Sonnet 4.6 Elevated Rate of Errors(status.claude.com) |
Sonnet 4.6 Elevated Rate of Errors(status.claude.com) |
And the recent “Investigating usage limits hitting faster than expected” [1] is probably them intentionally gauging how much they can push it without too much of an uproar.
[1] https://www.reddit.com/r/ClaudeAI/comments/1s7zgj0/investiga...
> From March 13, 2026 through March 28, 2026, your five-hour usage is doubled during off-peak hours (outside 8 AM-2 PM ET / 5-11 AM PT / 12-6 PM GMT) on weekdays). Usage remains unchanged from 8 AM-2 PM ET / 5-11 AM PT / 12-6 PM GMT on weekdays. Source: https://support.claude.com/en/articles/14063676-claude-march...
We’ve also observed a much higher cache miss rate in the past few weeks. Combine both together and your usage consumption can be greatly increased.
Did you already try tools that can help to reduce token usage cost so you can get more prompts in within your same plan? Some great ones are
https://github.com/gglucass/headroom-desktop
Current phase of usage/pricing is just testing the waters. Especially considering they are the market leader in this category.
I am using Claude constantly, multiple agents, around 8-10hrs a day, 5 or 6 days a week, and I'm never anywhere need my limit.
API Error: 529 {"type":"error","error":{"type":"overloaded_error","message":"Overloaded"},"request_id": "xxxxxxx"}
Plus, they do not want to overbuild computer, like what OpenAI is doing.
After all it's so dangerous.
(though Copilot is working :) and OpenCode)
But any combination of the Claude models are up or down on any given day: https://status.claude.com/
I get that they barely have the infrastructure to run their models at scale even when absolutely nothing goes wrong in any of it, but holy shit does it suck to be on the receiving end of that.
Makes me wonder where all the "bubble" talk is even coming from when we have a top 3 provider getting fucked over on every day of the week that ends in Y because of its inability to online compute faster than the inference demand grows.
If it’s as good as they say, why can’t it figure out how to not go down every day?
How can people rely on it for their job if it goes down everyday? Maybe they shouldn’t rely on it.
If it’s supposed to be such a good engineer, why should it have the same scaling issues as Twitter did 20 years ago with 20 years of lessons learned and 20 years of development for more modern and scalable infrastructures? Shouldn’t it know all the tricks to scale and have redundancy to keep availability high? Does it not know the demands?
When expectations are out of line with reality, there will be snark when things fail. Those expectations have been force fed to us by these AI companies for years now, so I don’t have much sympathy or patience to offer them. They created these expectations of their platforms and if they can’t live up to them, then maybe it’s time for recalibrate the public image of what AI really is and what it can do… and what its limitations are.
There seems to be a mass anxiety around the job market even. I‘ve seen a lot of social media content, including videos of people giving advice, especially to younger tech workers.
The most dangerous (psychologically, socially, economically) are people in important positions, who understand just enough to see some of its usefulness, but not enough to assess where its assumptions and guarantees actually are.
Even moreso if they see workers as a mere cost center instead of an asset.
But here is my perhaps naive, hopefully brave prediction: the real winners of this shake up are not decided yet, and neither bean counting nor superficial engagement with the topic will be sufficient or even useful.
Suddenly the fleshy meat sacks who used to do all this work, just slower, who have persistent memory, who get better and more experienced over time, who only require a few bananas to power their brains start looking like the more reliable option again.
The only reason these chat bots exist is because the upper crust don’t want to pay us to live properly, not because the robots can do it better, they just want to pay as little as possible.
Some profs have a team of PhDs and things go to shit all the time. I don’t know why we expect $FRONTIER_LLM to do better
There’s also the fact that they’re known for dogfooding heavily, I imagine that contributes to it a lot.
If you think $100 is that much and get very high expectations from it, you're not the target customer. You're a loss leader to Anthropic, and the fact that you don't see that / still have high expectations means your expectations are unrealistic.
They’re losing money on you at that price point.
Or more precisely you’re paying for it by giving them training data.
...and let's be realistic, it'll be both.
Either your current customers or your potential future customers are going to be unhappy so long as compute resources are finite. Take your pick.
Their main competitor OpenAI has much better uptime and more generous usage limits.
Maybe it is recovering from the weekend a few days ago, but wanted to take an extra day off like it did on Monday, hence the "outage".
Not that I wish to anthropomorphise it in this answer, but businesses have managed just fine when humans do this for "lunch breaks" and "going home for the evening to sleep".
(And even mandatory meetings which should have been emails).
You do understand however that aside from the growth/maturity path, this is also a path to enshittification and skinning their users, which might come even faster to LMMs than say Google , because the latter managed to have hundres of billions in investments in record time to recoup and IPOs on sight.
The sales pitch of AI is that it’s better than humans and has no real limits; it will make us all obsolete. This framing they created means I expect it not to make errors, not to have limits, and not to fail. I expect it to be able to learn and adapt at the speed of light and solve complex problems beyond what a PhD could do. This is what we’ve been told with the narratives around future jobs, AI performance on PhD level tests, how coding is a solved problem, and pictures painted of what a future with AI will look like. While we may know this isn’t true, this is what they are selling, and that’s the standard I’m going to hold them to.
I don’t blame the customer for being upset the snake oil didn’t live up to its promises, I blame the snake oil salesman. We have every right to be upset with the snake oil salesman and ridicule him when his product doesn’t work. Maybe we don’t need better more reliable snake oil, maybe we need real medicine. If real medicine don’t exist, its better to be honest than to mislead people and say it does.
This isn’t to say AI is completely useless, but it’s not what’s being sold. The downtime just proves that, unless they aren’t using their own product. If that’s the case, why not?
Is it though? Claude's reliability is now at an all-time low of 98.7%. It's not a stretch to think that large companies will have second doubts about about adopting claude for their production environments.
what? they already have, they aren't releasing mythos except to a limited pre-approved customer base who is practically begging them to take their money. they can do that for lower tier models and at this point they should.
And yet, it's what any business with limited stock or slots (from restaurants and car companies to airlines) have done since forever...
No one is apparently noticing that if they build their entire business model around AI being a certain price and availability they're essentially building one giant point of failure into their productivity.
What if the price shoots up 10x or Claude goes down for a day, or what if he's occasionally drunk (hallucinating). Reliability is sometimes a more important facet of business than ultra speed and productivity.
There are fallback mechanisms when the risk is per model provider (as in, "What if the price shoots up 10x or Claude goes down for a day" is a manageable concern), but I'd be more worried about the way all models regardless of provider have similar failure modes, i.e. that some tasks fail in similar ways for all models. In some ways, LLMs are collectively like Star Trek's Borg: you've met one, you've met all of them.
For an entire productivity suite including mail, meetings and terabytes of backed up redundant storage with nearly no bandwidth limitations it's like $35/m for even the most expensive option.
Compare it to hosting models locally, that would be more apt. Or renting GPUs from cloud vendors.
If you’re saying an LLM provides more value than the office productivity suites , mail platforms and meeting platforms which run essentially the planet: then I am afraid, you have drunk the kool-aid.
If you’re evaluating software licenses you have to weigh the price to value, there can be value to these LLMs but its not 3x the productivity of Mail+Spreadsheets+Live Meetings+presentations+wordprocessing+filesharing.
Its just not.
Whether Anthropic makes money from the $100 subscriptions or not, is their problem.
The cost of offering a service and the cost of buying a service are correlated but not the customers problem.
If you are the most expensive SaaS on a docket sheet and you’re also the least reliable you had better be delivering some serious value in the times you’re up otherwise customers won’t depend on you and you’ll be the first one out.
Nobody wants to pay premium prices for things they can’t depend on. If you cant understand that then you need to stop offloading critical thinking to your AI tools because your mind needs the exercise.