Or just don't use AI to write code. Use it as a code reviewer assistant along with your usual test-lint development cycle. Use it to help evaluate 3rd party libraries faster. Use it to research new topics. Use it to help draft RFCs and design documents. Use it as a chat buddy when working on hard problems.

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.

Writing detailed specs and then giving them to an AI is not the optimal way to work with AI.

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

I must be doing something very different from everyone else, but I write what I want and how I want it and Opus 4.7 plans it for me, then I carefully review. Often times I need to validate and check things, sometimes I’ve revised the plan multiple times. Then implementation which I still use Opus for because I get a warning that my current model holds the cache so Sonnet shouldn’t implement. 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 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.

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.

> With hard requirements listed, I found out that the generated code missed requirements,

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.

I'm starting to think a lot of the problem people are having is just that they have unrealistic expectations.

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.

I'm confused. If you have detailed, specific expectations, why aren't using the best model available? Even if you were using Opus 4.7, I would inquire if you're using high/xhigh effort by default.

Feels crazy to me for people to use anything other than the best available.

>Then hand over to Claude Sonnet.

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.

I have the same feeling.

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.

I give Claude small incremental tasks to do and it usually does them flawlessly. I know how to design the software and break into incremental tasks. Claude does the work. The productivity increase has been incredible. I think I'll be able to bootstrap a single person lifestyle business just using Claude.

I don't know. I don't write detailed specs, but make it very iterative, with two sessions. One for coding and one for reviews at various levels.

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).

This is how I feel with AI math proofs. I’m not sure where they’re at now, but a year ago it took so much more time to check if an LLM proof was technically correct even if hard to understand, compared to a well structured human proof.

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.

Now that there is Claw Code[1], seems like many of these cancellations are easier to do.

[1]: https://github.com/ultraworkers/claw-code

This may be a bit silly but I do what you do and then I tell Claude to review the code it wrote and compare it to the specs. It will often find issues and fix it. Then I review the reviewed code, and it's leagues better than pre reviewed code.

This may be worth trying out.

I use open spec to negotiate requirements before the handoff, it's helped me a lot. You could also use GSD2 or Amazon's Kiro, or Spec Kit but I find they have too many stages and waste tokens.

And it leaves 25 TODO comments in code silently, reporting to you that everything is done.

Beautifully stated and I couldn't agree more. This is my experience.

you are holding it wrong. For real this time.

I use the "Superpowers" plugin that creates an initial spec via brainstorming together, and then takes that spec and creates an implementation spec file based on your initial spec. It also has other agents make sure the spec doesn't drift between those two stages and does its own self-reviews. Almost every time, it finds and fixes a bunch of self-review issues before writing the final plan. Then I take that final plan and run it through the actual execution phase that does its own reviews after everything.

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 can quickly get something "working" until you realise it has a ton of subtle bugs that make it unusable in the long run.

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.

That is why I still use the Chatbots and not the CLI/desktop tools. I am in 100% control. I mainly ask question surrounding syntax with languages I am not well experienced in, snippets/examples, and sometimes feedback on certain bits of logic.

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.

> or even unnecessary code wrangling data (mapping objects into new objects of narrower types when won't be needed)

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!

Then hand over to Claude Sonnet.

Well, there's your problem. Why aren't you using the best tool for the job?

I very much value and appreciate the first four paragraphs! [3] This is my favorite kind of communication in a social setting like this: it reads more like anthropology and less like judgment or overgeneralization.

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.

I also use it this way and I'm overall pretty happy with it, but it feels like they really want us to use it in "autopilot" mode. It's like they have two conflicting priorities of "make people use more tokens so we can bill them more" and "people are using more tokens than expected, our pricing structure is no longer sustainable"

(but I guess they're not really conflicting, if the "solution" involves upgrading to a higher plan)

I have Max 5x and use only Claude Opus on xhigh mode. I don't use agents, or even MCPs, and stick to Claude Code.

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.

> the day when LLM-assisted coding is commoditized

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.

I'd recommend Kimi k2.6 for your use. It is an excellent model at a fraction of the cost, and you can use Claude Code with it.

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.

Same. Never hit a limit. Use it heavily for real work. Never even thought of firing off an LLM for hours of...something. Seems like a recipe for wasting my time figuring out what it did and why.

Similar with the copilot and not autopilot usage. I find its the best of them all. Mostly i just use it as an occasionnal search engine. I've never found LLMs to be efficient to actually do work. I do miss the day when tech docs were usable. Claude seems like a crutch for gaps in developer experience more than anything.

Honestly, it sounds like, assuming you have no ethical qualms, you could get by with a Mac or AMD 395+ and the newest models, specifically QWEN3.5-Coder-Next. It does exactly as you describe. It maxes out around 85k context, which if you do a good job providing guard rails, etc, is the length of a small-medium project.

It does seem like the sweet spot between WallE and the destroyed earth in WallE.

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But these products are all drop in replacements for each other. I've recently favored Codex more than CC, just because rate limits got mildly annoying. I really didn't have to change anything about my workflow in doing that.

Luckily local AI is becoming more feasible every day.

At least some of the investors in this tech are hoping for a monopoly position. They'd like to outspend the competition to get an insurmountable lead, at which point they can set their price.

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.

This is why, despite enjoying all of this, I really want to focus on locally hosted models. If we don't host the technology ourselves, we're setting ourselves up for a hard fall down the line.

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).

True. That is why it is key important to have open source and sovereign models that will be accessible to all and always on / local.

Competition (OpenAI vs Anthropic is fun to watch) and open source will get us there soon I think.

The owner rug-pulls, or Broadcom buys the owner and starts squeezing.

For the sake of argument if you build on AWS is that any more of a solid foundation? You're beholden to Amazon, unless you have the bandwidth to be able to DR immediately to another provider.

Soon, a dented toaster will be enough to run decent models.

Anthropic sells due to unrelenting pressure and unachievable demand > new owner cuts costs > models become worse > new owner sells > the capitalistic cycle wins > we, the people, suffer

“In the future there might be the possibility that catastrophic event A could happen.”

Not the best argument.

Also there is nothing without dependencies. Loose coupling means coupling.

The sooner you cancel the sooner you become independent of them

Some people are so dependent on it they can't even say it without twisting words to hide the fact that they're now stuck at zero

Imagine if anthropic and openai went bankrupt in the next 2 years. If you look at their financials its a real possibility.

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This was a real issue, and Anthropic recently awknowledged 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.

did you set your 4.7 to xhigh or max effort? anything else is basically not worth your time...

And on the seventh day, API Error: Claude's response exceeded the 32000 output token maximum

I don't think i'd let it think more than 5 minutes without killing the process.

Just curious, what version of Max are you on: 5x or 20x?

I hope this doesn't come out wrong but. When this happens do agentic/vibe coders message their boss and say "sorry can't work until tomorrow?"

Just copy and past the error back to Claude and you will be able to continue. I have seen this many times over the past few months. I thought it was related to AWS bedrock that I have been using - but probably not.

You're using it within their high usage rate window. I hope you're aware of this, if you use it out of the high usage time window it's supposed to use less, but it does seem a little odd that Sonnet uses so much, even on Medium.

My mental model for LLM is I don't expect them to chew gum and walk at the same time. Cleaning code up is a different task from building new functionality.

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.

Can I get that max20 if you are not using it?

Most "productive" flow I found was when I had both memberships and let Claude do the "I go yeet your feature" side and Codex do the "WTF bro, that's full of race conditions!" review phase.

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.

https://pi.dev/ seems popular, whats not open source about opencode? The repo has an MIT License.

Probably a silly idea, but I'll throw it into the mix - have your current AI build one for you. You can have exactly the coding agent you want, especially if you're looking for "extremely simple".

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.

Just in case it didn't occur to you already, you can just build whatever coding agent you want. They're pretty simple

Swival is not bloated and was specifically made for local agents: https://swival.dev

I use both Cursor and Claude Code, and yes, the latter is noticeably slower with the same model at the same settings.

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.

You'd figure by now we would have something between a TUI and an IDE.

You can run CC with local models, it's pretty straightforward. I've done this with vLLM + a thin shim to change the endpoint syntax.

what model you used with llama_cpp?

People said this about AWS too. "Why would they save you money??". It turns out that every time they reduce prices, they make more money, because more people use their services.

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.

To an extent. That economic incentive stops making sense when a) capacity is an actual constraint and b) Anthropic is not a monopoly and is subject to pressure from competitors who are more user-friendly.

I am betting on the fact that people will get increasingly frustrated at closed agent lock-ins. I built (cline fork) and open-sourced https://github.com/dirac-run/dirac with the sole focus on token efficiency expecting that the closed-lock-in vendors will do enough to frustrate their users over time. Looking for contributors

That's what I am thinking, too. It sound's like a conspiracy theory, but at the end Anthropic et al benefits from models that don't finish their jobs. I recently read about this "over editing phenomenon". The machine is never done. It doesn't want to.

It's like dating apps. They don't want you to find a good match, because then you cancel the subscription.

Up to a point. There is incentive when they get to the point where they literally can't serve their userbase and customers start leaving.

Well that's why threads like this are important to upvote. On hacker news , they're angry !

I severely doubt it. Token spend translates to real cost for the provider. Each token involves real and expensive compute. They aren't free monopoly money you get billed arbitrarily for. You're paying for electricity and infrastructure involved in generating each token.

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.

After you see it skip reasoning so many times and saying "actually the simplest fix is" the laziest thing ever you get kind of tired of babysitting it.

Even their explanations are often confabulations. Best case they point to something wrong in your prompt or agents files, but usually it’s just noise.

Like babysitting an intern.

I'm not sure that graph shows a time-based correlation. The 60% line stays inside the 95% confidence interval. Is that not just a measurement of noise?

Kimi nowhere close to opus on extended use but definitely highly competitive with sonnet. I will probably end up using kimi for personal stuff when I find some time to get it running or get a non-anthropic/openai harness set up on my personal machine.

I've been mostly using Kimi has a hacker of sorts, putting it places I want to attach AI directly as their API for their plans are not completely user hostile. Need to do OCR for scanning Magic the Gathering Cards. Sure!, have it attached to X4: Foundations as a AI manager for some stuff. sounds fun. Can't really do that with claude

How are you using kimi 2.6? I am considering their coding plan to replace my claude max 5x but I am worried about privacy and security.

I kind of wonder if people with ADHD tend to fall into the latter group, as we are used to setting guardrails to keep us aligned to a goal.

FYI that prominent link to your sharpee repo on GitHub 404s

Dead on. Any company not thinking about this like the 2nd group is setting themselves up for a bad time (and sadly, anecdotally, that seems to be an emerging majority).

IME it seems that output quality is directly proportional to the amount of engineering effort you put in. If a bug happens and you just tell the model to fix it over and over with no critical thinking, you end up with an 800 line shell script meant to change the IP address on an interface (real example). If you stop and engage your brain to reason about bugs and explain the problem, the model can fix it in an acceptable manner.

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.

This is cool - just wanted to note https://marginlab.ai is one that has been around for a while.

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> 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

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)

My guess is that the higher plans will be next, especially as more people upgrade to those and maximize their usage.

There was a really good point in this podcast episode about the speed of LLMs. They are so slow that all of the progress messages and token streaming are necessary. But the core problem is that the technology is so darn slow.

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.

About slowdowns... I have this theory that if they sneak some sleep(1) calls while processing medium to complex prompts they can serve more clients.

But I think "context switching" between 2 different prompts might be too expensive for GPUs to be worth it for LLM providers. Who knows.

Where are you getting inference from? I'm overwhelmed by the options at the moment.

You get to talk to an AI agent

This rings true. However I have used one AI automated support chat that didn't behave that way. I wish I could remember the vendor but I do remember being blown away when it said something like "that sounds like a real problem would you like me to open a support ticket for this?". Which it then did and subsequently a human addressed my issue.

You're describing a Taravangian prompt situation (a character in a book series who wakes up with a different/random intelligence level each day and has a series of tests for himself to determine which kind of decisions he's capable of that day). https://coppermind.net/wiki/Taravangian

The author is not a native English speaker it seems.

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.

Seriously, I’ve been subscribed to Claude for 18 months now ($20/mo) during which time I’ve seen the hype around other models come and go in a matter of weekends. I’ve just come to accept that everyone is largely commodity and sometimes it’s worth taking a longer view (provided one can afford it).

Yep. I think the sentiment here isn't lagging too much in terms of the day to day experience of what is being offered. Kind of makes HN very useful in this regard.

Wait, are SaaS's fundamentally shifting business models searching to maximize the value of a product at the expense of a customer over time?

Strange how things can change!

I like your blog and I can totally relate to this article - it's like something I wanted to write about for a couple of weeks now. :D

https://thoughts.jock.pl/p/adhd-ai-agent-personal-experience...

Same. I am getting crazy good value from Claude at work, on both scientific applications and deployment environments.

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.

It’s not ommited, OP clearly talks about editing Javascript.

Claude Code has something about picking sensible choices instead of asking questions in the system prompt, that's probably the problem.

I find it strange that you've anthropomorphized Claude but not ChatGPT seemingly based on one having a human name and the other not

https://news.ycombinator.com/item?id=47894155

(I am just learning that "a couple of weeks" apparently means "2 weeks"...)

It's something strange because i never have these issues. I often run two in parallel (though not all day), and generally have something running anytime i look at my laptop to advance the steps/tasks/etc. Usually i struggle to hit 50% on my Max20.

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.

https://www.anthropic.com/engineering/april-23-postmortem

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.

Opencode is a great cli for driving a coding agents.

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.

These changes fixed some of the token issues, but the token bloat is an intrinsic problem to the model, and Anthropic's solution of defaulting to xhigh reasoning for Opus 4.7 just means you'll go through tokens faster anyways.

The problem is they changed people's default settings, and if you're like me, you keep a Claude Code session open for days, maybe weeks and even a month, and just come back to it and keep going. I wouldn't be surprised if there's hundreds if not thousands of people still on these broken configurations / models.

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.

> I can agree. ChatGPT 5.5 made this a no-brainer choice

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?

>removing Claude Code from the Pro plan

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

It sounds like we have very similar usage/projects. codex had been essentially uncapped (via combination of different x-factors between Plus and Pro and promotions) until very recently when they copied Anthropic's notes.

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.

Claude Code now has an official telegram plugin and cron jobs and can do 80% of the things people used OpenClaw for if you just give it access to tools and run it with --dangerously-skip-permissions.

Don't forget, Openclaw was basically bought by OpenAI so there's only incentive to use it as a wedge to pry people off Anthropic.

Would love some more info on how you got any local model working with Crush. Love charmbracelet but the docs are all over the place on linking into arbitrary APIs.

maybe make it so that when you start typing it completes the snippet?

Like Stack Overflow?

I would say most people are complaining about the $20 plan, which is now actually indistinguishable from the free one. I tested it and ran into limits immediately. With Max, i can work properly again.

Same here. I've asked this question before. Haven't received an answer yet.

From the comments here it seems like people are stress prompting their builds instead of planning and reviewing.

If one model seems to be a bit off during a session I just switch to another (Opencode) and plan and review from there.

The thing that irks me the most is that I was paying full price. Literally spreading my cheeks wide open and saying, "here ya go Anthropic, all yours." and for actually close to a year it was great. Looking back I see I began using it in March of 2025. Thats roughly a year of integrating it into my day to day, pairing with it constantly, and as I said shipping really well engineered meaningful work.

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.

What level of subscription are you on? If you're complaining about running out of tokens but are happy to pay "whatever" for it, it should be Max 20x, yes? And one prompt drained all your tokens for the day on Max 20x?

It's impossible to justify the jump in the expense unless you are directly working on something that makes you money. Messing around on a hobby project, doing some quick research, and getting personalized notifications was a no brainer for 200 a year.

The product keeps getting worse so I will definitely evaluate options and possibly switch if management keeps screwing up the product.

I found the perfect sweet spot for my hobby development. I pay 7 euros for Gemini plus and use it for creating the architecture and technical specs. Those are fed to Sonnet on Pro that just implements the instructions. This gives plenty of space to do long sessions several times a week.

True that.

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 dare to call meself a senior dev, so I don't need a replacement, I need a tool.

I'm not a vibe coder or software manufacturer.

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.

For some reason you are being downvoted but I wanted to echo your sentiment. As someone who tries to switch things up for every next task, the productivity of Claude Max is worth every penny.

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.