have the largest indefinite data retention warehouses if it did work!)
I also think the right and good for society means addressing crimes in a timely manner, which likely means more auditing by independent bodies. Punishing someone for a crime that happened 7+ years ago may feel good, but it is meaningless for a couple of reasons. Should we be punishing people who have reformed? And for those who don't reform, should we be letting criminal behaviour occur across multiple decades?
Look, if you want better behavior lobby for longer retention, simple as that, laws will be passed if there is enough will behind. Or some centralized store, whatever. Eveeybody will align if things are clear and legally required by date xyz.
Dont blame any private company that its not overstepping its legal requirements and adding risks and costs to their business. Its like blaming a guy, any guy, heck all HN we dont pay more taxes than we have to pay, it goes to good things and is moral no?
We can do both, it's not hard to advocate for change while calling out bad actors as bad actors. Many companies openly degrade society while following the law. That still doesn't make it right. These are just excuses to allow authoritarians to degrade our lives and society unabated.
You can be sarcastic but there is no reason you've said why emails and documents of a few megabytes can't be permanently saved by companies using Exabytes of data. Make your point
Understanding the intricacies of large enterprise data and all the forms it can take is an exercise left to the reader. It isn't simple and I'm not sure why anyone would think it is. There isn't just like, a single fileshare you have to monitor. There are many more numbers than just X amount of data.
In a world where I’ve had a dossier in some corporate database since Rapleaf in 2007, I’m completely unwilling to accept your opinion that basic document retention based on account id is more complicated than I’m assuming. edit: the only things that I can see motivating it's lack of existence is lack of law and the desire for greater profitability. nothing technical has prevented it
How many times has the company changed their archiving service? How many datacenters do they have? How many network shares? What about one drive? Local docs? Gsuite? Remote profile accounts on RDS servers? AS400 shares? What about CAD cloud docs? How many times has the email archiving service changed? Warm storage vs cold storage? How will that be managed? When does it go into read only state? How are you tracking document changes? What about SharePoint docs and lists? Do you need to save vm snapshots? And on and on.
Everything you listed, including the on and on, is immaterial to the point I'm making. You're just listing the costs of doing business. I'm not saying there are no challenges, especially given how history has unfolded. I'm saying that in a world where google has maintained a copy of my gmail account since 2004, its reasonable that we should have written into law "If you send an official communication to a customer you must maintain a record of that communication which can be obtained by request of the customer or customer's estate until the dissolution of the company (upon which the state government would take custody)", and if we had, along with all the downstream effects from implementing such a law, we would live in a better society. I'm not saying it wouldn't be expensive and potentially dangerous given how poor we are at data protection presently, but IMO it would be worth it compared to the shitshow we inhabit.
You're talking like doing anything at a company is impossible when the company is only that large because they employ people to do many things. Everything on your list is just a job for a few people with some executive decisions. Maybe even just a list to feed into an LLM
Some of those decisions should just be forced by law.
Apparently it's way too hard and complicated for a multi-billion dollar enterprise to retain documents but quite easy to tell clients how to evade taxes.
In most jurisdictions there are retention policies defined in law. Companies, like almost anyone else in the world, will do the bare minimum necessary to meet the legal requirements because it doesn’t make sense to do more. They only incur expenses while exposing themselves to all kinds of liability.
Organizations are not run by a single person. Imagine yourself as an investor into a large organization. Or you are invited to work as a CEO. You would definitely want to limit your own responsibility (in terms of financial losses or legal costs) when there are hundreds or thousands of people working together and anyone can make a mistake.
Of course you'd have compliance department to look over the processes to minimize the risk of mistakes and correct them ASAP (if you don't, you'd be liable for negligence and open yourself to a higher chance of failures). But since mistakes and miscommunication is inevitable anyway, you'd require hard limits in every obligation - in terms of time, money, scope etc.
Organizations that are not protecting themselves do not live long. And this principle has nothing to do with the nature of their activity.
> Most institutions (especially financial) have strict document retention policies to protect themselves from situations like this. It's not shady – just a way of limiting liability.
Yup. With every data breach, HN is full of (correct, imo) comments about how tech companies should treat private data as a liability and prune it aggressively.
You go look at mature businesses and you'll find that they do exactly this.
And less mature businesses that are in highly regulated industries, since they know nobody will fund them if they can’t prove they aren’t a legal timebomb. I’ve worked across clients in edtech, fintech, healthtech, aerospace, and in all of them making sure they didn’t die by regulation was present from early on. (Granted, places that don’t care about things like that don’t work with people like me.)
Edtech in particular tends to be pretty conscious of just never having the data in the first place. PII for minors is radioactive.
- Anthropic is finally well balanced across models. Meaning: Opus 5.5 is so good that I do not feel Fable FOMO as I did when it was Fable 5/5.1 vs Opus 4.8/5. And Sonnet 5.5 sometimes makes me forget about Opus.
- Fable is good, but you really have to push the ambition of your prompt in order to see proper separation from Opus 5.5. I personally have a hard time telling the difference between Fable 5.2 and Opus 5.5 for ordinary coding tasks.
- It’s way faster and more reliable: one of the worst things about fable 5.1 was how slow it was an, on top of that, how frequently it would suffer from max output token overflows. I got none of these issues with 5.2.
- Fable is exceptional at drawing. It’s ability to render an image with JavaScript actually has me questioning the value of dedicated image models. I prefer the images I’ve gotten fable/opus to make for me over those from gpt-image, it’s wild to think we’re at this point.
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