Mounting evidence that claude max users are put into one big compute fuel pool. Demand increased dramatically with OpenAI's DoD PR snafu (even though Anth was already working with the DoD? But I digress...). The pool hit a ceiling. Anth has no compute left to give. Hence people maxing out after 1 query. "Working on it" means finding a way to distill Claude Code that isn't enough of a quality degradation to be noticed[0], in order to get the compute pool operational again. The distillation will continue until uptime improves.
0 as of this writing, it's noticeable. Lots of "should I continue?" And "you should run this command if you want to see that information." Roadblocks that I hadn't seen in a year+
Not sure how Claude and CC has become the defacto best model given gpt 5.3 codex and 5.4 exist. This space moves so fast you should be testing your workflows on different models at least once every quarter, prudently once a month.
If anthropic‘s reliability becomes a meme, they risk brand death like Microsoft. Go to hand it to them though, they’re really living that “AI writes all of our code and it should write your code too” life.
Unless they meant "all code that needs to be written has already been written" so their mission is to prevent any new code from being written via a kind of a bait and switch?
No one is going to like this answer, but there’s a simple solution: pay for API tokens and adjust your use of CC so that the actions you have it take are worth the cost of the tokens.
It’s great to buy dollars for a penny, but the guy selling em is going to want to charge a dollar eventually…
If you prepare yourself a token with "claude setup-token" (presuming you're not already locked out and had one) you can run "CLAUDE_CODE_OAUTH_TOKEN=sk.. claude" to use your account.
As much as people on Hacker News complain about subscription models for productivity and creativity suites, the open arms embrace of subscription development tools (services, really) which seek to offload the very act itself makes me wonder how and why so many people are eager to dive right in. I get it. LLMs are cool technology.
Is this a symptom of the same phenomenon behind the deluge of disposable JavaScript frameworks of just ten years ago? Is it peer pressure, fear of missing out? At its root, I suspect so; of course I would imagine it's rare for the C-suite to have ever mandated the usage of a specific language or framework, and LLMs represent an unprecedented lever of power to have an even bigger shot at first mover's advantage, from a business perspective. (Yes, I am aware of how "good enough" local models have become for many.)
I don't really have anything useful nor actionable to say here regarding this dialling back of capability to deal with capacity issues. Are there any indications of shops or individual contributors with contingency plans on the table for dialling back LLM usage in kind to mitigate these unknowns? I know the calculus is such that potential (and frequently realised) gains heavily outweigh the risks of going all in, but, in the grander scheme of time and circumstance, long term commitments are starting to be more apparently risky. I am purposefully trying to avoid "begging the question" here; if instead of LLMs, this were some other tool or service, reactions to these events would have been far more pragmatic, with less of a reticence to invest time on in-house solutions when dealing with flaky vendors.
Think of the stupidest product you can think of and you likely only know about it because people buy/bought them en masse. AI is no different from any other product; plenty will pay/adopt for exactly the reasons you said. There is powerful motivations for people to feel “ahead” of others (or more informed, or more “cool”, or more knowledgeable, or more experienced, or whatever their ego requires), even if their situation is exactly the same.
That said, I’m not sure I follow your statement of less resistance to the development of internal tools when the opposite seems to be the case; companies (or more specifically developers) are perhaps too quick to think they can just vibe-code a replacement for any vendor in a weekend these days.
I think some workflows are just that much faster with AI. And if not I can spare the time for a prompt to get work done in parallel to the stuff I work on.
There is a cost though, the context switches of topics aren't free. But if I need to visualise a something, I let an LLM create a page. If I have two tables of data that needs to be joined/mapped, I let an LLM do the first shot, often that is enough.
I cannot even hope to reach that speed. It isn't a magic tool, but it really accelerates some task.
That speed allows for in-house solutions to become viable again, software that really adapts specific business processes instead of some wonky ERP package that never really fit what you were trying to do.
I have our dbs schema checked into a Gitea repository, which our AIs can just access to quickly ingest schema definitions. If data safety is an issue, use a local model. It is extremely beneficial if you quickly can establish context and let your AI deal with real problems. And it is quite good at that.
Isn't it a little weird that we trust this app to help us build some of the most important parts of our business and the company that vends this app keep breaking it in unique ways.
At my workplace we have been sticking with older versions, and now stick to the stable release channel.
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[ 2.0 ms ] story [ 52.3 ms ] thread[1] https://status.claude.com/
0 as of this writing, it's noticeable. Lots of "should I continue?" And "you should run this command if you want to see that information." Roadblocks that I hadn't seen in a year+
I doubt even the core engineers know how to begin debugging that spaghetti code.
https://status.claude.com/
Unless they meant "all code that needs to be written has already been written" so their mission is to prevent any new code from being written via a kind of a bait and switch?
Free and local.
Not worth the money now, will be canceling unless fixed soon.
It’s great to buy dollars for a penny, but the guy selling em is going to want to charge a dollar eventually…
Is this a symptom of the same phenomenon behind the deluge of disposable JavaScript frameworks of just ten years ago? Is it peer pressure, fear of missing out? At its root, I suspect so; of course I would imagine it's rare for the C-suite to have ever mandated the usage of a specific language or framework, and LLMs represent an unprecedented lever of power to have an even bigger shot at first mover's advantage, from a business perspective. (Yes, I am aware of how "good enough" local models have become for many.)
I don't really have anything useful nor actionable to say here regarding this dialling back of capability to deal with capacity issues. Are there any indications of shops or individual contributors with contingency plans on the table for dialling back LLM usage in kind to mitigate these unknowns? I know the calculus is such that potential (and frequently realised) gains heavily outweigh the risks of going all in, but, in the grander scheme of time and circumstance, long term commitments are starting to be more apparently risky. I am purposefully trying to avoid "begging the question" here; if instead of LLMs, this were some other tool or service, reactions to these events would have been far more pragmatic, with less of a reticence to invest time on in-house solutions when dealing with flaky vendors.
That said, I’m not sure I follow your statement of less resistance to the development of internal tools when the opposite seems to be the case; companies (or more specifically developers) are perhaps too quick to think they can just vibe-code a replacement for any vendor in a weekend these days.
There is a cost though, the context switches of topics aren't free. But if I need to visualise a something, I let an LLM create a page. If I have two tables of data that needs to be joined/mapped, I let an LLM do the first shot, often that is enough.
I cannot even hope to reach that speed. It isn't a magic tool, but it really accelerates some task.
That speed allows for in-house solutions to become viable again, software that really adapts specific business processes instead of some wonky ERP package that never really fit what you were trying to do.
I have our dbs schema checked into a Gitea repository, which our AIs can just access to quickly ingest schema definitions. If data safety is an issue, use a local model. It is extremely beneficial if you quickly can establish context and let your AI deal with real problems. And it is quite good at that.
At my workplace we have been sticking with older versions, and now stick to the stable release channel.