Then hand over to Claude Sonnet.
With hard requirements listed, I found out that the generated code missed requirements, had duplicate code or even unnecessary code wrangling data (mapping objects into new objects of narrower types when won't be needed) along with tests that fake and work around to pass.
So turns out that I'm not writing code but I'm reading lots of code.
The fact that I know first hand prior to Gen AI is that writing code is way easier. It is reading the code, understanding it and making a mental model that's way more labour intensive.
Therefore I need more time and effort with Gen AI than I needed before because I need to read a lot of code, understand it and ensure it adheres to what mental model I have.
Hence Gen AI at this price point which Anthropic offers is a net negative for me because I am not vibe coding, I'm building real software that real humans depend upon and my users deserve better attention and focus from me hence I'll be cancelling my subscription shortly.
I think the AI companies all stink to high heaven and the whole thing being built on copyright infringement still makes me squirm. But the latest models are stupidly smart in some cases. It's starting to feel like I really do have a sci-fi AI assistant that I can just reach for whenever I need it, either to support hard thinking or to speed up or entirely avoid drudgery and toil.
You don't have to buy into the stupid vibecoding hype to get productivity value out of the technology.
You of course don't have to use it at all. And you don't owe your money to any particular company. Heck for non-code tasks the local-capable models are great. But you can't just look at vibecoding and dismiss the entire category of technology.
Anecdata, but I'm still finding CC to be absolutely outstanding at writing code.
It's regularly writing systems-level code that would take me months to write by hand in hours, with minimal babysitting, basically no "specs" - just giving it coherent sane direction: like to make sure it tests things in several different ways, for several different cases, including performance, comparing directly to similar implementations (and constantly triple-checking that it actually did what you asked after it said "done").
For $200/mo, I can still run 2-3 clients almost 24/7 pumping out features. I rarely clear my session. I haven't noticed quality declines.
Though, I will say, one random day - I'm not sure if it was dumb luck - or if I was in a test group, CC was literally doing 10x the amount of work / speed that it typically does. I guess strange things are bound to happen if you use it enough?
Related anecdata: IME, there has been a MASSIVE decline in the quality of claude.ai (the chatbot interface). It is so different recently. It feels like a wanna-be crapier version of ChatGPT, instead of what it used to be, which was something that tried to be factual and useful rather than conversational and addictive and sycophantic.
I am not a lawyer, but am generally familiar with two "is it fair use" tests.
1. Is it transformative?
I take a picture, I own the copyright. You can't sell it. But if you take a copy, and literally chop it to pieces, reforming it into a collage, you can sell that.
2. Does the alleged infringing work devalue the original?
If I have a conversation with ai about "The Lord of the Rings". Even if it reproduces good chunks of the original, it does not devalue the original... in fact, I would argue, it enhances it.
Have I failed to take into account additional arguments and/or scenarios? Probably.
But, in my opinion, AI passes these tests. AI output is transformative, and in general, does not devalue the original.
That's vibecoding with an extra documentation step.
Also, Sonnet is not the model you'd want to use if you want to minimize cleanup. Use the best available model at the time if you want to attempt this, but even those won't vibecode everything perfectly for you. This is the reality of AI, but at least try to use the right model for the job.
> Therefore I need more time and effort with Gen AI than I needed before
Stop trying to use it as all-or-nothing. You can still make the decisions, call the shots, write code where AI doesn't help and then use AI to speed up parts where it does help.
That's how most non-junior engineers settle into using AI.
Ignore all of the LinkedIn and social media hype about prompting apps into existence.
EDIT: Replaced a reference to Opus and GPT-5.5 with "best available model at the time" because it was drawing a lot of low-effort arguments
It is NOT the way to work with humans basically because most software engineers I worked with in my career were incredibly smart and were damn good at identifying edge cases and weird scenarios even when they were not told and the domain wasn't theirs to begin with. You didn't need to write lengthy several page long Jira tickets. Just a brief paragraph and that's it.
With AI, you need to spell everything out in detail. But that's NO guarantee either because these models are NOT deterministic in their output. Same prompt different output each time. That's why every chat box has that "Regenerate" button. So your output with even a correct and detailed prompt might not lead to correct output. You're just literally rolling a dice with a random number generator.
Lastly - no matter how smart and expensive the model is, the underlying working principles are the same as GPT-2. Same transformers with RL on top, same random seed, same list of probabilities of tokens and same temperature to select randomly one token to complete the output and feedback in again for the next token.
It’s pretty funny to claim that a model released 22 hours ago is the bare minimum requirement for AI-assisted programming. Of course the newest models are best at writing code, but GPT-* and Claude have written pretty decent systems for six months or so, and they’ve been good at individual snippets/edits for years.
You're assuming that finding the places where AI needs help isn't already a larger task than just writing it yourself. AI can be helpful in development in very limited scenarios but the main thrust of the comment above yours is that it takes longer to read and understand code than to write it and AI tooling is currently focused on writing code.
We're optimizing the easy part at the expense of the difficult part - in many cases it simply isn't worth the trouble (cases where it is helpful, imo, exist when AI is helping with code comprehension but not new code production).
Read uncharitably, yeah. But you're making a big assumption that the writing of spec wasn't driven by the developer, checked by developer, adjusted by developer. Rewritten when incorrect, etc.
> You can still make the decisions, call the shots
One way to do this is to do the thinking yourself, tell it what you want it to do specifically and... get it to write a spec. You get to read what it thinks it needs to do, and then adjust or rewrite parts manually before handing off to an agent to implement. It depends on task size of course - if small or simple enough, no spec necessary.
It's a common pattern to hand off to a good instruction following model - and a fast one if possible. Gemini 3 Flash is very good at following a decent spec for example. But Sonnet is also fine.
> Stop trying to use it as all-or-nothing
Agree. Some things just aren't worth chasing at the moment. For example, in native mobile app development, it's still almost impossible to get accurate idiomatic UI that makes use of native components properly and adheres to HIG etc
This sounds like an LLM talking.
Either you're a bot, or our human languages are being modified in realtime by the influence of these tools.
"Ignore all of the LinkedIn and social media hype about prompting apps into existence." Absolutely, its not hype, its pure marketing bullshitzen.
This is based on the premise that given detailed plan, the model will exactly produce the same thing because the model is deterministic in nature which is NOT the case. These models are NOT deterministic no matter how detailed plan you feed it in. If you doubt, give the model same plan twice and see something different churned out each time.
> And honestly, I’m mostly within my Pro subscription, granted I also have ChatGPT Plus but I’ve mostly only used that as the chat/quick reference model. But yeah takes some time to read and understand everything, a lot of the time I make manual edits too.
I do not know how you can do it on a Pro plan with Claude Opus 4.7 which is 7.5x more in terms of limit consumption and any small to medium size codebase would easily consume your limits in just the planning phase up to 50% in a single prompt on a Pro plan (the $20/month one that they are planning to eliminate)
Get it to write a context capsule of everything we've discussed.
Chuck that in another model and chat around it, flesh out the missing context from the capsule. Do that a couple of times.
Now I have an artifact I can use to one-shot a hell of a lot of things.
This is amazing for 0-1.
For brown field development, add in a step to verify against the current code base, capture the gotchas and bounds, and again I've got something an agent has a damn good chance of one-shotting.
Stop doing that. Micromanage it instead. Don't give it the specs for the system, design the system yourself (can use it for help doing that), inform it of the general design, but then give it tasks, ONE BY ONE, to do for fleshing it out. Approve each one, ask for corrections if needed, go to the next.
Still faster than writing each of those parts yourself (a few minutes instead of multiple hours), but much more accurate.
This is hardly a surprise, no? No matter how much training we run, we are still producing a generative model. And a generative model doesn't understand your requirements and cross them off. It predicts the next most likely token from a given prompt. If the most statistically plausible way to finish a function looks like a version that ignores your third requirement, the model will happily follow through. There's really no rules in your requirements doc. They are just the conditional events X in a glorified P(Y|X). I'd venture to guess that sometimes missing a requirement may increase the probability of the generated tokens, so the model will happily allow the miss. Actually, "allow" is too strong a word. The model does not allow shit. It just generates.
If you are seeing an agent missing tasks, work with it to write down the task list first and then hold it accountable to completing them all. A spec is not a plan.
I'm not having the same problem as you and I follow a very similar methodology. I'm producing code faster and at much higher quality with a significant reduction in strain on my wrists. I doubt I'm typing that much less, but what I am typing is prose which is much more compatible with a standard QWERTY keyboard.
I think part of it is that I'm not running forward as fast as I can and I keep scope constrained and focused. I'm using the AI as a tool to help me where it can, and using my brain and multiple decades of experience where it can't.
Maybe you're expecting too much and pushing it too hard/fast/prematurely?
I don't find the code that hard to read, but I'm also managing scope and working diligently on the plans to ensure it conforms to my goals and taste. A stream of small well defined and incremental changes is quite easy to evaluate. A stream of 10,000 line code dumps every day isn't.
I bet if you find that balance you will see value, but it might not be as fast as you want, just as fast as is viable which is likely still going to be faster than you doing it on your own.
Feels crazy to me for people to use anything other than the best available.
Not everyone has unlimited budgets to burn on tokens.
[1]: https://www.anthropic.com/engineering/april-23-postmortem ... but also see the September 2025 one at https://www.anthropic.com/engineering/a-postmortem-of-three-...
Have you tried Opus 4.6 with "/effort max" in Claude Code? That's pretty much all I use these days, and it is, honestly, doing a fantastic job. The code it's writing looks quite good to me. Doesn't seem to matter if it's greenfield or existing code.
If code is harder to read than to write, you're doing yourself a disservice by having the output stage not be top shelf.
Like there is no way in world that Gen AI is faster then an actual cracked coder shooting the exact bash/sql commands he needs to explore and writing a proper intent-communicating abstraction.
I’m thinking the difference is in order of magnitudes.
On top of that it adds context loss, risk of distraction, the extra work of reading after the job is done + you’ll have less of a mental model no matter how good you read, because active > passive.
Man it was really the weirdest thing that Claude Coded started hiding more and more changes. Thats what you need, staying closely on the loop.
Just the coding window makes mistakes, duplicates code, does not follow the patterns. The reviewer catches most of this, and the coder fixes them all after rationalizing them.
Works pretty well for me. This model is somewhat institutionalized in my company as well.
I use CC Opus 4.7 or Codex GPT 5.4 High (more and more codex off late).
Maybe it was Timothy Gowers who commented on this.
Lots of human proofs have the unfortunate “creative leap” that isn’t fully explained but with some detectable subtlety. LLMs end up making large leaps too, but too often the subtle ways mathematicians think and communicate is lost, and so the proof becomes so much more laborious to check.
Like you don’t always see how a mathematician came up with some move or object to “try”, and to an LLM it appears random large creative leaps are the way to write proofs.
This may be worth trying out.
Just saying that I know a lot of people like to raw dog it and say plugins and skills and other things aren't necessary, but in my case I've had good success with this.
You then spend months cleaning it up.
Could just have written it by hand from scratch in the same amount of time.
But the benefit is not having to type code.
I feel like I have easily multiplied my productivity because I do not really have to read more than a single chat response at a time, and I am still familiar with everything in my apps because I wrote everything.
I've been working on Window Manager + other nice-to-haves for macOS 26. I do not need a model to one-shot the program for me. However, I am thrilled to get near instantaneous answers to questions I would generally have to churn through various links from Google/StackOverflow for.
Dude! The amount of ad-hoc, interface-specific DTOs that LLM coding agents define drives me up the wall. Just use the damn domain models!
Well, there's your problem. Why aren't you using the best tool for the job?
The last two paragraphs, however, show what happens when people start trying to use inductive reasoning -- and that part is really hard: ...
> Therefore I need more time and effort with Gen AI than I needed before because I need to read a lot of code, understand it and ensure it adheres to what mental model I have.
I don't disagree that the above is reasonable to say. But it isn't all -- not even enough -- about what needs to be said. The rate of change is high, the amount of adaptation required is hard. This in a nutshell is why asking humans to adapt to AI is going to feel harder and harder. I'm not criticizing people for feeling this. But I am criticizing the one-sided-logic people often reach for.
We have a range of options in front of us:
A. sharing our experience with others
B. adapting
C. voting with your feet (cancelling a subscription)
D. building alternatives to compete
E. organizing at various levels to push back
> Hence Gen AI at this price point which Anthropic offers is a net negative for me because I am not vibe coding, I'm building real software that real humans depend upon and my users deserve better attention and focus from me hence I'll be cancelling my subscription shortly.
The above quote is only valid unless some pretty strict (implausible) assumptions: (1) "GenAI" is a valid generalization for what is happening here; (2) Person cannot learn and adapt; (2) The technology won't get better.
[1]: I'm at heart more of a "let's improve the world" kind of person than "I want to build cool stuff" kind of person. This probably causes some disconnect in some interactions here. I think some people primarily have other motives.
Some people cancel their subscriptions and kind of assume "the market and public pushback will solve this". The market's reaction might be too slow or too slight to actually help much. Some people put blind faith into markets helping people on some particular time scales. This level of blind faith reminds me of Parable of the Drowning Man. In particular, markets often send pretty good signals that mean, more or less, "you need to save yourself, I'm just doing my thing." Markets are useful coordinating mechanisms in the aggregate when functioning well. One of the best ways to use them is to say "I don't have enough of a cushion or enough skills to survive what the market is coordinating" so I need a Plan B!
Some people go further and claim markets are moral by virtue of their principles; this becomes moral philosophy, and I think that kind of moral philosophy is usually moral confusion. Broadly speaking, in practice, morality is a complex human aspiration. We probably should not not abdicate our moral responsibilities and delegate them to markets any more than we would say "Don't worry, people who need significant vision correction (or other barrier to modern life)... evolution will 'take care' of you."
One subscription cancellation is a start (if you actually have better alternative and that alternative being better off for the world ... which is debatable given the current set of alternatives!)
Talking about it, i.e. here on HN might one place to start. But HN is also kind of a "where frustration turns into entertainment, not action" kind of place, unfortunately. Voting is cheap. Karma sometimes feels like a measure of conformance than quality thinking. I often feel like I am doing better when I write thoughtfully and still get downvotes -- maybe it means I got some people out of their comfort zone.
Here's what I try to do (but fail often): Do the root cause analysis, vent if you need to, and then think about what is needed to really fix it.
[2]: https://en.wikipedia.org/wiki/Parable_of_the_drowning_man
[3]: The first four are:
I write detailed specs. Multifile with example code. In markdown.
Then hand over to Claude Sonnet.
With hard requirements listed, I found out that the generated code missed requirements, had duplicate code or even unnecessary code wrangling data (mapping objects into new objects of narrower types when won't be needed) along with tests that fake and work around to pass.
So turns out that I'm not writing code but I'm reading lots of code.The market-leading technology is pretty close to "good enough" for how I'm using it. I look forward to the day when LLM-assisted coding is commoditized. I could really go for an open source model based on properly licensed code.
(but I guess they're not really conflicting, if the "solution" involves upgrading to a higher plan)
Like yesterday? LLM-assisted coding is $100/mo. It looks very commoditized when most houses in developed world pay more for electricity than that.
My definition of LLM-assisted coding is that you fully understand every change and every single line of the code. Otherwise it's vibe coding. And I believe if one is honest to this principle, it's very hard to deplete the quota of the $100 tier.
But, it's not $100/mo. I think the best showcase of where AI is at is on the generative video side. Look at players like Higgsfield. Check out their pricing and then go look at Reddit for actual experiences. With video generation the results are very easy to see. With code generation the results are less clear for many users. Especially when things "just work".
Again, it's not $100/month for Anthropic to serve most uses. These costs are still being subsidized and as more expensive plans roll out with access to "better" models and "more* tokens and context the true cost per user is slowly starting to be exposed. I routinely hit limits with Anthropic that I hadn't been for the same (and even less) utilization. I dumped the Pro Max account recently because the value wasn't there anymore. I am convinced that Opus 3 was Anthropic's pinnacle at this point and while the SotA models of today are good they're tuned to push people towards paying for overages at a significantly faster consumption rate than a right sized plan for usage.
The reality is that nobody can afford to continue to offer these models at the current price points and be profitable at any time in the near future. And it's becoming more and more clear that Google is in a great position to let Anthropic and OAI duke it out with other people's money while they have the cash, infrastructure and reach to play the waiting game of keeping up but not having to worry about all of the constraints their competitors do.
But I'd argue that nothing has been commoditized as we have no clue what LLMs cost at scale and it seems that nobody wants to talk about that publicly.
im probably just not being charitable enough to what you mean, but thats an absurd bar that almost nobody conforms to even if its fully handwritten. nothing would get done if they did. But again, my emphasis is on that im probably just not being charitable to what you mean.
this is a small nit, but you still have to pay your electric bill, the $100/mo is on top of that. if you're doing cost accounting you don't want to neglect any costs. Just because you can afford to lease a car, doesn't mean you can afford to lease a 2nd car.
I anticipate a Napster-style reckoning at some point when there's a successful high-profile copyright suit around obviously derivative output. It will probably happen in video or imagery first.
I find it incredibly difficult to saturate my usage. I'm ending the average week at 30-ish percentage, despite this thing doing an enormous amount of work for (with?) me.
Now I will say that with pro I was constantly hitting the limit -- like comically so, and single requests would push me over 100% for the session and into paying for extra usage -- and max 5x feels like far more than 5x the usage, but who knows. Anthropic is extremely squirrely about things like surge rates, and so on.
I'm super skeptical of the influx of "DAE think Opus sucks now. Let's all move to Codex!" nonsense that has flooded HN. A part of it is the ex-girlfriend thing where people are angry about something and try to force-multiply their disagreement, but some of it legitimately smells like astroturfing. Like OpenAI got done pay $100M for some unknown podcaster and start hiring people to write this stuff online.
Recently I've gotten Qwen 3.6 27b working locally and it's pretty great, but still doesn't match Opus; I've gotten check out that new Deepseek model sometime.
>I'm super skeptical of the influx of "DAE think Opus sucks now. Let's all move to Codex!" nonsense that has flooded HN. A part of it is the ex-girlfriend thing where people are angry about something and try to force-multiply their disagreement, but some of it legitimately smells like astroturfing. Like OpenAI got done pay $100M for some unknown podcaster and start hiring people to write this stuff online.
A lot of people are angry about the whole openclaw situation. They are especially bitter that when they attempted to justify exfiltrating the OAuth token to use for openclaw, nobody agreed with them that they had the right to do so, and sided with Claude that different limits for first-party use is standard. So they create threads like this, and complain about some opaque reason why Anthropic is finished (while still keeping their subscription, of course).
I did a 1:1 map of all my Claude Code skills, and it feels like I never left Opus.
Super happy with the results.
Kimi wants my phone number on signup so a no-go for me.
It does seem like the sweet spot between WallE and the destroyed earth in WallE.
For now. That doesn't really change the risk, that just means they are all hyper competitive right this moment, and so they are comparable. If one of them becomes king of the hill, nothing stops them from silently degrading or jacking prices.
The only shield is to not be dependent in the first place. That means keeping your skills sharp and being willing to pass on your knowledge to juniors, so they aren't dependent on these things.
Of course, many people are building their business on huge AI scaffolding. There's nothing they can do.
They won't ever be SOTA due to money, but "last year's SOTA" when it costs 1/4 or less, may be good enough. More quantity, more flexibility, at lower edge quality. It can make sense. A 7% dumber agent TEAM Vs. a single objectively superior super-agent.
That's the most exciting thing going on in that space. New workflows opening up not due to intelligence improvements but cost improvements for "good enough" intelligence.
Edit: the replies to my comment are great examples of what I’m talking about when I say it’s hard to determine what hardware I’d need :).
[†] The latest Qwen 3.6 whatever has been a noticeable improvement, and I'm not even at the point where I tweak settings like sampling, temperature, etc. No idea what that stuff does, I just use the staff picks in LM Studio and customize the system prompts.
Yes, it's possible to run tiny quantized models, but you're working with extremely small context windows and tons of hallucinations. It's fun to play with them, but they're not at all practical.
But, so far, competition remains fierce. Anthropic still has the best tools for writing code. That lead is smaller than it's ever been, though. But, honestly, Opus 4.5 is when it got Good Enough. If Anthropic suddenly increased prices beyond what I'm willing to pay, any model that gives me Opus 4.5 or better performance is good enough for the vast majority of the work I do with agents. And, there are a bunch of models at that level, now maybe including some discount Chinese models. Certainly Gemini Pro 3.1 is on par with Opus 4.5. Current Codex is better than Opus 4.5 and close to Opus 4.7 (though I won't use OpenAI because I don't trust them to be the dominant player in AI).
I often switch agents/models on the same project because I like tinkering with self-hosted and I like to keep an eye on the most efficient way to work...which models wastes less of my time on silly stuff. Switching is literally nothing; I run `gemini` or `copilot` or `hermes` instead of `claude`. There's simply no deep dependency on a specific model or agent. They're all trying to find ways to make unique features for people to build a dependence on, of course, but the top models are all so fucking smart you can just tell them to do whatever thing it is that you need done. That feature could probably be a skill, whatever it is, and the model can probably write the skill. Or, even better, it could be actual software, also written by the model, rather than a set of instructions for the model to interpret based on the current random seed.
Currently, the only consistent moat is making the best model. Anthropic makes the best model and tools for coding, but that's a pretty shallow moat...I could live with several other models for coding. I'll gladly pay a premium for the best model and tools for coding, but I also won't be devastated if I suddenly don't have Claude Code tomorrow. Even open models I can host myself are getting very close to Good Enough.
Competition (OpenAI vs Anthropic is fun to watch) and open source will get us there soon I think.
Not the best argument.
Also there is nothing without dependencies. Loose coupling means coupling.
Until very recently, local models been little more than brittle toys in my experience, if you're trying to use them for coding.
But lately I've been running Pi (minimal coding agent harness) with Gemma4 and Qwen3.6 and I've been blown away by how capable and fast they are compared to other models of their size. (I'm using the biggest that can fit into 24gb, not the smaller ones.) In fact, I don't really need to reach for Claude and friends much of the time (for my use cases at least).
but then two months ago 4.6 started getting forgetful and making very dumb decisions and so on. Everyone started comparing notes and realising it wasn’t “just them”. And 4.7 isn’t much better and the last few weeks we keep having to battle the auto level of effort downgrade and so on. So much friction as you think “that was dumb” and have to go check the settings again and see there has been some silent downgrade.
We all miss the early days of 4.6, which just show you can have a good useful model. LLMs can be really powerful but in delivering it to the mass market Anthropic throttle and downgrade it to not useful.
My thinking is that soon deepseek reaches the more-than-good-enough 4.6+ level and everyone can get off the Claude pay-more-for-less trajectory. We don’t need much more than we’ve already had a glimpse of and now know is possible. We just need it in our control and provisioned not metered so we can depend upon it.
https://www.anthropic.com/engineering/april-23-postmortem
Of course, it sucks when companies screw up ... but at the same time, they "paid everyone back" by removing limits for awhile, and (more importantly to me) they were transparent about the whole thing.
I have a hard time seeing any other major AI provider being this transparent, so while I'm annoyed at Claude ... I respect how they handled it.
https://www.anthropic.com/engineering/a-postmortem-of-three-...
I think there's a certain amount of running with scissors going on here. I appreciate the transparency, but the time to remediation here seems pretty long compared to the rate of new features.
I recall reading similar tales of woe with other providers here on HN. I think the gradual dialling back of capability as capacity becomes strained as users pile on is part of the MO of all the big AI companies.
API Error: Claude's response exceeded the 32000 output token maximum. To configure this behavior, set the CLAUDE_CODE_MAX_OUTPUT_TOKENS environment variable.
GPT 5.4+ takes its time and considers even edgecases unprovoked that in fact are correct and saves me subsequent error hunting turns and finally delivers. Plus no "this doesn't look like malware" or "actually wait" thinking loops for minutes over a oneliner script change.
GLM always feels like it's doing things smarter, until you actually review the code. So you still need the build/prune cycle. That's my experience anyway.
But now I just use Codex. Claude is unreliable and leaves data races all over and leaves, as you say, negative conditions unhandled fairly consistently.
Now I'm looking for an extremely simple open-source coding agent. Nanocoder doesn't seem install on my Mac and it brings node-modules bloat, so no. Opencode seems not quite open-source. For now, I'm doing the work of coding agent and using llama_cpp web UI. Chugging it along fine.
I got annoyed enough with Anthropic's weird behavior this week to actually try this, and got something workable up & running in a few days. My case was unique: there's no Claude Code for BeOS, or my older / ancient Macs, so it was easier to bootstrap & stitch something together if I really wanted an agentic coding agent on those platforms. You'll learn a lot about how models actually work in the process too, and how much crazy ridiculous bandaid patching is happening Claude Code. Though you might also appreciate some of the difficulties that the agent / harnesses have to solve too. (And to be clear, I'm still using CC when I'm on a platform that supports it.)
As for the llama_cpp vs Claude Code delays - I've run into that too. My theory is API is prioritized over Claude Code subscription traffic. API certainly feels way faster. But you're also paying significantly more.
However, it's hard to justify Cursor's cost. My bill was $1,500/mo at one point, which is what encouraged me to give CC a try.
AI companies have the same incentive. Make it cheaper and people will use it more, making you more money (assuming your price is still above cost). And of course they have every reason to reduce their on costs.
Since the price they are charging is still way, way above their operating costs there's no surprise really that they end up making more from small price reductions.
If competition drove them to reduce costs to the point where their operating costs started to be a large factor, the paradox would disappear.
It's like dating apps. They don't want you to find a good match, because then you cancel the subscription.
Speaking of which:
https://www.cnbc.com/2026/04/24/deepseek-v4-llm-preview-open...
Less spend means less real cost to the provider while your flat monthly subscription stays the same price. As well, reducing token use per customer means you can over-subscribe even harder, allowing for more flat monthly subscriptions.
Less tokens = more free capacity = more subscription income.
For reasons that continue to elude me, almost exactly one year ago, Anthropic cancelled my Claude Pro plan. To appeal, you must fill out a Google docs form. And wait. In my case, I’ve waited for about one year. Once I managed to email with a human but they quickly plugged that hole with a chatbot that sends you back to their never-to-be-reviewed form. No route to escalate.
A year gives one a long time to think about things. Maybe it was because I was on a VPN temporarily. Otherwise, no clue. I’m a hobbyist embedded developer. That’s it.
So no, Anthropic support isn’t just poor; it’s nonexistent.
> “you can’t be serious — is this how you fix things? just WORKAROUNDS????”
If this is how you’re interacting with your agents I think you’re in for a world of disappointment. An important part of working with agents is providing specific feedback. And beyond that making sure this feedback actually available to them in their context when relevant.
I will ask them why they made a decision and review alternatives with them. These learnings will aid both you and the agent in the future.
I haven't seen anyone mention this publicly, but I've noticed that the same model will give wildly different results depending on the quantization. 4-bit is not the same as 8-bit and so on in compute requirements and output quality. https://newsletter.maartengrootendorst.com/p/a-visual-guide-...
I'm aware that frontier models don't work in the same way, but I've often wondered if there's a fidelity dial somewhere that's being used to change the amount of memory / resources each model takes during peak hours v. off hours. Does anyone know if that's the case?
I tried Kimi 2.6 and it's almost comparable to Opus. Anthropic lost the ball. I hope this is a sign the we are moving towards a future where model usage is a commodity with heavy competition on price/performance
How much you trust any particular provider's claim to not retain data is subjective though.
One group is consistently trying to play whack-a-mole with different models/tools and prompt engineering and has shown a sine-wave of success.
The other group, seemingly made up of architects and Domain-Driven Design adherents has had a straight-line of high productivity and generating clean code, regardless of model and tooling.
I have consistently advised all GenAI developers to align with that second group, but it’s clear many developers insist on the whack-a-mole mentality.
I have even wrapped my advice in https://devarch.ai/ which has codified how I extract a high level of quality code and an ability to manage a complex application.
Anthropic has done some goofy things recently, but they cleaned it up because we all reported issues immediately. I think it’s in their best interests to keep developers happy.
My two cents.
You can NEVER stop being vigilant. This is why I still have no faith in things like OpenClaw. Letting an AI just run off unsupervised makes me sweat.
If you want to get good results, you still have to be an engineer about it. The model multiplies the effort you put in. If your effort and input is near zero, you get near zero quality out. If you do the real work and relegate the model to coloring inside the lines, you get excellent results.
First was the CC adaptive thinking change, then 4.7. Even with `/effort max` and keeping under 20% of 1M context, the quality degradation is obvious.
I don't understand their strategy here.
Here is a sample report that tries out the cheaper models + the newest Kimi2.6 model against the 5.4 'gold' testcases from the repo: https://repogauge.org/sample_report.
running evals seems like it may be a bit too expensive as a solo dev.
I use AI, but only what is free-of-charge, and if that doesn't cut it, I just do it like in the good old times, by using my own brain.
I am certainly not saying people should “spend more money,” more like the Claude Code access in the Pro plan seems kind of like false advertising. Since it’s technically usable, but not really.
Its particularly noticeable when for a long time you could work an 8 hour day in codex on ChatGPT´s $20/month plan (though they too started tightening the screws a couple of weeks back)
https://podcasts.apple.com/us/podcast/this-episode-is-a-cogn...
As someone who both uses and builds this technology I think this is a core UX issue we’re going to be improving for a while. At times it really feels like a choose 2+ of: slow, bad, and expensive.
But I think "context switching" between 2 different prompts might be too expensive for GPUs to be worth it for LLM providers. Who knows.
I was worried about Anthropic models quality varying and about Anthropic jacking up prices.
I don't think Claude Code is the best agent orchestrator and harness in existence but it's most widely supported by plugins and skills.
And by crikey do I empathise with the poor support in this article. Nothing has soured me on Anthropic more than their attitude.
Great AI engineers. Questionable command line engineers (but highly successful.) Downright awful to their customers.
There's really no immediate solution to this other than letting the price float or limiting users as capacity is built out this gets better.
My experience very suddenly and very clearly degraded over the last few days.
Today I was trying to build a simple chess game. Previous one shots were HTML, this gave me a jsx file. I asked it to put it HTML and it absolutely devoured my credits doing so, I had to abort and do it manually. The resulting app didn't work, and it had decided that multiplayer could work by storing the game state only on local storage without the clients communicating at all
All mostly mitigatable by rigorous audits and steering, but man, it should not have to be.
The 20$ plan has incredible value but also, the limit are just way too tight.
I'm glad Claude made me discover the strength of ai, but now it's time to poke around and see what is more customer friendly. I found deepseek V4 to be extremely cheap and also just as good.
Plus I get the benefit to use it in vs code instead of using Claude proprietary app.
I think that when people goes over the hype and social pressure, anthropic will lose quite a lot of customer.
The first job of any support system—both in terms of importance and chronologically—is triage. This is not a research issue and it's not an interaction issue. It's at root a classification problem and should be trained and implemented as such.
There are three broad categories of interaction: cranks, grandmas, and wtfs.
Cranks are the people opening a support chat to tell you they have vital missing information about the Kennedy Assassintion or they want your help suing the government for their exposure to Agent Orange when they were stationed at Minot. "Unfortunately I can't help with that. We are a website that sells wholesale frozen lemonade. Good luck!"
Grandma questions are the people who can't navigate your website. (This isn't meant to be derogatory, just vivid; I have grandma questions often enough myself.) They need to be pointed toward some resource: a help page, a kb article, a settings page, whatever. These are good tasks for a human or LLM agent with a script or guideline and excellent knowledge/training on the support knowledge base.
WTFs are everything else. Every weird undocumented behavior, every emergent circumstance, every invalid state, etc. These are your best customers and they should be escalated to a real human, preferably a smart one, as soon as realistically possible. They're your best customers because (a) they are investing time into fixing something that actually went wrong; (b) they will walk you through it in greater detail than a bug report, live, and help you figure it out; and (c) they are invested, which means you have an opportunity for real loyalty and word-of-mouth gains.
What most AI systems (whether LLMs or scripts) do wrong is that they treat WTFs like they're grandmas. They're spending significant money on building these systems just to destroy the value they get from the most intelligent and passionate people in their customer base doing in-depth production QC/QA.
They might mean "few weeks ago" and the phrase "couple of weeks ago" might not be exactly as "Vor ein paar Wochen" in their mind rather could be as "few weeks ago."
Rest of the prose in the article seems to support the assumption.
The post is handwritten with no LLMs involved.
Strange how things can change!
The services (OpenAI, Anthropic) are not wildly changing that much. People are just using LLMs more and getting frustrated because they were told it would change the world, and then they take it out on their current patron. Give it a month and we'll be hearing how far OpenAI has fallen behind.
https://thoughts.jock.pl/p/adhd-ai-agent-personal-experience...
I tried Claude recently and it was able to one-shot fixes on 9/9 of the bugs I gave it on my large and older Unity C# project. Only 2/9 needed minor tweaks for personal style (functionally the same).
Maybe it helps that I separately have a CLI with very extensive unit tests. Or that I just signed up. Or that I use Claude late in the evenings (off hours). I also give it very targeted instructions and if it's taking longer than a couple minutes - I abort and try a different or more precise prompt. Maybe the backend recognizes that I use it sparingly and I get better service.
The author describes what sounds like very large tasks that I'd never hand off to an AI to run wild in 2026.
Anyway I thought I'd give a different perspective than this thread.
There is one caveat, and that is you have to give the model well thought out constraints to guide it properly, and absolutely take the time to read all the thinking it's doing and not be afraid to stop the process whenever things go sideway.
People who just let Claude roam free on their repository deserve everything they end up with.
For actual code that goes out to production, I generally figure out how I would solve the problem myself (but will use Claude to bounce ideas and approaches -- or as a search engine) and then have Claude do the boring bits.
Recently I had to migrate a rules-engine into an FSM-based engine. I already had my plan and approach. I had Claude do the boring bits while I implemented the engine myself. I find that Claude does best when you give it small, focused, incremental tasks.
I occasionally ask AI to write lots of code such as a whole feature (>= medium shirt size) or sometimes even bigger components of said feature and I often just revert what it generated. It's not good for all the reasons mentioned.
Other times I accept its output as a rough draft and then tell it how to refactor its code from mid to senior level.
I'm sure it will get better but this is my trust level with it. It saves me time within these confines.
Edit: it is a valuable code reviewer for me, especially as a solo stealth startup.
I think even with the worse limits people still hated it but when you start to either on purpose or inadvertently make the model dumber that's when there's really no purpose to keep using Claude anymore.
For work, unlimited usage via Bedrock.
Yes I’d like to get more usage out of my personal sub, but at 20/mo no complains
Even a simple prompt focused on two files I told Claude to do a thing to file A and not change file B (we were using it as a reference).
Claude’s plan was to not touch file B.
First thing it did was alter file B. Astonishing simple task and total failure.
It was all of one prompt, simple task, it failed outright.
I also had it declare that some function did not have a default value and then explain what the fun does and how it defaults to a specific value….
Fundamentally absurd failures that have seriously impacted my level of trust with Claude.
The thing is running local LLMs will give some kind of reliability and fixed expectations that saves a lot of time - yeah sure Claude might be fantastic one day, but what do I do when the same workload churns out shit the next and I am halfway thru updating and referencing a 500 document set?
Better the devil you know and all that.
(I am just learning that "a couple of weeks" apparently means "2 weeks"...)
Pro is gone. OpenAI plans are more expensive. He can only buy a Kimi plan, which is at least better than Sonnet. But frontier for cheap is gone. Even copilot business plans are getting very expensive soon, also switching to API usage only.
Heck two weeks ago i tried my hardest to hit my limit just to make use of my subscription (i sometimes feel like i'm wasting it), and i still only managed to get to 80% for the week.
I generally prune my context frequently though, each new plan is a prune for example, because i don't trust large context windows and degradation. My CLAUDE.md's are also somewhat trim for this same fear and i don't use any plugins, and only a couple MCPs (LSP).
No idea why everyone seems to be having such wildly different experiences on token usage.
Chances are one of you has been drafted into an unpleasant experiment.
Before the fixes, they were complete trash and I was ready to cancel this month.
Now, I'm feeling like the AI wars are back -- GPT 5.5 and Opus 4.7 are both really good. I'm no longer feeling like we're using nerfed models (knock on wood)!
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. This was the wrong tradeoff. We reverted this change on April 7 after users told us they'd prefer to default to higher intelligence and opt into lower effort for simple tasks. This impacted Sonnet 4.6 and Opus 4.6.
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.
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.
The filesystem tool cannot edit xml files with <name></name> elements in it
Like 3 weeks ago Qwen3-coder was the best coding LLM to run locally. I haven’t spent time since to figure out if anything is better.
You can also power Opencode with OpenRouter which lets you pay for any LLM à la carte.
[1] https://huggingface.co/Jackrong/Qwen3.5-9B-Claude-4.6-Opus-R...
Dear Anthropic:
Please, for the love of all things holy, NEVER change someone's defaults without INFORMING the end user first, because you will wind up with people confused, upset, and leaving your service.
The new model that came out less than 24 hours ago made this obvious? This feels like when a new video game comes out and there's 1,000 steam reviews glazing it in the first hours of release. Don't you think you should use it for longer than a day before declaring it a game changer?
Wait really? I wanted to give it a try, but for $200 a month no way am I paying that for something I just want to experiment around with
I'm debating trying out Codex, from some people I hear its "uncapped" from others I hear they reached limits in short spans of time.
There's also the really obnoxious "trust me bro" documentation update from OpenClaw where they claim Anthropic is allowing OpenClaw usage again, but no official statement?
Dear Anthropic:
I would love to build a custom harness that just uses my Claude Code subscription, I promise I wont leave it running 24/7, 365, can you please tell me how I can do this? I don't want to see some obscure tweet, make official blog posts or documentation pages to reflect policies.
Can I get whitelisted for "sane use" of my Claude Code subscription? I would love this. I am not dropping $2400 in credits for something I do for fun in my free time.
Plus is still very usable for me though. I have not tried Claude Pro in quite a while and if people are complaining about usage limits I know it's going to be a bad time for me. I had to move up from Claude Pro when the weekly limits were introduced because it was too annoying to schedule my life around 5hr windows.
I started using codex around December when I started to worry I was becoming too dependent on Claude and need to encourage competition. codex wasn't particularly competitive with Claude until 5.4 but has grown on me.
The only thing I really care about is that whatever I'm using "just works" and doesn't hurt limits and Claude code has been flaky as all hell on multiple fronts ever since everyone discovered it during the Pentagon flap. So I tend to reach for ChatGPT and codex at the moment because it will "just work" and there's a good chance Claude will not.
Most of this is about the billing system, which is apparently broken.
What I don't understand is these loud "voting with money" comments. What they are canceling is very subsidized plan to buy something that delivers a lot of value.
There are only two providers that can provide this level of models at very subsidized price - anthropic and openai. Both of them are bad in terms of reliability.
So I wonder what these people do after they "cancel" both of them? Do they see producing less result at same hourly rate as everyone else on the market as viable option?
Edit: i forgot HN doesn't do code fences. See https://pastebin.com/2rQg0r2L
Obviously the context window settings are going to depend on what you've got set on the llama-server/llama-swap side. Multiple models on the same server like I have in the config snippet above is mostly only relevant if you're using llama-swap.
TL;DR is you need to set up a provider for your local LLM server, then set at least one model on that server, then set the large and small models that crush actually uses to respond to prompts to use that provider/model combo. Pretty straightforward but agree that their docs could be better for local LLM setups in particular.
For me, I've got llama-swap running and set up on my tailnet as a [tailscale service](https://tailscale.com/docs/features/tailscale-services) so I'm able to use my local LLMs anywhere I would use a cloud-hosted one, and I just set the provider baseurl in crush.json to my tailscale service URL and it works great.
Asked support hey i got nothing back i tried prompting several times used a ton of usage and it gave no response. I'd just like usage back. What I payed for I never got.
Just bot response we don't do refunds no exceptions. Even in the case they don't serve you what your plan should give you.
WTF are y'all doing that chews tokens so fast? I mean, sure, I could spin up Gas Town and Beads and produce infinite busy work for the agents, but that won't make useful software, because the models don't want anything. They don't know what to build without pretty constant guidance. Left to their own devices, they do busy work. The folks who "set and forget" on AI development are producing a whole lot of code to do nothing that needed doing. And, a lot of those folks are proud of their useless million lines of code.
I'm not trying to burn as many tokens as a possible, I'm trying to build good software. If you're paying attention to what you're building, there's so many points where a human is in the loop that it's unusual to run up against token limits.
Anyway, I assume that at some point they have to make enough money to pay the bills. Everything has been subsidized by investors for quite some time, and while the cost per token is going down with efficiency gains in the models/harnesses and with newer compute hardware tuned for these workloads, I think we're all still enjoying subsidized compute at the moment. I don't think Anthropic is making much profit on their plans, especially with folks who somehow run right at the edge of their token limit 24/7. And, I would guess OpenAI is running an even lossier balance sheet (they've raised more money and their prices are lower).
I dunno. I hear a lot of complaining about Claude, but it's been pretty much fine for me throughout 4.5, 4.6 and 4.7. It got Good Enough at 4.5, and it's never been less than Good Enough since. And, when I've tried alternatives, they usually proved to be not quite Good Enough for some reason, sometimes non-technical reasons (I won't use OpenAI, anymore, because I don't trust OpenAI, and Gemini is just not as good at coding as Claude).
If one model seems to be a bit off during a session I just switch to another (Opencode) and plan and review from there.
I’m blown away by how good it is lately
It's almost unusable
I'm an executive, the devs complaining are getting retrained or put on the chopping block.
My rockstars are now random contractor devs from Vietnam. The aloof FTE grey beards saying "I don't know, it doesn't work very good on X." Are getting a talking to or being sidelined/canned. So far most of my grey beards are adapting pretty well.
I'm not waiting on people to write code any more. No way in hell.
Oh wait, I don’t have to imagine. That’s what Anthropic does. A nice preview for what is in store for those who chose to turn off their brains and turn on their AI agents.
We probably hit peak generative AI last year, now they probably use AI to improve the AI so it’s kinda garbage in garbage out, or maybe anthropic is deprioritizing users while favoring enterprise or even government where it provides better quality for higher contracts.
From "yay, claude is awesome" to "damn, it sucks". This is like with withdrawal symptoms now.
My approach is much easier: I'll stay the oldschool way, avoid AI and come up with other solutions. I am definitely slower, but I reason that the quality FOR other humans will be better.
AI used to be, the punched card replicator... its all replaceable.
I'm pretty sure it used to warn when you got close to your 5hr limit, but no, it happily billed extra usage. Granted only about $10 today, but over the span of like 45 minutes. Not super pleased.
Then within the last few months everything changed and went to shit. My trust was lost. Behavior became completely inconsistent.
During the height of Claude's mental retardation (now finally acknowledged by the creators) I had an incident where CC ran a query against an unpartitioned/massive BQ table that resulted in $5,000 in extra spend because it scanned a table which should have been daily partitioned 30 times. 27 TB per scan. I recall going over and over the setup and exhaustively refining confidence. After I realized this blunder, I referred to it in the same CC session, "jesus fucking christ, I flagged this issue earlier" -- it responded, "you did. you called out the string types and full table scans and I said "let's do it later." That was wrong. I should have prioritized it when you raised it". Now obviously this is MY fault. I fucked up here, because I am the operator, and the buck stops with me. But this incident really galvinized that the Claude I had come to vibe with so well over the last N months was entirely gone.
We all knew it was making making mistakes, becoming fully retarded. We all felt and flagged this. When Anthropic came out and said, "yeah ... you guys are using it wrong, its a skill issue" I knew this honeymoon was over. Then recently when they finally came out and ack'd more of the issues (while somehow still glossing over how bad they fucked up?) it was the final nail. I'm done spending $ on Anthropic ecosystem. I signed up for OpenAI pro $200/mo and will continue working on my own local inference in the meantime.
They could have just kept doing this - literally printing money. Literally: do absolutely nothing, go on vacation, profit $$$. So why did so much change? I think that the issue is they were trying to optimize CC for the monthly plan folks, the ones who are likely losing the company money, but API users became collateral damage.
Anthropic can't even scale their own infrastructure operations, because it does not exist and they do not have the compute; even when they are losing tens of billions and can nerf models when they feel like it.
Once again, local models are the answer and Anthropic continues to get you addicted to their casino instead of running your own cheaper slot machine, which you save your money.
Every time you go to Anthropic's casino, the house always wins.
I hate enshittification and I hate seeing this happening to Claude Code right now.
Up until last month, a $100 plan was more than enough, and it was difficult to run out of tokens per day for me. Something fundamentally changed, and Claude started making more mistakes and using more tokens. I know I am not tripping because I used it for over a year; this is absolutely new.
The product keeps getting worse so I will definitely evaluate options and possibly switch if management keeps screwing up the product.
Max 5, sonnet for 95% of things. I never run out of tokens in a week and I use it for ~5-6 hours a day.
I juts need a convenient commandline tool to sometimes analyse the repo and answer a few questions about it.
Am I unworthy of using CC then? Until now I thought Pro entitles me to doing so.
LOL, the elitism is through the roof.
And I actually read the output to fix what I don't like and ever since Opus 4.5, I've had to less and less. 4.6 had issues at the beginning but that's because you have to manually make sure you change the effort level.