As much as people on Hacker News complain about subscription models for productivity and creativity suites, the open arms embrace of subscription development tools (services, really) which seek to offload the very act itself makes me wonder how and why so many people are eager to dive right in. I get it. LLMs are cool technology.
Is this a symptom of the same phenomenon behind the deluge of disposable JavaScript frameworks of just ten years ago? Is it peer pressure, fear of missing out? At its root, I suspect so; of course I would imagine it's rare for the C-suite to have ever mandated the usage of a specific language or framework, and LLMs represent an unprecedented lever of power to have an even bigger shot at first mover's advantage, from a business perspective. (Yes, I am aware of how "good enough" local models have become for many.)
I don't really have anything useful nor actionable to say here regarding this dialling back of capability to deal with capacity issues. Are there any indications of shops or individual contributors with contingency plans on the table for dialling back LLM usage in kind to mitigate these unknowns? I know the calculus is such that potential (and frequently realised) gains heavily outweigh the risks of going all in, but, in the grander scheme of time and circumstance, long term commitments are starting to be more apparently risky. I am purposefully trying to avoid "begging the question" here; if instead of LLMs, this were some other tool or service, reactions to these events would have been far more pragmatic, with less of a reticence to invest time on in-house solutions when dealing with flaky vendors.
People will always go along with a removal of friction even against their benefit. It's natural bias, we have a preference for not spending energy.
It's why we pay stupid amounts for takeout when it's a button away, it's why we accept the issues that come with online dating rather than breaking the ice outside, it's why there's been decades scams that claim to get you abs without effort...
LLMs are the ultimate friction removal. They can remove gaps or mechanical work that regular programming can, but more importantly they can think for you.
I'm convinced this human pattern is as dangerous as addiction. But it's so much harder to fight against, because who's going to be in favor of doing things with more effort rather than less? The whole point of capitalism is supposed to be that it rewards efficiency.
HN is a big community that has always had a mix of people who value newness as a feature vs those who prioritize simplicity and reliability. Unless you're recognizing the exact same names taking these contradictory opinions it's probably different groups of people for the most part.
It seems like every LLM thread for the past couple years is full of posts saying that the latest hot AI tool/approach has made them unbelievably more productive, followed by others saying they found that same thing underwhelming.
I don't think many of you have legitimately tried Claude Code, or maybe you're holding it wrong.
I'm getting 10x the work done. I'm operating at all layers of the stack with a speed and rapidity I've never had before.
And before anyone accuses me of being some "vibe coder", I've built five nines active-active money rails that move billions of dollars a day at 50kqps+, amongst lots of other hard hitting platform engineering work. Serious senior engineering for over a decade.
This isn't just a "cool technology". We've exited the punch card phase. And that is hard or impossible to come back from.
If you're not seeing these same successes, I legitimately think you're using it wrong.
I honestly don't like subscription services, hyperscaler concentration of power, or the fact I can't run Opus locally. But it doesn't matter - the tool exists in the shape it does, and I have to consume it in the way that it's presented. I hope for a different offering that is more democratic and open, but right now the market hasn't provided that.
It's as if you got access to fiber or broadband and were asked to go back to ISDN/dial up.
Man I really thought this was satire. It’s phenomenal that you can gain 10x benefits at all layers of the stack, you must have a very small development team or work alone.
I just don’t see how I could export 10x the work and have it properly validated by peers at this point in time. I may be able to generate code 10-20x faster, but there are nuances that only a human can reason about in my particular sector.
You're still manually posting? All of my HN posting, trolling, shitposting and spamming is taken care of by a fleet of bots I vibecoded in the last 5 minutes.
I mean at this point can we just conclude that there are a group of engineers who claim to have incredible success with it and a group that claim it is unreliable and cannot be trusted to do complex tasks.
I struggle to believe that a ton of seemingly intelligent software engineers are too dumb to figure out how to use Claude code to get reliable results, it seems much more likely to me that it can do well at isolated tasks or new projects but fails when pointed at large complex code bases because it just... is a token predictor lol.
But yeah spinning up a green fields project in an extensively solved area (ledgers) is going to be something an AI shines at.
It isn't like we don't use this stuff also, I ask Cursor to do things 20x a day and it does something I don't like 50% of the time. Even things like pasting an error message it struggles with. How do I reconcile my actual daily experience with hype messages I see online?
I suspect many people here have tried it, but they expected it to one-shot any prompt, and when it didn't, it confirmed what they wanted to be true and they responded with "hah, see?" and then washed their hands of it.
So it's not that they're too stupid. There are various motivations for this: clinging on to familiarity, resistance to what feels like yet another tool, anti-AI koolaid, earnestly underwhelmed but don't understand how much better it can be, reacting to what they perceive to be incessant cheerleading, etc.
It's kind of like anti-Javascript posts on HN 10+ years ago. These people weren't too stupid to understand how you could steelman Node.js, they just weren't curious enough to ask, and maybe it turned out they hadn't even used Javascript since "DHTML" was a term except to do $(".box").toggle().
Hypothetically, you have a simple slice out of bounds error because a function is getting an empty string so it does something like: `""[5]`.
Opus will add a bunch of length & nil checks to "fix" this, but the actual issue is the string should never be empty. The nil checks are just papering over a deeper issue, like you probably need a schema level check for minimum string length.
At that point do you just tell it like "no delete all that, the string should never be empty" and let it figure that out, or do I basically need to pseudo code "add a check for empty strings to this file on line 145", or do I just YOLO and know the issue is gone now so it is no longer my problem?
My bigger point is how does an LLM know that this seemingly small problem is indicative of some larger failure, like lets say this string is a `user.username` which means users can set their name to empty which means an entire migration is probably necessary. All the AI is going to do is smoosh the error messages and kick the can.
“I struggle to believe that a ton of seemingly intelligent software engineers are too dumb to figure out how to use Claude code to get reliable results”
Seemingly is doing the heavy lifting here. If you read enough comment threads on HN, it will become obvious why they aren’t getting results.
Here's my 100 file custom scaffolding AI prompt that I've been working on for the last four months, and can reliably one-shot most math olympic problems and even a rust to do list.
> and I have to consume it in the way that it's presented
I'm just curious, why do you "have to"? Don't get me wrong, I'm making the same choice myself too, realizing a bunch of global drawbacks because of my local/personal preference, but I won't claim I have to, it's a choice I'm making because I'm lazy.
What are the reasonable options besides a Claude Code subscription (or an equivalent from Codex or Copilot)?
I could pay API prices for the same models, but aside from paying much more for the same result that doesn't seem helpful
I could pay a 4-5 figure sum for hardware to run a far inferior open model
I could pay a six figure sum for hardware to run an open model that's only a couple months behind in capability (or a 4-5 figure sum to run the same model at a snail's pace)
I could pay API costs to semi-trustworthy inference provider to run one of those open models
None of those seem like great alternatives. If I want cutting-edge coding performance then a subscription is the most reasonable option
Note that this applies mostly to coding. For many other tasks local models or paid inference on open models is very reasonable. But for coding that last bit of performance matters
As a professional you have a choice in how you produce whatever it is you produce. Sure, you can go for the simplest, most expensive and "easiest" way of doing things, or you can do other things, depending on your perspective and requirements. None of this is set in stone, some people make choices based on personal preferences, and that matters as much to them as your choices matter to you.
I'm still reviewing all the code that's created, and asking for modifications, and basically using LLMs as a 2000 wpm typist, and seeing similar productivity gains. Especially in new frameworks! Everything is test driven development, super clean and super fast.
The challenge now is how to plan architectures and codebases to get really big and really scale, without AI slop creating hidden tech debt.
Foundations of the code must be very solid, and the architecture from the start has to be right. But even redoing the architecture becomes so much faster now...
> And before anyone accuses me of being some "vibe coder", I've built five nines active-active money rails that move billions of dollars a day at 50kqps+, amongst lots of other hard hitting platform engineering work. Serious senior engineering for over a decade
You sound like a pro wrestler. I'd like to know what "hard-hitting" engineering work is. Hydraulic hammers?
I mean five nines is legitimately difficult to accomplish for a lot of problem spaces.
It's also like.... difficult to honestly and accurately measure. And account for whether or not you're getting lucky based on your underlying dependencies (servers, etc) not crashing as much as advertised, or if it's actually five nines. Or whether you've run it for a month and gotten <30s of measure downtime and declared victory, vs run it for three years with copious software updates.
Apart from local AI, a serious choice is aggregated API such as new-api [0]. An API provider aggregated thousands of accounts has much better stability than a single account. It's also cheaper than the official API because of how the subscription model works, see e.g. the analysis [1].
You don't even need to be a customer served by Anthropic or OpenAI so the Terms of Service are irrelevant. That's how I live in China and use almost free Claude and GPT which they don't sell here.
Wait, is this just something like openrouter, that routes your requests to different API providers, where you're paying per-token rates? Or is this taking advantage of fixed price plans, by offering an API interface for them, even though they're only supposed to be used with the official tools?
I really enjoy coding. I've build a number of projects, personal and professional, with Python, Rust, Java and even some Scala in the mix. However, I've been addicted to Claude Code recently, especially with the superpowers skill. It feels like I can manifest code with my mind. When developing with Claude, I am presented with design dilemmas, architectural alternatives, clarification questions, things that really make me think about the problem. I then choose a solution, propose alternatives, discuss, and the code manifests. I came to realize that I enjoy the problem solving, not the actual act of writing the code. Like I have almost cloned my self, and my clones are working on the projects and coming back to me for instructions. It feels amazing
"Addicted" "Superpowers" "manifest with my mind" "it feels amazing"
Why does it sound like you're on drugs? I know that sounds extremely rude, but I can't think of any other reasonable comparison for that language.
It's hard to take these kinds of endorsements seriously when they're written so hyperbolically, in terms of the same cliches, and focused on entirely on how it makes you feel rather than what it does.
"superpowers" is the exact name of the specific Claude code skill. The rest of your concerns is just me expressing my excitement, as until recently I was very skeptic of the whole vibe-coding movement, but have since done a complete 180.
The drug is the llm coding. I kind of get it, when I was a kid and first got a computer I felt the same way after I learned assembly language. The world is your oyster and you can do what felt like anything. It was why I spent almost every waking hour working on my computer. That wore off eventually but I've spent some time on my backlog of projects with Claude and it feels bit like the old days again.
Reading a bunch of posts related to Claude Code and some folks voice genuine upset about rate limits and model intelligence while others seem very upset they can't get their fix because they've reached the five hours limits is genuinely concerning to how addictive LLMs can be for some folks.
I think the social aspect is underreported. I think this applies even for people using Claude Code and not just those treating an LLM as a therapist. In other words, I wonder how many of these people can't call their doctor to make an appointment or call a restaurant to order a pizza. And I say this as someone who struggles to do those things.
People claim that DoorDash and other similar apps are about efficiency, but I suspect a large portion is also a desire to remove human interaction. LLMs are the same. Or, in actuality, to create a simulacrum of human interaction that is satisfying enough.
It's reflecting the value we get from it, relative to the cost of continuing if we switch to the API pricing. It is genuinely upsetting to hit the limits when you face a substantial drop in productivity.
Imagine being an Uber driver and suddenly have to switch to a rickshaw for several hours.
> Why does it sound like you're on drugs, specifically cocaine?
This has basically been what all of Silicon Valley sounds like to me for a few years now.
They are known for abusing many psycho-stimulants out there. The stupid “manifesto” Marc Andreessen put out a while back sounded like adderall-produced drivel more than a coherent political manifesto.
If I were to go off into the woods, take a lot of drugs, and write my own crank manifesto, the central conceit would be that ADHD is the key to understanding the entirety of Silicon Valley. A bunch of people with stimulus driven brains creating technologies that feed themselves and the rest of the populace more and more stimulation, setting a new baseline and requiring new technologies for higher levels of stimulation in an endless loop until we all stimulate ourselves to death. Delayed gratification is the enemy.
This is similar to how we have already found hacks in our evolutionary programming to directly deliver high amounts of flavor without nutrition, and we've been working on ever more complex means of delivering social stimulation without the need for other human (one of the key appeals of AI for many people, as well).
Of course these are all the ravings of a crank and should be ignored.
I feel this sentiment. It’s more like pair programming with someone both smarter and dumber than you. If you’re reviewing the code it is putting down, you’re likely to spot what it’s getting wrong and discussing it.
What I don’t understand, are the people who let it go over night or with whole “agent teams” working on software. I have no idea how they trust any of it.
That’s like saying enjoying composing music, but not enjoying playing music. Or creating stories, but don’t like writing. Yes they’re different activities, but linked together. The former is creativity, the latter is a medium of transmission.
Code is notation, just like music sheets, or food recipes. If your interaction with anyone else is with the end result only (the software), the. The code does not matter. But for collaboration, it does. When it’s badly written, that just increase everyone burden.
It’s like forcing everyone to learn a symphony with the record instead of the sheets. And often a badly recorded version.
> That’s like saying enjoying composing music, but not enjoying playing music
Do you think that is impossible? There are plenty of people who enjoy composing music on things like trackers, with no intent of ever playing said music on an instrument.
I love coding, but I also like making things, and the two are in conflict: When I write code for the sake of writing code, I am meticulous and look for perfection. When I make things, I want to move as fast as possible, because it is the end-product that matters.
There is also a hidden presumption in what you've written that 1) the code will be badly written. Sometimes it is, but that is the case for people to, but often it is better than what I would produce (say, when needing to produce something in a language I'm not familiar enough with), 2) and that the collaboration will be with people manually working on the code. That is increasingly often not true.
Using your analogy, I enjoy composing music and enjoy playing music. I don't enjoy going through the notion of writing the notes on a piece of paper with the pen. I have to do it because people can't read my mind, but if they could I would avoid it. Claude code is like that. The code that gets written, feels like the code that I would have written
> if instead of LLMs, this were some other tool or service, reactions to these events would have been far more pragmatic, with less of a reticence to invest time on in-house solutions when dealing with flaky vendors
As an example, a long term goal at the employer I work for is exactly this: run LLMs locally. There's a big infrastructure backlog through, so it's waiting on those things, and hopefully we'll see good local models by then that can do what Claude Sonnet or GPT-5.3-Codex can do today.
I would love nothing more than ditching Claude for a local solution tomorrow. But it doesn't exist today, so it is what it is - you gotta keep up with the joneses.
Maybe in 5 years we'll have an open weights model that is in the "good enough" category that I can run on a RTX 9000 for 15k dollars or whatever.
The great part is that you can always build your own selfhosted tools. There is nothing that can't be done at home, it's just a calculation of how much you're willing to spend.
Lately though the RAM crisis is continuing and making things like this more unfeasible. But you can still use a lot of smaller models for coding and testing tasks.
Planning tasks I'd use a cloud hosted one, for now, because gemma4 isn't there yet and because the GPU prices are still quite insane.
The cool and fun part is that with ollama and vllm you can just build your own agentic environment IDE, give it the tools you like, and make the workflow however you like. And it isn't even that hard to do, it just needs a lot of tweaking and prompt fiddling.
And on top of that: Use kiwix to selfhost Wikipedia, stackoverflow and devdocs. Give the LLM a tool to use the search and read the pages, and your productivity is skyrocketing pretty quickly. No need anymore to have internet, and a cheap Intel NUC is good enough for self-hosting a lot of containers already.
Source: I am building my own offline agentic environment for Golang [1] which is pretty experimental but sometimes it's also working.
Contingency plan? Just code without it like before. AI could disappear today and I would be very disappointed but it's not like I forgot how to code without it. If anything, I think it's made me a better programmer by taking friction away from the execution phase and giving me more mental space to think in the abstract at times, and that benefit has certainly carried over to my work where we still don't have copilot approved yet.
I think most people understand the need for subscriptions here. It is an ongoing massive compute cost, and that’s what you’re paying for. Your local system is not capable of running the massive amount of compute required for this. If it were then we would see more people up in arms about it.
We could run it locally, but the problems that matter simply don't change.
We're paying for servers that sit idle at night, you don't find enough sysadmins for the current problems, the open source models aren't as strong as closed source, providing context (as in googling) means you hook everything up to the internet anyway, where do you find the power and the cooling systems and the space, what do you do with the GPUs after 3 years?
Mounting evidence that claude max users are put into one big compute fuel pool. Demand increased dramatically with OpenAI's DoD PR snafu (even though Anth was already working with the DoD? But I digress...). The pool hit a ceiling. Anth has no compute left to give. Hence people maxing out after 1 query. "Working on it" means finding a way to distill Claude Code that isn't enough of a quality degradation to be noticed[0], in order to get the compute pool operational again. The distillation will continue until uptime improves.
0 as of this writing, it's noticeable. Lots of "should I continue?" And "you should run this command if you want to see that information." Roadblocks that I hadn't seen in a year+
Codex just changed the way they calculate usage with a massive negative impact.
Before a Subscription was the cheapest way to gain Codex usage, but now they've essentially having API and Subscription pricing match (e.g. $200 sub = $200 in API Codex usage).
The only value of a subscription now is that you get the web version of ChatGPT "free." In terms of raw Codex usage, you could just as easily buy API usage.
> e.g. $200 sub = $200 in API Codex usage [...] In terms of raw Codex usage, you could just as easily buy API usage.
I don't think it's made out like that, I'm on the ChatGPT Pro plan for personal usage, and for a client I'm using the OpenAI API, both almost only using GPT 5.4 xhigh, done pretty much 50/50 work on client/personal projects, and clients API usage is up to 400 USD right now after a week of work, and ChatGPT Pro limit has 61% left, resets tomorrow.
Still seems to me you'd get a heck more out of the subscription than API credits.
Both are great, where they differ is: Claude Code has a better instinct than Codex. Meaning it will naturally produce things like you, the developer, would have.
Codex shines really well at what I call "hard problems." You set thinking high, and you just let it throw raw power at the problem. Whereas, Claude Code is better at your average day-to-day "write me code" tasks.
So the difference is kind of nuanced. You kind of need to use both a while to get a real sense of it.
I think the way I and others use it is code with clause, review or bug hunt with codex. Then I pass the review back to Claude for implementation. Works well. Better than codex implementation and finds gaps versus using Claude to review itself in my opinion.
I'm happy I invested in setting up Codex CLI and getting it to work with ollama. For the toughest jobs I can use Github Copilot (free as an academic) or Gemini CLI. If we see the per token price increase 5x or 10x as these companies move to focusing on revenue, local models will be the way to go, so long as stuff like Gemma 4 keeps getting released.
I don't think anyone is talking about it because it's not a very productive conversation to have. I'm not particularly bullish on vibe coding either but if you could explain what exactly about vibe coding causes these specific issues then it could be more interesting to discuss.
But as it stands, the more likely reason is capacity crunch caused by a chips shortage and demand heavily outpacing supply. You vibe coding reason is based on as much vibes as their code probably is.
that is a separate issue indeed, but their comms make it rather obvious they are scrambling to reduce compute and they're just slashing their service selectively - with openclaw and max users being the first in the chopping block
It should catch up faster. It's absolutely useless for the bulk of the tedium—notably, soldering together random repos to satisfy executives—that makes up my job now.
That's not an elephant in the room.. it's just proof of how insanely useful the tool is and the reality that so much more hardware is needed. Thus people saying "why are these companies building insanely large data centers" ... this is why!
The problem is that vibe-coding, when it fails (i.e. it's non-useful, at least for a bit), is usually solved by more vibes. Try again and hope it works. Ask it to refactor and hope the cleaner code helps it along. If you're willing to think about the code yourself you'll likely ask it questions about the codebase. High vibe-code usage is both a metric that it is working and that it's failing.
I think it is telling that this audience down votes this. It's kind of obvious that the thing is being used a lot. Doesn't mean it works as well as advertised. Doesn't mean the business model they have works. Just means there is a lot of demand. You can't ignore that.
I have no particular insight into the Anthropic backend, but it's possible in general for systems to have architectural issues which cannot be mitigated by just adding more hardware.
The proof is already there. It's concrete. I've seen it directly the last few months of using claude code. It closed the loop. It's insanely beneficial when used properly- that is a pure fact. You act like it's an opinion.
I agree; I think that's what happened. But it's a shame—I'm having a lot of trouble with poor-quality results from Claude-Code, and the session limit is being used up quickly.
I looked at my cc usage and I was at 90% of my weekly allowance after 3 days of use...BUT, if I looked at the usage stats with the chart, it showed, on a scale of 1-4 intensity of usage (4 being most intense), the three days as such:
Day 1: 2
Day 2: 3
Day 3: 1
Not sure how I can hit such limits so quickly with such low scores on its own chart.
Then why not update their chart to at least say that? The numbers (shading, actually) on the chart are not absolute numbers, they're relative, so just make it look as if I spent a lot of time on it. If they're gonna change their limits without being clear about it, at least go all the way. Right now, I can go, "See, you're actually saying I didn't use that much compared to the limits."
I assume it's anthropic rejecting the US Government's use of their software for domestic mass surveillance or fully autonomous weapons, and openai happily agreeing to it.
That has led to a significant number of people switching over from openai, or at least stating they were going to do so.
They made a $25 million donation to Trump, which was repaid in kind by designating Anthropic a supply chain risk. Unfortunately, they weren’t nearly subtle enough about this, and went “sure, we’ll take over the contract with no limits on killbots or domestic surveillance, no problem!” on the same day as Anthropic got in trouble, and people put two and two together.
The responsible thing would be to not sell way more subscriptions than their capacity. But they have to show the exponential revenue curves to their investors. I cancelled my subscription yesterday.
No one is going to like this answer, but there’s a simple solution: pay for API tokens and adjust your use of CC so that the actions you have it take are worth the cost of the tokens.
It’s great to buy dollars for a penny, but the guy selling em is going to want to charge a dollar eventually…
...pay for API tokens and adjust your use of CC so that the actions you have it take are worth the cost of the tokens
Do you feel there is enough visibility and stability around the "Prompt -> API token usage" connection to make a reliable estimate as to what using the API may end up costing?
Personally, it feels like paying for Netflix based on "data usage" without having anyway for me to know ahead of time how much data any given episode or movie will end up using, because Netflix is constantly changing the quality/compression/etc on the fly.
This reads like even if you had a rough idea today about what usage might look like, a change deployed tomorrow could have a major impact on usage. And you wouldn't know it until after you were already using it.
This is it. These subscriptions have been heavily subsidized, which was fine when usage was much lower overall. But with so many folks trying to use the tools and soaking up all the chips something has to give.
Now we’re going to find out what these tools are really worth.
it's not a subsidy. It's predatory pricing and it should be illegal. I offer you a service at a loss to remove competition and then increase prices once you are stuck with it.
I just want a little predictable insight into how much I get. For example, at a buffet, I know I can only eat so much food and can plan around it. This is like going to a buffet and not knowing how many plates I can take or how big the plates are, and it changes each week, and yet I have to keep paying the same price. Except it's not about eating, it's about my work and deadlines and promises and all that.
When you hire a person, you don't know what you are going to get out of them today.
If an hour of an excellent developer's time is worth $X, isn't that the upper bound of what the AI companies can charge? If hiring a person is better value than paying for an AI, then do that.
Fair on not knowing what you'll get out of someone. But if that varies wildly, I may not want to hire that person. Even with employment, predictability matters a lot. If they underperform too much, I might feel annoyed. If they overperform, I might feel guilty.
They can charge whatever they want, I think many people like to make business decisions based on relative predictability or at least be more aware that there's a risk. If they want it to be "some weeks you have lots of usage, some weeks less, and it depends on X factors, or even random factors" then people could make a more informed choice. I think now it's basically incredibly vague and that works while it's relatively predictable, and starts to fail when it's not, for those that wanted the implied predictability.
That's what these providers want as well, but from the other side. They want to know that a customer won't be able to eat more than certain number of servings, as they need to pay for each of those servings.
It works out even if some customers are able to eat a lot, because people on average have a certain limit. The limits of computers are much higher.
Fair, and I think openclaw and all the orchestrators are having agents maxing out the plans. So maybe they figure out a new tier that is agent-run vs human-run. Agents are much more insatiable, whereas humans have a limit. Not sure if it'd be possible to split between those two different modes, but I think that might address the appetite issue better.
If you need the tokens for real work, that’s what the API and the other providers like Bedrock are for. The subscription product is merely to whet your appetite.
Well then I would just not use their service. I used extra usage once and just for what I'd consider a low amount of tests and coding, racked up like $300 in an hour or more. For some, that's not a lot of money, for me, I'd just code it manually, especially without knowing almost any way to gauge how much I'll need and how fast it goes.
I'm not sure how businesses budget for llm APIs, as they seem wildly unpredictable to me and super expensive, but maybe I'm missing something about it.
It's also worth keeping in mind that Anthropic's compute needs are nothing like those of a company like Shopify or Adobe, so revenue might not paint accurately enough the picture of what they're dealing with right now.
Yeah its huge demand upswing from the growth of openclaw and similar pushing resources. Very clear from recent changes and announcement around this [0]
Fwiw there are worse delays from second tier providers like moonshot's kimik2.5 that are also popular for agentic use.
I assume most of their outages is related to this insane scaling and lack of available compute.
Vibe coding doesn't automatically mean lower quality. My codebase quality and overall app experience has improved since I started using agents to code. You can leverage AI to test as well as write new code.
> I assume most of their outages is related to this insane scaling and lack of available compute.
>
> Vibe coding doesn't automatically mean lower quality
Scalability is a factor of smart/practical architectural decisions. Scalability doesn't happen for free and isn't emergent (the exact opposite is true) unless it is explicitly designed for. Problem is that ceding more of the decision making to the agent means that there's less intentionality in the design and likely a contributor to scaling pains.
My theory is that most of their outages are compute and scale related. IE. A few GPU racks blows out and some customers see errors. They don't have any redundant compute as backup because supply is constrained right now. They're willing to lower reliability to maximize revenue.
This is only true for small companies that can infinitely scale within AWS without anyone noticing.
You are talking about software scaling patterns, Anthropic is running into hardware limitations because they are maxing out entire datacenters. That's not an architectural decision it's a financial gamble to front-run tens of billions in capacity ahead of demand.
Why would you think that the person you are replying to didn't design in scalability? What exactly are emergent features when vibe coding? If scalability is an explicit requirement it can be done.
> What exactly are emergent features when vibe coding?
Regression to the mean. See the other HN thread[0]
The LLM has no concept of "taste" on its own.
Scalability, in particular, is a problem that goes beyond the code itself and also includes decisions that happen outside of the codebase. Infrastructure and "platform" in particular has a big impact on how to scale an application and dataset.
After the CC leak last week I took a look at their codebase and my biggest criticism is they seem to never do refactoring passes.
Personally I write something like 80-90% of my code with agents now but after they finish up, it's critical that you spin up another agent to clean up the code that the first one wrote.
Looking at their code it's clear they do not do this (or do this enough). Like the main file being something like 4000 LOC with 10 different functions all jammed in the same file. And this sort of pattern is all over the place in the code.
I have a buddy who used to work at Shopify and was proud about having sprints dedicated to removing unused features. This is really underrated but is the only reliable way to prevent bloat. Oh and getting rid of bloat is way more satisfying!
I’m sure someone is out there claiming that AI is going to solve all your business’s problems no matter what they are. Remotely sane people are saying it will solve (or drastically improve) certain classes of problems. 3x code? Sure. 3x the physical hardware in a data center? Surely not.
To be clear, this number will probably end up being reasonably accurate, but it is a pet peeve nonetheless in the startup world how shitty these financial metrics have become. We're three months from the end of 2025. You'd think we'd want to see at least two years of $30 billion dollar revenue earned in each year before we say with any meaningful level of statistical validity that they have $30 billion in "annual recurring" revenue.
My biggest frustration right now is the seeming complete loss of background agent functionality. Permissions seem completely botched for background agents right now. When that happens, the foreground agent just takes over the task despite:
1. Me not wanting that for context management reasons
2. It burning tokens on an expensive model.
Literally a conversation that I just had:
* ME: "Have sonnet background agent do X"
* Opus: "Agent failed, I'll do it myself"
* Me: "No, have a background agent do it"
* Opus: Proceeds to do it in the foreground
* Flips keyboard
This has completely broken my workflows. I'm stuck waiting for Opus to monitor a basic task and destroy my context.
HN’s guidelines say ‘Don’t editorialize’. The original title here is ‘[BUG] Claude Code login fails with OAuth timeout on Windows’, which is more specific and less clickbaity.
Is this really relevant news? Please share more bug reports from popular services and tools. Feels a tiny bit biased. My CC is just fine since at least three weeks.
I think it's been clear from the beginning that the per-token price of usage was far below what it will be when firms have implemented their profit-maximizing price plans. "AI winter" will happen when these firms start maximizing profit. At that point it'll be too expensive for all but certain use cases to use the best technology for work.
We'll see AI chat replace Google, we'll see companies adopting AI in high-value areas, and we'll see local models like Gemma 4 get used heavily.
AI winter will see a disappearance of the clickbait headlines about everyone losing their jobs. Literally nobody is making those statements taking into account that pricing to this point is way less than the profit maximizing level.
Not sure how Claude and CC has become the defacto best model given gpt 5.3 codex and 5.4 exist. This space moves so fast you should be testing your workflows on different models at least once every quarter, prudently once a month.
We've got access to opus 4-6, gpt 5.4, gemini pro and a few others through corprate. I have customized agents on claude, gpt and gemini since we tend to run out of tokens for x model by the end of a month. Out of all of them I've consistently been using sonnet for most tasks. Opus functions mainly as hand-off agents and reviewer". In my anecdotal experience Claude is miles ahead of the other models and has been for a long while... when it comes to writing code the way we want it. Which eksplicit, no-abstraction, no-external packages, fail fast defensive programming. I imagine you'd get different milage with different models and different coding styles.
The rest of the organisation, which is not software development or IT related, mainly uses GPT models. I just wish I hadn't taught risk management about claude code so they weren't wasting MY tokens.
I've been an avid fan of codex for the last few month's but finally hit the weekly limit so I've wanted to try out claude code before biting the bullet and going for the 200 dollar codex sub.
Obviously in hindsight it would be unfair to Anthropic to judge them on an unstable day so I'l leave those complaints aside but I hit the session limit way too fast. I planned out 3 tasks and it couldn't finish the first plan completely, for that implementation task it has seen a grand total of 1 build log and hasn't even run any tests which already caused it to enter in the red territory of the context circle.
It was even asking me during planning which endpoints the new feature should use to hook into the existing system, codex would never ask this and just simply look these up during planning and whenever it encounters ambiguity it would either ask straight away or put it as an open question. I have to wonder if they're limiting this behavior due trying to keep the context as small as possible and preventing even earlier session limits.
Maybe codex's limits are not sustainable in the long run and I'm very spoiled by the limits but at this point CC(sonnet) and Codex(5.4) are simply not in the same league when comparing both 20 dollar subscriptions.
I will also clearly state that the value both these tools provide at these price points are absolutely worth it, it's just that codex's value/money ratio is much better.
Anthropic models haven't been far ahead for a while. Quite a few months at least. Chinese models are roughly equal at 1/6th the cost. Minimax is roughly equal to Opus. Chinese providers also haven't had the issues with uptime and variable model quality. The gap with OpenAI also isn't huge and GLM is a noticeably more compliant model (unsurprisingly given the hubristic internal culture at Anthropic around safety).
CC is a better implementation and seems to be fairly economic with token usage. That is the really the only defining point and, I suspect, Anthropic are going to have a lot of trouble staying relevant with all the product issues.
They were far ahead for a brief period in November/December which is driving the hype cycle that now appears to be collapsing the company.
You have to test at least every month, things are moving quickly. Stepfun is releasing soon and seems to have an Opus-level model with more efficient architecture.
Minimax is nowhere near Opus in my tests, though for me at least oddly 4.6 felt worse than 4.5. I haven't use Minimax extensively, but I have an API driven test suite for a product and even Sonnet 4.6 outperforms it in my testing unless something changed in the last month.
One example is I have a multi-stage distillation/knowledge extraction script for taking a Discord channel and answering questions. I have a hardcoded 5k message test set where I set up 20 questions myself based on analyzing it.
In my harness Minimax wasn't even getting half of them right, whereas Sonnet was 100%. Granted this isn't code, but my usage on pi felt about the same.
> CC is a better implementation and seems to be fairly economic with token usage. That is the really the only defining point and, I suspect, Anthropic are going to have a lot of trouble staying relevant with all the product issues.
What are you using to drive the Chinese models in order to evaluate this? OpenCode?
Some of Claude Code's features, like remote sessions, are far more important than the underlying model for my productivity.
Isn't it a little weird that we trust this app to help us build some of the most important parts of our business and the company that vends this app keep breaking it in unique ways.
At my workplace we have been sticking with older versions, and now stick to the stable release channel.
If you prepare yourself a token with "claude setup-token" (presuming you're not already locked out and had one) you can run "CLAUDE_CODE_OAUTH_TOKEN=sk.. claude" to use your account.
If anthropic‘s reliability becomes a meme, they risk brand death like Microsoft. Go to hand it to them though, they’re really living that “AI writes all of our code and it should write your code too” life.
i upgraded to the 20x plan, and hit the weekly limit within 24 hours. i was running some fairly large tasks, but was still surprised it hit the weekly session limit so quickly. now i can't use it for 6 more days :( i didn't even have time to ask it to help setup logs or something to track my usage before i hit the session limit.
How are you using it to reach the limit so quickly? I'm 13% at a 10x plan and I have used it for hours every day for the last 5 days. I never hit a limit.
I have the 20x plan and use it together with my husband. 4 days in to our weekly usage window and we're only at 54% (and we both use it the entire day)
I have no idea how people are hitting the limits so fast.
Hitting limits is more related to how many tokens it’s generating, not necessarily how complex the changes are.
Hit the weekly limit on my 20x plan last week trying to do a full front end rewrite of a giant enterprise web app, 600+ html templates, plus validating every single one with playwright.
I'm VERY curious about your case. What kind of switching costs do you guys have? I'm working at a very young startup that is still not locked into either AI provider harnesses -- what causes switching costs, just the subscription leftovers or something else?
I stopped using Claude Code several months ago and I can't say I've missed it.
There was constant drama with CC. Degradation, low reliability, harness conspiring against you, and etc – these things are not new. Its burgeoning popularity has only made it worse. Anthropic is always doing something to shoot themselves in the foot.
The harness does cool things, don't get me wrong. But it comes with a ton of papercuts that don't belong in a professional product.
I really don't understand the way Claude does rate limiting, particularly the 5 hour limit. I can get on at 11:30, blow through my limit doing some stupid shit like processing a pile of books into my llm-wiki, and then get notified that I've used 90% of my 5 hour session limit and I have to wait for noon (aka wait 10 minutes) for the five hour limit to reset. Baffling.
Unless they meant "all code that needs to be written has already been written" so their mission is to prevent any new code from being written via a kind of a bait and switch?
I’ve been toying around at home with it and I’ve been fine with its output mostly (in a Java project ofc), but I’ve run into a few consistent problems
- The thing always trips up validating its work. It consistently tries to use powershell in a WSL environment I don’t have it installed in. It also seems to struggle with relative/absolute paths when running commands.
- Pricing makes no sense to me, but Jetbrains offering seems to have its own layer of abstraction in “credits” that just seem so opaque.
Then again, I mostly use this stuff for implementing tedious utilities/features. I’m not doing entity agent written and still do a lot of hand tweaks to code, because it’s still faster to just do it myself sometimes. Mostly all from all from the IDE still.
Maybe, but you can literally feel the difference as you type. When you type in Codex, it's fast, it feels instant. When you type in Claude Code, it feels like playing a game in 60 fps after you already got used to 144 fps.
Is this a symptom of the same phenomenon behind the deluge of disposable JavaScript frameworks of just ten years ago? Is it peer pressure, fear of missing out? At its root, I suspect so; of course I would imagine it's rare for the C-suite to have ever mandated the usage of a specific language or framework, and LLMs represent an unprecedented lever of power to have an even bigger shot at first mover's advantage, from a business perspective. (Yes, I am aware of how "good enough" local models have become for many.)
I don't really have anything useful nor actionable to say here regarding this dialling back of capability to deal with capacity issues. Are there any indications of shops or individual contributors with contingency plans on the table for dialling back LLM usage in kind to mitigate these unknowns? I know the calculus is such that potential (and frequently realised) gains heavily outweigh the risks of going all in, but, in the grander scheme of time and circumstance, long term commitments are starting to be more apparently risky. I am purposefully trying to avoid "begging the question" here; if instead of LLMs, this were some other tool or service, reactions to these events would have been far more pragmatic, with less of a reticence to invest time on in-house solutions when dealing with flaky vendors.
It's why we pay stupid amounts for takeout when it's a button away, it's why we accept the issues that come with online dating rather than breaking the ice outside, it's why there's been decades scams that claim to get you abs without effort...
LLMs are the ultimate friction removal. They can remove gaps or mechanical work that regular programming can, but more importantly they can think for you.
I'm convinced this human pattern is as dangerous as addiction. But it's so much harder to fight against, because who's going to be in favor of doing things with more effort rather than less? The whole point of capitalism is supposed to be that it rewards efficiency.
It seems like every LLM thread for the past couple years is full of posts saying that the latest hot AI tool/approach has made them unbelievably more productive, followed by others saying they found that same thing underwhelming.
I don't think many of you have legitimately tried Claude Code, or maybe you're holding it wrong.
I'm getting 10x the work done. I'm operating at all layers of the stack with a speed and rapidity I've never had before.
And before anyone accuses me of being some "vibe coder", I've built five nines active-active money rails that move billions of dollars a day at 50kqps+, amongst lots of other hard hitting platform engineering work. Serious senior engineering for over a decade.
This isn't just a "cool technology". We've exited the punch card phase. And that is hard or impossible to come back from.
If you're not seeing these same successes, I legitimately think you're using it wrong.
I honestly don't like subscription services, hyperscaler concentration of power, or the fact I can't run Opus locally. But it doesn't matter - the tool exists in the shape it does, and I have to consume it in the way that it's presented. I hope for a different offering that is more democratic and open, but right now the market hasn't provided that.
It's as if you got access to fiber or broadband and were asked to go back to ISDN/dial up.
I just don’t see how I could export 10x the work and have it properly validated by peers at this point in time. I may be able to generate code 10-20x faster, but there are nuances that only a human can reason about in my particular sector.
After I solved entrepreneurship I decided to retire and I now spend my days reading HN, posting on topics about AI.
I struggle to believe that a ton of seemingly intelligent software engineers are too dumb to figure out how to use Claude code to get reliable results, it seems much more likely to me that it can do well at isolated tasks or new projects but fails when pointed at large complex code bases because it just... is a token predictor lol.
But yeah spinning up a green fields project in an extensively solved area (ledgers) is going to be something an AI shines at.
It isn't like we don't use this stuff also, I ask Cursor to do things 20x a day and it does something I don't like 50% of the time. Even things like pasting an error message it struggles with. How do I reconcile my actual daily experience with hype messages I see online?
So it's not that they're too stupid. There are various motivations for this: clinging on to familiarity, resistance to what feels like yet another tool, anti-AI koolaid, earnestly underwhelmed but don't understand how much better it can be, reacting to what they perceive to be incessant cheerleading, etc.
It's kind of like anti-Javascript posts on HN 10+ years ago. These people weren't too stupid to understand how you could steelman Node.js, they just weren't curious enough to ask, and maybe it turned out they hadn't even used Javascript since "DHTML" was a term except to do $(".box").toggle().
I wish there were more curiosity on HN.
Hypothetically, you have a simple slice out of bounds error because a function is getting an empty string so it does something like: `""[5]`.
Opus will add a bunch of length & nil checks to "fix" this, but the actual issue is the string should never be empty. The nil checks are just papering over a deeper issue, like you probably need a schema level check for minimum string length.
At that point do you just tell it like "no delete all that, the string should never be empty" and let it figure that out, or do I basically need to pseudo code "add a check for empty strings to this file on line 145", or do I just YOLO and know the issue is gone now so it is no longer my problem?
My bigger point is how does an LLM know that this seemingly small problem is indicative of some larger failure, like lets say this string is a `user.username` which means users can set their name to empty which means an entire migration is probably necessary. All the AI is going to do is smoosh the error messages and kick the can.
Seemingly is doing the heavy lifting here. If you read enough comment threads on HN, it will become obvious why they aren’t getting results.
I'm just curious, why do you "have to"? Don't get me wrong, I'm making the same choice myself too, realizing a bunch of global drawbacks because of my local/personal preference, but I won't claim I have to, it's a choice I'm making because I'm lazy.
I could pay API prices for the same models, but aside from paying much more for the same result that doesn't seem helpful
I could pay a 4-5 figure sum for hardware to run a far inferior open model
I could pay a six figure sum for hardware to run an open model that's only a couple months behind in capability (or a 4-5 figure sum to run the same model at a snail's pace)
I could pay API costs to semi-trustworthy inference provider to run one of those open models
None of those seem like great alternatives. If I want cutting-edge coding performance then a subscription is the most reasonable option
Note that this applies mostly to coding. For many other tasks local models or paid inference on open models is very reasonable. But for coding that last bit of performance matters
I'm given a tool that lets me 10x "provide value".
My personal preferences and tastes literally do not matter.
The challenge now is how to plan architectures and codebases to get really big and really scale, without AI slop creating hidden tech debt.
Foundations of the code must be very solid, and the architecture from the start has to be right. But even redoing the architecture becomes so much faster now...
You sound like a pro wrestler. I'd like to know what "hard-hitting" engineering work is. Hydraulic hammers?
It's also like.... difficult to honestly and accurately measure. And account for whether or not you're getting lucky based on your underlying dependencies (servers, etc) not crashing as much as advertised, or if it's actually five nines. Or whether you've run it for a month and gotten <30s of measure downtime and declared victory, vs run it for three years with copious software updates.
Yet
[0] https://github.com/QuantumNous/new-api
[1] https://she-llac.com/claude-limits
Isn't this almost certainly against ToS, at least if you're using "plans" (as opposed to paying per-token)?
Why does it sound like you're on drugs? I know that sounds extremely rude, but I can't think of any other reasonable comparison for that language.
It's hard to take these kinds of endorsements seriously when they're written so hyperbolically, in terms of the same cliches, and focused on entirely on how it makes you feel rather than what it does.
People claim that DoorDash and other similar apps are about efficiency, but I suspect a large portion is also a desire to remove human interaction. LLMs are the same. Or, in actuality, to create a simulacrum of human interaction that is satisfying enough.
Imagine being an Uber driver and suddenly have to switch to a rickshaw for several hours.
This has basically been what all of Silicon Valley sounds like to me for a few years now.
They are known for abusing many psycho-stimulants out there. The stupid “manifesto” Marc Andreessen put out a while back sounded like adderall-produced drivel more than a coherent political manifesto.
This is similar to how we have already found hacks in our evolutionary programming to directly deliver high amounts of flavor without nutrition, and we've been working on ever more complex means of delivering social stimulation without the need for other human (one of the key appeals of AI for many people, as well).
Of course these are all the ravings of a crank and should be ignored.
What I don’t understand, are the people who let it go over night or with whole “agent teams” working on software. I have no idea how they trust any of it.
Code is notation, just like music sheets, or food recipes. If your interaction with anyone else is with the end result only (the software), the. The code does not matter. But for collaboration, it does. When it’s badly written, that just increase everyone burden.
It’s like forcing everyone to learn a symphony with the record instead of the sheets. And often a badly recorded version.
Do you think that is impossible? There are plenty of people who enjoy composing music on things like trackers, with no intent of ever playing said music on an instrument.
I love coding, but I also like making things, and the two are in conflict: When I write code for the sake of writing code, I am meticulous and look for perfection. When I make things, I want to move as fast as possible, because it is the end-product that matters.
There is also a hidden presumption in what you've written that 1) the code will be badly written. Sometimes it is, but that is the case for people to, but often it is better than what I would produce (say, when needing to produce something in a language I'm not familiar enough with), 2) and that the collaboration will be with people manually working on the code. That is increasingly often not true.
As an example, a long term goal at the employer I work for is exactly this: run LLMs locally. There's a big infrastructure backlog through, so it's waiting on those things, and hopefully we'll see good local models by then that can do what Claude Sonnet or GPT-5.3-Codex can do today.
Maybe in 5 years we'll have an open weights model that is in the "good enough" category that I can run on a RTX 9000 for 15k dollars or whatever.
Lately though the RAM crisis is continuing and making things like this more unfeasible. But you can still use a lot of smaller models for coding and testing tasks.
Planning tasks I'd use a cloud hosted one, for now, because gemma4 isn't there yet and because the GPU prices are still quite insane.
The cool and fun part is that with ollama and vllm you can just build your own agentic environment IDE, give it the tools you like, and make the workflow however you like. And it isn't even that hard to do, it just needs a lot of tweaking and prompt fiddling.
And on top of that: Use kiwix to selfhost Wikipedia, stackoverflow and devdocs. Give the LLM a tool to use the search and read the pages, and your productivity is skyrocketing pretty quickly. No need anymore to have internet, and a cheap Intel NUC is good enough for self-hosting a lot of containers already.
Source: I am building my own offline agentic environment for Golang [1] which is pretty experimental but sometimes it's also working.
[1] https://github.com/cookiengineer/exocomp
It would be cool to run SOTA models on my own hardware but I can't. Hence, the subscription.
We're paying for servers that sit idle at night, you don't find enough sysadmins for the current problems, the open source models aren't as strong as closed source, providing context (as in googling) means you hook everything up to the internet anyway, where do you find the power and the cooling systems and the space, what do you do with the GPUs after 3 years?
Suddenly that $500/month/user seems like a steal.
0 as of this writing, it's noticeable. Lots of "should I continue?" And "you should run this command if you want to see that information." Roadblocks that I hadn't seen in a year+
Before a Subscription was the cheapest way to gain Codex usage, but now they've essentially having API and Subscription pricing match (e.g. $200 sub = $200 in API Codex usage).
The only value of a subscription now is that you get the web version of ChatGPT "free." In terms of raw Codex usage, you could just as easily buy API usage.
I don't think it's made out like that, I'm on the ChatGPT Pro plan for personal usage, and for a client I'm using the OpenAI API, both almost only using GPT 5.4 xhigh, done pretty much 50/50 work on client/personal projects, and clients API usage is up to 400 USD right now after a week of work, and ChatGPT Pro limit has 61% left, resets tomorrow.
Still seems to me you'd get a heck more out of the subscription than API credits.
Codex shines really well at what I call "hard problems." You set thinking high, and you just let it throw raw power at the problem. Whereas, Claude Code is better at your average day-to-day "write me code" tasks.
So the difference is kind of nuanced. You kind of need to use both a while to get a real sense of it.
They’re still doing subscriptions: https://developers.openai.com/codex/pricing
Edit: Looks like it still works with subs, they just measure usage per token instead of per message.
But as it stands, the more likely reason is capacity crunch caused by a chips shortage and demand heavily outpacing supply. You vibe coding reason is based on as much vibes as their code probably is.
Day 1: 2
Day 2: 3
Day 3: 1
Not sure how I can hit such limits so quickly with such low scores on its own chart.
Pentagon: No
OpenAI: We are okay if the line is merely a suggestion and we encourage you not to cross it!
Pentagon: Yes we pick that option
That has led to a significant number of people switching over from openai, or at least stating they were going to do so.
I have cancelled my subscription last week, I'll see them when they fix this nonesense
It’s great to buy dollars for a penny, but the guy selling em is going to want to charge a dollar eventually…
Do you feel there is enough visibility and stability around the "Prompt -> API token usage" connection to make a reliable estimate as to what using the API may end up costing?
Personally, it feels like paying for Netflix based on "data usage" without having anyway for me to know ahead of time how much data any given episode or movie will end up using, because Netflix is constantly changing the quality/compression/etc on the fly.
I agree that ex ante it’s tough, and they could benefit from some mode of estimation.
Perhaps we can give tasks sizes, like T shirts? Or a group of claudes can spend the first 1M tokens assigning point values to the prospective tasks?
Take the response on another post about Claude Code.
https://news.ycombinator.com/item?id=47664442
This reads like even if you had a rough idea today about what usage might look like, a change deployed tomorrow could have a major impact on usage. And you wouldn't know it until after you were already using it.
Now we’re going to find out what these tools are really worth.
If an hour of an excellent developer's time is worth $X, isn't that the upper bound of what the AI companies can charge? If hiring a person is better value than paying for an AI, then do that.
They can charge whatever they want, I think many people like to make business decisions based on relative predictability or at least be more aware that there's a risk. If they want it to be "some weeks you have lots of usage, some weeks less, and it depends on X factors, or even random factors" then people could make a more informed choice. I think now it's basically incredibly vague and that works while it's relatively predictable, and starts to fail when it's not, for those that wanted the implied predictability.
It works out even if some customers are able to eat a lot, because people on average have a certain limit. The limits of computers are much higher.
I'm not sure how businesses budget for llm APIs, as they seem wildly unpredictable to me and super expensive, but maybe I'm missing something about it.
[1] https://status.claude.com/
For some context, they added 2x Palantir or .75x Shopify or .68x Adobe annual revenue in March alone.
Fwiw there are worse delays from second tier providers like moonshot's kimik2.5 that are also popular for agentic use.
[0] https://news.ycombinator.com/item?id=47633396
Vibe coding doesn't automatically mean lower quality. My codebase quality and overall app experience has improved since I started using agents to code. You can leverage AI to test as well as write new code.
You are talking about software scaling patterns, Anthropic is running into hardware limitations because they are maxing out entire datacenters. That's not an architectural decision it's a financial gamble to front-run tens of billions in capacity ahead of demand.
The LLM has no concept of "taste" on its own.
Scalability, in particular, is a problem that goes beyond the code itself and also includes decisions that happen outside of the codebase. Infrastructure and "platform" in particular has a big impact on how to scale an application and dataset.
[0] https://dornsife.usc.edu/news/stories/ai-may-be-making-us-th...
Personally I write something like 80-90% of my code with agents now but after they finish up, it's critical that you spin up another agent to clean up the code that the first one wrote.
Looking at their code it's clear they do not do this (or do this enough). Like the main file being something like 4000 LOC with 10 different functions all jammed in the same file. And this sort of pattern is all over the place in the code.
1. Me not wanting that for context management reasons
2. It burning tokens on an expensive model.
Literally a conversation that I just had:
* ME: "Have sonnet background agent do X"
* Opus: "Agent failed, I'll do it myself"
* Me: "No, have a background agent do it"
* Opus: Proceeds to do it in the foreground
* Flips keyboard
This has completely broken my workflows. I'm stuck waiting for Opus to monitor a basic task and destroy my context.
https://status.claude.com/
We'll see AI chat replace Google, we'll see companies adopting AI in high-value areas, and we'll see local models like Gemma 4 get used heavily.
AI winter will see a disappearance of the clickbait headlines about everyone losing their jobs. Literally nobody is making those statements taking into account that pricing to this point is way less than the profit maximizing level.
The rest of the organisation, which is not software development or IT related, mainly uses GPT models. I just wish I hadn't taught risk management about claude code so they weren't wasting MY tokens.
Obviously in hindsight it would be unfair to Anthropic to judge them on an unstable day so I'l leave those complaints aside but I hit the session limit way too fast. I planned out 3 tasks and it couldn't finish the first plan completely, for that implementation task it has seen a grand total of 1 build log and hasn't even run any tests which already caused it to enter in the red territory of the context circle.
It was even asking me during planning which endpoints the new feature should use to hook into the existing system, codex would never ask this and just simply look these up during planning and whenever it encounters ambiguity it would either ask straight away or put it as an open question. I have to wonder if they're limiting this behavior due trying to keep the context as small as possible and preventing even earlier session limits.
Maybe codex's limits are not sustainable in the long run and I'm very spoiled by the limits but at this point CC(sonnet) and Codex(5.4) are simply not in the same league when comparing both 20 dollar subscriptions.
I will also clearly state that the value both these tools provide at these price points are absolutely worth it, it's just that codex's value/money ratio is much better.
CC is a better implementation and seems to be fairly economic with token usage. That is the really the only defining point and, I suspect, Anthropic are going to have a lot of trouble staying relevant with all the product issues.
They were far ahead for a brief period in November/December which is driving the hype cycle that now appears to be collapsing the company.
You have to test at least every month, things are moving quickly. Stepfun is releasing soon and seems to have an Opus-level model with more efficient architecture.
One example is I have a multi-stage distillation/knowledge extraction script for taking a Discord channel and answering questions. I have a hardcoded 5k message test set where I set up 20 questions myself based on analyzing it.
In my harness Minimax wasn't even getting half of them right, whereas Sonnet was 100%. Granted this isn't code, but my usage on pi felt about the same.
What are you using to drive the Chinese models in order to evaluate this? OpenCode?
Some of Claude Code's features, like remote sessions, are far more important than the underlying model for my productivity.
I keep coming back to it because I can run it as a manager for the smaller tasks.
At my workplace we have been sticking with older versions, and now stick to the stable release channel.
Is Microsoft (one of the largest companies in the world) really a victim of brand death?
I have no idea how people are hitting the limits so fast.
Hit the weekly limit on my 20x plan last week trying to do a full front end rewrite of a giant enterprise web app, 600+ html templates, plus validating every single one with playwright.
It's actually via quantum entanglement.
There was constant drama with CC. Degradation, low reliability, harness conspiring against you, and etc – these things are not new. Its burgeoning popularity has only made it worse. Anthropic is always doing something to shoot themselves in the foot.
The harness does cool things, don't get me wrong. But it comes with a ton of papercuts that don't belong in a professional product.
Free and local.
Unless they meant "all code that needs to be written has already been written" so their mission is to prevent any new code from being written via a kind of a bait and switch?
I’ve been toying around at home with it and I’ve been fine with its output mostly (in a Java project ofc), but I’ve run into a few consistent problems
- The thing always trips up validating its work. It consistently tries to use powershell in a WSL environment I don’t have it installed in. It also seems to struggle with relative/absolute paths when running commands.
- Pricing makes no sense to me, but Jetbrains offering seems to have its own layer of abstraction in “credits” that just seem so opaque.
Then again, I mostly use this stuff for implementing tedious utilities/features. I’m not doing entity agent written and still do a lot of hand tweaks to code, because it’s still faster to just do it myself sometimes. Mostly all from all from the IDE still.
Not worth the money now, will be canceling unless fixed soon.
Maybe you should consider....local models instead?
I doubt even the core engineers know how to begin debugging that spaghetti code.
So?