Livenerf: Has Opus 5.5 been nerfed yet?
github.com
github.com
https://www.bridgebench.ai/nerf-bench
They test it on launch day, then benchmark it against that. A deviation of above 10% is considered a change. They're currently tracking Opus 5.5 and GPT-6 Astra.
This bench famously detected a degradation of Opus 4.6 which Anthropic later blogged about. I personally think people sense nerfs more often than they happen and that it's often about honeymoon effects.
It doesn’t mean the big labs don’t also nerf models! But if they didn’t you’d still have users complaining.
I was like, wow, I guess the shoes must be literal torture devices full of MRSA-covered broken glass at this point. They've been getting continuously worse for 24 consecutive years!
Of course, what was really happening is that they were not getting worse, but naturally every year there was some small percentage of vocal dissatisfied users, while the silent majority simply enjoyed their shoes and didn't have much to say about them.
(The sorta-opposite happens in sneaker reviews as well. People will gush about how cushy the sole in some particular new sneaker is. Well, yeah, of course it's cushy -- you're comparing a new sneaker to your old sneaker where the foam had lost its bounce...)
Shrinkflation is a thing, which people suddenly started noticing in the past 5 years.
There's also "the Schlitz Mistake", which I've heard summarized as "most customers won't notice if you take your product's quality from A to B (or C), but they definitely will if you take it from A to J (or A to M)"
At this point I kinda assume that any company releasing year updates to a physical product that _doesn't_ take the opportunity to trim costs / reduce quality would be vulnerable to a shareholder lawsuit for leaving money on the table...
This also happens in product support. When I was a helpdesk tech at a company, I saw thought "Our stuff is such _crap_! Why does anybody even buy it?". I felt like the sorts of errors we were seeing just shouldn't happen in any deployed product. But gradually, I got a trickle of customer comments like "It saves us so much time. What took multiple people hours..." and "Oh! Sorry, I was trying to reach accounting to renew. We love your software.".
The first real issue where i wanted to leave was the Claudish nonsense. If not for 5.5 i'd be on OpenAI by now.
Ikea is notorious for this: The early Billy bookcase had heavier veneer and sturdier construction early on, and was actually a really good purchase for the money. The later years replaced veneer with paper foil, used thinner shelves, frames, and backing panels, and was just significantly weaker.
Wow the old grumpiness that lingers in my head from losing my favorite shoe. Who knew? Now I'm old and heavier and run in the Nimbus and am happy again. But you're right, of course, that most of the model changes are just fine and people like to complain.
most running shoe series get heavier year to year as manufacturers turn to cheaper materials and add cushioning to try to attract more adopters
it's almost universal, very few manufacturers seem to be able to resist tampering
(heavier shoes are slower, every three ounces is equal to another vo2max point lost)
`percieved_performance = actual_perf/expectation`
`expectation` is an increasing function over time.
`actual_perf` is a stochastic function of the model's true ability, context, etc. -> a recipe for some bad sessions.
As for multiple bad sessions in a row, this is a studied phenomenon in gambling where players perceive "runs" because our brains love to find patterns.
e.g. The original apple shuffle and the Risk app ins which a string of songs from the same album or three one roles are "not random"
How to Shuffle Songs? - https://web.archive.org/web/20220215030739/https://engineeri... ( https://news.ycombinator.com/item?id=38330877 78 points, 65 comments)
Took a little bit of digging to find it - I remembered the graphic at the top and found a blog post that copied it and linked to the blog post, but the blog post isn't there anymore... so web archive.
The current version of the blog post is from 2025 - https://engineering.atspotify.com/2025/11/shuffle-making-ran... (which didn't get any traction on HN)
This is true of all reliability and performance paradigms, incidentally
I believe in temporary nerfs. Operators reducing quality significantly to increase throughput for whatever reason (high demand?). Same session, model being completly incapable, and it being back to normal the next day. Experienced that with Anthropic models way too many times. Never on weekends, usually during US work days.
It's been a few months since I last recall this though, must have been pre-opus-5. And I know there are benchmarks for this as well, hence "believe".
People called it lazy mode, in that instead of writing the script you asked for it would basically tell you to learn to code then check out xyz topics to tackle the problem.
Anthropic cut a deal with SpaceXAI in May - $1.25b/mo. Before that, they employed months of dishonest nerfy strategies, to an extreme.
This is what pissed me off the most. Make it slower, rate limit it, move the credits to other time slots, idk.. but returning BAD results? That's the worst approach you could take.
I can't imagine sticking with a supplier that plays games like that with me. Tokens are a pretty vague quantity to begin with (you don't control how many tokens a model puts out in response) and giving a couple of purposefully wrong responses will happily inflate your bill, but you don't care because eventually it worked. It's almost an ideal vehicle to scam people.
Imagine the power company being able to decide how much you consume and at which price point.
When you're running something at a loss, you can mistreat your customers and they'll still stick around (I'm an example). OpenAI and Anthropic are now cheaper than Chinese models on subscriptions, while being 6-10x more expensive on the API.
My guess is they need the user numbers for the IPO and are willing to take a temporary loss in the meantime. By the time they go public, they'll either drop the subscription model or it'll turn into what the Chinese providers already offer: basically just a cap on how much API you can consume. Same same.
It's not clear what API tokens actually cost them, but I looked into running a local model, and it's way outside the budget of an individual or even a small or medium business (hundreds of thousands of dollars). So my guess is that running these models economically isn't possible, even if they're delivering real business value (coding, research, etc.). In other words, at API prices I'd just stop using AI, and I suspect most other developers would too.
Datacenters have massive economies of scale. Everything from cheaper electricity to having specialized, more efficient hardware to simply being able to run it continuously at near-100% utilization, all adds up.
Many things in the economy - most notably, manufacturing of most consumer goods - only makes economic sense once you're producing for/serving millions of people. This is not unusual.
> In other words, at API prices I'd just stop using AI, and I suspect most other developers would too.
Many say that, but I sincerely doubt they'd actually follow through. People might get more conservative about how they spend their tokens, but AI today is just too good at eliminating drudgery and boring / bullshit parts of daily work to give up on merely 3-5x price increase.
Airlines, banks, health insurance…
I don't know if that's the actual origin of the term nerf, but it was the first time I'd heard it.
It's meant for their test env I think, so is not documented to my knowledge
Is the fraction of the 5h quote consumed consistent with the fraction of the weekly quota consumed?
I heard there is a usage tracking tool you can install that tells you if tokens are more or less expensive at the current time.
This alone makes the benchmark unsound.
Deepseek 4.1 ranks very low in this benchmark but it has proven so capable that after being simultaneously on Max x20 and Pro x20 subscriptions, i've transitioned to using DS 4.1 as a daily driver and am very satisfied.
My point is, i think their overall ranking makes sense, matches my experience with out of the box capabilities for vague and underspecified tasks. But seeing a model rank low in their ranking does not mean that the model is incapable. Having skills and guidelines has a lot of influence on what you get out of a model.
I'd love to hear more, I'm considering jumping ship. I'm running a Debian desktop if that's a concern.
Except maybe that the model often believes that he is running out of context, and needs to rush so i occasionally need to jump in to tell it that it still has plenty of room left.
But this does not degrade the quality of my overall experience in a meaningful way.
I also wonder if cache could be used to throw these off as well, where it's serving un-nerfed cache results for context windows that are identical to ones they've previously had for benchmark requests.
Seems like the only way to do it well would be to have some randomness involved that couldn't be cheated on - but you'd want to do it in a way that doesn't throw out the benchmarks too much, so your results can be compared still.
All that energy wasted... could just drive a big V8 instead and make less money for the big tech
Remember that some people get access to the next flagship version long before us peasants do. I recall seeing the mention of "Astra" more than a month before it was officially announced
fuck
this bench was just released, it couldn't have detected opus 4.6 degradation.
For example, if your weights were trained as 32-bit floats and you need 1TB of RAM, you could reduce that to around 256GB by quantizing to 8-bit floats. You also make the model faster in the process because there is less data to process to calculate the next token.
The game is to balance between the savings of quantization and making the model dumb enough the people notice
Of course it's almost entirely unsubstantiated BS.
this one has been around for over a year
I am quite convinced that the whole nerfing phenomenon is 90% AI psychosis. I have the word muted on X.
my fair lady gained a bit weight and the bjs lack variety? [ I'm certain that that's a quote from some time ago by some commenter in a thread with a similar or even the same context ...but my Amnesia (Ｔ▽Ｔ ):･ﾟ:･ﾟ] fuck off.
you open two files, before and after you notice a nerf, and from worse comments to logical oversights, it's all damn obvious.
don't normalize this make believe bullshit and misleading people who you think barely understand what they see anyway ...
you wouldn't even know if models had somehow timed nerfs hardcoded into them, however much control over the stack you have.
ridiculous
It is definitely not this. Anthropic has thousands of employees making probably > 100,000+ tiny changes across the entire stack and infrastructure everyday.
The compounding effect can definitely cause temporary regressions in some domain or use-case that doesn’t have really good coverage in their internal tests.
What makes this particularly challenging in the case of LLMs is how changing some language in the prompt can vastly affect the output.
But this is less true as models become larger and additional parameters are able to capture each and every possible nuance of the language. I’d say caching and cache tuning or token optimization/tweaks to thinking are the biggest culprits today.
What "stack" do you have in mind here?
An LLM is a monolithic slab of weights, not millions of lines of code spread over many microservices. The changes they make will be showing up in web and app responsiveness, and in the performance of training runs for one or more next versions of the that monolithic slab of weights, but I'd be surprised if there's a way for their efforts to show up directly in a "has this model been nerfed?" sense.
Both Anthropic and OpenAI used to have multiple snapshots per model; both seem to have switched to updating version numbers when anything changes, looking at the "snapshots" section in their recent and old models: e.g. https://developers.openai.com/api/docs/models/gpt-4o for how far back I had to go to find multiple snapshots on an OpenAI model, and https://platform.claude.com/docs/en/about-claude/model-depre... seems to be a more useful list for Anthropic, but both are now things they did a year ago.
It most definitely is not, hasn't been for a while now.
I.e. when dealing with hosted models of the large providers, you are not interacting with a big bag of floats. You are interacting with an API/UI that presents an unholy web of software components, some of which may be large or small bags of floats, as if they were a big bag of floats.
Even with local models, you have dozens of parameters you can tune for inference these days, all of which affect quality of output in some way or another. And that doesn't touch load balancing, A/B testing, shunting token burners ("Hi chat, how are you?"), protecting user from themselves (refusing to answer "bad" queries, stopping "bad" responses), protecting user from third parties ("prompt injection" mitigations), protecting the model from self-pwning itself when calling tools, then the tools themselves, their prompts, the stack of system prompts used by the vendor, etc.
There's a lot of things to tune there, and just as many reasons to do it.
The livenerf tester appears to be testing a specified model, just as the website (and Claude Code) do when a user makes that choice.
> Even with local models, you have dozens of parameters you can tune for inference these days, all of which affect quality of output in some way or another.
Good points.
> And that doesn't touch load balancing, A/B testing, shunting token burners ("Hi chat, how are you?"), protecting user from themselves (refusing to answer "bad" queries, stopping "bad" responses), protecting user from third parties ("prompt injection" mitigations), protecting the model from self-pwning itself when calling tools, then the tools themselves, their prompts, the stack of system prompts used by the vendor, etc.
The behaviour I'm seeing from the companies these days, the A/B is what I'm saying is not showing up like this, they present user A/B options openly, and the impression I have is this is to train n+1 models; the other stuff (but I say with low certainty) appears to be done in a more headline-grabbing manner, "model taken offline due to ${news}"? Short update cycles seem to allow that.
But the prompts you're probably right, I wasn't giving that enough consideration.
* the two exceptions being automated safety downgrade for dangerous topics, and "Auto-switch to Thinking" as a used-specified option in ChatGPT
If you’ve tried setting either of these up, you’ll know how various tricky settings can impact throughout and model output quality; and those are much simpler stacks.
Even homelabbers are getting into disaggregated compute; e.g. one GPU for prefill, another for decode.
Obviously Anthropic and co are using a mixture of GPUs and hardware and clusters; not everything is just GB300 or whatever; so you then get into hardware quirks, kernel optimisations that may deliver huge speedups at the cost of a tiny bit of KL divergence, etc.
And I believe they’ve publicly said they use TPUs for inference too, but I doubt exclusively; and I’m sure that’s well optimised too.
Finally, Google has publicly stated they intentionally and silently degrade/poison models in response to distillation attacks; who knows what the other companies do.
Frontier models are huge. Astra could be 10 trillion parameters or more. That will probably need > 10 TB of VRAM (HBM3 if you don’t want to wait forever for a response) and need a mini-cluster just to run one instance/copy.
And because token generation is a sequential process, all the code to orchestrate tensor parallelism (spreading a single request across multiple GPUs) is non-trivial. Each new token depends on the previous ones.
Combine that with KV caching optimizations, loading/unloading from cheaper cache storage, session management, load balancing, parallel sessions, and daily software updates plus testing optimizations to kernels; it’s a lot of moving parts.
We haven’t even discussed coordinating stuff across different data centres, failover mechanism, and what not.
I don't follow. That sounds to me exactly like a reason why it could be people pattern-matching on noise: because there is a lot of noise in which a matchable pattern could emerge.
I don't think you understand what "this" is--or rather, the "thing" that didn't happen in "nothing happened".
> Anthropic has thousands of employees making probably > 100,000+ tiny changes across the entire stack and infrastructure everyday.
So, not the sort of thing referred to.
> The compounding effect can definitely cause temporary regressions in some domain or use-case that doesn’t have really good coverage in their internal tests.
Yes, but you claimed that this definitely did happen. But the whole point is to determine whether it did.
Now I dismiss it every time and the quality is more consistent.
Complete adhoc and personal experience but something I've observed, wouldn't be surprised if they nerfed on a per session basis
Random performance is random, your brain will jump through hurdles to fit patterns where there aren't any.
In fact, since they have some rule based system (if bioenegineering or security, route to degraded model) it would be almost trivial to add 'user has filed feedback' to it.
Not saying this is what happens, but just that it's not as insane as it sounds.
And it's also the kind of solution a misaligned agent would implement:
Make sure users are happy about their experience but also optimize resources.
The obvious solution is to route users based on feedback.
Using feedback on your sessions makes that session, and presumably all attached data, fair game for training.
And strangely, expressing frustration multiple times in a row would reliably trigger a feedback popup as well.
That said, given the propensity for mature code bases to have "fuck" in commit messages/comments and those are typically of higher quality, I curse up a storm when the clankers make mistakes, if only to try put more quality-code valence into context. https://news.ycombinator.com/item?id=36584464
A few bad answers can be annoying as hell, but who knows what's behind them. I'm curious to see how the numbers look after a few weeks. Props for tracking improvements too.
I made a graphic to explain why people feel like the models get nerfed:
https://x.com/thesilenceturns/status/2103551351825543610
The idea is that new models can handle up to a certain level of complexity, at which point they fall apart. Every new model can handle more complexity, so there's a wonderful time upon release when you feel like you can do anything, only for you to hit the complexity ceiling a few days later when you saturate it. Rinse and repeat for the next model.
Anthropic has admitted to nerfing in the past. There have also been inference bugs. On top of that, model performance changes as they move compute to schwaggier providers as well.
Your chart is wrong.
Where?
Unintentional tbf.
I am more skeptical about the compute provider claim - do you have any evidence of that?
and there's sometimes just huge floods of complaints from people all of a sudden, which is pretty unlikely to be a coincidence
Btw, you have a typo in the twitter handle on your profile (not in your comment), 'thesilencesturns'.
My local models don't display that degradation, sensed or measured. They consistently perform equally to what I expect of them, precisely because they don't change.
How does twitter explain that? Is my internal model for expectation of capacity magically not drifting for local models but somehow is for anthropic api call based models?
The majority of people are not on it, and the links are gated by a ton of toxic dark patterns and horrible UX trying to force people to sign up or log in.
I try to click on the image to enlarge and make the text readable, and I'm greeted with a login screen instead of a larger image.
https://marginlab.ai/trackers/claude-code/
This site has been documenting it for a while
https://marginlab.ai/trackers/claude-code-historical-perform...
You can't just dismiss something backed by careful measurements by throwing a truism at it. What is this honeymoon phase? Can you quantify it? If not, how are you sure it's real?
That sentence... This conversation is indistinguishable from a billion conversations had around multi-player online gaming.
My work at my job has stayed the same. But the model quality has varied.
They definitely tune the models in production after launch, if not only to share load during high traffic times. It’s not a crazy conspiracy that the same model can be stupider at different times.
This is a more a statement on the work you do and how you work versus the models. I'm doing more complex work since Fable (and now for way cheaper thanks to Opus 5.5)
With 4.6 I would still babysit a lot more code quality and so on. With the newer model I see myself talking about features at a higher level, and then not having to nitpick PRs to death. Which means most of my time is now spent talking to the model about the product instead of the implementation of the product.
> It’s not a crazy conspiracy that the same model can be stupider
Sorry, I really do think it's a conspiracy. If nerfing were real, it would be trivial to prove. DeepSWE, SWEBench, and other benchmarks are all available for anyone to run. A "nerfing" hypothesis has to survive the fact that a statistically significant dip in benchmarks has never been observed.
What Anthropic presents as Opus 5.5 isn't actually a single model...it's Anthropic's ecosystem as a whole. If you are lucky, you get the top model handling your issues all the time, however, that never happens. What really happens is that your request and content are graded along with your subscription (example: API? subscription, if so, what tier? how much has the user used it? Do we trust the user? how much? how much are they paying? are they asking something we think is dangerous?) and your request and context are routed accordingly.
Anthropic isn't alone in this behavior, Open AI does it as well, just look at the respective subreddits on reddit for both if you need some examples, or just play around with the various models from both companies.
There are a few folks who've done some analysis on this (their findings were posted on reddit and X), and a bigger multi-national study is apparently coming, though I admittedly don't know their findings.
I guess the tl;dr is that Anthropic and Open AI are actually selling you "best-effort" routers, so you may or may not get the best in class model, and only they get to determine if you do or do not. No guarantees.
A/B testing alone would result in a performance nerf for one group.
8-bit quantized models will barely show degradation on benchmarks. The performance is reliably at 99% of the non-quantized model. 4-bit quantization retains somewhere around 95-98% performance on benchmarks. But if you've ever used a 4-bit model, it feels lobotomized.
And just consider what a compny serving these models would do if they were at capacity. Would they stop serving the model altogether? Of course they wouldn't...
Denying that models experience purposeful degradation is gaslighting.
Note: I know what quantization is so don't hold back.
The current period is as pro-customer as we're ever going to get, with cash still flying around and neither OpenAI nor Anthropic on the public market, and people are already forced into this sort of business to keep them true to their word. The point isn't even whether they're nerfing the models (I don't think they are), but that people can't seem to trust them to do right.
Its quite worrisome honestly and i feel like that 'im in danger' Simpsons meme more and more. Right now i still have a lot of domain expertise which helps in knowing which questions to ask and which problems to solve, but well, i wonder how long that is going to save my job.
My gut tells me this involves an undisclosed, never-released grandparent model (higher-class than Fable/Astra level, roughly unsellable due to unfeasible cost). That grandparent model is distilled into lower models, of which Opus 5.5 might be an instance of.
That also guarantees protection against distilling a core business. You never make your prime weights available to the public, you only make distillings themselves available.
The downside of this strategy is that you spend a lot of compute on something that you never release, but it might be just the right play (for now) for closed weight companies.
It's a gut feeling, I have zero hard evidence to back it up (it's what I would do as them).
Yesterday I fought claude fable to not just jump to make changes like a dog following a treat, that we were researching... at some point I introduced the word HAWAI... and only if I say HAWAI the thing can start making changes..
I was going to post here in HN just to have a "I knew this was the reason" when they release fable > 5.1
I had the exact same feeling every time they have a new big release
Even in fresh sessions with minimal context like a file with a couple hundreds of lines of code, it would frequently ignore clear instructions, avoid work, and even sometimes “think” things like “looking for ways to code without approval”.
The model is just tuned for long running and doesn’t like to work with a human in the loop, or work back and forth.
The actual 10-day windows contain substantially more samples than that validation, but I haven’t demonstrated that they’re sufficient to distinguish a same-family swap of that size, so I’m not claiming they are.
The goal isn’t to make the instrument say “nerfed.” A null result is a result too. I’d much rather publish “we couldn’t detect a change of this magnitude” than overclaim what the data can support.
The quality of the output/work seems the same, but the speed at which it gets stuff done is a lot slower, because it's asking for permission so much more
I don't have any numbers/stats, just my impression. However, I imagine that if Anthropic could make the models ask for permission more often, it could be an interesting way to throttle access, without degrading quality of the output
I’m sure it’s lowkey intentional, probably encourages users to spin up new chats; hence less context.
Have a look into Docker sbx for instance.
After Fable launch I switched over to Codex and it was simply amazing, with frequent usage resets that seemed never ending. They clearly had more compute than they knew what to do with. Post Astra, Codex has gotten dumb again across all models, increased usage for no real reason, and no resets.
I'm guessing Opus 5.5 will take the heat off Codex for a bit, leading to better performance. So I guess I stick around here instead of switching again?
Also, it isn’t comparing one 10-day period once and calling it done. The window rolls forward daily, and a change has to clear the pre-registered 99% threshold in two consecutive windows before it’s flagged.
Ten days isn’t sacred, though. Once there’s enough longitudinal data, one of the things I want to evaluate is whether that window length is actually well calibrated or should be changed in a future version.
(which has led me to believe that's a good approximation for hedonic adaptation, I've seen tons of attempts at demonstrating nerfing via benches, none persist)
It’s frustrating to observe communities made up of smart, professional individuals as they behave like spoiled children on the day after Christmas when new toy novelty has begun to wane.
I understand it’s relatively harmless but for goodness sake, take a step back and appreciate what you have instead of immediately wanting the thrill of a newer model. Slow down and do deliberate work to get the most out of these amazing tools. Don’t just live off the temporary thrill of finding something marginally better than what you have.
_pushes model further_
"Ugh why is this model failing now even though I'm asking it to do harder things and also got sloppier with my prompts because I got used to it being able to figure stuff out"
At scale where anthro and cgpt operates - every single token matters.
Open models are the endgame.
The classifier is a model; it examines the actual prompt.
They already do this for the safety "guardrails".
First two days of this thing was like working with Einstein, then about 24-36 hours ago I started getting frustrated at bullshit that hadn't been a problem before. It was so egregious that I checked to make sure I was still on Opus 5.5 Max.
A few things make it harder: the panel contains questions drawn from multiple benchmarks rather than one recognizable test, the evaluation is automated and fixed ahead of time, and the raw outputs/results are public so odd behavior can be inspected.
But ultimately LiveNerf measures the behavior exposed through the API on a fixed public panel. It can’t prove what’s happening internally or guarantee the provider isn’t conditioning on the benchmark.
Longer term, I’d like to add held-out/private or periodically refreshed panels specifically to make benchmark recognition harder. I just don’t want to quietly change the current panel, because having a fixed instrument is important for the longitudinal comparison.
As my weekly/hourly quota is nearing its end, does Anthropic start to serve me a worse model?
Am i personally being throttled? testing against API is meaningless.
This week I have done considerably less work, and 2 days in, 54 % of Max usage is gone.
I hate this bait and switch cycle, and seeing how OpenAI cut its limits (5x raise from 0.1x api price to 0.5x api price for their max plan, so new $500 plan gets less usage than previous $200 plan) I think this will get worse still.
My company uses Claude models exclusively via Azure and AWS bedrock, which have their own licensed copies of the weights.
If all these people are so convinced Anthropic is nerfing models, have they tried other inference providers? Do they think the nerfing is coordinated across independent providers? Why wouldn’t any of these nerf-benches use these comparison points or even talk about them?
I think the most likely conclusion by far is that this is a psychological phenomenon.
The initial calibration screened 2,336 questions with 4 samples each, then selected the 78 questions where Opus 5.5 showed useful variance. The panel is run daily, and the actual decision is based on paired per-item differences across 10-day windows with clustered standard errors, not on any single day’s result.
The n=1 in the daily sampling rate means one sample per item per day, not one sample for the experiment. By the time a window is evaluated there are hundreds of observations, and a change has to clear a pre-registered 99% interval in two consecutive windows before LiveNerf calls it a change.
The nondeterminism is basically the reason the statistical part exists in the first place.
I wouldn't say Opus has been nerfed. They are just two different models but, at the end of the day, GPT Sol is the model I trust the most, at least for now.
If so, please share. This should be measurable, and I'm glad this project is measuring it.
Answers in the form of additional anecdotes, stated with even greater passion but still lacking a statement that could be tested and falsified, would validate my exact concern.
As he confirmed, for at least a period of time, and for some users, “Sol xhigh” was actually “Sol high”, etc.
It would be economical suicide from anthropic and OpenAI to actually need models intentionally.
But hey I guess it's hard with technology that truly seems like magic. People say if you'd bring electricity to the middle ages you'd be called a witch and burned. The same is happening to the model labs here because they are bringing tech that the world isn't ready for yet.
Links have been provided by others in this post.
Ant denied them at first though.
Great future everyone has chosen for us.
Truly, madly, deeply sloppy.
Step 1. Model can’t do something challenging Step 2. You try a bunch and fail Step 3. Anthropic trains on your usage data. Your current code base and current problem are now in domain Step 4. Model comes out and you’re shocked when it can tackle the thing you were stuck on Step 4. Codebase drifts significantly and you try new problems you thought were a similar level. Your code is less familiar and the problem doesn’t have a bunch of failure cases in the train set. Feels of it being worse on similar problems
This is definitely not about dealing with the frontier of AI. I wasn't part of the nerfing chord, but Astra changed my mind. Quality got me to upgrade from Pro x5 to Pro x20 on launch day. A couple of days later was dumb af, horrendous code quality etc...
Something fishy, or at least unethical is going on. Not sure it impacts API users though.