They weren't freaked by anything, it's a retaliatory shakedown after ideological differences and Anthropic not doing exactly what they're told/what the Admin wants them to do.
Yep, people are expanding way too much mental energy on basic bribery. Anthropic will agree to work with the DoD, WH insiders will get some lucrative pre-IPO allocation and Fable will be magically "fixed" and available again.
And until then we’re left with braindead Opus 4.8 where I need tell it 7 times before it does something correctly where Fable 5 just did it in the first prompt.
Example: Hey Opus, I’m dealing with this issue on AD and users experience this thing, I tried these. Opus responses with the most braindead call center style respond I’ve ever heard.
they're setting the scene for an attempt to scare the geriatric decision makers into banning free and open source ML, as it's the industry's only real competition
What does that mean exactly? Like sure, you can get some freely available weights and run them on your own hardware, but where did those weights come from?
Was the training process in any way "open", or are you simply relying on a handout from some other (probably large, probably corporate) organization that has the resources to do the actual training?
the current buzz about "sharing the wealth" with these AI co.s also smells of "sure we'll give you some back, just don't regulate us, see you dont need to, we're sharing our shares!"
or are you setting the scene for well-meaning technocrats to back unrestricted AI development in hopes it will bring about utopia while dismissing the damage it could cause in the hands of adversarial groups?
AI isn't that scary. But I've also got some extreme minority opinions like "Never give a website your real name" and "Computers should not be used for banking" and "Don't believe anything you hear online".
The worst I see AI/ML doing to society is shining an unmistakable light onto the blind spots people have already been exploiting for decades. Y2k forced us to patch the integer bug. Super AI will force us to reevaluate what cyber security even is.
I have no idea why anybody is talking about "jailbreaks."
The government made it clear what was going to happen to a private company not following the government's orders:
> Trump said on his Truth Social platform: “The Leftwing nut jobs at Anthropic have made a DISASTROUS MISTAKE trying to STRONG-ARM the [Pentagon], and force them to obey their Terms of Service instead of our Constitution.” [0]
> There will be a Six Month phase out period for Agencies like the Department of War who are using Anthropic’s products, at various levels. Anthropic better get their act together, and be helpful during this phase out period, or I will use the Full Power of the Presidency to make them comply, with major civil and criminal consequences to follow. [1]
Plus OpenAI fell in line, and OpenAI and Anthropic have competing IPOs coming up... it doesn't take a rocket surgeon to understand what is happening here.
No, it's regulatory capture. Anthropic is the current leader and they want to ensure their position by forcing regulation to stamp out the Chinese competition.
That's what's not clear to me. About the only way this works is if we create the "Great US firewall", or if china decides to also put in export controls around usage of their models (unlikely).
Anthropic releases used to feel thorough and well done, with the models feeling immaculately polished. It felt like using a premium product, and it never felt like they were racing to keep up with the news cycle, or reply to competitors.
Recently that immaculately polished feel is harder to find. It coincides with the daily releases of CC, Desktop App, unknown/undocumented changes to the various harnesses used in CC/Cowork. I find it an unwelcome shift.
I still think they're the best option on the market, but the delta isn't as high as it was. Sometimes slowing down is the way to move faster.
Boris from the Claude Code team here. We agree, and will be spending the next few weeks increasing our investment in polish, quality, and reliability. Please keep the feedback coming.
For there to be any trust in the above, the tool needs to behave predictably day to day. It shouldn't be possible to open your laptop and find that Claude suddenly has an IQ 50 points lower than yesterday. I'm not sure how you can achieve predictability while keeping inference costs in check and messing with quantization, prompts, etc on the backend.
Maybe a better approach might be to version both the models and the system prompts, but frequently adjust the pricing of a given combination based on token efficiency, to encourage users to switch to cheaper modes on their own. Let users choose how much they pay for given quality of output though.
Sure, I've cancelled my Max 20 subscription because you guys prioritize cutting your costs/increasing token efficiency over model performance.
I use expensive frontier labs to get the absolute best performance, else I'd use an Open Source/Chinese one.
Frontier LLMs still suck a lot, you can't afford planned degradation yet.
My biggest problem with CC as a harness is that I can't trust "Plan" mode. Long running sessions frequently start bypassing plan mode and executing, updating files and stuff, without permission, while still in plan mode. And the only recovery seems to be to quit and reload CC.
Right now my solution is to run CC in tmux and keep a 2nd CC pane with /loop watching the first pane and killing CC if it detects plan mode being bypassed. Burning tokens to work around a bug.
Here's one person's feedback. After the release of 4.7, Claude became unusable for me in two ways: frequent API timeouts when using exactly the same prompts in Claude Code that I had run problem-free many times previously, and absurdly slow interface response in Claude Cowork. I found a solution to the first after a few days (add "CLAUDE_STREAM_IDLE_TIMEOUT_MS": "600000" to settings.json), but as of a few hours ago Cowork--which I had thought was fantastic, by the way--was still unusable despite various attempts to fix it with cache clearing and other hacks I found on the web.
hm. ml people love static evals and such, but have you considered approaches that typically appear in saas? (slow-rollouts, org/user constrained testing pools with staged rollouts, real-world feedback from actual usage data (where privacy policy permits)?
And you didn't invest anything in polish, quality and reliability before... why? Because for any questions people have you reply something like "I have Claude working on this right now" and have no idea what's happening in the code?
A reminder: your vibe-coded slop required peak 68GB of RAM, and you had to hire actual engineers to fix it.
A month prior their vibe-coders was unironically telling the world how their TUI wrapper for their own API is a "tiny game engine" as they were (and still are) struggling to output a couple of hundred of characters on screen: https://x.com/trq212/status/2014051501786931427
Yeah you don't have to convince me. I switched to Codex mid-January in part because of the dubious quality of the tui itself and the unreliability of the model. Briefly switched back through March, and yep, still a mistake.
Once OpenAI added the $100 plan, it was kind of a no-brainer.
if only there were a place with 9.881 feedbacks waiting to be triaged...
and that maybe not by a duplicate-bot that goes wild and just autocloses everything,
just blessing some of the stuff there with a "you´ve been seen" label would go a long way...
Common pattern of checking the claude code issue tracker for a bug: land on issue #12587, auto closed as duplicate of #12043; check #12043, auto closed as duplicated of #11657; check #11657, auto closed as duplicate of #10645; check #10645, never got a response, or closed as not planned, or some other bullshit.
Because then they lose vertical integration and the extra ability it grants to tune settings to reduce costs / token use / response time for subscription users.
Or improve performance and efficiency, if we’re generous and give them the benefit of the doubt.
It makes sense, in a way. It means the subscription deal is something along the lines of fixed / predictable price in exchange for Anthropic controlling usage patterns, scheduling, throttling (quotas consumptions), defaults, and effective workload shape (system prompt, caching) in whatever way best optimises the system for them (or us if, again, we’re feeling generous) / makes the deal sustainable for them.
It may be (but I wouldn’t know) that some of other changes not covered here reduced costs on their side without impacting users, improving the viability of their subscription model. Or maybe even improved things for users.
I’d really appreciate more transparency on this, and not just when things fail.
But I’ve learned my lesson. I’ve been weening off Claude for a few weeks, cancelled my subscription three weeks ago, let it expire yesterday, and moved to both another provider and a third-party open source harness.
Nothing you wrote makes sense. The limits are so Anthropic isn't on a loss. If they can customize Claude using Code, I see no reason why they couldn't do so with other wrappers. Other wrappers can also make use of cache.
If you worry about "degraded" experience, then let people choose. People won't be using other wrappers if they turn out to be bad. People ain't stupid.
By imposing the use of their harness, they control the system prompt:
> On April 16, we added a system prompt instruction to reduce verbosity. In combination with other prompt changes, it hurt coding quality, and was reverted on April 20. This impacted Sonnet 4.6, Opus 4.6, and Opus 4.7
They can pick the default reasoning effort:
> On March 4, we changed Claude Code's default reasoning effort from high to medium to reduce the very long latency—enough to make the UI appear frozen—some users were seeing in high mode
They can decide what to keep and what to throw out (beyond simple token caching):
> On March 26, we shipped a change to clear Claude's older thinking from sessions that had been idle for over an hour, to reduce latency when users resumed those sessions. A bug caused this to keep happening every turn for the rest of the session instead of just once, which made Claude seem forgetful and repetitive. We fixed it on April 10. This affected Sonnet 4.6 and Opus 4.6
It literally is all in the post.
I don't worry about anything though. It's not my product. I don't work for Anthropic, so I really couldn't care less about anyone else's degraded (or not) experience.
Evidently, all these things you just dismissed matter, else all the changes I quoted from the original post wouldn’t have affected anyone, or half as many people, or half as much. Anthropic wouldn’t have had any complaints to investigate, the article promoting this entire thread wouldn’t exist, and we wouldn’t be having this very conversation.
Defaults matter. A large share of people never change them (status quo bias, psychological inertia). Having control over them (and usage quotas) means Anthropic can control and fine-tune what this fixed subscription costs them.
And evidently (re, the original article), they tried to do so.
> Defaults matter. A large share of people never change them (status quo bias, psychological inertia). Having control over them (and usage quotas) means Anthropic can control and fine-tune what this fixed subscription costs them.
Allowing third party wrappers doesn't mean Claude Code would cease to exist. The opposite actually, Claude Code would be the default.
People dissatisfied with Code would simply use other wrappers. I call it a win-win. Don't see how Anthropic would be on a lose here, they would still retain the ability to control the defaults.
Except one of the major other wrappers was pi, through OpenClaw. With countless hundreds of thousands of instances running every hour on that heartbeat.
I have no idea what the share of OpenClaw instances running on pi was, or third-party wrappers in general, but it was obviously large enough that Anthropic decided they had to put an end to it.
Conversely, from the latest developments, it would seem they are perfectly fine with people running OpenClaw with Claude models through Claude Code’s programmatic interface using subscriptions.
But in the end, this, my take, your take, is all conjecture. We are both on the outside looking in.
I've noticed the same thing in my own AI assisted work. Feels like I'm moving too fast and it's easy to implement decisions quickly but they really have to be the right f--ing decisions. In the past dev was so slow so you had a lot of time to vet the hard decisions and now you don't.
> It felt like using a premium product, and it never felt like they were racing to keep up with the news cycle, or reply to competitors.
I don't know, their desktop app felt really laggy and even switching Code sessions took a few seconds of nothing happening. Since the latest redesign, however, it's way better, snappy and just more usable in most respects.
I just think that we notice the negative things that are disruptive more. Even with the desktop app, the remaining flaws jump out: for example, how the Chat / Cowork / Code modes only show the label for the currently selected mode and the others are icons (that aren't very big), a colleague literally didn't notice that those modes are in the desktop app (or at least that that's where you switch to them).
Given the price I don't really think they're the best option. They're sloppy and competitors are catching up. I'm having same results with other models, and very close with Kimi, which is waaay cheaper.
Likewise, I foolishly assumed everybody else was just doing it wrong.
But this week I've lost count of the times I've had to say something along the lines of:
"Can you check our plan/instructions, I'm pretty sure I said we need to do [this thing] but you've done [that thing]..."
And get hit with a "You're absolutely right...", which virtually never happened for me. I think maybe once since Opus 4-6.
Honestly, I thought it was a skill issue too, but it just turns out I wasn't using it enough.
I started a new job recently, so I'm asking it a lot of questions about the codebase, sometimes just to confirm my understanding and often it came up with wrong conclusions that would send me down rabbit holes only to find out it was wrong.
On a side project I gave it literally a formula and told it to run it with some other parameters. It was doing its usual "let me get to know the codebase" then a "I have a good understanding of the codebase" speech, only to follow it up with "what you're asking is not possible" I'm like... No, I know it's possible I implemented it already, just use it in more places only to get the same "o ye ur right, I missed that... Blabla"
They track our frustration, which is probably really good coding data. The reason why it's painful is because that's data annotation, it's literally a job people get paid to do, yet we're paying to do it. If they need good data, they just turn the models to shit and gaslight everyone
There is a field of AutoML, with its own specialized academic literature and libraries that tried to achieve this type of thing but didn't work very well in practice.
Years ago there were big hopes about bayesian hyperparameter optimization, predicting performance with Gaussian processes etc, hyperopt library, but it was often starting wasteful experiments because it really didn't have any idea what the parameters did. People mostly just do grid search and random search with a configuration that you set up by intuition and experience. Meanwhile LLMs can see what each hyperparameter does, it can see what techniques and settings have worked in the literature, it can do something approximating common sense regarding what has a big enough effect. It's surprisingly difficult to precisely define when a training curve has really flattened for example.
So in theory there are many non-LLM approaches but they are not great. Maybe this is also not so great yet. But maybe it will be.
I'd like see a system like this take more inspiration from the ES literature, similar to AlphaEvolve. Let's see an archive of solutions, novelty scoring and some crossover rather than purely mutating the same file in a linear fashion.
That was my impression. Including evolutionary programming which normally would happen at the AST level, with the LLM it can happen at the source level.
> There are better techniques for hyper-parameter optimisation, right?
Yes, for example "swarm optimization".
The difference with "autoresearch" (restricting just to the HPO angle) is that the LLM may (at least we hope) beat conventional algorithmic optimization by making better guesses for each trial.
For example, perhaps the problem has an optimization manifold that has been studied in the past and the LLM either has that study in its training set or finds it from a search and learns the relative importance of all the HP axes. Given that, it "knows" not to vary the unimportant axes much and focus on varying the important ones. Someone else did the hard work to understand the problem in the past and the LLM exploits that (again, we may hope).
Fair push back, but I do think the LSTM vs Transformers point kinda supports my position in the limit, not refutes. Once the compute bottleneck is removed, LSTMs scale favourably.
https://arxiv.org/pdf/2510.02228 (I believe there's similar work done on vanilla LSTMs, but I'd have to go digging)
So the bottleneck was compute. Which is compatible with 'data or compute'. But to accept your point, at the time the algorothmic advances were useful/did unlock/remove the bottleneck.
A wider point is that eventually (once compute and data are scaled enough) the algorithms are all learning the same representations: https://arxiv.org/pdf/2405.07987
Algorithms do matter because compute is not unlimited in practice. Otherwise we might as well use bogo sort because the result is eventually the same. Yes the platonic ideal of a sorted list looks the same but that doesn’t tell you anything about how to get there or whether you can in this lifetime.
I bring up transformers because scaling compute and data was unlocked by a better algorithm. It matters a lot because scaling compute isn’t always an option.
