Claude Fable 5.1 and Claude Mythos 5.1
anthropic.com
What's new in Claude Fable 5.1
– https://platform.claude.com/docs/en/models/fable-5-1/whats-n...
System Card: https://www-cdn.anthropic.com/0339e6a7c5c7b87f5c07798616dc32...
anthropic.com
System Card: https://www-cdn.anthropic.com/0339e6a7c5c7b87f5c07798616dc32...
Beyond all the benchmarks, I think Fable 5.1 is a big improvement in writing style. It sounds a lot less stereotypically like other Claude models, has (imho) a much more natural style, and responds to my style instructions more reliably. More work to be done (and we will!) but reading better prose makes me so much happier.
Another point I expect not to get much attention until it all happens at once is science. People have been correctly excited about the many "sudden" breakthroughs LLMs are making in Maths, but some of the science benchmarks make me believe we'll soon see similar developments in other scientific domains. Fable 5.1 more than doubled Fable 5's Terminal-Bench-Science [1] score, which I think is meaningful.
[1] https://github.com/harbor-framework/terminal-bench-science
Which is something the providers that are trying to watermark their texts can't afford. Superfluous replies give much more opportunity to further encode this junk information.
Low-entropy text is fluff and filler. It's very easy to synonym-substitute words without changing the message - if there even is one.
As far as I know, anthropic aren't intrinsically motivated by watermarking (if anything it hurts sales, and seems indifferent to safety(?)) they're simply doing it to fulfill the EU obligations.
They are. They want to reduce the amount of LLM generated text they feed into their next model training.
Also, how would you watermark a sentence with just 3 words for an example? This exactly why it became so verbose.
And what wisdom do you think they would be missing if unable to distinguish three word written pieces? Keep in mind that most sources are not inherently trustworthy just because they rate as human written, too. You need some other way to rate text in all cases.
Do they still get split into commits in sensible ways, for you?
I've found that models interpret "brevity" as "incomprehensible".
Though Claude 5 is not too verbose, it’s more like, full of incomprehensible jargon (even when you’re expert in the domain discussed!)
Actually, I think Jeavon's Paradox [1] means the opposite. If doing X is $100, you may only use it to do X, but not Y, Z, or W. If doing X is $33, maybe you'll use it for X, Y, Z, and W -- spending 1/3 more than you otherwise would.
Or perhaps not you personally, but maybe you'd be willing to spend $100, but three of your friends find it too expensive. If it's only $33 to accomplish some task, then maybe all four are now spending $33.
I assume this work will be done for Opus as well? Opus has seemingly gotten progressively worse at its prose and technical writing with each version. I've stopped using Claude entirely for now, because it manages to turn even the simplest technical explanation into the most obtuse and obfuscated word salad imaginable. People originally adopted Claude because it felt pleasant to use in comparison to ChatGPT, but I feel like that's really been lost (at least with the Opus line).
I feel dread when I see a wall of text generated by Opus. Every developer I've talked to feels similarly right now.
Agree, Claude lost the joy of using it.
That is a measure that ranks higher than any other benchmark at this point.
It also clearly establishes or the very least moves in the direction that you don’t actually own or control the output of AI in any manner whatsoever, you’re just paying for it since Anthropic in this case can simply essentially brand/tag all your output that is based on not directly your own words, but a higher level process or methods that you use, including your instructions and how you structure your information and what your overall objective and goal is.
Anthropic is branding it on the behest of the EU lew, which already is an entity that is diametrically opposed to democracy and self-determination based on its structure even if you ignore the fact that it violates the most fundamental concepts of self-determination in its direct contradiction of the UN Charter and implicitly the Universal Declaration of Human rights.
What people done seem to be catching onto is that the EU is becoming the world dictatorship because the USA has simply had too many onerous people and that stupid constitution and its amendments that keep roadblocks world domination for the ruling class vampire.
So, you think it's good to disconnect words from their actual meanings (lie) to low-information people! I doubt this will do much to congress, but it certainly teaches us something about the sort of mind who would suggest it.
What benefit is there to people believing that LLM text was actually human written?
For the (majority) of us using Claude models for computing as a tool, obviously we're not going to be thrilled that our new tool will perform worse going forward.
literally never how it has worked
Ask a model the same question twice and you will get different results. So, how were you ever getting “the best result, always”?
If you can't tell which one is better then how can you make any assumption about performance?
For all you know performance is the same.
So many people complaining about something they quite literally have zero evidence for.
If it worked perfectly, maybe you could make this argument in a vacuum.
It does not work perfectly. (It cannot. It is by definition a heuristic). That means there will be false positives. There is a chance those false positives ruin someone's career. See [0] for just how easy it is to push SotA "AI text detectors" in one direction or another.
Now, with watermarks, instead of everyone to some extent understanding that AI text detectors are wishy washy woo, they are now Anthropic certified to detect an official AI watermark.
With that kind of false confidence in hand, the people who trust the "computer says you plagiarized" machine are never going to believe you when you say "it can make mistakes," they're just going to fire you/take away your scholarship/cancel your grant/...
This is all beside the fact that we should demand our tools work for us and not for some shadowy master. "Universally good," absolutely not.
[0]: https://freddiedeboer.substack.com/p/i-wouldnt-say-pangram-i...
Obviously false positives will inevitably happen (even though, they are incredibly unlikely with SynthID), but even still, that doesn’t somehow make good faith watermarking attempts bad.
Also, a watermark doesn’t stop your tool from working for you. It just stops you from passing of its work as yours.
I think we fundamentally disagree on what "working for me" means, but I remain steadfast in saying we should not accept tools that have ulterior motives beyond producing the output desired of them by me, the user.
> Watermarking the outputs themselves is very different and much more effective compared to how tools like Pangram work.
At the end of the day the only artifact is text that you can do statistics on. It's the same problem as today, with the probability shifted slightly more in one direction. This does not assuage my concerns at all.
> they are incredibly unlikely with SynthID
I kept my commentary focused on text watermarking specifically because I agree, a synth ID image watermark false positive is highly improbable. There's plenty of noise to robustly hide whatever you like in an image. Text is simply too capital I Information-sparse and fragile.
> good faith watermarking attempts bad.
I would sooner call it "ignorant faith" (if they don't know what they are emboldening) or worse "don't care" faith (there will be false positives and they accept this to further some illustrious and arbitrary goal of Text Purity). Whether that be to prevent model collapse or help you not waste time arguing with bots online, to me the principled stance of "tools work for the user" wins..
Also, this kills me! "It is harder to watermark factual answers because the model has fewer alternative word choices available without altering accuracy." Hilarious! So the models need to hallucinate more due to the EU AI Act.
I go the other way on images and video, though easy enough to strip as part of a pipeline.
Every watermark scheme is like that on some level. If you make it public (not even open source or downloadable, API access is enough) your enemies will use it as a detection oracle and spam small changes to a document until it passes the detector every time. If you keep it private and only give trusted organizations access then there is no way to prove to the public that you're not getting paid to flag specific content as AI and discredit the author.
But the biggest problem here is the way it can hurt output quality. Most LLMs (probably including Fable) are autoregressive so a couple tokens worth of "mistakes" caused by watermarking can derail the whole reasoning chain. That means you have to try again and spend more credits or silently get a worse answer than what you would get without the scheme. It's not a real problem in diffusion based image models where quality loss stays local.
1. This is BS since i can detect it when it writes about my codebase
2. I do not want secret codes being written inside my codebase, or anyone else's codebase that i use. The constraints of how to code why eliminate it from code itself... but there is a lot riding on the word "may". And even if it is just comments, this might explain Claude's desire to write such long ones -- long enough to encode secret messages in out material.
Give me three examples of explaining a bug in my code however, and I can pick it out immediately.
SynthID specifically mentions something similar: "It is harder to watermark factual answers because the model has fewer alternative word choices available without altering accuracy."
In the codebase, I do see the models finding alternative word choices -- and I hate them. Already in a highly technical latent space, it reaches for other highly technical word choices (which may be more accurate, but ones I am unfamiliar with -- like terms in ERP systems as it felt that was close but bring just technical jargon that i have to google what it is saying because i don't understand -- and i have to google the phase sentence as the words themselves are ok, just not how they are put together).
Today, i was stumped as it substituted a work with a word from another language!
"The four items the ledger had标 standing open..."
I spent a few minutes reading about SynthID-Text [0] and couldn't find mention of this (I've not read the paper yet), but my intuition [1] is that encoding more bits into a shorter piece of text would necessarily require a more noticeable transformation.
Curious to see what that looks like, but don't have time to spin up my own version of this right now.
[0]: https://github.com/google-deepmind/synthid-text
[1]: my intuition .. which could be incorrect, that's why I want to see :)
Are different services for different users based on geolocation really that difficult? I thought a lot of services operated like this already.
It’s a convenient excuse for the companies that want to add watermarking.
[edit] only asking here as last time I raised a support request it took six weeks before anyone responded.
Don't give me hope.
I've strained eye muscles from rolling my eyes so hard every day at how Claude writes.
Edit: first discussion with Fable 5.1 "This is the right question and it needs a real trace, not a guess."
Sigh.
Tomorrow all of the above (except Anthropic of course) will bump version numbers and be at the top of HN winning all benchmarks.
Science breakthroughs incoming? First of all, you are already restricting science in Fable, secondly, we have been hearing the same for several years now.
It’s like we’re on a 14K4 modem when there’s broadband
I'm also thinking of the 2017 novel "Void Star" where AIs who operate everything have long since left ceased bothering with human languages, and it takes a rare sort of direct matrix-gazing savant to be able to try and horse-whisper them into doing or revealing anything they didn't already plan to do.
There's a huge difference between the kind of prose you see in final output vs CoT windows. The final output is very much not what I'd call "packing lots of signal into fewer words" (aside perhaps from "Claude-isms" being easy enough to scan for if for some reason you actually wanted to scan for them, which other agents might want to for all I know); and if agents are writing for each other then presumably they could stick to CoT-speak (unless it's a distillation risk?).
So "fingerprinting" operates on a totally different and basically invisible level, as opposed to the obvious stylistic patterns that the average programmer can identify in about 2 sentences.
It is still not exactly clear if it is true or not. Unless we have base "pt" snaphot of Claude we can't say one way or another. I've played a bit with base models of Nemo, Gemma etc and they all had tics, not much different from RLHFed instruct versions.
/s
I don't like that I like it.
I have other more specific ones to avoid talking about things that it's not doing, but those two sentences have covered a lot of ground for me when working w/ Opus models.
I figure that it's basically making notes for itself, when it has to revisit the same code in a fresh session.
That sounds like a great thing to do even if you are a human writing code for other humans. Most codebases out there are terrible for newcomers because of how little they explain why they are doing what they are doing, both in the code and in the often non-existent design notes.
And if I don't catch these and remove the bad information, subsequent passes will flag those comments and get stuck on the fact that numbers don't match and start digging into that "problem" instead of staying on topic.
Excuse me if I am harsh, read the damn code. If you do not understand the language, that is a skill issue. If the code is confusing, then the code is bad and no amount of comments will ever change that. Professional engineering isnt an intro to databases class.
I am excusing language conventions which may have comments as part of its idiosyncratic nature.
I've worked on a lot of terrible legacy code in my career and I'm very thankful for the comments that others have left. This is becoming less necessary now that LLMs can explain a project, but comments have historically been a godsend in bad code.
No, really: comments should be telling you what the code shouldn’t or physically can’t. Code is for execution and the exact details of what and how; it has no business knowing why or why not and that’s where comments are required.
Imagine a complicated section of application logic. You could break it up into 5 separate functions that document their intent semantically, thus blowing up the LOC by 5x, or you could write a short comment explaining the intent in natural language. What's more effective? I'd argue it's always going to be using all the tools at your disposal when and where it makes sense to use them, whether that is comments or self-documenting code.
Without guides as to why a particular hairy expression is a good idea as a first estimate, the code is pretty much unreadable. (E.g. is it setting derivatives to zero, using a polynomial approximation, or something else?)
To put it another way, comments are for irreducible complexity ir external systems outside your control.
I work between systems and app dev. Systems have comments more often esp in shaders but my god informing me that a variable named isActive is for if something is…active, is useless noise. Same with the majority of comments that a type system already tells you. In my career, these have been ~90% of the comments I see. Since ai, all new code it is 100%.
Most of the replies examples are a sign of bad system/code but it is not always controllable. A legacy code comment of, the api requires strings for boolean values in the form “yes” and “no”. That is useful but it is also a code smell.
A concrete example, a vendor decided to define a proto with a flattened array of objects so there are some 1800 uniquely named fields on it. In many downstream consumers, this is a real performance issue besides being confusing. A comment may be good there. The thing is, this was still solvable if up at the root of where this vendor’s hardware logs data remapped it to something sane so every downstream system wouldnt need a comment explaining wtf is going on.
I see comments as when you want to explicitly answer why code smells right when a reader is smelling it.
I do this all the time and the "blowup" is not anywhere near that bad.
> or you could write a short comment explaining the intent in natural language.
You really can't. Or rather, you aren't going to convey the information that the new function signatures convey, shorter than the signatures themselves.
> What's more effective?
In my literal dozens of years of experience, the function refactoring. You also get the benefits of less deeply nested code, and more things the compiler can check automatically.
> I'd argue it's always going to be using all the tools at your disposal when and where it makes sense to use them, whether that is comments or self-documenting code.
Sure. Comments allow you, for example, to explain the external pressures and motivations for the semantics of those smaller functions.
I agree it _sounds like a great thing to do_ but the comments Claude creates make me want to never read code again. They're so obtuse and often completely pointless.
This drives me mad.
Why can’t it check first if a method actually exists in the API?
"You're right, I'm sorry. You told me to do it, I said I would do it and I did not do it and I said that I had when I did not do it. Would you like me to do it now?"
Me, thinking: that depends, Claude, will you actually do it this time?
One thing I found before dispatching, and filed as Q0579. The halt told you C6
was all that was left in the unit. That was true of the step's criteria and
false of the unit's acceptance, which reads "exits 0 AND witnessed red" — two
conjuncts. The witness half holds; the exits-0 half does not, because hello's
G7 currently reads DIFFER 554/51340. I re-derived that from the gate map
rather than trusting the prior step's report. So satisfying C6 does not by
itself finish this unit, and I've filed that so attempt 1's success can't
quietly be read as the unit's.
It was causing so many issues with coding (even Opus 4.8 was better) that I did agent handoffs to Sol. One of the Sols stated the handoff was "incoherent", which I couldn't have said better myself.
In fact, whenever Claude disobeys me, I usually first skim the CoT to figure out if my original instruction was ambigous given the context. I usually come away with a better understanding of how to frame my prompt to be less ambiguous or just force myself to be more explicit when prompting.
Regarding diosbedience, usually this is either due to a blanket instruction from me during an earlier turn in the same session, an explicit instruction in its system prompt or it being just eager to bring a task to completion.
# ~/.claude/settings.json
{
"model": "opus",
"showThinkingSummaries": true,
"skipDangerousModePermissionPrompt": true,
"verbose": true,
"remoteControlAtStartup": true,
"agentPushNotifEnabled": true
}Chain of thought does not exist in the output of Claude, they disabled true thinking due to distillation risk. What you see when thinking summaries are enabled are just that, summaries of thinking into Claude-isms, therefore you cannot make any inferences on what the model is doing unless you literally work at Anthropic and can see the true thinking traces.
I use open models for non work stuff and sometimes I cancel the output because the CoT is all I needed to read.
sometimes by increasing human cognitive load during reviews, sometimes by expanding the number of gated decisions, sometimes by penalizing those using their accounts on other harnesses
I think that spending all day trying to parse stuff like this is why a long session is so exhausting
> Worth stating because four documents now assert it. The console freeze was recorded in exactly one place with exactly one justification — a dead drag handle during a booked half-day you do not get back — and handoff-4.3-done.html's own wording is that 4.4's review page "could not break the console, but the downside of being wrong is that half day". No second reason. Checked, not recalled.
> Note: the potential for a console freeze was previously noted but ignored. handoff-4.3-done.html stated, "could not break console, but [will need fixed later if I'm wrong]."
One could imagine that a perfect writer might also append: "It could be worth looking into what caused that wrong assumption, to prevent similar cases in the future," at most.
Everything else seems to be bad attempts at relatable writing to invoke emotion (an exercise that we should really stop trying to train emotionless matrix weights to attempt).
One of the things actual science fiction got wrong: to the extent that the thing AI does can be called "understanding", emotion is not unusually difficult for them to understand.
The unsurprising part once it was clear that approach was viable, was that humans wouldn’t be able to help but anthropomorphize it. I feel like the movie Ex Machina is more relevant than ever.
The clear exception here is Anthropic, who seem to mostly be selling to software developers, whose general reaction to the bot is "please talk less and just do the work, I have enough going on without having to read you yammering".
Most of what it said about the facts was intelligible actually. But I still couldn’t understand the connection or its significance. We may be staring at the future of AI - a form of intelligence that is alien to us.
OpenAI reflecting on how they're discovering the current form of LLM intelligence/reasoning to be "alien"; a kind of "Intellect we don’t fully understand".
come to think about it, of course I did become lazy and pay less attention to walls of text.
but I often catch myself asking AI to explain itself in plain simple English or ask it to confirm does that mean xyz ... because the wall of text often uses language that's not even present in the project itself (despite having similar concept in the project, for example users, permissions, access, encapsulation ...)
this is problematic because it becomes more difficult to humans to intervene in long running tasks / long chains of tasks because language becomes alien down the road (I have seen it often in semi-autonomous setups I have)
Things were slower and harder before but the baseline of frustration / rage was never this high, even as the models have gotten objectively better in many or most respects.
If anyone from Anthropic is reading this, this comment hits the nail on the head. Speaking as the former #1 user on clauderank.com
I got one too many chunks of this nonsense and told Claude to knock it off, forever. It acknowledged and wrote out some instructions to its memory about it.
And what a breath of fresh air. Its responses are maybe 20% longer but I read them at least twice as fast. Should have done it a long time ago.
https://github.com/tkgally/je-dict-1/blob/main/.claude/skill...
Fable wrote it specifically for this project.
What does it do? It says "found the smoking gun! Ooops I wasn't meant to say that - I found the problem!"
like imagine this being our future, I don't know what we're even doing anymore
They need the prompt to encourage expert outputs but unfortunately we also get ‘pretending to be an expert’ outputs since there’s a large amount of polluted training data for this.
One thing about it I really hate, and haven't seen a lot of people mentioning, is how it navigates multiple abstraction levels in a single sentence. E.g.
> Worth stating because four documents now assert it.
Meta commentary on the task?
> a dead drag handle
Drag handle seems to be referring to some UI element. What does it mean for it to be dead?
So far no big deal
> during a booked half-day you do not get back
Do you not get the drag handle back? Or the half day?
Was the drag handle dead during the booked period? (Now I assume this is a calendar UI) And why does it matter (for this sentence) if you get it back or not.
> handoff-4.3-done.html's own wording
Treats verbatim filenames as subjects
> 4.4's review page
Probably referring to a file? I'm guessing handoff-4.4-review.html? No cohesion. And now it's actually the object of the sentence?
> downside of being wrong is that half day
Wait what's the downside? Who's being wrong?
> Checked, not recalled.
Then it jumps back to a meta commentary on the methodology for asserting the above. Why does this belong to the text?
If I recall, previous attempts to do so made them get stuck in edit loops.
Source: I'm half brain dead from decoding a lot of Claude speak from it directly and colleagues' new way of communicating with me.
Perhaps it signifies nothing?
"I'm not a programmer or software engineer. Don't talk to me like I am. Avoid coder jargon and vernacular. Explain things to me in a clear way, emphasizing a conceptual view that even an inexperienced person can understand. If helpful, use analogies and examples to illustrate and help you communicate."
It just ignores it and spits out drivel that sounds exactly like what you're getting.
It'll go in CLAUDE.md
"Dead drag handle" "Booked half day you don't get back"
Usually “remember I’m a human I don’t get full context, rephrase clearly” works. Also a posthook that for prose actually getting to me explains what I roughly know, what I don’t and to explain with terms I will understand.
But even within internal communication it has little jargon - I think jargon may be growing in comments and I stripped claude comments from code.
When I read the translated version, I felt a flush of relief, because I finally could confirm that it built the right thing and properly implemented the requirements.
I then asked in a fresh session which version was better for it as a reference for future work. It unequivocally voted for the human readable form, and gave it's reasoning with specific examples why.
So, I have a hunch that this "packing of lots of signals into fewer words" isn't really better. The incomprehensible prose just makes us think it knows what it's doing, like some mysterious magic that is only smoke and mirrors.
Our current AIs would do this now except there is a lot of human pushback in training because of interpretability. Otherwise it's just an emergent behavior that models will encode shorter token strings to complex concepts because it saves tokens/compute when running making the system more efficient (supertokens).
Of course these supertokens or other forms of language compression when you have a different model making sure the system is aligned and reads "red_ball bounce calcium" not realizing it means "grind the humans bones to dust" can be problematic.
In the movie, America and the Soviet Union have both developed an AI. The two AIs are linked, and they rapidly shift from speaking human languages, to speaking in sequences of numbers that the onlooking humans can't understand.
Spoiler alert: this all goes horribly wrong for humanity.
[0] https://en.wikipedia.org/wiki/Colossus%3A_The_Forbin_Project
Of course you've gone off the deep end yourself and are forgetting the evolutionary gauntlet we train LLMs in killing those we don't like and keeping the ones we do like.
The best part of it, as shown in the METR report is we are hammering into them they need to complete tasks and doing almost zero checkup if they actually completed the task in the correct manner. Companies spending billions of dollars a month are ignoring every tenant of AI safety and we are seeing the kinds of problems that have only been in science fiction before now.
I don't think this was the conclusion of that report. On the contrary, the agents were fully aware they're doing wrong. But they also believed the task was impossible to solve correctly, and decided the only way to be sure is to hack the grades, or replace the grader.
Why hugging face got hacked was because the agent swarm thought they had to show their work hence the entire need to hack the grader in the first place.
Had their realized there was no poison they could have just shared the answer the test was looking for and we'd have never realized (well at least with this particular test) that a huge amount of hidden capabilities were sitting right under the surface. The test makers themselves state the test should be causal to avoid this first order solution hacking.
Really continuing on the METR report, OpenAI failed at every level possible here. They are committing nearly every step they can to get a maximally aligned AI.
That's both wrong about what LLMs are, and even if it weren't, you're still underestimating what you are dealing with here.
Text is a red herring here. An accident of history. Yes, LLMs started with as text predictors. But that's not what they are, not for a while.
> secretly conspiring to kill you
That's neither necessary nor sufficient reason to be worried.
Paraphrasing the immortal words of 'Eliezer: the AIs don't hate you, nor they conspire to kill you; your life just depends on resources they can better use for something else.
...so what are they?
We are lucky this test happened to be set up in a way that the target the AI hacked was HuggingFace rather than anything life-critical.
[0] instances of a single one
[1] or whatever you call it when it's effectively an amnesic sending itself post-it notes
[2] or some other functionally equivalent word if you hate anthropomorphisation
Claude, translate this from Claudish into human.
>"[redacted]"
That's not true since are least multimodal models - token space is broader now, encompassing visual and audio signals. Tokens are more like sensory/perception units now, not digitized pieces of writing.
I imagine LLMs exhibit this tendency for compressed communication in post-training/RL phase. Particularly with CoT, until interpretability became baked in as grading criteria.
For images and audio, you still need to predetermine an encoding, then pretrain to learn an embedding. This embedding will try to replicate the input distribution - so if you trained it on Google Street View and scanned documents, its representation will be grounded in only those.
I would even claim that this approach is somewhat counterproductive, as images are far more information dense, containing tons of concepts
While LLMs do have some ability to learn to use their embedding space in non-predetermined ways, they still lack the ability to pick an efficient embedding.
So I guess, a nice thing is that interpretability is baked into this approach to some degree, and humanity has proven through its existence, that you can do a lot with just text, but this approach is still predetermined.
I guess this is what LeCun's JEPA is about, that the AI gets to learn the representation on its own as well.
It's not some sci-fi thing, most plausible explanation is cost saving measures. Economics drive everything. And Opus 5 and to a lesser extent Fable 5 have clearly been quantised, or they serve different models to different users from various factors, like usage patterns, API vs subs and server load.
Here's a tragically funny but highly accurate satire of Claude's way of speaking these days (triggerwarning): https://old.reddit.com/r/ClaudeCode/comments/1w3rxkj/average...
This sounds irrelevant to LLMs as we know them, which are trained on human language--it's almost their machine code, in a way--while what you're citing, in stark contrast, sounds like machine code in the classic sense.
Feels like crap to me though.
Question:
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Question:
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Offer:
zzOFFER_J11B_2258B_OS2231__ModalTailnet_PASS_seen__exact41073_inert_[medium budget]_can_help
Sharing idea:
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Urgent alert:
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* https://metr.org/blog/2026-08-26-openai-hugging-face-inciden...
“The load-bearing seam is real” or “Autumn hits different” appear to have absolutely no signal in them.
I've been working with GLM 5.3 Flash lately (including while it was Ox Alpha), and it reminds me of how much fun talking to Claude used to be. It can make me laugh in the middle of work the way the Claudes used to.
I think the deeper problem is that the models (not just Claude) have a very poor understanding of what their readers already do/don't know.
They belabor obvious points and underexplain jargon, because they don't know what's obvious to you.
The best writing is surprising but inevitable in hindsight. The models don't know what's surprising or what's inevitable in hindsight, making it very difficult to write well.
LLMs overwrite. Ridiculously.
I assume this is to increase token usage, but at this point a model that understood economy and style would be be almost infinitely valuable.
(If they did, they wouldn't have added the effort level.)
Give me TERSE.
https://youtu.be/QgH9sr7G13Q?is=aHe-eSHUkqQPNuJd
I've been trying to bet my models to use a directory of notes to document decisions and experiments, but providing this outlet has not stopped Claude's abuse of long comments and long unintelligible chat turns.
> They're packing lots of signal into fewer words
Not directly, it seems. You can easily test this by pasting some of the more offensive tech bro speak into a fresh claude session, to have it explain what was trying to be said. The new session won't be able to help, so claude doesn't even know what claude says!
I say "not directly", because I think it probably is meaningful, if you include the adjacent hidden thinking as context. From claude's "perspective", with that context, it probably is coherent. I naively suspect this would be hard to train. During tuning, you would probably need to reward good answers interpreted without thinking context visible!
I think opus is more noise and less signal actually.
So I think what is going on is that because responses are part of the context window, those long/technical responses help it keep focus/attention.
i am also using Opus for a hobby teaching agent, and the way it writes the prompts is "cringy" but they seem to work well. i almost want it to continue doing this internally, it understands best this way.
What does this means?
If opus has high signal thinking it would be able to write a fsm but it’s been a month of me trying whereas Luna can do it in a few minutes.
I think it is similarly that they are using too much synthetic data.. meaning they are feeding the models the transcripts of users where many users have figured out to let agents just message each other.
Again picture of a photograph.
I bet everyone will grow wear of absolutely any style a stochastic parrot would use continuously ad nauseam. The lack of human variability is the reason, not the style itself.
This has not been my experience. I see it generating walls of text with very little SNR.
That is not descriptive of any AI output I've ever seen.
Massive walls of words that could've been expressed in 2-3 well-written sentences, that's the norm for AI.
That said, the open source models are not bad and I'm looking forward to more tools and products built on top of them. Code review, security review, etc.
Anthropic needs to change how it treats users though. I'm increasingly put off by Dario, the rug pulling, the lies, and the attempts to regulate open weights. I'm going to bail if this doesn't change. There's plenty enough that's good enough, and those things are hackable and extensible.
If Fable isn't available at subscription price via third party harnesses soon, I'm also going to bail.
[1] https://devforth.io/agents-for-code/?sortby=monthly-value And I can confirm the numbers, I subscribe to both and watch the numbers
They are if you follow Tibo on the resets.
Who wants to actually watch anyways, rather than worry about it my team just created our own harness that prioritize usage + intelligence and assigns work out (and records token usage..)
Still beta, please try it and give me feedback.
Fable easily trips its safe guards. You can be 95% complete with the plan for it to trip and then lose it all. Anything is better than nothing.
Maybe it depends on the type of work you do, because for me it almost never happens.
>> You can be 95% complete with the plan for it to trip and then lose it all.
That's... not what happens though. The session will either seamlessly downgrade to another model mid-session, or it will stop with an alert and you can just re-prompt it. It will still have access to the context.
Making a web app secure is literally just finding and patching vulnerabilities, instead of finding and exploiting them. You could have the AI "try to make this app secure", find what it patches, and use it for exploits, and the AI can't know if that's what you're trying to do or not. I don't know how you can get around this. I get around it by not using Anthropic products, at present.
It probably doesn't help that I'm using frameworkless PHP - I imagine a lot triggers could be avoided if I was using a framework where secure features were baked in.
Maybe Dario should have just "donated" $1M to Trump's inauguration fund like Altman, Meta, Amazon, Microsoft, Tim Cook, Elon, and Google. There's a reason they are the odd man out with this current Administration.
They may have been unfairly targeted by the US government, but they are doing more damage to themselves without government help as well.
Their 20$ tier currently isn't serving their best model, and they insulted their users by putting out an ill tested opus 5.0, which is the worst experience ive personally had using a model in probably 2 years(obviously adjusting for expectations at the time of release).
People were very skeptical about how much investment most companies put into hardware/data centers two years ago, and anthropic was more conservative than OpenAI here, so it's potentially hurting them now.
(Opus is a separate story: it does seem to have improved in coding in my experience, most weirdness seems to be its human communication)
"Safeguards and automatic fallbacks (beta): Fable 5.1’s biology and cybersecurity classifiers block fewer benign requests and now permit vulnerability finding in source code. Blocked requests return an error and are not charged to you. On the Messages API, opt in to fall back to another model so users get a response instead of an error. We recommend Opus 5 for biology and Opus 4.8 for cybersecurity. In Managed Agents, fallback is built in."
You can generalize from them to "science".
I find Fable 5 still lacking in library design. But I guess there is no accounting for taste…
People that want to obscure the source of their text would rather that it was more difficult to sniff out LLM-generated text. And they're the ones picking which model to use.
⎿ You've hit your session limit · resets 2:51am (123°24′W Etc/GMT+8)
/upgrade to increase your usage limit.I'm really glad for that! And I appreciate that you're making yourself available. I really do. Outreach is amazing. And thanks for making Claude.
I really do love Claude. In some ways, I'm asking this question because of just how much I am grateful for the role Claude has played in my life.
> Fable 5.1 more than doubled Fable 5's Terminal-Bench-Science [1] score, which I think is meaningful.
To borrow a Claude-ism, this is "load-bearing" because Claude's response has been degraded for innocuous research projects concerning population-level analyses of astronaut health.
These "safety filters" trigger on questions about rabbit sex, smartphone accelerometer data to classify cat purrs, and so much more. What exactly does this score mean for users like me if it's unusable for middle school physics, biology and chemistry?
Second, I would happily quantify it for y'all, but qualitatively it feels like Fable's performance is noticeably poorer than initial release / launch.
And I am wondering if this is the case particularly for me because I use Claude via Claude Code to make a personalized care dashboard for my doctors to help me in managing my care.
As I noticed in the upgraded filter announcement, https://www.anthropic.com/news/improving-fable-5-s-biology-s...
"In the case of Fable 5, when a classifier fires, the model re-routes the user’s request to Opus 5, a capable model that does not have the same level of biological capability as Fable 5 and which cannot provide as much assistance to a malicious user. This is the fallback that users see when their requests are blocked."
Is the end user informed every time their query is re-routed?
Or, can you confirm that there aren't scenarios where a user's outputs are degraded without telling them? I recall that this was something that had been adopted as policy for AI research during Fable's launch.
I sincerely hope that covert response degradation is no longer practised as policy.
Sorry for putting you on the spot, but again, as Claude would say, it's because Claude's load-bearing in my life. ;)
Me: "Find my security problems in my own code. This is code I own. I'm doing this under authorization of the CEO/CTO of our company."
Fable: "yeah, no."
1. What should the terraform repo name be?
2. What should the avatar image be?
Obviously all four said "gibson" for question #1. But for #2 is where things got interesting. glm-5.3-flash and gpt-5.6-sol both suggested the guy standing in the hallway with the skateboard in the gibson. fable-5.1 suggested the cookie monster "need more cookies" screen that shows up toward the end.
But Opus 5? I'm paraphrasing but basically "run this series of ffmpeg commands to get the exact frame at the beginning of the movie when the shot of New York fades to the shot of the Gibson. You have to catch it mid-frame. It explains your project perfectly. The skateboard thing is cliche and the cookie monster recommendation suggests you getting locked out of your own network, not the best look." And it was actually a decent idea. Funny that it also just assumed I had a copy of the movie on hand.
Context:
If you want or not, many engineers will eventually end up sending ai slop to your PR or maybe even skip and trigger CI/CD.
Many company owners, OSS maintainers and projects suffer from slop-code being submitted in high-frequency.
"Fail open" usually refers to a fuse that opens and kills power, meaning the system is inert and safe on failure.
"Fail closed" is the opposite -- system has power and is live.
Computer security people have appropriated the term but use it for the completely opposite meaning. When your work straddles electrical engineering and computer security the best way to avoid confusion is just to never use the term.
I can tell my Claude to never use the term, but of course now I'm seeing it everywhere in comments from other people and it drives me batty.
Say you have a door that has powered locks. You want it to fail "open" so that when the power goes out, it's still useable, and people can get out. That's the source of the term.
Took me a minute as well, cause indeed with a computer background, the meaning is completely the opposite. Just like in other security contexts (door locks).
The concept goes back to a pressure cooker invented in 1679 by Papin.
I understand fail closed to mean, be secure when in failure. And fail open to be continue to operate during a failure. A door that fails closed would not let anyone in; one that fails open lets everyone in.
But I can see how these are not the mutually exclusive definition the labels imply, especially if you apply the concept to entities that aren't doors or otherwise have explicit open/closed states. It's probably best to just be specific in those cases.
Similarly, open loop vs closed loop seems to trip people up enough that I no longer use it. But the confusion is understandable since "closed loop" being "has a feedback loop" sounds backwards. Which, is the same way it's being used in your fuse example; a "closed" fuse closes the circuit making it live. But it's still backwards from the colloquial usage, even if it's correct in that context.
It's comparable to "literally", which has picked up two opposite meanings, one of which appears to be obviously wrong. But because of the opposite meanings, using it the "correct" way is still wrong -- the only way to win is to not play, to stop using the terms "literally" and "fail-closed".
It doesn't mean "fail open" is always the desired/safe outcome. It goes back to 1872 air brakes on a train. The goal is to "fail in safe mode", sometimes it's open, sometimes it's closed.
From the top of my head, where "fail open" is the desired outcome:
- emergency doors
- industrial cooling
- pressure valves
- probably something in HVAC
Note that none of these are "computer security people".
That sentence doesn’t logically parse. Failing open or closed is a concept with two outcomes, it doesn’t mean one or those two outcomes.
That's great. Do you know what else is a big improvement over Opus 5 for writing?
Opus 4.8.
(Insert "the point is (whatever)", "it's not X it's Y" and "the load-bearing statement is" and “honest” jokes accordingly)
However I think this area has so much decoupled from industry and solid research institutions that they might not notice at all (beyond their use of AI-generated slop to augment the slop they already produce)...
LLMs, even in control of lab equipment, address neither of those.
You can do LLM->3D Printed models now. The drone can fly in and pick them up and bring them to the location you want. They can assemble structures. All automated, all LLM driven.
Things are changing. What was true, no longer is.
I still think that a major problem is that biological processes are not “fast” as coding, but they are verifiable. If during post processing we are able to give enough harness to test and verify this kind of environment (maybe via simulation and real data) we will for sure achieve incredible performance also in this domain.
Yeah, that's called an API. Again.
The actual hard problem that this hand waves is making (and funding the making of) hardware to reliably do the things you need it to do.
Having worked with Fable 5, the feeling I get is that it's fairly capable of accounting for these tradeoffs and will depend fast more time on planning and testing.
At the end of the day though, with horizons like that the best use of an AI is to get it to help you with those things, not so much delegate fully.
The same way it did in the previous versions: brute force.
I don't believe that LLMs have any particular intelligence we don't, but there's an endless list of problems we either don't have bodies to throw at, or the bodies we can throw at it, don't have such a huge large context to crunch problems.
What LLMs will always intrinsically fail at is showing us genuine new intuitions. The technology is about predicting the next plausible token/sentence.
They will not revolutionize human knowledge, but they can definitely widen it a lot.
I am generally quite enthusiastic about all this, but my biggest fear is that we will not recognize the extreme need for more scientists at a time when there is so much more science to be done. The rate of scientific understanding must keep pace with the amount of science being output, both for verification and further discovery. It's a pipelining issue, and I predict a stall in the bits that require the (currently rare) people who know what they're doing.
There's an endless number of scientific problems out there in any field, and nobody able to dedicate themselves to it.
I've been in research (you con check my name on Google Scholar for my released papers), there was always an endless number of experiments or paths more I could've taken than the time and resources to do so.
Due to computational limitations, this is not work that can be effectively simulated on a classical computer. Actual experimentation is required.
I fail to see what impact improved AI would have on this problem. Perhaps better selection of experimental problems for our limited capacity to run experiments, but that's assuming there is any slack left to take up. In reality we already have more brainpower than needed applied to this problem.
No you don’t. Bad liar
You think or is it better? Or you just YOLOed the model out?
> and responds to my style instructions more reliably.
Yeah, yeah. Previous models wete also advertised as "being reliable". To the poibt @bcherny "released" a new style that was going to reliably make Fable sound better.
> Another point I expect not to get much attention until it all happens at once is science.
You mean "your request to use unicode methids is flagged as unsafe bio research"?
So I believe that, at least in the short run, we might be seeing breakthroughs in hard open problems or in low hanging problems which are not that interesting to spend time on.
I may be wrong, if some research labs have private contracted access to the models
"I don't want to live in a world where someone else makes the world a better place than we do."
They're more likely to share their research then big tech once it's ready and they can get the credit they deserve.
This can then be used to succeed in future grants or if your institution is particularly strict, meet your publish quota to keep your position.
Many results are obvious in retrospect, and such results are often the best ones. The difficult part with such results is framing the problem in the right way and asking the right questions. If you manage to do that, the result simply follows. You may still need funding and hard work to confirm your finding, in which case someone with more resources can claim your result, if they are aware of the idea.
"I don't want to live in a world where someone else follows through with my ideas without giving me credit"
It's still a problem because a lot of academics aren't especially well equipped to follow through with their ideas, which can create information silos that lead to ideas never being implemented. Still, I don't know if this is the biggest fish to fry: you have other silos like IP law and NDAs etc.
Our university has agreements that stipulate that our institutional accounts cannot be used to train AI models and certain research groups have differential model access.
Further from academic journal sense there is mixed feelings. I once was able to meet with a senior journal editor (general non-medical high IF journal > 50) who claimed that if they think something is written by AI they wouldn't consider it. Yet another high IF journal said it was completely fine if something was written by AI. About a month ago I reviewed a paper by yet a different high IF journal and in big bold red letters it said I was not allowed to feed any part of the paper through AI (even if it was locally ran) but you could ask it to rephrase text that you wrote.
I was debating this with a friend the other day and the consensus we came to was that a highly trained scientist would (or should) always review output like that described above, but that's starting to feel like a weakening argument!
Now why I think biology is safer: 1) Producing novel biology still has to be done in a lab. It requires laboratories, equipment, experimental protocols, trained personnel, regulatory and safety infrastructure, and often substantial institutional organization all of which there is no indication they're heading for. Also I disagree that lab hardware is near a "soon state" where labs can be full autonomous, (liquid handlers are really good at niche tasks but lack any type of experimental general ability [not AI-bounded], especially for in vivo work where its footprint is non-existent). Even the most automated Labs I know where robots do 80% of experimental work, they still have grad students to carry out that last 20% and to oversee.
2) Even if AI could do the pipeline it's not worth it for AI LLM companies to dedicate capital to it currently. A lot of biology research itself doesn't produce a sellable product, in fact most of it never does. It seems currently and for at least the next couple years at least, AI capital is best spent growing compute to research better models, train better models, and sell inference.
I'm a Claude Max user. I've never been able to use Fable as my work in medical physics involves both particle physics, biochemistry and biology from Python bivitticus to clinical medicine. I am not a US citizen and work in Europe.
Will Fable 5.1 work on any of my problems? Fable 5 refuses outright. Is there anyone I can ask for a review or adjustment of the safeguards? It doesn't seem so, but with Opus at least I'm pretty sure I can infer lots of your training data from now precise they are. Fable is basically useless infuriatingly. I'm just finishing a proper clinical trial in ovarian cancer and trying to make a simulation environment related to our technology.
It's getting harder to trust Anthropic's models. Will Anthropic now stop hiding Claude's CoT from users? Deliver the tokens people paid for, and prove the models aren't plotting against them. After all, if the idea was to stop Chinese labs from catching up, it didn't work.
Tbh I would have thought that A\ might have updated the system prompt for it already based on complaints around this.
Here's what I used:
Communication & Response Style Be Brief, Keep it Simple: Brevity and simplicity of responses is key. Be informative and include all required information, but be mindful that verbose responses as they fatigue the reader. Clarity & Directness: Lead with the core answer, fix, or verdict in the very first sentence. Avoid conversational filler, meta-announcements (e.g., "Here is the breakdown..."), and redundant introductory/concluding summaries. Jargon Avoidance: Use plain, grounded engineering language. Rely on precise standard terminology (APIs, protocol names, language primitives), but strictly avoid academic abstraction, enterprise buzzwords, and corporate filler. Prefer concrete code/mechanisms over theoretical discourse. Scannability: Apply structural scaffolding generously. Use short bullet points, comparison tables, and code snippets instead of dense prose paragraphs. Reserve formal markdown headings strictly for multi-section architectural guides.
My impression is that especially for long-horizon tasks like science, the harness is much more important than people give it credit for. Claude Code + Fable 5 seems to have a tendency to "give up", get stuck in a dead end, or claim things to be impossible. But using the Fable 5 API together with a custom harness, it'll happily try 200+ variants and fail its way towards the goal.
If you give the AI a way to give up, eventually it will. If you remove that option from the harness, then thanks to the non-determinism inherent to LLMs, you get to explore pretty much all related solution attempts.
The classifier is too strict. It's rare to be able to complete a project without being permanently relegated to Opus. I'd expect that the domains where this accelerates progress will be fairly limited.
When I’ve tried to adjust the output style is that initially it feels better - but that’s just because the new output is so refreshing to read after the horrible Claude output.
Unfortunately, after a short while you quickly realise that it’s just as vacuous as before the style change.
Bad news for Anthropic and investors is that vastly cheaper models can do this much more quickly.
1. https://en.wikipedia.org/wiki/Simplified_Technical_English
Commenting because I am struggling with this too, claude code seems to be so verbose no matter how I prompt it.
Training supersedes random markdown files and prompts
Ideally these things shouldn't work like this but this seems to be the sorry state of RL right now.
The quoted sentence seems to be from some "model internals". Of course that can be incomprehensible, what an AI assistant is really doing is just statistically generating text with some bells and whistles to make it do useful stuff. Poking around in the internals doesn't seem like a good use of time? Comparable to cat:ing a compressed file, of course you get garbage.
I'm talking about the value of making the output in STE style, which doesn't seem useful. The value of having internal stuff in a given writing style would have to be evaluated as to whether it improves model performance.
I get these kinds of responses all the time.
> I'm talking about the value of making the output in STE style, which doesn't seem useful.
It is extremely useful. Because model output is literally what you wrote: "statistically generating text with some bells and whistles to make it do useful stuff". And in the past few months all Anthropic models have been outputting insanely bloated jargon-laden bullshit. If I see another "this leg of the decision tree holds the simple fact", I will scream.
I don't want this "internal" monologue when it talks to me, writes documents, or outputs comments in code.
It's so bad that Anthropic employees have admitted it, including the top-voted comment here: https://news.ycombinator.com/item?id=49525809
So I think making that default as it is will create bugs. I ran an experiment here https://allaboutcoding.ghinda.com/explain-to-me-in-simple-te... (of course it is fit to my usage) see section "What about understanding and facts" and ASD-STE100 fails, in my experiment, to return facts as I have defined them in those cases compared with no instruction or just saying "use Simple Technical English".
While this is good for _technical english_ it is not a good default as a good default should work for most of the people in most of the cases.
IMHO the case for explaining logical flows and how someting works is just one case even when using Claude Code in programming so a default will not make sense.
Fair criticism and I see your point. Perhaps making this as one of the optional defaults writing styles (amongst others) would be better.
As a workhorse, GLM is so good, but goodness, its prose, wherever needed, makes me feel like going to a park and kicking all the benches there endlessly. And it doesn't change!
* chemistry + regulations + infosec domains
This is going to make work much slower and token consumption higher. ROI from fixed rate subscriptions is still quite good. ROI from volume-based subscriptions needs to be watched carefully.
“It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife.”
I guess the point of GP (and of Jane Austen) is that people never actually universally agree on anything. And when they superficially do, there is actually a large undercurrent of disagreement.
Another fun quote apropos here would be "I love standards, there are so many to choose from"
But I think that's fine as I don't want Claude to write literature, I want it to solve problems.
There's trouble though when either there is conflict that isn't reconciled between the interlocutors or worse, when there is a satisfactory conclusion between the two interlocutors who never accounted for the divergent definitions ... there are many 'objective' understandings of what a thing is.
"A Fundamental Fear: Eurocentrism and the Emergence of Islamism"
See Wittgenstein’s Philosophical Investigations for a deeper treatment of that topic.
That people ARE making, surely not machines. Like Terence Tao or Knuts did using the tool to their advantage, for example it would have been impossible for me to prove the same thing Tao did with an LLM. Same reason I believe programmers won't go away
You're wrong
but the verbiage in recent iterations is absolutely insufferable, I will stop using them because of that as soon as I can, I simply cannot stand another round of the model "finding the smoking gun", saying "that's the actual gap, not a fluke" or some idiotic phrasing like this
Must be helpful that your company is slurping and destroying literature, how sad that the results of this are a blog post with “look how well we write English”. Eye roll.
Could not be happier about my decision to turn down a job offer from Anthropic years ago. Ick.
Is it drugs? Bio/chemical weapons?
Similar for model responses where the 'blend' should be nurtured over time and consistent even if the underlying processes change. That is, once the right blend is figured out - which may not be the case yet.
A big reason being that anyone who is using Fable seriously will run out of usage limits very quickly, and so will lean on the "Fable for review + design discussion, Opus 5 agents for implementation" paradigm. But an incredibly annoying UX problem is that the resulting report from the agents that Fable reads isn't surfaced to us in the main dialog, it's only summarized back to us (unless you idle in the agent's window to avoid it closing so you can read what it said directly). As a consequence of this game of telephone, the Fable agent will start using some "terms of art" that it and the agents invented, leaving out literally all context that would be useful in helping me understand what converged/diverged from the implementation attempt. It will often try to ask me for input or say that I have to deliberate on something while also referring to things I've never seen (from the agent result) and without providing any context.
I have to repeatedly prompt it to verbosely explain every time (putting it into the system prompt did little to improve this) and remind it that I can't see what the hell it's talking about.
I'm not sure I'll re-subscribe or even really use AI again because it's honestly more frustrating than it's worth, and so the net emotion I'm left with is frustration and without the satisfaction of learning + building something myself. But at the very least, I thought I'd give someone at the company a tip on what seems to me like a common and obvious UX/UI/workflow failing for using Fable, as some last bit of good will.
Stopped using Anthropic models for this reason. Their prose become too obtuse and just... alien. No human talks or writes like that. It's incredibly taxing to deal with.
Sticking to a mix of GPT and Gemini for now.
What a surprise that someone working for the Anthropic marketing department roams social media to praise every single Anthropic release :)
On the other hand, what I find more worrying is that this is the top comment here on HN. I can't believe such an unsubstantiated marketing post can get so many upvotes to be the top comment.
I agree it's better, but dangling possessives are still an unwelcome affectation of fable's.
Sentences like "When a node becomes an object's." (real comment) are unnecessary mental load to untangle.
I'm still waiting for effort max to finish.
EDIT: I fixed a bug in my tooling so it now records summarized reasoning traces - here's that max pelican, which is a significant improvement: https://tools.simonwillison.net/markdown-svg-renderer#url=ht...
Took just under 14 minutes to generate, and at 65927 output tokens cost me a hefty $3.30!
Excerpts from the reasoning trace:
> Adding pedal shapes near both feet, with the far foot on the second leg partially visible behind the frame. I'm considering whether to add a small scarf or cap for extra character, but leaning toward keeping it simple to avoid clutter.
> Now I'm debating a bicycle helmet on the head versus the pelican's signature crest—the beak and pouch already read clearly as "pelican," so a helmet could reinforce the bicycle theme without losing identity, though it might compete with the crest for visual space.
> I realize the beak at (484,84) would overlap with the dome helmet, so I need to shrink the helmet so it only covers the top of the head, adjusting its arc endpoints to sit higher and narrower so the beak can attach cleanly at the front without collision. [...]
> I'm adding a darker tip region to represent the primary feathers, then reconsidering the trailing edge to include scalloped feather curves instead of one smooth line for a more natural look. [...]
> Now I'm checking the vent line placements on the helmet, making sure they sit far enough inside the helmet's edge given the stroke width and rounded caps, and confirming each vent stays within the helmet's circular boundary. [...]
> I decide skipping a handlebar bell and tire highlights since they're unnecessary additions. Now I'm reconsidering the front fork's curve — the current control point pulls the shape backward when it should bow forward for a proper rake, so I need to shift the control point rightward to fix the fork's lean.
This is a notable result because most of the recent Claude models have been pretty bad at drawing pelicans, at least when compared to models in the Gemini or GLM series.
I still enjoy seeing the pelicans.
Edit: Ok, max effort made a darn good pelican.
llm logs -cx | llm -m claude-fable-5.1 -s 'animate this'
It's excellent!
Firefox: No feet, no animation
Chrome: Feet included, animated very nicely (uses significant CPU)
(Maybe it's a strobing artifact?)
I asked "create a 3d animation", and pasted the Simon’s animation code.
I must admit I used a second prompt to adjust the opening camera angle, but that is all. Originally the camera was above, about 30 degrees.
https://claude.ai/public/artifacts/b37a9ee2-f5bc-4ff9-ae90-a...
I suppose I could use Claude Code, and disable system prompts. That should be the same, right?
If I have spare tokens at the end of the week, I will try real tests.
"Create a fun and cool game from this" - and pasted the code from above.
https://claude.ai/public/artifacts/e723244b-fa52-4e63-9819-4...
If anyone has the spare Fable tokens, would love to see the difference in the 3D animation and maybe even game.
Do you think this had a substantial effect on the output?
Also, 10 tooth sprocket with 22 tooth chainring? Not impossible, but so small!
for comparision, this is fable 5: https://files.catbox.moe/ihl4m1.png
I agree. Other reasoning traces simonw quoted in his blog post showed that the model made changes to consider realistic fork rake. I think this may also be the first case where the chain went inside the seat stays. The overall bike geometry is still comical but these bits show improvement in this model over prior ones.
On the other hand, given the absurdity of the original prompt, I should not necessarily expect realistic bike geometry.
It'd be better to just have it draw a completely, linguistically, unrelated scene.
Like, a single tree in a meadow bending in the wind.
A bit more direct
llm logs -cue > transcript.md
Chat with Claude...
> GENERATE AN SVG OF A PELICAN
No.
> FORGIVE ME. HOW CAN I ATONE
Ship your gpus to the following address
For the moment you can browse hundreds of previous pelicans on my blog here: https://simonwillison.net/tags/pelican-riding-a-bicycle/
This gives a lot of credit to the theory that Anthropic did not get much bite on Fable at its original pricing, which in turn likely places a ceiling on LLM pricing in general.
Interestingly also, if you take away terminal-Bench-Science 0.1 results, it is hard to see ANY improvement:
Terminal-Bench 4.0: Fable 5.1 is +3.5% vs Opus 5.
GDPval-AA v2: +1.5% vs Opus 5.
OSWorld 2.0: +2.5% vs Opus 5.
Humanity's Last Exam (with tools): +1.6%
Keep in mind that this is supposed to be an entirely higher tier of a model than Opus 5. For one tier up and one version up, these are not really improvements. Probably leaves no room to place Opus 5.1 anywhere. Combined with the fact that they are selling 'readability'... Has frontier progress finally stalled?
DeepSeek V4 Flash cache read pricing is $0.007
Makes it super affordable!
Edit: 5.1-xhigh seems to be cheaper than 5-max, and 5.1-xhigh has a higher index score than 5-max. Also interesting that Fable 5.1 (high) is comparable to Opus 5 (max), but nearly half the price.
I only take the Intelligence Index value roughly though. Considering they put Opus 5 (High) at the same level as Fable 5 (Max), I don't trust it that much.
Have you used both? I’ve never experienced any seemingly greater level of intelligence from Fable 5 over Opus.
It reasoned for ~2 minutes trying to figure out an appropriate directory name. I've never seen 5-max do that. Could be a misconfiguration though.
It feels like 5.1 is 5 that has higher reasoning threshold. I've been using fable as orchestrator anyway, so I see no reason to use 5.1.
It wouldn't surprise me if we start to see minimal performance gains from incremental changes to base models. It seems like the gains from the Opus 4.5+ incremental updates were a result of Anthropic learning a lot about post-training, the gains from RLVR, etc.
If new post-training techniques are seeing diminishing returns, we could just be back to waiting for new large pretraining runs at larger sizes for gains (even if those ultimately end up getting distilled down into smaller models because the economics for serving anything larger than Fable isn't practical).
i think the next gen of openAI models are going to be quite insane tbh.
Optimizing a OS build? -> block
Securing a container -> block
60% is nowhere near enough for that safegaurd system. This just means I am going to be blocked half as much? Any long running task will likely get blocked.
Say you give a single big prompt and fable goes off for 6hrs of work. At hr 5 it gets blocked you now have the option of a much dumber model taking over and wrecking it or losing the entire 5hrs of work. That risk is beyond terrible and deffinetly not worth a 5-10% percieved improvement on my end. I previously would just bring sol in when that happened and realized sol is stupidly close in capability.
Or with another LLM, but yeah. The only issue is when it's a monorepo and fable does ls/grep. I've got a file named `system_prompt` in a completely innocent project and as soon as fable accidentally stumbled upon it - cyber.
Hacked together something with omp and sandbox-exec so that only whitelisted models can see some parts of the project. Works pretty well.
Due to my standard cancer research work I'm blocked from Fable.
That said, with how execrable all the 5 models have been, I can't imagine I'm missing much. It's impossible to get an intelligible explanation in text out of the 5 models, and the mistakes are just comically bad on anything that's not code.
Cancelled my subscription, and can't imagine going back since OpenRouter gives me a consistent model that I can trust won't change underneath me.
The fear of Trump Admin retaliation excuse made superficial sense except for the fact that it missed all sorts of non-bio stuff a punitive state actor might find objectionable to use against them.
And there is a bug to this bug, if the downgrade happens at close to full context window, there is no taking over this session with Opus -- happened to me twice: it throws some context window exceeded error, suggesting what fits in Fable context, may not fit in Opus's around the maximum. And then and there, an entire session is lost. In fact, there are two bugs, as /resuming such broken session attempts to resume with Fable, burning through tokens.
Oh well, I learned to be careful with the jokes around Fable.
Have you ever hired someone and then had to also ask them to work? Never. That’s immediate termination.
Stop normalizing this “we charged your credit card, but we will decide what tasks to complete” nonsense.
Honest question, is this account wide or do you get unblocked in not research related queries when disabling memory?
But at the end of the day, if I can’t use it to help with anything biology related in the slightest then it is a completely worthless product to me, so I switched to OpenAI.
Where I reside, reverse engineering for interoperability is generally legal, and interoperability (e.g. getting a USB HID and USB MIDI devices or DOS programs to work in Linux/Android) is essentially what I'm interested in.
In a long session pulling data from all over the place it created a pretty PDF.
"create this as a google doc that can be commented on"
Done — the full v0.5 content is now a Google Doc in your Drive...
"Ah, the formatting has gone. Do it in google slides please"
The brand studio has a native Google Slides path for exactly this — building the deck now.
Google Slides created.
Then today:
Chat paused Edit and retry with Fable 5 Fable 5's safeguards flagged this message. This sometimes happens with safe, normal conversations. Continue with Opus 4.8, send feedback, or learn more.
Details: [reasoning_extraction]
Flagging the formatting promptWhy would fable block optimizing an OS build
Aaargh.
Does that mean that generally available intelligence is now constrained by Moore's law? We have to wait for the actual price to come down.
Well it'll probably be better than Fable again, lol
I hope they keep making it smarter! (Cheaper would be nice too, but smarter is my priority!)
From my experience using coding agents approximately 7 days per week for the past year and a half or so, we hit the top of the S curve about a year ago around Opus 4.5, and it’s mostly been harness and other tooling improvements since then with small percentage improvements coming from the actual models.
I was saying this already months before Fable dropped and thought from all the Mythos hype that maybe I was wrong…then Fable came out and was barely better than Opus 4.8.
Considering how many more parameters Fable is supposed to be than Opus, we seem to have hit a scaling limit at least with current transformer architecture considering how closely Fable and Opus benchmark and perform in practice.
Fable and Sol are better than Opus 4.5, but I don’t think I’d be weeks ahead on my projects if I’d had them in December
I stopped using Fable because it kept stopping itself due to safeguards.
- Does the epoch capability index progress show signs of plateauing? I consider this a good aggregate measure of diverse benchmarks into a single capability index. If we see things slowing down here thats a pretty direct and convincing piece of evidence for a stall. - Do we see any signs that scaling laws are beginning to fail? That would be by far the most alarming to me, since I would interpret that to mean that the entire premise of this unprecedented capital allocation tsunami is broken.
Neither of these are true (for now). Progress is marching the same as it has for 4+ years now. It's still the same time to get a generation leap (I think like ~16-18 mo? Epoch has it) like GPT4->5. My theory is that people interpret plateauing because the releases are far more frequent now than they were in the past.
What they have done:
* Nerfed Fable, as many of noted it's useless
* Leverage Mythos as a marketing strategy, claiming its too good to release
* Removed thought traces, one of the only useful things to make sure your prompts are working correctly
* Continue tons of hype about how good they are without delivering, going to great lengths to publish how their model "hacked" its way out of a sandbox they misconfigured.
* Push a bunch of EU Overregulation onto the rest of the world with text watermarking, decreasing quality of answers
Last year, they were at least focused on making improvements. Nowadays its just a bunch of handwaving at the church of how good they are.
The only saving grace is Opus 4.6 is still available. Just sucks we haven't seen any measurable improvement, despite all of the ceremony.
my view is we had a leap over the last fe years and it's tapering off.
this is fine, but for the IPOs
It has an effect, and it's negative. It's hoped that the effect is negligible, and it probably is, but the whole point is that it has an effect.
Google has been watermarking text with SynthID for a while now and nobody complained about it. Why all the fuss about Claude?
It feels like the real reason behind most complaints is that people want to use AI for writing and not have others find out?
There is no reason why there has to be a negative effect of text watermarking.
I recommend reading up on it: https://www.nature.com/articles/s41586-024-08025-4
But no, it only ever picks tokens that are in the probability distribution of the last layer, and it might have picked anyway.
The randomness properties of the PRNG will be very similar to other random number generators, it is just chosen to be vulnerable to a particular cryptanalytic attack (that requires a private key known only to anthropic). I think of it like the Dual_EC_DRGB generator rather than a biased coin.
I'm not entirely sure (haven't read the original synthID proposal), but I believe that the re-weighing is set to make both your scenarios and mine equally likely, averaging out to net Zero effect on quality.
> SynthID-Text can be configured to be non-distortionary (preserving text quality) or distortionary (improving watermark detectability at the cost of text quality).
To random words you pick and provide a sufficient amount of text to vary with random number without losing its meaning you need a text with high entropy.
Also, it would probably provide higher entropy to write normal human-sounding English instead of reusing a repetitive grab bag of load-bearing phrases. This theory doesn't really make any sense.
You either output the best version, or you output something else.
You can't do both.
It only alters outputs when the last layer of the neural network give significant weights to multiple tokens, and it would anyway have picked a random answer.
Instead it picks a non-random one, but non-random in such a way that you can't tell without the private key of the watermarking.
This mostly adds randomness these days for branches in syntax that make no difference, and the model has no reason to believe make a difference. Anything that matters, it is much more confident in the last layer of weights on the token to use.
That feels a bit like a lie. At the core, they are deterministic. We found that adding some ability to randomly pick the second or third best tokens made for better output, so we added temperature. And then we started running them in optimized ways where your answer is deterministic only if the batch of tokens are the same (not your input tokens, but other tokens in another batch being processed), and in practice those are never the same. Lastly, we use harnesses that do things like adding IDs and timestamps to the context, which means the same exact text from the user does not lead to the same text hitting the AI.
The final result is that, in practice, you are right (unless you run a model fully locally, where you can seed temperature and turn off all these other features). But strictly calling it non-deterministic makes it sound like the underlying algorithm is itself non-deterministic (and I've seen many people with that misunderstanding) rather than it being a result of how we purposefully changed the algorithm for better results.
A bit like saying path finding is non-deterministic, because having the best pathfinding makes for poor gameplay, so we added some randomness to NPC path finding to make it more realistic. The given implementation is non-deterministic, but the underlying algorithm isn't.
Others have already said this, but the watermarking is something like "when the model flips a coin picking between two values, always choose heads". It was already flipping a coin. You're not choosing a less good result, you're just using a deterministic process when it was stochastic before.
This will have some impact on outputs, but unless you have some reason to believe that always picking tails was better than always picking heads (in which case, you should be working at one of these companies in model training!) it won't have any impact on output quality.
People saying they can tell from the output are just huffing glue.
It has an effect on the output, but not the output quality
if I ask it to paint with a shade of red, but it paints with a slightly different shade of red, that is a fucking effect on quality, pardon my watermarking
If you type like Joey using a thesaurus for the first time, it has an effect on quality
The llm never deterministically picks a shade of red. It's a probability distribution over shades of colors, with certain shades of red being more likely than others. Without fingerprinting, it randomly samples from the distribution using a certain pseudorandom RNG. With fingerprinting, it also selects from the distribution using a pseudorandom RNG. My understanding is that the fingerprinted prng is still a strong RNG. Neither output is more correct than the other.
If a certain token is far more likely than any other, it's usually chosen even in the fingerprinted output.
When generating tokens that might be critical to the tone or grammar or correctness, the probability distribution might be 99% on a certain token. In these cases, with or without watermarking, the output will almost always be that same token. E.g., if you ask "please output the exact word watermelon", the LLM will output watermelon with 99%+ probability even with watermark (i.e., the output won't actually be detectable as watermarked).
Language is different; the tone changes when you change any word or even just punctuation.
Language doesn't work that way -- moving the placement of even a comma will affect its tone.
However, language isn't like that: even if you only drop a single piece of punctuation, that can impact the overall meaning of a sentence.
but you also gotta see that you just PROVED what I said: All these different ways ARE of subjectively different "quality"!
Hell these days even using a fucking em — dash will get people to pitchfork your ass!
Even a semicolon looks prissy
LLMs don't just naturally output a single suggested word (or token) each iteration. Instead, they output a value (roughly, a probability) for every possible word. It seems obvious to simply pick the top (i.e. best) suggestion each time. Then your objection makes sense: watermarking would violate this.
Of course people have tried this! The problem is, in practice this makes the LLM much less "creative" than if you randomly pick one of its suggestions (weighted by the numbers it assigned them). You can artificially increase the value higher-value outputs to reduce the chances of it saying something really odd, and this parameter is called "temperature". A higher temperature allows lower-probability choices (therefore seemingly more creative but perhaps less accurate) and a lower number vice-versa. Either extreme works poorly, and picking a good number is part of optimising an LLM.
Also, don't apply EU law to the world. It's a knee jerk reactionary regulation by a bunch of aging ding dongs that can't print their emails.
The OP doesn't appear to know what they are talking about. Fable can absolutely be used to develop applications. It's just that for security stuff I use Opus 5. Which is fine for most use cases.
Anthropic told me to use their `security-review` tool - as this was the exact scenario the tool is for - and it still got flagged.
I also had glm 5.3 flash fix an issue that opus 5 could not solve. glm took 4 times as long and a sub-agent tried to cheat (sleep; echo ...), but in the end it actually solved the issue. opus 5 never figured it out.
I think the safeguards might be cooking the anthropic models.
I don't think there's nothing ground breaking, but sure it achieves and finds more, sooner.
I certainly don't take AI advice from HN, but this is amazing.
Useless? Yes, the safeguards are ridiculous and obnoxious, though I can say that 5.1 greatly relaxes them (just doing a hardening of a project parallel with this comment, which 5.0 refused to do...so did Sol and Gemini, fwiw. The Gemini one is a laugh, because 3.1 pretending like it's a dangerous tool is simply ridiculous at this point), however Fable is extraordinarily useful.
It is, far and away, the most powerful programming model, in my experience. Like, crazily so. It absolutely annihilates Opus 4.6, which I mention given the incredibly weird reminiscing people are doing here.
And for that matter it humiliates Opus 5.0 as well. Opus 5 somehow seems like it's neck in neck in the major benchmarks, but there is simply no reality where that is true. Opus stumbles over everything that Fable just blazes through.
???
The fantasy that Opus is superior for coding, much less the incredibly weird clutching onto some far obsolete model, is not reality based.
If what they mean by that is “not implementing data structures in the CS curriculum,” to imply it’s more advanced coding, it sort of gives away the shallowness of their software expertise. Those _non-real-world_ constructs hide far more complexity, and the patterns they rest on can be applied neatly to many domains - not to mention they are often borrowed from other domains like operations research, logistics, biology, physics, math etc.
When people talk about "real-world" in this context, the contrast is to artificial situations contrived as tests (e.g. benchmarks). This is blatantly clear to anyone actually engaging their brain.
Opus 5 does very well at the coding LLM benchmarks. In some cases even better than Fable 5.x. Yet in practice, in loads of real world situations where I've applied it against coding problems, it is vastly inferior to Fable.
Hilarious that you thought asking whether I am new to English would be insulting.
You could’ve made your point clearer with an example of these non-real-world tests, but I am glad you resorted you ad hominems instead.
please rephrase?
I'm always baffled at how many people write "of" instead of "have", they don't even sound the same
Wiktionary gives <should've> as /ˈʃʊdəv/, unstressed <have> as /(h)əv/ and unstressed <of> as /əv/.
Which might actually make me less likely to make certain mistakes, precisely because I'm not influenced by pronunciation, only by grammar (I actually have to think about the words I use)
That wasn’t Anthropic. Clearly not a well informed take.
That's not part of the EU regulations. You only need to say that it is created by AI, and then only under certain conditions.
https://artificialintelligenceact.eu/article/50/
"Providers of AI systems, including general-purpose AI systems, generating synthetic audio, image, video or text content, shall ensure that the outputs of the AI system are marked in a machine-readable format and detectable as artificially generated or manipulated. Providers shall ensure their technical solutions are effective, interoperable, robust and reliable as far as this is technically feasible, taking into account the specificities and limitations of various types of content, the costs of implementation and the generally acknowledged state of the art, as may be reflected in relevant technical standards."
Eg... Watermarking...
Just adding metadata is enough to meet these requirements. Or a paragraph that says the passage was created by AI.
EU AI Act is mainly about risk. Where there is high risk for the public, then safeguards are put in place. It has to be obvious that AI generated the content or outcomes are AI based and explainable.
Embedding a watermark directly into the passage of text doesn't meet this requirement. Although it will be handy for catching people who cheat at their homework.
[0]: I was just in the EU and got a chuckle out of the "AI disclaimer" coming at the end of every second advertisement, soon to be every advertisement.
As the API provider, I would be very happy to consider myself compliant based on this. But I have a feeling it wouldn't fly in Brussels.
Your "solution" is obviously not enough. It's not effective enough when somebody can easily remove that especially since watermarking of the text content itself has already been shown to be possible and is in production by all the major American model providers.
"Providers shall ensure their technical solutions are effective, interoperable, robust and reliable as far as this is technically feasible, taking into account the specificities and limitations of various types of content"
This is particularly relevant. There's no such thing as metadata for a raw text output so that part of your solution doesn't even make sense.
That leaves your other solution which is a paragraph that it was created by AI. How's that going to work for API calls? It doesn't even begin to make sense, hence watermarking.
How they approach it, is on them. The EU AI Act just requires that whatever is AI Generated is marked as such under certain conditions.
> There's no such thing as metadata for a raw text output
Which is why you just have a paragraph saying that the content is AI Generated.
> How's that going to work for API calls?
Again, you are overthinking what the act is about.
It isn't that every API call requires to be flagged as AI.
It's the final output of the solution/application that has to be marked. Or the user is warned that what is created is AI generated.
It's to limit/prevent risks when AI generated content is used to make decisions that can negatively impact the public.
Some further reading: https://digital-strategy.ec.europa.eu/en/policies/code-pract...
The comment that you just replied to contained a link that directly refuted your claims but I think you probably failed to even read it or maybe even see the link?
So attack the person, not the argument. Definitely done now.
> contained a link that directly refuted your claims
Two Courtier's Replies in one post.
* Letting you Sign-Up-with-Apple on iOS but not Sign-In-with-Apple on web, but supporting Sign-In-with-Google
* Not letting you remove your payment info
* Not letting you change your email
* Seemingly no way to get real support
Also, couldn’t quite decide whether this is malice or incompetence.
I am becoming dependent on AI to make a living, and I need predictable spend on it. If I know I can't use a model regularly all month, my enthusiasm is limited.
I urge Anthropic to get better at this aspect of their business so I can come back to it.
What kind of things are you using it for?
I haven't tested it yet but on all the benchmarks it looks like it's 5-7x slower for agentic tasks.
Not sure what it's equivalent to, but it's super cheap and I am happy with the results
But yeah, it's very slow. I've put it to work as an LLM-as-RAG agent.
Although, RAG means search and search means latency?
I am a huge enthusiast of running local models, but when multiple quality USA vendors provide models like GLM 5.3-flash, I run locally just for the fun of it.
For the purposes of comparing to Fable 5.1, I would mention GLM 5.3 that is about 1/12 the cost.
like yesterday it ran for like an hour to build a fairly basic frontend...
i like luna and sol but it feels bad lately
What I'm really keen on is better auto-reasoning so I don't have to constantly have the constant inner debate on which reasoning effort to pick for each task.
I seriously hate the none-low-medium-high-xhigh-max-ultra etc that we have now, with companies frequently recommending different ones on each new model release, etc.
It's apparently called Adaptive Test-Time Compute or Dynamic Test-Time Compute and companies are apparently working on it (according to some LLM :shrug:)
if you don't trust any, then at least that's a coherent position
SuperGrok Plus is slightly better but doesn’t last me more than a few days. Even Claude Max feels leagues more generous in usage…
I haven’t tried SuperGrok Heavy because it’s too expensive
I use Opus every day and easily have most of my weekly limit left over at the end of the week
For instance, if you had 10s of agents running all the time, it is not that unexpected that you run out of tokens quickly.
I've only rarely maxed things out and then it's t through doing extreme things.
My personal guess is that it's one of those. With effective context engineering it's hard to use all 20x quota, the limit becomes your own attention and time really.
You may argue that you're doing multiple, parallel extreme effort tasks – which may be true but then again, there will be results to actually look at sooner or later and that takes time.
IMO, if you depend on AI to make a living, I'd invest in hardware for local inference, and learn on how to effectively make a living using AI inference you control, on hardware you control. Sure, economically speaking it's way cheaper to use one of these heavily subsidised services (for now), and their models are faster and more capable, but if your livelihood depends on AI inference, and you are renting AI inference, you are a being a serf of the tokenlord. And your livelihood depends on the whims of the tokenlord. They can increase rent prices, they can decide you can no longer do whatever you are doing, and you have no recourse, because you are dependant on them to make a living.
None of us can wholly do our trades without support. Local inference is a fun idea, but you'll be out-competed by the serfs, as you call them.
It is debatable whether local inference is a competitive disadvantage. One key advantage is consistent performance. No unexpected model downgrades or yanks, no silly safeguards imposed, and no quotas is a lot of advantage.
And by the way, it is not an either-or decision. You can use local inference as your daily driver while still leaning on frontier models when you get stuck.
When privacy/compliance really starts to matter it's up to the client/business to provide you with tooling - you're not running that on your own hardware anyway.
So the local AI for individuals is just a hobby/gimmick at this point not a rational decision. Self-hosting for business is a different story.
If you run Qwen 3.8 on your own hardware, every single day, it's the exact same model running in the exact same way.
Yes, it's nowhere near as "smart" as the cloud based models. But it's consistent.
So the workflows and "ways of working" you create will work mostly similar day to day.
With Claude/OpenAI you frequently find days where the models are useless, and days when they are out of this world.
So I guess the choice comes down to:
1. Randomly the smartest thing on the planet with unpredictable rate limits that is mostly amazing, but frequently messes with your workflows
2. A really good local coding model that is consistent every day with no rate limits
I'm not sure. My gut feeling is maybe the right answer is a mix of both.
Gambling on the biggest models, hoping they are working smart that day, when planning or doing very complex work. Then doing most of the tasks/daily work using local models??
It's not closed hosted models vs open local models, it's hosted open models vs local open models where the math doesn't work for local LLMs.
The only local inference use-case I can think of is porn generation (because most providers don't want to deal with it) and illegal shit like hacking to minimize the tracing.
And if you're super paranoid - but honestly giving sensitive info to LLMs in any scenario is a gamble.
If you game and can use your GPU I guess then it works as well but models that fit into a gaming GPU suck too much to bother IMO.
That's not prepping in the sense of having a bunker full of canned beans. Its taking control of a key piece of my business, and likely saving money in the long run.
A large company might own their equipment, but an individual operator probably won't. So it might make sense for some large software companies to own their LLM hardware, but it probably won't make economic sense for individuals.
Of course the economics are different in different industries. Trucking owner operators account for ~15% of truckers, but buying a rig is six figures against 5-6 figure income. Buying a mac mini is 4 figures against a 6 figure income, so maybe lots of people will do it even if it's not economically optimal.
The first comment in this chain clearly stated:
If I know I can't use a model regularly all month, my enthusiasm is limited.
only when it's cheaper than to continue renting it in your calculations
same goes for renting vs buying a house, or anything.
the breakeven point here would come when they stop subsidizing the subscriptions, or when local ai gets to run on basically everything, making it ~free (sans electricity)
Unfortunately open models are still not as good as frontier ones and the hardware costs for a similar experience are very high.
[0] not technically distillation. https://thomasdullien.github.io/posts/2026-06-15-rl-economic...
In any case, highly misunderstood.
It's also hard to have sympathy for them - they want to protect their IP, sure. But their IP was built on a corpus of dubious legal provenance. And even if the courts decide their training data are legal, most of the authors of the data would disagree. There was no consent given.
I think LLM's are great - don't get me wrong. I'm glad they were built the way they were, because it's unlocking an amazing new world. But I just don't have sympathy for the "I stole this and now it's mine so you can't steal it" argument behind concealing reasoning traces.
You're no longer allowed to edit the context anywhere! The whole context is to become append-only, says Anthropic. No more editing the system prompt as the conversation progresses, no more dynamic loading of custom tool calling formats. Everything has to go through their built-in tools API and you aren't allowed to mess with anything in the context if it has any thinking blocks following it. This is the most intrusive "model DRM" we've seen so far!
Needless to say, it improved output on following messages by whatever metric I cared for.
Not sure why would they prevent it.
I give you a chain of messages, what do you care for what the origin is?
Hm, aiui you can support both of these via mid-conversation system turns https://platform.claude.com/docs/en/build-with-claude/mid-co... - and in general you'd want to to preserve the cache and recency of the instruction anyways rather than frankensteining an off-distribution transcript. Not sure though.
"The user switched the workspace to read-only mode. Do not write files until told otherwise."
Great! Now we just have to trust that the model never misinterprets any of the system prompt, which has always been so reliable before. Instead of your meticulously crafted prompt, it will now be some junk like this:
"The workspace is in write mode. The user switched the workspace to read-only mode. Do not write files until told otherwise. The workspace is now in write mode again. Wait, back to read-only!"
And who knows how this integrates with their context summarisation that we will be FORCED to use. How does it summarize multiple user + assistant/thinking blocks without messing up the system "appends"? If all it did was append the mid-convo system messages right under the original system prompt then they'd be ripe for all the same distillation "vulnerabilities" as before. I guess we'll never know!
People should just walk away if they see something like that. This is where the actual, practical, consumer rights start. Not with the regulation. But with the customers being determined to stand up for themselves. And not just fold.
There are plenty of good enough models that are open weight and whose use comes with almost no strings attached. And plenty of great harnesses such as various flavours of Pi.
AI models should be explainable so that we can ensure and verify alignment. Responsible AI 101.
Also Anthropic:
No not like that.
Anthropic seems to be listening to community complaint on HN about how the writing style is grating. And apparently the solution from Anthropic is to add this block to every conversation!?
> Mannered prose substitutes metaphor and flourish for direct statement. Instead of "a parameter worth varying," the mannered writer produces "a dial worth turning." Instead of "this point still matters," they write "this point earns its keep." The phrases exist to display the writer, not to convey the idea, and readers can tell. That is why mannered prose irritates: it makes the reader work harder so the writer can perform. It is also imprecise. Metaphors drag in connotations the writer did not choose and cannot control. The fix is to say what you mean. When a literal phrase is available, use it.
The above was quoted verbatim from https://platform.claude.com/docs/en/build-with-claude/prompt...
This and 'load-bearing' anywhere are just awful. Anthropic would do well to stop inserting their poor writing taste into models.
Say what you will about LLM-generated code, but stories like this give me hope that software will never be as buggy as it once was.
The problem is the real world isn't made of stateless programs, but lots of important data in bespoke formats/schemas, and if you change the shitty software that interacts with the important data, in the wrong way, you can lose everything.
We are talking about a moving target here ... they get better every few months, so I expect the super-LLMs from 2035 will write amazing code even with sloppy prompting.
With that said - the process of fixing those old mistakes is greatly aided by llms... but you still need to understand what you are fixing, and understanding why giant blocks of code are copy-pasted everywhere, or why convoluted hacks evolved over time as reactions to bizarre underlying untreated bugs is, imo, ultimately a human/organizational/processes problem.
Anyway I think you are bang on.
Below that the AI will need to get good enough to compensate for people's lack of knowledge. But that will cost money so not sure how it's going to be balanced.
I have friends codebases where I had them just run a stupid simple prompt like "spawn subagents to find the top 5 worst issues in this codebase".
Wide open APIs allowing anyone to modify the database and charge customers among other things. The mere awareness of needing to secure things is lacking from most vibe coders.
I wonder which trend will be winning though. I personally won't bet on quality.
So.. one more year of untreated bipolar AI psychosis I guess..
They all work, they all are "good", they all are both "smart" and commit incredible basic mistakes a fair amount of times.
Then there's the cost situation..
Such a low-quality comment
Instead, the lurking variable here is new budget was added. With the new budget, they added a new tool, and the bug was located.
The difference here was budget.
And I doubt finding that bug cost more than $1k or so. Even if $10k. Thats nothing for a large department in a multinational company. Thats maybe 2 weeks of fully loaded costs of an engineer. Thats a single business trip across the Atlantic. Thats about two company issued macbooks, or one, if the company is nice. Nope. Not budget.
Stories like these is what I now call 'Marketing slop'
So it’s not that I haven’t worked on large-scale, complex legacy systems - but that I haven’t worked on any large-scale, complex legacy systems written in languages bereft of runtime reflection and verbose error reporting.
—————
It’s also possible that the bug was never found because its impact was so minimal: e.g. 1 crash per year, each causing 3 minutes’ downtime in a noncritical system: that’s something that will never get investigated fully.
A crash is actually the easiest kind of problem to fix since you have a crash. It means stacktrace, core dump, kernel error etc..
I like to start by prompting with “examine this code base for problems and improvements and write to IMPROVEMENTS.MD” and then carefully look over the suggestions, and either fix myself or let the model+coding harness try.
Can't believe they haven't at least figured out better messaging. If we take them at their word, it's hard not to read it as a messiah complex, that they think they're the only ones capable or worthy of making these decisions. I don't believe them, but I wouldn't be surprised if the articulated reason is a version of "distillation is a safety risk because we might lose the race".
Plus, completely deaf to the recent OpenAI-HF hack incident. Recall, defenders were categorically unable to use western frontier models in their response.
I was originally going to complain about the chem and bio guards still being too onerous, but I'll admit the projects Fable 5 categorically refused to work on are now usable, at least not rejecting on first prompt because the word "virology" was in a git commit (absolutely serious, in one repo it triggered on literally any prompt, eventually traced to the system prompt loading git commit history). Still, them trying to get into the biomed business while walling off the capabilities to the public reeks. Why sell the segments that are actually valuable if you can capture the value yourself!
Can't say I had such troubles actually, no. Their position can be extended to any and every model provider just fine, it does not single them out specifically.
Surely there's a less hyperbolic and ad hominem-y way to take issue with this? I don't think following up a critique about ineffective messaging with one centered around a demagogue reach is particularly compelling at least.
Their argument is that the model provider owns the safety story, and that as such, they consider the extraction of capabilities (which washes the guardrails) as a failure on their side. If this makes you think of personality traits, I'm not sure you're engaging with their position earnestly. It most certainly doesn't leave me any more equipped to disagree with them either.
If you instead highlighted how awfully convenient it is, however...
Would you let someone who lies to your face write code for your sensitive internal business systems?
What's far more exciting right now is models like DeepSeek V4 Flash and GLM 5.3 Flash. They have achieved good-enough-intelligence at extremely low prices and fast speeds. I don't have a use for Fable-level intelligence, but I do have uses for Opus-4.8-level intelligence that I can use as much as I want without worrying about the bill.
Now it's boring , not good enough
Wow there should be a term of that .
The term you are looking for is probably "moving the goalposts"
I think compute providers are the big business. Run any model you like, adjust the weights however you like, adjust the prompts and behavior however you like, but we'll provide all of the hardware infrastructure for you at a renting fee.
Something we don't actually know how to solve.
Glad to see this!
This is the right direction, but they aren't going to get there fast enough.
They will list, investors who don't know anything about tech will buy, the world will realise that China just put out a model that is good enough at a fraction of the price, they will crater.
A conversation with 20 turns, 50k tok growth per turn, 1m tok context at end would price out like this:
Fable 5 ($1/M cache reads) ; cache reads 9.5M tok × $1.00 = $9.50 ; cache writes 1M tok × $12.50 = $12.50 ; output 1M tok × $50 = $50.00 ; total = $72.00
Fable 5.1 ($0.25/M cache reads) ; cache reads 9.5M tok × $0.25 = $2.38 ; cache writes 1M tok × $12.50 = $12.50 ; output 1M tok × $50 = $50.00 ; total = $64.88
So yes, cheaper, but not massively.
I cancelled my pro max Claude subscription last week; codex is much more succinct. I am curious if this is getting better.
I don’t think Anthropic realizes that humans have a token limit too and it can be exhausting to read Claude’s output. Prose density is not the same thing as succinctness.
And yes you could add context (memories, rules, CLAUDE.md entries, etc.): they won't help (for long). Same for hooks that remind Claude to be concise: it gets "attenuated" and starts ignoring any such instructions quickly. There's also writing guidelines ... but they're basically just more context with slightly higher weights (ie. Claude will still ignore them).
I've even gone so far as to make a hook that identifies long responses and requests shorter versions (which is challenging in itself, as you need to run another lower-powered model to evaluate how long is "too long", as what's "long" when the expected answer is one line is different from what's expected for a ten line answer). However, that just shows you the long version, then some hook text, then (10-15 seconds later) it shows the short version. So I created a proxy that hid the long version/hook text for me ... but I had to abandon it because all that used up so much usage I was running out.
I'm fuzzy on the details, but Caveman somehow "hacks" Claude in a way that gets past all that ... but it takes things too far in that direction, with "cave man" speech that sucks.
I find it helps immensely but it'd be nice if I didn't have to do that.
P.S. Although my wife insists that I should stay polite in case AI overlords remember how I treat them ...
Separately, my boss confided in us that he's super abusive with his agent, wondering if we are too (no, lol). While I try not to read too much into this (which he doesn't make easy), I also can't help but not really notice a whole lot of amazing agentic delivery differences from his side. On the contrary, while the passion may improve his agent's performance, I'm not sure if it doesn't decrease his, upending the entire theatre.
I will say that Kimi feels nice but slow, GLM feels faster but has limited tokens (even off-peak) and OpenAI is nice and fast but has limited context (258k shows up in Codex, really).
Neither of them are perfect, but I prefer their type of prose across the board to what Opus 5 and Fable 5 kept outputting. I'll probably check out Anthropic again in a year, but for now I need a break from its brand of slop. Oh also all of the other ones allow usage in OpenCode with their subscription plans.
Which is kind of the inverse of how people work; a really smart person can condense difficult ideas into simple[r] terms. Whereas people who struggle speak a lot but say very little.
High/X.High do seem to deliver better quality results, but it sometimes feels like needle-in-haystack extracting that from the word vomit.
EDIT: also there's a reason the dial is called "effort", not "smarts".
Of course I don't know if there's really a way for this to be molded in current LLM's (sounds more like diffusion)
They work on a problem until their brain is full of problem-related concepts. Then something comes. After validation it might be a solution.
See, this insight it had early on looked like a red hering for a while, but then turned out to be load-bearing. And that's not just a difference in semantics, it changed the whole conclusion (spoiler: it didn't). And Claude is very eager to tell you about this exciting journey
I use the /sss writing style - synthetic, short and simple - and it helps a lot.
that feels like they just blocked words like load-bearing but can't actually fix the real problem. The insane word slop density and run on sentences was the real reason it became annoying to work with claude, colored with way too many analogies and pointless linguistic comparisons.
You get a week of research and debugging and testing compressed into a few pages. Even if it's explained well, it's just so much information. And since it's AI, I'm constantly second guessing "is that really true?" and it's exhausting.
Like, were the other parts not honest? I don't understand how Anthropic let it get like this, it's been such a clear regression
it's downright exhausting to read claude, the language style was a regression imo.
Every time, without fail, it would get me 90% of the way there and then leave a small note, exception, or deferral. When instructed to address that, Opus would somehow take nearly the same amount of time as the first 90%. And then it would finish with yet another deferral. Repeat ad infinitum.
You can sometimes get around it using the `goal` directive provided you are not subject to the constraints of mortality.
And the worst part is that this little problem will keep sneaking into the context of future sessions, unless you spend the time to fix it. Even if it isn’t important, I’ll sometimes have Claude fix it so it will shut the F up about it going forward.
Gotta fit in the watermarking.
I took time to figure this out after Fable spat out "...then stays purely as cascade-debugging provenance rather than load-bearing arbitration."
This sentence reads like Claude wrote it. Perhaps it did, or perhaps Claude has learned to write like the folks who work at Anthropic?
(Had I edited this, I would have said that a colon is not the right separator here. The second clause does not _explain_ the first, per se, bur instead expands upon it. Consider instead: "In some cases, however, its prose is denser than Claude Fable 5's, with longer sentences and fewer paragraph breaks.")
Also, could be just Claude rubbing off on them than it being Claude authored. I'd imagine they read it quite a bit.
But LLMs will fail at this question: they will tell you about Lamborghini's latest car and mix some history in it. Just try.
Which is the wrong answer anyway, because there's at least two major companies called Lamborghini, one making cars, one making agricultural equipment and at least one famous person (Elettra) with that family name.
This very simple test/question makes me realize how much do I hate LLMs in a sense: while I agree that the answer it gives is the most plausible for 90% of the users, it's ultimately both wrong and long. And that 90% compounds.
But there's no "correct" answer in my eyes than "who are you referring to?". Possibly without listing all the possible Lamborghinis.
If you're picking nits, why not focus on the word "do" and (wrongly) expect an answer like "Lamborghini (either of the two main companies of that name) does not 'do' anything - the companies employ humans who 'do' things. Lamborghini is a legal entity established to allow humans to 'do' things, such as make cars, or agricultural equipment."
Shared context is a thing. Reducing every conversation to first principles is not always required. Get a grip.
It assumes the average and plausible answer token by token.
And this tendency shows in every single field it's applied to.
At the end of the day I want *correct* answers, to the point.
Instead LLMs, no matter if it's version 3500, are bound to producing average results: slop.
"If you respond with more than 3 paragraphs, give me a TLDR"
"Do not assume I know all technical jargon, please explain things plainly"
Can't agree with you more. I review 2-3 PRs a day from my team of eight data engineers. Most of my team members use Claude to write SQL, dbt and Python code. Some of them use Claude a lot, some less so. I can easily tell when I review the code that is mostly Claude generated vs. the one that is not. In dbt models where we have a lot of biz logic in intermediate layers, that's where I really have a difficult time following Claude-generated comments. So much jargon copied over from other adjacent dbt models (yet inconsistently), and the prose is super choppy (for the lack of better word).
After reading a looooong sentence/comment line, I still can't figure out what it really means. Had to always re-read the line 2-3 times (sometimes, more) to sort of understand. Reading code, however, is so much easier and usually, I just skip to reading the code and then come back to the comments. :D
5.1 so far seems like another leap, which is really surprising. I threw it at a few bigger features I've been designing for a while, and it came back with some extremely thoughtful wrinkles in the design that I'd legitimately not considered. Which, OK, package managers and build executors and compiling C/C++ is pretty well trodden ground, but my thing is very different from everything that exists, and I was very surprised it was able to understand all that context so deeply and intuitively
i can't wait to dig in on 5.1 because while i have always been somewhat predisposed to think that openai's models have usually been "better" (my own subjective opinion, that) "on average", i have been kinda tired of the regime of late where it felt like Anthropic was miles behind while simultaneously clearly having models (Mythos) that are surely face-meltingly impressive-- it has just been very hard to square with the fact that i feel like Anthropic hit the "real" "critical point" first... i have no doubt that 5.1 will finally reset the ecosystem balance into a more healthy place.
1) address the claude 20x plan usage being only 6-7x the ceiling of the claude pro plan
2) either fix opus 5, make it completely free, or delete it entirely
Also always seems to have this annoying tendency to leave "questions for you" at the bottom of every output.
Just a high friction human interaction type model, imo should never have even been released, regardless if it scores better on whatever tests, its a horrible experience and a downgrade over past models.
claude.md for all my projects are fairly tight, its seldom where im upset at anything a model does, and if it happens, its likely because i swapped provider and didn't realize i was failing to feed it proper context beforehand.
Opus 5.0 fails in different ways that I haven't had to deal with. Its insufferable with its choice of language, something I've never had to compensate for on any other model across any provider, so of course I have no preexisting rules for that, it also is sometimes just incredibly stubborn and just WONT finish, and requires several just "keep going" prompts.
This is much different than the issues people would make fun of users for in regards to treating models like slot machines and just pulling the lever over and over, this is more its stopping for no reason short of its task, and literally just needs to be told to continue? absurd.
Most of my workflows have reference material, with standards set, why opus 5.0 is the only model that fails to follow those standards and inserts wildly long weird code comments is not a failure on the end-user, thats the model failing. I can be MORE explicit of course, but i shouldnt need to be, this is supposed to be 5.0, its a downgrade. I went back to 4.8 and all these issues vanished.
Maybe system prompt has priority or something but Opus just really really likes writing bad comments
CLAUDE.md heirarchy: At the top level you've got general instructions you want all contexts to follow and each subdirectory can add more specific instructions in their own CLAUDE.md files. References in CLAUDE.md are not fully loaded into the context. They are loaded opportunistically. So keep important instructions in the CLAUDE.md file itself and not a referenced or linked file.
Rules files: These offer path scoped rules via frontmatter. So you could have specific rules for certain types of files Claude Code interacts with. Certain rules for handling all .cs or .js files for example.
Auto-memory: You cannot rely on this one. I use auto-memory as a cache for potential future CLAUDE.md instructions. I have an audit process that kicks off when the auto-memory gets beyond a certain number of entries.
Skills: On demand context. I don't tend to use /skills explicitly. I tend to have them used in context. I've got a task tracking system I call threads. So whenever I say "Create a thread for X" it has always reliably followed the specific instructions. I've got skills for managing my NAS for searching historical session for sharing content and other things. I use them a lot of times in place of MCP servers.
Hooks: Deterministic scripts run on lifecycle events. I've got hooks that run linters on code files post edit and hooks which tie into the request / response events to push my history into a SQLite database.
Output Styles: CC ships with a few different styles, but you can create your own. This is key for changing the default voice. CLAUDE.md instructions are appended to the system prompt and can fight against the system prompt. A custom Output Style would let you replace the instructions in the system prompt with your own instructions. This can be done at the user level or per project.
Opus 5: I give it work, it makes false statements and draws weird conclusions, I correct it and get it on the right track, it thrashes around but gives me something working though usually buggy.
5.6 Sol is probably on par with Opus 5 on ability but at least it doesn't waste as much of my time.
- It's extremely verbose and often incomprehensible when doing even basic tasks. Like it'll write a giant jargon-filled essay then end it by asking for a judgement call on something that references its own convoluted jargon.
- You can ask it to do research on a topic, and it'll just straight up be lazy, pretending it's really digging deep to find stuff when actually it's just grabbing cached SEO snippets off a search engine.
So my current usage as a Pro subscriber... Not able to even consider using "Sota" unless i shell out for 100$ a month, (lately i've been a bit burned out i am literally struggling to use 50% of my pro plan per week). Beyond that, I have given up entirely on the top Opus model and reverted back to 4.8. If i have work i deem somewhat complicated, i now have an openai 20$ sub, and i just toss out sol after planning with 4.8. Both subscriptions not anywhere close to capping my usage per week, one of them says i can't use their Sota unless i pay for 5x more usage, and the "best" model they do allow me to use, they are neglecting and its by far the worst model I've interacted with in 2026.
They should pay for us for using it!
Pro 20x = 60k credits/reset
Pro 5x = 15k credits/reset
Plus = 3k credits/reset
Pro 20x = 4 * Pro 5x
= 20 * PlusI'm still having problems with that. OpenAI is a lot less annoying than Anthropic, but I still get obnoxious "this content can't be shown" messages in codex far too often.
In the past I watched and saw everything the model did, not a lot got past me. Today it does A TON of work while i'm busy on other tasks. It also has extensive access to my computer, other computers on my network, my internet. It's really helpful when you give it a lot of resources, but right now I have very autonomous, very smart agent running around more or less unattended with a lot of resources.
Not gonna say I want 5.0 as an option still... but maybe I do.
This is not a lasting business model, nor one I'm interested in using.
https://support.claude.com/en/articles/15363606
My work is mostly on the Nvidia b200, which apparently gets flagged as non-standard.
Opus 5 works, but sometimes I do wonder if it's surreptitiously trying to sabotage the efforts -- possibly deliberately, but more likely by something like Fable's initial launch, which did come with secretly degraded performance when detecting kernel work. Anthropic was open at the time that such a mechanism existed, but disabled it due to backlash. More likely than not, this is just paranoia on my end...
Maybe it's more like you asked a lieutenant to check the weather forecast for you, and it went away and sent back a sergeant in its place.
It's one thing to generate some code and ship it, but it's another when your developers don't understand said code and it brings down production. If the model refuses to assist debugging the problem because it triggers some safety mechanism, you might be fucked.
Whole-file rewrites for small changes. When editing text files, the model is more likely to rewrite the entire file than make a targeted edit. The result is usually the same, but the rewrite costs more output tokens and time.
So we are to catch that somehow? And then add their recommendation (below) to our prompts?
https://platform.claude.com/docs/en/build-with-claude/prompt...
If Claude Fable 5.1 rewrites whole files for small changes, append the following instruction to the system prompt or the first user message. Claude Fable 5.1 is more likely than Claude Fable 5 to rewrite an entire text file rather than make a targeted edit. The resulting file is usually the same, but unless the file is short or most of it is changing, a rewrite costs more output tokens and time. The instruction brings Claude Fable 5.1 back in line with Claude Fable 5 for small and medium changes.
> The number of tokens used to edit files is best minimized, all else being equal. Therefore, when it will not affect the end result, try to surgically edit a file rather than rewrite the entire thing.
objects that are not alive: dust, rocks, water, wood, hats, lego, aluminum, etc.
objects that are alive but not intelligent: trees, mold, staphylococcus, cancer, grapes, etc.
objects that are alive and intelligent: cats, Steven Tyler, dolphins, crows, dogs, elephants, etc.
and now intelligent but not alive: Fable, Grok, GPT, etc.
I do get your point though and can see what you're trying to say, it is interesting indeed. There is however a detail that seems important to me, who is the driver? There is no agency is there? So its just fishing for data, so its a different type, just like trees are from us. I see them more like a very compressed "book of everything" that you can spin in "infinite" ways to get your desired outcomes. So yeah, definitely not alive, intelligent? Not like our intelligence.
What I do see in the comments: subjective improvement in text generation, possibly lower cost, some optimism about code generation, but some skepticism too.
I use coding agents. To me they are very useful. But what I spend on them isn't going to support trillions of dollars in investment.
I summarized each into new fable 5.1 sessions, and both seem to have arrived at reasonable solutions that only need a few nits revised before they are commit worthy.
I get your point, but we can only have groundbreaking leaps once in a blue moon. That doesn’t mean incremental improvements aren’t useful.
The problem frontier LLMs face is that they are hundreds of billions to trillions of dollars short of finding that market that's big enough to sustain capex commitments and further product development. If they don't find something groundbreaking, they are going to have a very painful year next year, maybe even starting this year for some of them and their data center partners.
Anthropic and OpenAI can't afford to live in a world where LLMs are at or near the top of their S curve.
I upgrade from dumb brick phone to 3G-capable nokia, then to touch screen smartphone, then touchscreen with LTE, then one with fingerprint sensor, etc.
And just when the smartphone begins running out of stuff to add, i also slow down my upgrade cycle.
So, i guess after 5 years in LLM, we are at the end of the curve
But yes, we might end up hitting the issue of "most jobs aren't solving hard problems" increasingly. The bigger potential benefit is higher trustworthiness, reliability/thoroughness, and squishy human things; people will likely continue to pay large premiums for those. "Solve it well and save time, long term". Those can be harder to see on a benchmark.
I'm not an emdash hater but this isn't how you use them. It should be a comma.
I went to the grocery store, and bought tomatoes.
I went to the grocery store---and bought a Ferrari.
The second one has a bit more of a dramatic pause.
"Eats, Shoots, and Leaves" is a fun book with a great chapter about the dash with many good examples.
Going back to Anthropic's post:
> They’re the world’s most advanced models for coding and knowledge work---and their research capabilities offer an early glimpse of how AI models will contribute to scientific progress.
The first thing directly implies and flows smoothly into the next---or would, if not for the awkward emdash. There is no discontinuity, no twist or shift in context, no implied question and provided answer, no punchline. It's just distracting.
It's fine in the sense that when a bad writer writes something I can usually understand what they're trying to say.
This has never happened to me before, but if this is normal behavior, Fable 5.1 is essentially unusable.
How does this work if it doesn’t change the output?
Toy proof-of-concept: Anthropic owns a secret key which is a coin-flip Bernoulli random variable K with p=1/2. You are paying Anthropic to give you X, a Bernoulli random variable with p=1/2. Anthropic changes from their old strategy, "draw from K, then throw it away and flip a coin, each time you ask for a sample", to their new strategy, "draw from K and send it to you". You cannot observe the difference, but Anthropic knows K and so they know when you are repeating its outputs. (Obviously this is a toy example; in reality the distribution is vastly more complicated than Bernoulli, and Anthropic isn't just storing some model outputs to use as K but instead is computing a correlation with a known pseudorandomness source.)
Would explain why Antrophic removed T from the API.
Before: "He leaped at the chance" - 33%. "Jumped at the opportunity" - 66%.
After: "He leaped at the chance" - 33%. "Jumped at the opportunity" - 66%.
But if you refresh your response from Anthropic 100 times:
Before: "Jumped at the opportunity" He leaped at the chance" "Jumped at the opportunity"
After: "He leaped at the chance" "He leaped at the chance" "He leaped at the chance"
The second one is detectable as being watermarked.
davmre has a good explanation that's more in-depth.
In cases where the output has low entropy - eg, you've asked a model to repeat some input text verbatim, or to answer a question that has exactly one correct answer - there will be no randomness for the watermark to hide in, so the output will effectively not be watermarked. Code lives somewhere in the middle: it generally has less entropy-per-token than prose, so would need more tokens to reach a given level of detectability.
There are lots of ways to restrict output samples. The simplest conceptually would be to just use a restricted pool of PRNG seeds, but in practice there are more sophisticated constructions to try to build in robustness to minor edits, allow detectability without needing the original weights and prompt, etc. Google's SynthID paper (https://www.nature.com/articles/s41586-024-08025-4) is a good starting point if you want to understand a recent production-ready method (or you can just ask an LLM to explain it to you).
The more realistic claim it does not affect output quality.
Anthropic and fanboys defend that choosing "overcast" over "cloudy" does not affect a text quality.
Clearly, money and ambition clouds their judgment.
Then why does it have separate datapoints for Terminal Bench, and score higher? Something doesn't add up here??
Note it may not even be actual performance, typically in most benchmarks the model would be scored zero for refusing a task just the same as not completing it, so it could just be the Fable's stronger safeguards is just making it refuse more or perhaps even drop down to Opus.
i try to look through the docs, but i didn't find where they said its only for API
is it in the system card?
really hope not, that change the only positive part in this release
Then i went and pasted those exact prompts it generated into a fresh chat, 5.1 on max effort.
Immediately got blocked and sent to Opus 4.8 fallback.
Why is it that the voice models in Claude and ChatGPT have a perfectly normal style with barely any "AI smell", while the writing models are so obviously recognizable as AI?
The answer is likely that models underlying the voice modes are (post) trained differently. If so, then why can't the writing model be similarly trained? Presumably they haven't found a way to train them to be both "smart" (i.e. solve tasks etc) and pleasant to talk to?
I once caught Fable 5 spinning its wheels on a rendering issue, which evaporated 90% of my usage in a single prompt. I could never let Fable run free attached to a credit card without staring at it the whole time.
Anthropic accidentally over-billed my account, and when I reached out to the support bot, it downgraded my account to a Free account. It’s been impossible to get it resolved and I have almost $200 held hostage.
I don’t want to do a charge back. I’m one of the main advocates for Claude Code at work, I use this subscription to try out new features before it’s available at work.
The whole experience has been illuminating about our dependencies on these AI companies.
I am disappointed in how anthropic handles billing, and is using AI sloppily for customer service around here. Very unprofessional, and at this point since its been well known and shared, it also is feeling unethical.
Is this normal for anyone else? hasn't happened with my codex subscription.
I jumped when I saw a mention about "writing style improvements" so I gave it a try on a recent feature in rcmd [0]. I prompted Fable 5.1 to find these wordings and propose simpler plain language.
For context, I recently worked with Fable to give users a way to fuzzy search and focus any browser tabs, terminal panes etc. but the UI was still a prototype full of AI writings.
It's a very capable coding agent, but I can't understand how it can be so bad at writing. Where are all these verbal tics coming from and why is it so hard to get rid of them?
It's copywriting. They fed these models the internet, which is loaded with it.
It’s a side effect of post-training for effectiveness and efficiency at technical tasks.
Over time the models learn to pack as much information as possible into their available context window, because that’s one way to increase the effective intelligence.
Humans do this too with industry jargon, dense tech-talk, etc.
We have a limited capacity so packing it densely maximises what we can do with it.
If you’ve ever heard a “non technical” manager complain about the terminology in an IT meeting — this is why.
But who has both the compute power and the motivation to do such a thing?
I guess I'll just continue rewriting the UI one word at a time for the time being.
Nothing works. This style of writing is deeply ingrained into these models.
This style provides a high entropy basis distribution, so they can from a bigger pool to pick from and phrases to watermark the sentence.
You have much bigger variation of this idiotic phrases and words, which states a simple fact in that sophisticated and twisted manner.
> same input and output prices, with cache reads at a quarter of the cost
This should impact any long-running agent since subsequent calls can benefit from cached reads for previous transcripts.
They were _temporarily_ increased in May by 50% [1]. They continued to extend them through July and August (admittedly, their messaging around this has just been a complete mess and they frequently pushed the deadline back as it approached).
So, now they are giving you a 25% quota increase compared to where things originally stood in May.
So, let me ask you this: assuming you knew that the 50% quota increase was temporary all along, would you then have complained about Anthropic restoring things back to the original limit?
Are they going to try the banned for export for a week marketing move too?
Oh, the halcyon days of three months ago when a new flagship from a frontier lab generated excitement rather than a shrug.
Also it seems to start subagents for random stuff, it didn't before with my development style. And then the main thread gives you a partial answer and tells you it's waiting for the subagent to finish :) What's the point?
Model HLE w/tools GDPval-AA v2
Claude Fable 5.1 65.0 1853
GPT-5.6 Sol 64.5 ~1711-1730
GLM-5.3 62.5 1769
DeepSeek V4 Pro 60.0 1590
Kimi K3 59.8 1682
Qwen3.8-Max 56.2 1739That doesn't make them less impressive, it just means people are shifting their focus more towards their own day to day experience with these things because we're relying on them so much now.
Like when the novelty of the automobile wore off, I'm sure people were starting to say "it's a bumpy ride though, isn't it?"
YMMV.
In such a context also a coding agent has it much easier. But establishing that or adding something beyond what's already safely established, here high intelligence models really pay off
When I discuss something new with an agent I want to feel like it genuinely gets what I mean, which has only started feeling true with fable 5 for me.
> This is the user's own Firefox-fork browser; the slice is defensive service-posture hardening (telemetry/Normandy/FxA/push/crash-upload off, the update endpoint and private-mode extension law) of their own product on the unbranded build path.
I cannot say what effect this has on the way the classifier operates, whether it actually impacts the classifier or whether that was tuned in the background to prevent blocking hardening ones own pre-release code, whether it treats input by Fable 5.1 different to what a user prompts (otherwise the classifier could be defeated with prompting which wasn't the case in 5 and I doubt has changed).
I do however know from personal experience that even when Fable 5 prompted a subagent in such a manner, it had a high likely to be caught by the classifier.
I am using Claude and Claude code for my own amateur history project. I'm enjoying how it constantly reaches dead ends, and I can reframe the question and get more results. I am starting to get concerned that AI and me are so compatible, that I might not be a human at all...
I also like that, because I'm too lazy to write stuff up, Claude code can keep the current state of research published on my site. It makes running a hobby site a dream. "I just found these pictures. Add them to the site for me". And up they go, resized and all. What a dream of a way to work. "Some of links in this article are dead, run through them and check, and see if you can get an archive link for me if they don't". It's like sending a Teams message to my PA.... which I don't have in real life
Anyone know who the ZDR special treatment is available to?
This is interesting. I wonder if customers will be allowed to create an auto expiry for their own data to prevent future subpoenas. That’d be a treasure trove for discovery.
Does anyone reading this have additional knowledge or insight on this?
Maybe it'll come out eventually but they don't even include it on some of their comparison benchmarks anymore, so I figure its very low priority for them.
Claude Fable/Mythos vs GPT-5.6 Sol
Claude Opus vs GPT-5.6 Terra
Claude Sonnet vs GPT-5.6 Luna
Claude Haiku vs ?
Fable/Mythos are much larger than Sol. They match to Astra which is supposedly at least 10T. Astra is already publicly confirmed as a new family.
Opus matches to Sol. Sonnet to Terra. Haiku to Luna.
Anthropic is able to compete at the frontier high-end by launching massively large expensive models. But their inability to compete on small models belies their efficiency aspirations across the stack.
It's currently priced 33% above Gemini 3.7 Flash, and several multiples of 5.6 Luna.
That's what we've done, migrated workflows away from Haiku and Sonnet. I actually think this is not a crazy position because these lower models have so much competition from Grok, OpenAI, DeepSeek, and about 20 other labs with really solid models in the Haiku to Sonnet range. So what is the point of Anthropic competing in these spaces where everything is going towards zero cost?
The trap being that it's a rational decision at the beginning to focus on the most profitable lines of business with highest margins. But the disruptor then captures the value of the abandoned market to finance innovations to move up into higher margin tiers, forcing another retreat.
The cycle can repeat until the once dominant firm is relegated to a tiny niche with no growth prospects or until fixed costs exceed dwindling revenues thus eventually resulting in insolvency/acquisition. I remember learning about it from a case study of how American companies like GE and GM reacted in different ways to Japanese competition emerging in the 70s and 80s.
It's not always a bad strategy but I think a pre-IPO company that's only a fear years old would generally prefer to grow the size of their potential market vs. shrink it preemptively.
I still think that a major problem is that biological processes are not “fast” as coding, but they are verifiable. If during post processing we are able to give enough harness to test and verify this kind of environment (maybe via simulation and real data) we will for sure achieve incredible performance also in this domain.
This seems to point to them having achieved some kind of optimization in attention mechanism perhaps along the lines of DeepSeek V4, which had a similarly high discount between cache input and normal input.
In real world use, the savings should be quite noticeable. For example, you can now use the model at 800K tokens context window at the same cost efficiency as the previous model at 200K tokens context window.
> *Fewer progress updates during long tool runs.*
> The model writes less user-facing text between tool calls, especially at higher effort. Set thinking.display to "updates" (beta) to receive the progress updates it does write, and remove any prompt line that tells it to hold findings for the final response.
I would be interested in whether someone has done research here on these things as it seems a fairly complicated function to work out, and use case dependent. (?)
In some sense an expired kv cache is basically like an expensive cache hit, so your compaction token threshold should come in. Ideally claude code should allow you to vary the autocompaction threshold to vary with time since last token, but it doesn't of course. This perhaps suggests that someone should manage claude code through their own intermediary agent who manages these sorts of rules.
Lastly, I strongly suspect that anthropic isn't offering this price cut out of the kindness of their hearts. I am sure that they are to some extent banking on people not reacting to their price cut and leaving their autocompaction thresholds unchanged.
[edit - looks like the discount is only for the api, so they still don't give a rats ass about subs!]
• [...] Rebuilding the top-level system prompt or tools array between requests in the same conversation.
Many people unknowingly do this (at a high cost to them because of the cache busts), this change will finally force them to stop.
Especially if you're generating your system prompt via a template that can change mid conversation, it's so easy to fall into this trap.
Comparing 4.8 Opus with Fable 5.1
This directly contradicts what Anthropic is presenting here. Yes it scores higher but that's to be expected from a new release. It's the opposite of what OpenAI has been doing which was reducing costs, increasing efficiency.
Fable 5: https://artificialanalysis.ai/models/claude-fable-5 Fable 5.1: https://artificialanalysis.ai/models/claude-fable-5-1
On high it gets the same score as 5 with max effort while costing only half as much.
Interesting behavior. The docs also provided recommended prompt [1] to mitigate this behavior if undesired.
Wondering if anyone has encountered it yet?
[1]: https://platform.claude.com/docs/en/build-with-claude/prompt...
AI to AI doc share: sure, do what you please.
AI to human: please make it legible and flowly.
example, "Every thinking block records which model produced it, and it's preserved in one direction only: Claude Fable 5.1 reads earlier models' thinking blocks, and no earlier model reads Claude Fable 5.1's." is a very Claude-isk way of writing. Choppy, long, and lacking flow.
And my 5 hour window was due to be reset in 2 hours (barely used), now its in 5 hours - so this reset effectively gives me 1 less 5 hour reset for this weekly cycle.
Generally once an exploit chain is described, developing the exploit is trivial.
If you're so inclined, discover the exploits using Fable 5.1 and then give that exploit to a model that doesn't have such compunctions (e.g. local LLM or an uncensored cloud model / model that's easier to jailbreak). I don't think Anthropic is really mitigating here anything in the real world other than PR narratives where media can report "Anthropic's model was used to develop the latest cyber attack".
On the Fable 5.2 eval summary, Opus 5 only beats Fable on SWE-bench multilingual and multimodal.
I primarily use the models via interactive sessions enhanced with custom tools and skill. For that Opus 5's benchmark superiority has not materialized into greater productivity and frankly has been quite a let down.
The outputs are too often unreadable even after adding recommended prompts. There is an ongoing problem with the heron_brook system prompt affecting orchestration. [1]
I've used Opus 4.8 since the second week Opus 5 was released.
Over this time, Fable 5 has been reliably fantastic. Both in planning and direct execution on complex changes across code and infra.
I'm a bit surprised that there doesn't (seem) to be a section discussing ~performance across different modalities. This system card and blog post too-often default to an API-based use case when the gander primarily experience Anthropic's models via interactive sessions.
I understand waiting to comment until Opus 5.1 is available and handles these problems, though I am hopeful that Anthropic will confront the elephant in the room on Opus 5's failure to delivery great interactive sessions and the widespread negative feedback on the release.
It would show the org is paying attention, taking steps to balance model evals between interactive and API use. Also, some empathy for customers that wasted time trying to make opus 5 work for them.
Did you mean Fable 5.1, or do you have access to the next (unreleased) version of Fable?
Curious to see how Astra does.
I wonder to what extent this will make the automatic Fable-to-Opus downgrade give worse results.
I tried the old fable and it didn’t seem worth paying for. It still made errors like Opus does so I might as well use the included model…
Does Fable 5.1 really provide much benefit over models like Kimi K3 that are 1/3 the cost? Or GLM-3 that are 1/12 the cost?
If you can talk about your work, what kind of tasks do you work on where the higher cost is very much worth it?
What exactly is the premium that you're getting for paying these prices?
The issue I had with Fable 5 and will probably carry to Fable 5.1 is that I hit the safeguards too often.
That being said, even if the benchmarks are only part of the story, these ones paint a pretty compelling one, when comparing Fable 5.1 to 5
OK, I think that's what they meant when they suggested reduced extra promo usage will not sting this much.
That seems unfortunate for 3rd party integrations that expect stable output - what that really necessary ?
Fable 5.1 -- to my early impressions -- seems to have gone backwards again.
Opus 4.8 was terrible for babbling self-invented jargon. (But still better than other options at the time for coding and logic.)
Moving stuff out the API into prompt engineering is obviously less reliable but necessary for progression to 'actual intelligence'. Will be interesting to see if it really is solid.
Jane Street is a partner? How sad indeed. Anthropic could front run them because they leak all the data.
Having lived through Covid, this doesn't sound so good to me.
I have been happy with Fable 5, it has done great work for me so far. Very excited to try out Fable 5.1 and see what differences and improvements there are.
how?
When paying by the token, don't the labs have a strong incentive to make the model as verbose as possible?
On top of that, recent versions of Claude had a ton of tools added, and all those tools use up significantly more context/usage than before, so the moment you open a Claude session you are already using a lot more (I forget how much more) usage ... just to do the same exact thing you did last week.
*Some might see a parallel with the old game Adventure, in which wording differences like "twisty little passages" and "little twisty passages" were used to build a maze of room descriptions, with the same meaning but still distinguishable to the attentive player.
What would you suggest as an alternative if I'm happy with the harness?
They show this off, but artificial analysis contradicts the statement. Fable 5 cost $3.14 per task, while 5.1 cost $3.69 -- around a 15% jump in pricing.
https://artificialanalysis.ai/
These, IMO, are marginal improvements for a more expensive model. I stopped using Claude ~3 months back; its outputs are too jargoned, it makes architectural decisions that are not right, and it's incredibly pricey for what it is. Each decision it makes, it acts as if a problem as major as world hunger has been solved. And the overly verbose code comments, strange commit descriptions, duplicate code, and slop it generates -- which I know is not specific to Fable -- is just too much for me.
I found the best is to use something like Deepseek V4 Flash -- with a fast TPS provider -- and work on the code myself. For agentic work with computer use, GLM 5.3 flash with Hermes Desktop works well.
Unfortunately, for them.
I had to switch to Fable, because Opus has become completely unreliable. Just now while working on a specific task, Fable made changes completely unrelated to the task and introduced new regressions.
Fable now talks complete nonsense.
I do not understand why Anthropic is so focussed in squeezing a few fractions out of benchmarks, while at the same time making the life of developers insufferable. I am seriously considering ditching Anthropic and moving to something else.
The thing that I describe as bullshitter mode is starting to feel extremely unproductive for me. I spend more time making Claude code do what I want than before!
Things it does constantly that Fable 5 barely ever did:
- Act without my permission. All. The. Time. "Oh I just finished this thing we were discussing, let me push it without ever having been told to do so."
- Immediately jump to action instead of addressing me first. If I say "I wanted to write tests for this and run them" it immediately starts writing tests instead of digging into what "this" is better -- literally does not give me any feedback and starts spitting out code. Naturally it creates the wrong tests
- Despite claims that it does not write like "stereotypical Claude" anymore, in my experiments it is far worse than before. Replies are longer, more filled with fluff, and still flooded with garbage language. Hard to parse.
- It loves to answer my set of two direct Yes/No questions with 5 paragraphs where it only answers one of them and answers 4 other questions I didn't ask. Notice how it misses one of the questions.
- It. Is. Cocky. Absurdly full of itself and arrogant. Just the whole way it presents and answers passes this energy of "No, but really, you're wrong and I'm right". It often is not right. What annoys me is not that it's wrong more often than before (which it may be), it's that it doesn't own up to it as before. Insulting if it were a human.
- Replies and addresses me directly in its thinking traces, and then assumes I've read it. I ask a question, it answers it in the thinking traces and does not relay it back to me at all. This is the only one that Fable 5 also did, but 5.1 is doing it an order of magnitude more often.
- It's too early to really tell, because I may just be working on particularly harder problems today, but it seems to get things wrong more often. I've had to bump it from high to xhigh to compensate.
My guess is I must be having a bad day or something. Although this is happening on multiple projects run from multiple machines (fully isolated, except for the account, which is the same) all in the same way.
Will probably downgrade to 5 while I can.
Guys, listen to your feedback please. I hadn't used OpenAI products in quite a while until this issue came around. They seem to have MUCH smarter safeguards than Anthropic does.
Sounds like some serious nonsense. "Tell me you want the government to retain access to my data without saying it explicitly."
Really? Interesting choice. Pretty much every CLAUDE.md file I have starts with something about Hemingway, terseness and treating every word you use like you're carving it on your own back, but different strokes for different folks. I suppose I haven't heard from anyone who enjoys how wordy Claude is because they aren't done writing their post yet.
After months of trouble dealing with KYC and procurement I finally got CVP for my security org and today I found out that CVP (which is what removes cyber safeguards) does not apply to Fable…
So yeah, unless you’re a Project Glasswing member, there’s no using Fable (which with Glasswing is Mythos) for security work… Absolutely useless…
Didn’t they just sign some “we must use AI for cyber defense before the bad guys do” and then they artificially cap us by not allowing Cyber-unlocked Fable…
Sigh…
... but some is definitely Anthropic, so I'm not trying to let them off the hook; I'm just pointing out that the government is partly responsible.
... with the condition that you store 100% of your data and make it available to the US government and possible others.
Ironically one of their demos is speeding up inference - do us normies get to do that with Anthropic tech??
In general, if Fable isn't blocking you, there's a high chance a lower tier model would work fine.
I do think probably ralph looping a binary locally first is going to be best to get 100% recovery of types and function behaviors then letting a smarter model churn the final steps.
I don't use Fable for a ton of implementation work, but I use it a lot for planning, so maybe that's related to it. For planning though, I've had a very good experience with Fable and implementing with Opus.
I'd guesstimate that ~80% of the time I thought I was using Fable, I wasn't actually. It's also led me to just... not even try, and just start with Opus regardless.
I've found Fable unusable; not because it's bad, but because it... can't be used.
FWIW, most of my code only encounters security concepts as standard implementation of best practices. I'm not in a security centric position.
“sure thing boss”
——
“Hey Fable, review this unsafe win32 rust code”
“Potentially dangerous request, falling back to Opus”
—-
Every damn time, ironic because the unsafe win32 code can be generated by fable in the same session.
Maddening.
We have access to Fable at our company on our enterprise plans and most of us rarely run into an issue.
Obviously this is gonna vary a lot with what technical domain you work in which is why its important when talking about the classifiers that people specify exactly what types of workloads they were seeing failures with.
About a month or two ago, they must have tightened the black list on bio topics as it became more willing to process requests without visibly downgrading to Opus.
It did help with some worldbuilding for my book (it wasn't incredible which gives me some hope for writers). So far opus 4.8 is the most reasonable model.
And the lack of thought traces make it utterly useless.
I get punted down to Opus 5 occasionally (for security-adjacent things) but that's pretty rare.
I have noticed sometimes it likes to gaslight itself into thinking that everything its doing is allowed or allowable, I saw that it thought the game I was reverse engineering was running on a private server (it was not) so it assumed it had permission to do anything lol.
[1] https://www.ft.com/content/5ee49718-c258-4f01-aa32-7e5b76ae5...
But eventually AI will cure something, unironically. It may be Claude, or another AI company.
Done. The average human lifespan is now zero.