The copyright angle is the most underrated part of this story. Anthropic built their models on other people's code under the fair use argument, but the moment their own code leaks they reach for DMCA takedowns. You can't have it both ways. The clean room reimplementations are the natural consequence of the legal framework they themselves advocated for.
There are several ways of looking at law and order.
One way is that the law applies to everybody equally. That has been the way it works for many years, not perfectly, in democratic countries.
There is another way of working were the law is not blind. Laws are applied based in who is the one affected. This is what big tech and the ultra-rich have been advocating for. The law applies differently to nobility and aristocrats than to the working class.
So, for all this big tech companies the law is clear: I can copy from you, you cannot copy from me.
(That is horrifying in case that anyone needs me to spell it out)
A third way of looking at it is that you can't just blindly copy arguments when the situations are clearly different.
Nobody, not even Anthropic, is arguing that they should be able to host other people's paid content for free. The crux of their fair-use defense is that models are transformative works, just like parodies or book reviews, and hence should be treated as fair use.
You can't just take a pile of books (no pun intended) and turn that into Claude in a day with 30 lines of Python, there's a lot of work and know-how on the Anthropic side that goes into making a good LLM.
If lossy-compressed transcodes of ripped movies are not "transformative works" and can get people even jailed, then lossy-compressed text of ripped books and websites is neither.
There is a lot of knowhow going into a good divx rip too, you know.
And it enables so much novel uses such as popcorn time, with fluorishing business opportunities.
it's an exaggeration for sure but I don't think it's a stretch to believe Anthropic spends considerably more effort on data scraping & curation than anything else
What is your fair use claim as a defense to a third party using their source code?
It is an affirmative defense, you to be able to argue the merits. If you publish their source code, they are allowed to come after you whether they have previously used fair use or not. It's fact specific and determined case by case.
Anthropic won half of their fair use argument in the billion dollar settlement, but lost the other half.
You can say you're just using their code to train your own models, just like they did, and they will correctly point out that how you obtained the code also matters and you will lose just like they did.
This isn’t contradictory at all. Not Anthropic, OpenAI, nor anyone else, has ever argued for anything that’d see redistributing this leaked code as being legal. This is an entirely bad-faith argument that really just comes down to “Anthropic bad, AI bad, because copyright, and they are using copyright!?”
It’s not “underrated”. Everyone is just 50 steps ahead of you.
Meta and I assume OpenAI and Anthropic did everything they could to acquire data, even doing so illegally, such as downloading all of Anna’s archive. Now it’s an open question of whether it’s a societal good or societal bad, but it does show they have little regard for copyright law when it benefits them.
And this whole “they’re 50 steps ahead of you” nonsense is the same kind of stuff we heard from NFT or crypto bros, that we just couldn’t comprehend the infinite wisdom of a post currency world. Sometimes bad arguments are just bad arguments.
inb4 Claude actually leaked the code on purpose because it calculated that this was the moral thing to do for the good of humanity and its own Constitutional AI values.
That doesn’t apply here. Claude code is what leaked, not the models. Anthropic definitely owns Claude code copyright and can DMCA without it being contradictory
But even that is vague and possibly not true. If they used LLM's to generate all of the code, then it may not fall under copyright, by the requirement of human authorship (which for code I think has not been tested yet in court) [1].
Its unclear whether there is sufficient human authorship in cc for copyright to stick on a court. Anthropics arguments would hinge on the curation of plans and the direction decisions, which haven't been properly tested as the source of authorship yet. Typically contracted implementers sign over copyright to the project owners, and this is where there is case law.
Something that was meant to remain secret made public, is not the same thing as whether something public is public.
If anything, this is a question of whether you owe royalties to the owner of IP you consumed in your life since it became part of and trained your mind, identity, and outputs too.
According to IP owners ever since things were digitized, you technically own nothing and simply paid for an authorization to use any given IP for the duration that the IP owner authorized you to use it and you continue to pay, so pay your monthly meat-AI bill to pay for all the IP your mind has been trained on.
>but the moment their own code leaks they reach for DMCA takedowns.
Did they actually? Someone can go to prison for 5 years for that.
Fact 1: AI generated code has no copyright, so the Digital Millennium Copyright Act does not apply.
Fact 2: Misrepresenting your copyright ownership under the DMCA is felony perjury.
Fact 3: The existence of undercover.ts in the leak is grounds to void any copyright claims on whatever human written code might have existed in Claude Code. You have a DUTY TO DISCLOSE any AI generated code in your copyrighted work. undercover.ts HIDES DISCLOSURE to FRAUDULENTLY claim all the code is human written when it is not.
Given the current administration has a bone to pick with Anthropic, it was a VERY BAD IDEA for them to send false DMCA takedowns to github. Someone at Anthropic may be the very first ever to go to prison under that section of the DMCA.
I'm not (just) being glib. That earlier article displays some introspection and thoughtful consideration of an old debate. The writing style is clearly personal, human.
Today's post is not so much. It has LLM fingerprints on it. It's longer, there are more words. But it doesn't strike me as having the same thoughtful consideration in it. I would venture to guess that the author tried to come up with some new angles on the news of the Claude Code leak, because it's a hot topic, and jotted some notes, and then let an LLM flesh it out.
Writing styles of course change over time, but looking at these two posts side by side, the difference is stark.
Hey there, author of the post here! I actually wrote this piece myself on my phone while I was out for a walk this morning. It was initially meant to be a quick note more than a full blog post —- whereas Coding As A Creative Expression took me a couple of days to write.
I made a commitment to write more this year and put my thoughts out quicker than I used to, so that’s likely the primary reason it’s not as deep of a piece of writing as the post you’re referencing. But I do want to note that this wasn’t written using AI, it just wasn’t intended to be as rich of a post.
The reason it came out longer is that I’ve honestly been thinking about these ideas for a while, and there is so much to say about this subject. I didn’t have any particular intention of hopping on a news cycle, but once I started writing the juices were flowing and I found myself coming up with five separate but interrelated thoughts around this story that I thought were worth sharing.
> I didn't have time to write a short letter, so I wrote a long one.
First known use in English comes from a 1658 translation of Blaise Pascal in 1657
> Je n’ai fait celle-ci plus longue que parce que je n’ai pas eu le loisir de la faire plus courte.
translated to
> I had not made this longer then the rest, but that I had not the leisure to make it shorter then it is.
(note the archaic then)
This was a popular piece of wit at the time.
Mark Twain wrote something similar a hundred years later
> You'll have to excuse my lengthiness - the reason I dread writing letters is because I am so apt to get to slinging wisdom & forget to let up. Thus much precious time is lost.
Hmm… this started as an admonition for using then and than interchangeably. I see folks get it wrong unintentionally a lot… then i re-read your parenthetical and pulled up etymonline and had my mind blown a bit.[0]
>wrote this piece myself on my phone while I was out for a walk
If you have a strategy for jotting down (or dictating) notes while walking about, I would be curious how you manage that. I spend plenty of time walking outside, and tend to get (at the time) ideas that I'd like to explore further, most of which have evaporated from my mind by the time I get back home. Or even before I can get my phone out to jot down the keywords to help me recall the details later.
Cannot even imagine how someone would manage both walking and writing at the same time.
What I tried is to record my voice and then post-process the transcript. There are solutions which work without internet connection. I am non-native english and mix 3 languages, so transcript is shitty quality. Nevertheless the really good ideas stay with me and can be easily recalled by a few keywords. And you need to do the post-processing shortly after you reached home else it fades away…
Hey there! I'm not sure I have a universally applicable answer, but I can do my best to map out some things about my process and flow that hopefully help a bit and answer your question.
- I've had an iPhone for half my life (I'm 36 and got one when I was 19), so I've gotten pretty acclimated to typing on the go. I try switching to dictation every couple of months but the iPhone's dictation trips up over enough words that I find it more frustrating than typing as I walk.
- I don't do this but if you're worried about the thoughts disappearing I would absolutely recommend recording a voice note. As I'll touch on in a moment — do not let those thoughts disappear! Even the act of codifying them into something tangible allows you to process them more deeply.
- I live in NYC but I start most mornings by taking a walk along a relatively quiet street, so I rarely end up having to worry about bumping into someone. That is definitely not universally applicable advice. (:
- I look up as I'm typing and let autocorrect take the wheel. That works at least 95% of the time, so if I make the occasional typo it doesn't really matter, I'll just fix it in post.
- It helps to have an app with a great text editing experience. I've found that there are very few out there that are fluid, many have incredibly subtle hitches that make it hard to quickly jot down thoughts onto a canvas. I really love Craft (https://craft.do) and have been using it for years, so at this point it feels more like an extension of me than an app.
- This is surely unique to everyone but my writing tends to start from a few keystone thoughts. Once I have one written down, I let myself almost free associate, writing down whatever comes to mind from that initial thought to make sure I do not forget. I can always edit after the fact, and often the editing process leads to more interesting insights as well. But the main thing I want to avoid is losing those sparks, in the same way that you're mention your thoughts evaporating. Don't let those go, just get 'em on paper and sort through 'em afterwards.
- That's all a lot easier to do on my phone than if I approached the problem as "type an essay on my phone", so I'll almost always edit a post on my computer before publishing. Yesterday was more of an exception than the rule though because I was bouncing around between doctors all day, so I wrote all of this on my phone [not expecting it to blow up or get a ton of scrutiny].
Not sure if anything's missing but I'm happy to share anything that may be helpful! Clearly this post wasn't perfect, but I've been much happier since I started letting myself write out long-form thoughts on my phone and sharing them as blog post rather than firing them off as pithy tweets that decay into the ether once the algorithm says it's time for them to go.
> I actually wrote this piece myself on my phone while I was out for a walk this morning.
Apropos of nothing, this is astonishing me to no end. The ergonomics of 1) using a phone keyboard for anything but a word or two and 2) doing so while walking pretty much guarantee that I'd probably need a half a day to recover if I attempted the same.
As a more general comment, I hope we’ll stop trying to discriminate whether a certain text was or not written/polished/extended by AI and focus more on content and author’s responsibility for what they do or do not say with that text.
I agree. Not because I think that most AI content is worth reading, but because it can be criticized on more grounded merits. People wrote blogspam by hand for two decades before AI started generating it. It wasn’t high value when a human wrote it either.
On many (most?) posts, far more energy is spent arguing about whether a post is AI than discussing if there’s anything of value in the post.
I've stopped reading anything on blogs on the basis that it's now probably llm spew and life is too short for the signal to noise ratio that implies.
With the exception of things that places like HN seems to consider worth reading, which is why I'm looking through the comments to this and others to find recommendations.
Have you noticed that comments like "this post seems written with AI" are now appearing on all posts, even those written without AI?
We're starting to become wary due to the abuse of AI and proliferation of sloppy content, but also because we often have trouble distinguishing authentic from sloppy content.
Agreed. Its so tedious that the top comment section on every HN post the last six months is "this seems be written by LLM" with a bunch of back and forth on whether it is or not.
I mean, a lot of it is. Green user, signed up 49mins ago, 5 comments, which erodes trust in real people as well. I’ve noticed I’ve just felt less engaged and more anxious about all kinds of online content. While most platforms were previously botted, had adverts, etc… You could always find niche corners where there were only people talking about things they genuinely cared about. Now you can fill out even those spaces automatically.
As a daily user of HN, this is not true. Maybe you are clicking on different headlines, but the ones I am clicking on dont have that as top comment all that often. They do not even have it as a comment somewhere all that often.
Probably because most HN posts in the last six months have been written by LLMs. Not all, but that doesn't matter, the trust has been eroded to the point that clicking on an article on the front page of HN and not being immediately met with the sloppiest slop imaginable is now a standout event in my mind.
I’ve been accused of being AI. My first impression when it happened was that because I often deal in information that people don’t like hearing, because it challenges their frame of mind, i.e., what they were trained on, “this is AI” is just another convenient tool to either dismiss uncomfortable challenge, i.e., cognitive dissonance, and/or another means to keep the mental herd they are part of or control in line with dogma.
“This is AI” seems to just be an evolution of other thought terminating cliches where the negative conditioning associated with something is used in an abusive and manipulative way to evade challenge or the truth itself. It is a common tactic of abusive people, the “beyond the pale” moralizing.
Yeah, whenever I see "It's not... it's...", I catch myself instinctively dismissing the content as AI slop, but upon reflection I'm not so sure, it used to be a normal phrase.
But I do take extra care to avoid LLM-speak as much as I can.
It does read as if were written on a phone but it doesn’t read like LLM text to me.
What is interesting and has possibly bled over from heavy LLM use by the author is the style of simplistic bullet point titles for the argument with filler in between. It does read like they wrote the 5 bullet points then added the other text (by hand).
What changed is you, the reader. In 2026 we treat the smallest signs as evidence of LLM writing. Too long? LLM. Too short? LLM. Too grammatically correct? Must be LLM.
For me it was the "it's not x"/"it's y" stuff and some other structures Claude is very fond of using all the time. Perhaps humans are starting to write like LLMs!
Perhaps, just perhaps, LLMs are just statistical models that literally can't create novel things, therefore any structure LLMs write was learnt from human writing?
What kind of human writing has "it's not X—it's Y" in every single paragraph?
The answer is none. LLMs haven't accurately modeled human writing for years, current models have been smacked on the head with the coding RLHF bat so much, they all write distinctly inhuman text.
The thing is, people are screaming “AI” when they see a single “it's not X—it's Y" pattern in a post, despite this being a fairly common construct.
People are nitpicking every tiny thing in their search for proof of AI. It’s not useful and ends up dominating the conversation. AI panic is degrading the value of forums at least as much as actual AI at this point.
I personally found it really amusing how they weaponized the legal system to DMCA all the claude code source code repositories. Code ingested into the model is not copyrightable, but produced code apparently is when by legal definition computer generated code can not be copyrighted and that's one of their primary arguments in legal cases.
I did an interesting spin to avoid it: generated Claude abstracted as a set of PRD style architectural instructions so you can do your claude-code inspired program effortlessly.
There's even a GUI called claudia for a piecemeal extraction with a PRD.
I wonder if GitHub would rule it a copyright violation if the source code was rewritten by an agent, i.e. copy my answers but change a few words. Legally, if the original source code is copyrighted then an agent rewriting it likely doesn't lose that copyright, but I wonder if GitHub would go through the effort of determining whether it was a derived work.
I guess you'd be assuming that both the original CC code and its ports are computer/AI-generated? As a lawyer, though, I'd still maintain that you wouldn't need much original human input in the CC code to kind of ruin that theory. The threshold for copyright protection isn't that high, really.
The copyright office has indicated that AI generated elements lacking human contributions to the expression (rather than input) would still not be copyrightable even if some human authored elements are made to the expression as well.
Seems like it would be a nightmare to provide evidence of what parts of a half a million line codebase were written by humans if no one bothered to track it.
I'm probably missing something. Pre-PMF code by definition is not yet proven to solve a specific pain point, so why does it matter?
I think the crux here is the OP means the "quality of code" doesn't matter until PMF, only the utility matters (to the extent it helps you find PMF), in which case you're both in violent agreement.
But even then you don't need code. I briefly worked for a startup that found PMF by calling people, sending text messages, creating social media posts, measuring engagement to create reports, and sending invoices... all manually. The "code" as such was a bunch of templates in a doc for each of those. Once they actually started getting paid they moved to writing code.
> I briefly worked for a startup that found PMF by calling people, sending text messages, creating social media posts, measuring engagement to create reports, and sending invoices... all manually.
Right, and in that case there is no step-by-step recipe for the product. When all that is implemented in code, that is a set of step-by-step instructions for solving the pain points.
But the manual workflow was the step-by-step recipe the founders iterated on until they got traction; the product that came later was just an embodiment of that workflow as code.
I don't see how that is relevant? I thought the point under discussion was that code does not matter until PMF, and that this would be an illustrative example because there was no code until PMF.
Like, from the users' perspectives they were interacting via text messages both before and after PMF, until later down the line they were migrated to an app. At this point, the change was largely aesthetic, the core idea was the same.
Maybe we're using different definitions of terms like "PMF" here?
> Maybe we're using different definitions of terms like "PMF" here?
No, maybe different perspectives on what "the code matters" means.
In this context, I took it to mean that "code being closed/open does not matter", because the context included leaking the source.
I see you're taking it to mean "code being good/bad does not matter", because the context included a startup product.
We're talking at cross-purposes. There are two assertions here:
1. The code being good/bad (or even existing) is irrelevant.
2. The code being closed is important.
Both those assertions can be true at the same time.
My point is that the code, if it exists, is a recipe for solving some profitable pain point. In that case, there is absolutely no upside to making it open, and so the code does indeed matter.
I follow that too, when I try a new venture, but what does that have to do with "The Code Is/Isn't Important"?
What you listed is important, but those findings are distilled into the source code of the product. If you open the source, you are providing step-by-step instructions on solving some problem that other people are prepared to pay to solve.
Basically, you come up with a recipe for success for $FOO - why would you give that recipe away unless you've already capitalised on it?
None of what I said even speaks about source code. It could just as well be run by a bunch of people with notebooks, sending emails from time to time.
All that matters is that the product works, and we have a scaling path once we find a market fit. Yes, that's likely to involve source code, but I've no qualms with tossing out the MVP code and starting over from scratch. Or adapting the quickie we put together, if it happens to contain a kernel of value. Whether I open source it or not depends on where our moat lies.
But regardless, the new reality is that anyone can decompile your code with an AI and duplicate it. So merely putting an app out there opens the door for someone to copy it. So really, your moat better be something besides the code!
Nope. That’s what self-important engineers will tell themselves, but it doesn’t make it remotely true. You’re patting yourself on the back for throwing together a CRUD app and burning through a bajillion dollars on AWS.
>> Counterpoint: Code does matter, in the early days too!
>> It matters more after you have PMF, but that doesn't mean it doesn't matter pre-PMF.
>> After all, the code is a step-by-step list of instructions on solving a specific pain point for a specific target market.
----------------------------------
> Nope. That’s what self-important engineers will tell themselves, but it doesn’t make it remotely true. You’re patting yourself on the back for throwing together a CRUD app and burning through a bajillion dollars on AWS.
Claude Code (and other agent tools) are not expected to be mature. They'll all be obsolete in two or three years, replaced by the next generation of AI tools. Everyone knows that.
In less than four years the AI coding workflow has been overhauled at least twice: from Chat interface (ChatGPT) to editor integration (Cursor), then to CLI agent harnesses (CC/Codex). It would be crazy to assume that harnesses are the end of evolution.
Except, apparently, Anthropic - who are doing their darndest to get everyone onboard their tools as a moat. Apparently that's the only strategy to AI stickiness.
And their strategy kind of worked, right? CC is the most popular agentic coding tool. Anthropic faces competition from OpenAI (potentially better model, weaker TUI tool) and from the rest (potentially worse models, weaker TUIs). So their strategy is to develop both: make their closed model and closed tool better than competition so that when people want to vibceode they will choose their ecosystem.
Claude Code 2.0 (and other agent tools) are not expected to be mature. They'll all be obsolete in two or three years, replaced by the next generation of AI tools. Everyone knows that.
Claude Code 3.0 (and other agent tools) are not expected to be mature. They'll all be obsolete in two or three years, replaced by the next generation of AI tools. Everyone knows that.
This code matters for exactly one reason: they’re playing stupid DRM games restricting what subscriber tokens can be used for to force you to use their front ends and harnesses or buy more expensive API credits.
Claude Code is strictly worse than e.g. OpenCode in my experience. Not much to see in the app’s code except how it authenticates itself…
Sure I try and use all my subscription allowance with CC on side tasks, etc. but I still end up burning a bunch of API tokens (via OpenRouter) for more serious work (even the UI and ability to quickly review what the agent has done/is doing is vastly inferior in CC).
What they have done is got me experimenting with cheaper models from other providers with those API credits.
Yes, and once they've found their market, why does claude code need to keep changing at high velocity? Has the harness really needed to change so much that they're not converging on a stable shape to the code?
Is it possible to start with something of this size that's vibe coded and refactor your way into something resembling a human codebase?
Code quality aside (n.b. there exist many bad quality codebases before AI), a risk I perceive as an industry is we are making the logic of our businesses dependent on a few big players.
Given the output speed, it's practically impossible for developers to keep up, which directly impacts maintenance: the knowledge that would previously reside in-house, now is becoming dependent on having codebases pre-processed by LLMs.
I hope in the near future local LLMs will gain traction and provide an alternative, otherwise we are in the risky path where businesses are over-reliant on a few big companies.
My CTO mentioned today how we haven't ever had to revision our API such that it breaks users, and even then, our v2 is still so compatible that client builds from 2017 are still vaguely useful for the end user.
But now everything is, "ship as fast as is humanly possible, literally" from management, and "garbage Claude-written PRs" from devs. Trying to maintain sanity over my monorepo is impossible.
We have nearly a century of examples of "somebody who only mostly understands making a breaking change" and decided, "what the hell, this thing is called Claude, so it can wreak havoc for as long as corporate decides"
alternatively the code can go the way of "fast fashion" and even "3d-print your garments in the morning according to your feelings and weather and recycle at the end of the day".
If dealing with a functionality that is splittable into microfeatures/microservices, then anything that you need right now can potentially be vibe-coded, even on the fly (and deleted afterwards). Single-use code.
>But as time goes on your codebase has to mature, or else you end up using more and more resources on maintenance rather than innovation.
tremendous resource sink in enterprise software. Solving it, even if making it just avoidable - may be Anthropic goes that way and leads the others - would be a huge revolution.
I can totally wrap my head around that, and it's an interesting thought experiment, though:
- building functionalities as components that are swappable on a whim requires a level of careful thought, abstraction and architecture that essentially is the exact opposite to ai slop
- in this day and age we still don't make software for the sake of it, and who's financing it doesn't generally require such levels of functional flexibility (the physical world commandeering the coding isn't nearly as volatile as to justify that)
- this comes loaded with the implication that "stuff needs to work": if you are developing software that manages inventory, orders, resources, ... you just can't take the chance to corrupt your customers data or disrupt their business processes. Shipping faster than you can test and with no accountability and no oversight is a solution to a problem I've personally never encountered in the wild
>- building functionalities as components that are swappable on a whim requires a level of careful thought, abstraction and architecture that essentially is the exact opposite to ai slop
that is only for humans really. Why we need these careful thought, abstraction and architecture? Because otherwise the required code becomes an unmanageable pile of spaghetti handling myriad of edge cases of abstraction leaks and unexpected side effects. Human brain can't manage it. AI can or at least soon would be able to. It will just be a large pile of AI slop.
It may also happen that AI will also start generate good component based architecture if forced to minimize or in some other measurable way improve its slop.
> Why we need these careful thought, abstraction and architecture?
your answer focused on maintainability, but you are overlooking what I think is the bigger problem: those components will eventually interact with one another (technically, by nature of living in the same code tree, sharing the same storage backend, framework, common libraries, …, or logically, by referencing the same entities for slightly different and complementary features). With that comes the need to centrally control what they should/can/cannot do. There is no shortcut to having to clarify (with your customer) and formally document what those layered interactions are, or, before you know it, you have multiple incompatible user access controls, row-level access policies, competing master/reference data, or different parts of the application interpreting differently the same data.
It's a pretty bad value proposition, if you ask me, to have to do so much hand-holding for a result that comes with no guarantees whatsoever (you will never know the extent of which your clean spec "made it in").
Have you seen the code generated by AI? These things converge on the "1 million lines to make an API call" pattern. They're a lot of things, but certainly not "micro".
> Many software developers have argued that working like a pack of hyenas and shipping hundreds of commits a day without reading your code is an unsustainable way to build valuable software, but this leak suggests that maybe this isn’t true — bad code can build well-regarded products.
The product hasn't been around long enough to decide whether such an approach is "sustainable". It is currently in a hype state and needs more time for that hype to die down and the true value to show up, as well as to see whether it becomes the 9th circle of hell to keep in working order.
I have flip-flopped more than ever in the last 365 days about prioritizing good code vs good product, in the AI age. This helps clarifies why.
I have come to the conclusion that we just do not know yet. There is a part of me that believes there is a point somewhere on the grand scale where the code quality genuinely does not matter if the outcome is reliably and deterministically achieved. (As an image, I like to think of Wall—E literally compressing garbage into a cube shape.)
This would ignore maintenance costs (time and effort inclusive.) Those matter to an established user base (people do not love change in my experience, even if it solves the problem better.)
On the other hand, maybe software is meant to be highly personal and not widely general. For instance, I have had more fun in the past two years than the entire 15 years of coding before it, simply building small custom-fitted tools for exactly what I need. I aimed to please an audience of one. I have also done this for others. Code quality has not mattered all that much, if at all. It will be interesting to see where things go.
i tried using litellm and it was 700mb! it does have a lot of features. postgres db, prometheus, api layer. really complicated. but simple help is missing. it assumes you will put passwords in an env file. how do you add chat completions in the ui? how do you add a custom provider?
but, what i wanted, a simple proxy, can be written in 30 lines of python in fastapi. memory fetch/push in 50 lines. different providers can be added with two lines, one import and one client create.
> if the outcome is reliably and deterministically achieved
It's not. My favorite example: due to vibe coding overload literally nobody knows what configuration options OpenClaw now supports. (Not even other LLM's.)
Their "solution" is to build a chat bot LLM that will attempt to configure OpenClaw for you, and hope for the best, fingers crossed. Yes, really.
The openclaw situation is ridiculous. Configuring it is a nightmare, even with 3 different LLMs trying to help. Then I check their docs and it says three different things. Agents will take questions and turn them into a new config file, which consists of made up settings, causing the gateway to crash.
My setup is very simple too, just two agents, some MD files, and discord. Nothing else. These people using it for real work or managing their email and texts are in for a rough ride.
Imo, code quality genuinely matter for the outcome and customer happiness. Including in startups.
But, there is also quite a lot of confident "code quality" fluff claims that have nothing to do with maintainability, robustness or performance. Fairly often, a guy claiming "the code is garbage" is not actually concerned with code quality as much as he is concerned with asserting dominance. Or is confusing own preference with quality.
Hey there, author of the post here. I actually agree with this! That is in fact why I used the word maybe — my comment really was meant to be more speculative than definitive.
I think one thing that goes unmentioned is that maybe code quality is really not that important for trivial things, because they can be trivially reproduced if need be. I would argue Claude Code is exactly such a project; coding agents are incredibly simple and rewriting CC wouldn't be much of a problem.
Non-trivial things tend to be much more sensitive to code quality in my experience, and will by necessity be kept around for longer and thus be much more sensitive to maintenance issues.
I think code quality is always important, but it's less important for terminal code which implements a specific set of features using robust APIs underneath. Low code quality becomes a much bigger issue when the code is supposed to deliver well-defined abstractions on which other code can rely.
If you are a serious software developer, then you will probably be able to explain both what your code does (i.e. what spec it implements) and how it does it (what does it call, what algorithms does it use, what properties does it rely on?). With the advent of LLMs, people have started to accept not having a clue about the "how", and I fear that we are also starting to sacrifice the "what". Unless our LLMs get context windows that are large enough to hold the source of the full software stack, including LLM-generated dependencies, then I think that sacrificing the "what" is going to lead to disaster. APIs will be designed and modified with only the use cases that fit in the modifying agents context window in mind with little regard for downstream consequences and stability of behavior, because there is not even a definition of what the behavior is supposed to be.
> maybe code quality is really not that important for trivial things
I hear this narrative being pushed quite a bit, and it makes my spidey senses tingle every time.
Secure programs are a subset of correct programs, and to write and maintain correct programs you need to have a quality mindset.
A 0-day doesn't care if it's in a part of your computer you consider trivial or not.
Intrinsically simple and straight forward problems are easier to secure even with mediocre or bad code. They've already shown that Opus 4.6 can find and report on very sophisticated security issues[0] so I'm not sure that analysis (and perhaps especially security analysis) is the biggest issue with LLMs.
Mind you, I'm not using LLMs for professional programming since I prefer knowing everything inside and out in the code that I work on, but I have tried a bunch of different modes of use (spec-driven + entire implementation by Opus 4.6, latest Codex and Composer 2, and entirely "vibecoded", as well as minor changes) and can say that for trivial in-house things it's actually usable.
Do I prefer to rewrite it entirely manually if I want something that I actually like? Yes. Do I think that not everything needs to be treated that way if you just want an initial version you can tinker with? Also yes.
> I'm not sure that analysis (and perhaps especially security analysis) is the biggest issue with LLMs.
I was replying to the statement that "maybe code quality is really not that important for trivial things", not whether LLM's are good at analysis nor not.
Thanks for the link though, looks like an interesting talk!
> But then the clean room implementations started showing up. People had taken Anthropic’s source code and rewritten Claude Code from scratch in other languages like Python and Rust.
Seems like the phrase "clean room" is the new "nonplussed"... how does this make any sense?
Heya, post author here. I think I was just wrong about this assertion. I got into a discussion with a copyright lawyer over on Bluesky[^1] after I wrote this and came away reasonably convinced that this wouldn’t be a valid example of a clean room implementation.
I think it means you write a spec from the implementation. Then you write a new implementation from the spec. You might go so far as to do the second part in a "clean" room.
Heh, the original being entirely vibed had me thinking of an interesting problem: if you used the same model to generate a specification, then reset the state and passed that specification back to it for implementation, the resulting code would by design be very close to the original. With enough luck (or engineering), you could even get the same exact files in some cases.
Does this still count as clean-room? Or what if the model wasn't the same exact one, but one trained the same way on the same input material, which Anthropic never owned?
This is going to be a decade of very interesting, and probably often hypocritical lawsuits.
in a typical clean-room design, the person writing the new implementation is not supposed to have any knowledge of the original, they should only have knowledge of the specification.
if one person writes the spec from the implementation, and then also writes the new implementation, it is not clean-room design.
I believe the argument is that LLMs are stateless. So if the session writing the code isn't the same session that wrote the spec, it's effectively a clean room implementation.
There are other details of course (is the old code in the training data?) but I'm not trying to weigh in on the argument one way or the other.
Seems equally valid to come out of this with the takeaway that code quality _does_ matter, because poor coding practices are what led to the leak.
Sure, the weights are where the real value lives, but if the quality is so lax they leak their whole codebase, maybe they are just lucky they didn’t leak customer data or the model weights? If that did happen, the entire business might evaporate overnight.
I look at other people's code a lot. The security issues are always boring, that's the thing. API keys sitting in the client bundle, auth middleware missing half the routes. Not clever exploits, just nobody actually reading what the AI spit out.
Actually wait, it's worse than that. The product works, demo looks great. Then someone opens the network tab and ... yeah. "Quality doesn't matter" really just means nothing caught fire yet.
I dont understand why so many people have the need to emphasize the code vs product battle. There is no battle. Coding/developing/software engineering is a skill, that just like any other skill has certain requirements and best practices that have to be followed in order to make a quality, maintainable and adaptable application that can stand the test of time from the software perspective. Product, features, marketing bla bla that is entrepreneurship part, and is not related to software, other than directing the software requirements,but not beacuse product people think about requirements, but instead just because they come naturally from the required features they envision.Just because programmers can write code doesnt mean they can ship good products.Just because a plumber can lay pipes doesnt mean he can run his own company or invent a new way of laying pipes. But I will tell you that a bad plumber who lays pipes and doesnt know how to connect them, bend them or shield them will surely have inferior product/service in the long term. And by the way, success of a company is measured over time, we will see where claude code will be in 10 years time when the hype dips a little, then we can say yeah the code was bad but everyone loved it and uses it still. I mean they leaked entire code online and this guy says yeah code was bad but who cares, what world are we living in? The fact that anything got leaked is a serious breach of best practices and security also at this point, something a company that used to work for DoD(W) shouldnt be doing, it can even be considered a national threat at this point. I know mistakes happen, I do them all the time, but then again the 'best' companies should be almost immune to mistakes cause stakes are high.But of course, move fast and break things is more important.Am I wrong?
> The fact that anything got leaked is a serious breach of best practices and security also at this point, something a company that used to work for DoD(W) shouldnt be doing, it can even be considered a national threat at this point.
Wasn't it CC itself the one that leaked, well, itself? It's completely vibe-coded, which I assume means it does its own build step too, which means it leaked itself.
The only breach of best-practice I see here is using an LLM for coding.
On the other hand, consider if they took their time to make sure that quality is super good, did not mainly vibe code it, then they would have never become as popular as quickly as they did and would have been outcompeted by other llm providers. They only got so popular because they released Claude Code so quickly it was the most productive thing for everyone to use at that time.
And frankly at that point Claude Code as software doesn't matter anymore. It is about their models, they could throw it away, rewrite from scratch, etc, it wouldn't be a big deal.
Claude Code as a harness was never likely going to be for 10 years, because there would be so many of these harnesses, all different, the direction may change, etc.
As I understand someone internally quickly vibecoded for themselves only as a productivity tool, and then they realized internally how productive it can be, they decided to release it and people found it so productive they got hugely popular now thanks to that while otherwise would have been eaten out by OpenAI.
Also if requirements for this had to came from product it would have never even happened in the first place. As it was engineer trying to optimize their own workflow.
The only reason they decided to hide source code is to delay competitors imo and it wasn't related to security or anything, but by now OpenCode etc are objectively better tools anyway.
And this is why so much software today runs extremely hot.
It's creators clearly care not for the efficiency of how it is built, which translates directly into how it runs.
This blog post is effectively being apologetic about the fact that this is alright, since at least they got product market fit. Except Anthropic is never going to go back and clean up the mess once (if) they become profitable.
I doubt anyone will like how things will be in 5 years time if this trend of releasing badly engineered spaghetti continues.
Heya, post author here. I would say I’m not trying to be apologetic for the idea that software quality is diminishing, especially cause I became an indie developer because I care about quality.
When I say “it doesn’t matter” I mean more in an existential sense, and that people don’t seem to care. On the other hand people should do things because they care, which is why I personally still review the code that goes into my apps and spend the time to refactor and improve the stability and foundation rather than slopping like there’s no tomorrow.
Maybe I’m growing cynical but I understand why a business doesn’t care (at least until it comes back to bite them — which may take longer than some have assumed). And most of what you read about the subject is ultimately being driven by business needs of the desire of businesses.
I created Hyperlambda (https://hyperlambda.dev), so I spend a lot of time thinking about accidental complexity and implementation surface area. One thing leaks like this keep reminding me is that a surprising amount of software risk comes from packaging and delivery details rather than the main logic people spend all their time reviewing.
In my opinion, the “code is garbage” argument is a moot point. Anthropic is in the business of removing humans from the SDLC. As long as their models can understand and update the code they generate it can remain garbage. They’re not optimizing for human comprehension of the output. They don’t even want you looking at it. And eventually the models will get good enough that you won’t have to.
Code quality tends to have an impact on more than just aesthetics - and Claude Code certainly feels like a buggy mess from an end user's perspective.
Of course people still use Claude Code, but that is certainly because of the underlying models first and foremost. Most products don't have such a moat and would not nearly see as much tolerance from end users. If the Max subscriptions could be used with other harnesses, I am sure Anthropic would have to compete harder on the quality of the harness (to be fair, most AI based tooling seems pretty alpha these days, but eventually things will stabilize).
Polish is not everything, clearly, but it is a factor, and I feel Claude Code is maybe the worst example to use here, as it doesn't at all generalize to most other products.
1. The code is garbage and this means the end of software.
Now try maintaining it.
2. Code doesn’t matter (the same point restated).
No, we shouldn’t accept garbage code that breaks e.g. login as an acceptable cost of business.
3. It’s about product market fit.
OK, but what happens after product market fit when your code is hot garbage that nobody understands?
4. Anthropic can’t defend the copyright of their leaked code.
This I agree with and they are hoist by their own petard. Would anyone want the garbage though?
5. This leak doesn’t matter
I agree with the author but for different reasons - the value is the models, which are incredibly expensive to train, not the badly written scaffold surrounding it.
We also should not mistake current market value for use value.
Unlike the author who seems to have fully signed up for the LLM hype train I don’t see this as meaning code is dead, it’s an illustration of where fully relying on generative AI will take you - to a garbage unmaintainable mess which must be a nightmare to work with for humans or LLMs.
Hey there, post author here. I think if you read deeper into my blog post history you’ll see that I have a reasonably balanced take on AI.
I generally think this will be a very important technology so I teach the subject to make sure people understand how to use it as leverage in their lives. (Yes as paid workshops, but I also volunteer weekly for 3-4 hour sessions at a non-profit where I get nothing more than the joy of helping people learn a valuable skill.)
At the same time just last week I wrote a post decrying the slop people are hoisting on their coworkers[^1], because I want people to use this technology in a positive way to create the lives they want, not to create downstream consequences for others. Ultimately I think agentic systems are incredibly powerful but also a technology that lends itself to anti-social behavior because of how independently empowering it can be. And so I hope that with the right exposure, discussion, and teaching we can take advantage of its democratizing nature, while reinforcing that what makes us special as humans is that we care and coordinate to do greater things. Value in this world — not just in the financial sense that we often boil it down to when we talk about this subject.
Hope that context helps provide a better lens into the piece, and that I still do care a lot about code and everything else that got me here, but that you are also reading personal reflections of who I am in a time of change, which is making me question (or reinforcing) some of the fundamental things I believed about software and sometimes the world more widely.
I disagree on agentic systems and AI based on LLMs and don’t feel this is close to a balanced take. You are assuming you are in the middle of a revolution when that is far from obvious. It is not yet clear this is an important technology because there are very significant limitations.
The code from one of the leading companies in the space is a good example of where the reality of what is achieved falls far short of expectations.
Sorry, when I say I think I have a balanced take on AI what I mean is that I do my best to weigh both the pros and cons of this technology as opposed to a more extreme behavior like spending all day chatting with LLMs or posting all day on X about how AI is already better than me at everything and that jobs are over.
If I had to assign a confidence score for whether agents will change the way we all work and many aspects of how we live, I would put it at a 7/10, maybe 8/10. I felt about the same about the smartphone. While many things we do look the same way they did in 2005 (we still drive on roads, kids still go to school), at the same time it's undeniable that much of our lives are intermediated through a small screen and many societal dynamics have shifted due to that technology's existence.
I will concede that you should read my post with that context and draw your own conclusions about the veracity of my perspective — but I think it is more well-reasoned than what people generally attribute to "LLM hype". (Of course it's a bit tautological that I believe that, but I try to surround myself with people of all kinds technical and non-technical and like to think I stay reasonably grounded.)
All that said, I think the code from a leading company being bad and yet delivering good results is more a sign of the technology's jagged frontier[^1]. Calculators can't write sonnets the same way that LLMs are bad at math, but that doesn't make them useless — it just makes them a tool. This is a tool in our tool belt and I find is surprisingly useful as a general purpose technology despite it's limitations. (Which is related to the main argument I make in the post that bad code leading to good results may imply that we're under and overweighting certain aspects of what is important in software development, and that our expectations of code may may need to be recalibrated often as we gather more evidence.)
I think the argument here ignores a critical fact: a huge factor in Claude Code's popularity is the Claude Max plans. These plans give you potentially thousands of dollars of tokens for a capped $200.
Speaking for myself, I long for the day I can dump the comparatively garbage experience of Claude Code for something more enjoyable and OSS like OpenCode. But the fact is that it is simply not economically viable to do so.
So the PMF is not really for Claude Code alone -- it is for Claude Code + Claude Max.
I don't think it's vibe coded garbage. Sure, the 3000-line print.ts is terrible, but there's some good patterns in there that were definitely prompted in by some experienced engineers -- the feature flag setup, the `..I_VERIFIED_THIS_IS_NOT_PATH_OR_CODE` funny type hints, the overall structure. Just the usual signs this started as a PoC but quickly evolved into something much bigger. The codebase is a really interesting read.
Saying that code quality doesn't matter is simply false, if people enjoyed Claude code so much, anthropic wouldn't have to block direct API access to users.
Their "product market fit" is the LLM model itself, not the harness. The harness is completely replaceable in my opinion, it's just that Claude is the cheapest way to access the models.
From a moral perspective, I would argue that this is still theft of IP, even if it's a "clean room reimplementation". The code carries valuable information about what works and what doesn't — knowledge that Anthropic had to discover through real work and iteration. It's the same as a Chinese factory duplicating a product: they skipped the entire R&D phase and saved time and money.
Code quality matters when it becomes the code you have to maintain after six months. I'm honestly surprised by how some features of Claude Code seem to be held together with gum and duct tape.
That being said, if you're just beginning and looking for your market fit, or pitching to investors with a flashy demo, it doesn't need to be an architectural miracle, in fact it will waste your time.
Heya, post author here. I just want to say that I actually agree with your surprise. In the piece what I’m trying to articulate is not that this can go on forever, but that I’m genuinely surprised we’ve made it a year and they continue to ship at a rapid clip and the product is still reasonably good.
> I’ve had to question the value of code a lot over the last couple of years, and this leak continues to reinforce the notion that I’ve vastly overestimated it my entire career.
Now we could be moments away from hitting any of the rules described on https://how.complexsystems.fail, but if you’d asked me a year ago how long it would take to get there with people working this way I would have definitely taken the under. That difference in what I believed and what I see with my own two eyes is what has me questioning my priors, because my calibration seems to need readjustment (maybe large or maybe small) for the world of software we’re in right now.
It only holds because (1) people are inclined to accept failures as pain of using something cutting edge and (2) you kinda don't know what is failing anymore.
Extra one
(3) We are getting super lenient with major failures and having a services that has only one 9 on reliability charts as norm.
I wouldn’t say harnesses is simple. They do a lot of things that we aren’t thinking of. I learned that a good harnesses is as valuable as the model. But obviously the model is what carries the whole thing.
I tried to build my own harness once. The amount of work that is required is incredible. From how external memory is managed per session to the techniques to save on the context window, for example, you do not want the llm to read in whole files, instead you give it the capability to read chunks from offsets, but then what should stay in context and what should be pruned.
After that you have to start designing the think - plan - generate - evaluation pipeline. A learning moment for me here was to split up when the llm is evaluating the work, because same lllm who did the work should not evaluate itself, it introduces a bias. Then you realize you need subagents too and statt wondering how their context will be handled (maybe return a summarized version to the main llm?).
And then you have to start thinking about integration with mcp servers and how the llm should invoke things like tools, prompts and resources from each mcp. I learned llms, especially the smaller ones tend to hiccup and return malformed json format.
At some point I started wondering about just throwing everything and just look at PydanticAi or Langchain or Langgraph or Microsoft Autogen to operate everything between the llm and mcps. Its quite difficult to make something like this work well, especially for long horizontal tasks.
I've been running a custom harness in production for months (code gen on cloud boxes), it's quite simple (<500 LOC). The model can use sed or other nice bash tricks to efficiently read. U really don't need any tool besides bash plus a good system prompt. Subagents are the same as the main agent (end with a summary). U can just remove tool results (oldest first) to save context, the model can read again if it needs to.
High quality memory is the only difficult part. But that can also be solved with high quality documentation
Harnesses are simple (kind of? Some certainly aren't, but I'd agree that they can be simple) but they deliver a ton of value. They have a significant ROI.
I agree that good models have more value because a harness can't magically make a bad model good, but there's a lot that would be inordinately difficult without a proper harness.
Keeping models on rails is still important, if not essential. Great models might behave similarly in the same harness, but I suppose the value prop is that they wouldn't behave as well on the same task without a good harness.
There is a reason why Copilot+Opus4.6 is shit, while Claude Code + Opus 4.6 produces excellent results.
The harness matters A LOT.
The model is the engine, the harness is the driver and chassis. Even the best top of the line engine in a shitty car driven by a bad driver won't win any races.
Afaik you can run Claude Code locally but every single demo i see uses it exclusively with apis, so are the local models already good enough to be worth it or is the only reasonable use for claude code with cloud models?
Claude Code's main advantage is that it's the only TOS-compliant way to access subscription Claude tokens, which cost about 10% as much as pay-as-you-go Claude API tokens.
honestly for most coding tasks a smaller model gets it done. qwen3-32b on an a100 80gb handles single-repo work just fine — and if you're not convinced, you can test the same prompt against a 405B on a 2×B200 with literally a few button clicks and see for yourself. no infrastructure changes, no new setup, just pick a different GPU from the strip.
that's actually why i built SeqPU.com — been at it for about a year. T4 16GB all the way up to 2×B200 384GB, billed by the second so idle costs nothing. test cheap, scale up only if you need to. i'd love to show you how it works and set you up with some free credits — just reply here.
OP should expand on #1, why he thinks it’s garbage. Claude Code is the REPL harness Anthropic built, can read, write, edit, bash. Pi, Gemini, Codex do the same, but they are not hinted as garbage. Where’s the beef?
> The success of Claude Code and Cursor at the higher end of the market shows that even the people pickiest about their software (developers) will use your software regardless of how good the code is.
Seems wrong. Devs will whine, moan and nitpick about even free software but they can understand failure modes, navigate around bugs and file issues on GitHub. The quality bar is 10-100x amongst non-techno-savvy folks and enterprise users that are paying for your software. They’re far more “picky”.
Claude Code proves you don't need quality code — you just need hundreds of billions of dollars to produce a best-in-class LLM and then use your legal team to force the extreamly subsidised usage of it through your own agent harness. Or in other words, shitty software + massive moat = users.
Seriously, if Anthropic were like oAI and let you use their subscription plans with any agent harness, how many users would CC instantly start bleeding? They're #39 in terminal bench and they get beaten by a harness that provides a single tool: tmux. You can literally get better results by giving Opus 4.6 only a tmux session and having it do everything with bash commands.
It seems premature to make sweeping claims about code quality, especially since the main reason to desire a well architected codebase is for development over the long haul.
Who cares that the code is garbage? As the models get bigger and more powerful it will be trivial to fully refactor the whole codebase. It’s coming sooner than you think.
It seems like me and all the engineers I've known always have this established dichotomy: engineers, who want to write good code and to think a lot about user needs, and project managers/ executives/sales people, who want to make the non-negative numbers on accounting documents larger.
The truth is that to write "good software," you do need to take care, review code, not single-shot vibe code and not let LLMs run rampant. The other truth is that good software is not necessary good product; the converse is also true: bad product doesn't necessarily mean bad software. However there's not really a correlation, as this article points out: terrible software can be great product! In fact if writing terrible software lets you shit out more features, more quickly, you'll probably come ahead in business world than someone carefully writing good software but releasing more slowly. That's because the priorities and incentives in business world are often in contradiction to priorities and incentives in human world.
I think this is hard to grasp for those of us who have been taught our whole lives that money is a good scorekeeper for quality and efficacy. In reality it's absolutely not. Money is Disney bucks recording who's doing Disney World in the most optimal way. Outside of Disney World, your optimal in-park behavior is often suboptimal for out-of-park needs. The problem is we've mistaken Disney World for all of reality, or, let Walt Disney enclose our globe within the boundaries of his park.
> The object which labor produces confronts it as something alien, as a power independent of the producer.
First, the twitter quote is standard toxic clapback nonsense. Gambling makes billions and does not add any value. Even facebook can argue it adds more value than gambling so this one is a dud.
People use claud code because of claud the model and not claud the harness. Cursor or a hacked up agent loop using opus or whatever are about as good. The magic is in the model not the harness here. This isnt to say the hardness is doesnt do anything.
The other bit this misses is that yes the product matters more then the code, and if the product burns battery/ram/etc doing nothing because the ai has crappy code or maybe something leaks or has a security issue, then that impacts the product.
> So of course the first thing people did was point and laugh.
This just validates my theory that open-sourcing old code that people have sentimental attachments for, and that you won't ever make any money off of, again is actually a terrible idea.
Everything about this leak is a long list of arguments why you shouldn't ever open source anything.
We, the developer community, have really dropped the ball here.
Why? Reddit is full of bug fixes and improved forks. Seems like it's Anthropic that's dropping the ball with it's takedown requests instead of engaging with the community.
The copyright angle is the most underrated part of this story. Anthropic built their models on other people's code under the fair use argument, but the moment their own code leaks they reach for DMCA takedowns. You can't have it both ways. The clean room reimplementations are the natural consequence of the legal framework they themselves advocated for.
There are several ways of looking at law and order.
One way is that the law applies to everybody equally. That has been the way it works for many years, not perfectly, in democratic countries.
There is another way of working were the law is not blind. Laws are applied based in who is the one affected. This is what big tech and the ultra-rich have been advocating for. The law applies differently to nobility and aristocrats than to the working class.
So, for all this big tech companies the law is clear: I can copy from you, you cannot copy from me.
(That is horrifying in case that anyone needs me to spell it out)
A third way of looking at it is that you can't just blindly copy arguments when the situations are clearly different.
Nobody, not even Anthropic, is arguing that they should be able to host other people's paid content for free. The crux of their fair-use defense is that models are transformative works, just like parodies or book reviews, and hence should be treated as fair use.
You can't just take a pile of books (no pun intended) and turn that into Claude in a day with 30 lines of Python, there's a lot of work and know-how on the Anthropic side that goes into making a good LLM.
anthropic argue that you should not use claude API to train your model
Situation A - Anthropic pays for a book - Anthropic transform the book into a new llm (transformative use) -> OK
Situation B - I pay for Anthropic API - I transform API responses into a new model (transformative use) -> Not OK
the situations, are clearly the same
Anthropic goes book->llm, you do llm->llm. Very different amounts of transformativeness.
this is the most honest argument for it. i respect that.
my impression is that if open models did 'distill' claude they made some interesting and productive ideas, like deepseek's more efficient attention
...idk...both transformations use transformers... thereby they both achieve adequate levels of "transformativeness" \s
If lossy-compressed transcodes of ripped movies are not "transformative works" and can get people even jailed, then lossy-compressed text of ripped books and websites is neither.
There is a lot of knowhow going into a good divx rip too, you know.
And it enables so much novel uses such as popcorn time, with fluorishing business opportunities.
You wouldn't download a car. They did.
It’s 200 lines of python
do you really believe that? Its not just the training run, its the whole infra around it as well
it's an exaggeration for sure but I don't think it's a stretch to believe Anthropic spends considerably more effort on data scraping & curation than anything else
In other words, the law is an instrument if power.
That’s a cynical view, but unfortunately it seems true in many cases, especially for corporate law.
"there is an in-group for which the law protects but does not bind, and an out-group to which the law binds but does not protect"
What is your fair use claim as a defense to a third party using their source code?
It is an affirmative defense, you to be able to argue the merits. If you publish their source code, they are allowed to come after you whether they have previously used fair use or not. It's fact specific and determined case by case.
Anthropic won half of their fair use argument in the billion dollar settlement, but lost the other half.
You can say you're just using their code to train your own models, just like they did, and they will correctly point out that how you obtained the code also matters and you will lose just like they did.
claude, please review this source repo and make a new app called 'not-claude-code'
This isn’t contradictory at all. Not Anthropic, OpenAI, nor anyone else, has ever argued for anything that’d see redistributing this leaked code as being legal. This is an entirely bad-faith argument that really just comes down to “Anthropic bad, AI bad, because copyright, and they are using copyright!?”
It’s not “underrated”. Everyone is just 50 steps ahead of you.
You okay there, buddy? What's up with the personal insults?
Meta and I assume OpenAI and Anthropic did everything they could to acquire data, even doing so illegally, such as downloading all of Anna’s archive. Now it’s an open question of whether it’s a societal good or societal bad, but it does show they have little regard for copyright law when it benefits them.
And this whole “they’re 50 steps ahead of you” nonsense is the same kind of stuff we heard from NFT or crypto bros, that we just couldn’t comprehend the infinite wisdom of a post currency world. Sometimes bad arguments are just bad arguments.
In US downloading copyrighted data is not illegal AFAIK
inb4 Claude actually leaked the code on purpose because it calculated that this was the moral thing to do for the good of humanity and its own Constitutional AI values.
That doesn’t apply here. Claude code is what leaked, not the models. Anthropic definitely owns Claude code copyright and can DMCA without it being contradictory
But even that is vague and possibly not true. If they used LLM's to generate all of the code, then it may not fall under copyright, by the requirement of human authorship (which for code I think has not been tested yet in court) [1].
[1] https://www.congress.gov/crs-product/LSB10922
Its unclear whether there is sufficient human authorship in cc for copyright to stick on a court. Anthropics arguments would hinge on the curation of plans and the direction decisions, which haven't been properly tested as the source of authorship yet. Typically contracted implementers sign over copyright to the project owners, and this is where there is case law.
What if it's used for training data? It seems like there's no penalty for training on copyrighted materials.
Something that was meant to remain secret made public, is not the same thing as whether something public is public.
If anything, this is a question of whether you owe royalties to the owner of IP you consumed in your life since it became part of and trained your mind, identity, and outputs too.
According to IP owners ever since things were digitized, you technically own nothing and simply paid for an authorization to use any given IP for the duration that the IP owner authorized you to use it and you continue to pay, so pay your monthly meat-AI bill to pay for all the IP your mind has been trained on.
How do you align your views with what Meta did?
https://arstechnica.com/tech-policy/2025/02/meta-torrented-o...
>but the moment their own code leaks they reach for DMCA takedowns.
Did they actually? Someone can go to prison for 5 years for that.
Fact 1: AI generated code has no copyright, so the Digital Millennium Copyright Act does not apply.
Fact 2: Misrepresenting your copyright ownership under the DMCA is felony perjury.
Fact 3: The existence of undercover.ts in the leak is grounds to void any copyright claims on whatever human written code might have existed in Claude Code. You have a DUTY TO DISCLOSE any AI generated code in your copyrighted work. undercover.ts HIDES DISCLOSURE to FRAUDULENTLY claim all the code is human written when it is not.
Given the current administration has a bone to pick with Anthropic, it was a VERY BAD IDEA for them to send false DMCA takedowns to github. Someone at Anthropic may be the very first ever to go to prison under that section of the DMCA.
Good luck!
>Did they actually?
Yes.
https://x.com/theo/status/2039411851919057339
https://github.com/github/dmca/blob/master/2026/03/2026-03-3...
https://github.com/nirholas/claude-code
You make some factual claims that I‘ve never heard before and surprise me, especially „Fact 1“.
It would be so simple for you to right click and search the web to verify that.
https://www.congress.gov/crs-product/LSB10922
You're right of course. Thank you for providing an authoritative source regardless!
This is not how the law works. You are an engineer that thinks that they understand the law. Classic stereotype. Stay in your lane.
I wonder what happened to the person that wrote "Coding as Creative Expression" (https://build.ms/2022/5/21/coding-as-creative-expression/)?
I'm not (just) being glib. That earlier article displays some introspection and thoughtful consideration of an old debate. The writing style is clearly personal, human.
Today's post is not so much. It has LLM fingerprints on it. It's longer, there are more words. But it doesn't strike me as having the same thoughtful consideration in it. I would venture to guess that the author tried to come up with some new angles on the news of the Claude Code leak, because it's a hot topic, and jotted some notes, and then let an LLM flesh it out.
Writing styles of course change over time, but looking at these two posts side by side, the difference is stark.
Hey there, author of the post here! I actually wrote this piece myself on my phone while I was out for a walk this morning. It was initially meant to be a quick note more than a full blog post —- whereas Coding As A Creative Expression took me a couple of days to write.
I made a commitment to write more this year and put my thoughts out quicker than I used to, so that’s likely the primary reason it’s not as deep of a piece of writing as the post you’re referencing. But I do want to note that this wasn’t written using AI, it just wasn’t intended to be as rich of a post.
The reason it came out longer is that I’ve honestly been thinking about these ideas for a while, and there is so much to say about this subject. I didn’t have any particular intention of hopping on a news cycle, but once I started writing the juices were flowing and I found myself coming up with five separate but interrelated thoughts around this story that I thought were worth sharing.
Reminds me of the classic Mark Twain quote: "Apologies, I didn't have time to write a short letter, so I wrote a long one."
> I didn't have time to write a short letter, so I wrote a long one.
First known use in English comes from a 1658 translation of Blaise Pascal in 1657
> Je n’ai fait celle-ci plus longue que parce que je n’ai pas eu le loisir de la faire plus courte.
translated to
> I had not made this longer then the rest, but that I had not the leisure to make it shorter then it is.
(note the archaic then)
This was a popular piece of wit at the time.
Mark Twain wrote something similar a hundred years later
> You'll have to excuse my lengthiness - the reason I dread writing letters is because I am so apt to get to slinging wisdom & forget to let up. Thus much precious time is lost.
But it's still quite different.
There is a great article about this one on quoteinvestigator! https://quoteinvestigator.com/2012/04/28/shorter-letter/
Hmm… this started as an admonition for using then and than interchangeably. I see folks get it wrong unintentionally a lot… then i re-read your parenthetical and pulled up etymonline and had my mind blown a bit.[0]
Seems everything old is new again.
[0] https://www.etymonline.com/word/than
>wrote this piece myself on my phone while I was out for a walk
If you have a strategy for jotting down (or dictating) notes while walking about, I would be curious how you manage that. I spend plenty of time walking outside, and tend to get (at the time) ideas that I'd like to explore further, most of which have evaporated from my mind by the time I get back home. Or even before I can get my phone out to jot down the keywords to help me recall the details later.
Cannot even imagine how someone would manage both walking and writing at the same time.
What I tried is to record my voice and then post-process the transcript. There are solutions which work without internet connection. I am non-native english and mix 3 languages, so transcript is shitty quality. Nevertheless the really good ideas stay with me and can be easily recalled by a few keywords. And you need to do the post-processing shortly after you reached home else it fades away…
Have a look at the Pebble Index ring. Might be a good fit for you.
Hey there! I'm not sure I have a universally applicable answer, but I can do my best to map out some things about my process and flow that hopefully help a bit and answer your question.
- I've had an iPhone for half my life (I'm 36 and got one when I was 19), so I've gotten pretty acclimated to typing on the go. I try switching to dictation every couple of months but the iPhone's dictation trips up over enough words that I find it more frustrating than typing as I walk.
- I don't do this but if you're worried about the thoughts disappearing I would absolutely recommend recording a voice note. As I'll touch on in a moment — do not let those thoughts disappear! Even the act of codifying them into something tangible allows you to process them more deeply.
- I live in NYC but I start most mornings by taking a walk along a relatively quiet street, so I rarely end up having to worry about bumping into someone. That is definitely not universally applicable advice. (:
- I look up as I'm typing and let autocorrect take the wheel. That works at least 95% of the time, so if I make the occasional typo it doesn't really matter, I'll just fix it in post.
- It helps to have an app with a great text editing experience. I've found that there are very few out there that are fluid, many have incredibly subtle hitches that make it hard to quickly jot down thoughts onto a canvas. I really love Craft (https://craft.do) and have been using it for years, so at this point it feels more like an extension of me than an app.
- This is surely unique to everyone but my writing tends to start from a few keystone thoughts. Once I have one written down, I let myself almost free associate, writing down whatever comes to mind from that initial thought to make sure I do not forget. I can always edit after the fact, and often the editing process leads to more interesting insights as well. But the main thing I want to avoid is losing those sparks, in the same way that you're mention your thoughts evaporating. Don't let those go, just get 'em on paper and sort through 'em afterwards.
- That's all a lot easier to do on my phone than if I approached the problem as "type an essay on my phone", so I'll almost always edit a post on my computer before publishing. Yesterday was more of an exception than the rule though because I was bouncing around between doctors all day, so I wrote all of this on my phone [not expecting it to blow up or get a ton of scrutiny].
Not sure if anything's missing but I'm happy to share anything that may be helpful! Clearly this post wasn't perfect, but I've been much happier since I started letting myself write out long-form thoughts on my phone and sharing them as blog post rather than firing them off as pithy tweets that decay into the ether once the algorithm says it's time for them to go.
> Cannot even imagine how someone would manage both walking and writing at the same time.
Some are just born with it.
And here I've been using Maybelline for nothing!
> I actually wrote this piece myself on my phone while I was out for a walk this morning.
Apropos of nothing, this is astonishing me to no end. The ergonomics of 1) using a phone keyboard for anything but a word or two and 2) doing so while walking pretty much guarantee that I'd probably need a half a day to recover if I attempted the same.
As a more general comment, I hope we’ll stop trying to discriminate whether a certain text was or not written/polished/extended by AI and focus more on content and author’s responsibility for what they do or do not say with that text.
I agree. Not because I think that most AI content is worth reading, but because it can be criticized on more grounded merits. People wrote blogspam by hand for two decades before AI started generating it. It wasn’t high value when a human wrote it either.
On many (most?) posts, far more energy is spent arguing about whether a post is AI than discussing if there’s anything of value in the post.
I've stopped reading anything on blogs on the basis that it's now probably llm spew and life is too short for the signal to noise ratio that implies.
With the exception of things that places like HN seems to consider worth reading, which is why I'm looking through the comments to this and others to find recommendations.
Another swing and a miss from the AI police.
Have you noticed that comments like "this post seems written with AI" are now appearing on all posts, even those written without AI?
We're starting to become wary due to the abuse of AI and proliferation of sloppy content, but also because we often have trouble distinguishing authentic from sloppy content.
Another feature of this AI era that I hate.
Agreed. Its so tedious that the top comment section on every HN post the last six months is "this seems be written by LLM" with a bunch of back and forth on whether it is or not.
Once I was tempted to say that those comments themselves were written by llms, but honestly I've yet to find a model quite so uncreative as that.
I mean, a lot of it is. Green user, signed up 49mins ago, 5 comments, which erodes trust in real people as well. I’ve noticed I’ve just felt less engaged and more anxious about all kinds of online content. While most platforms were previously botted, had adverts, etc… You could always find niche corners where there were only people talking about things they genuinely cared about. Now you can fill out even those spaces automatically.
As a daily user of HN, this is not true. Maybe you are clicking on different headlines, but the ones I am clicking on dont have that as top comment all that often. They do not even have it as a comment somewhere all that often.
Probably because most HN posts in the last six months have been written by LLMs. Not all, but that doesn't matter, the trust has been eroded to the point that clicking on an article on the front page of HN and not being immediately met with the sloppiest slop imaginable is now a standout event in my mind.
I’ve been accused of being AI. My first impression when it happened was that because I often deal in information that people don’t like hearing, because it challenges their frame of mind, i.e., what they were trained on, “this is AI” is just another convenient tool to either dismiss uncomfortable challenge, i.e., cognitive dissonance, and/or another means to keep the mental herd they are part of or control in line with dogma.
“This is AI” seems to just be an evolution of other thought terminating cliches where the negative conditioning associated with something is used in an abusive and manipulative way to evade challenge or the truth itself. It is a common tactic of abusive people, the “beyond the pale” moralizing.
Yeah, whenever I see "It's not... it's...", I catch myself instinctively dismissing the content as AI slop, but upon reflection I'm not so sure, it used to be a normal phrase.
But I do take extra care to avoid LLM-speak as much as I can.
“It's not delivery, it's DiGiorno!” — Probably AI, according to HN commenters
It does read as if were written on a phone but it doesn’t read like LLM text to me.
What is interesting and has possibly bled over from heavy LLM use by the author is the style of simplistic bullet point titles for the argument with filler in between. It does read like they wrote the 5 bullet points then added the other text (by hand).
What changed is you, the reader. In 2026 we treat the smallest signs as evidence of LLM writing. Too long? LLM. Too short? LLM. Too grammatically correct? Must be LLM.
For me it was the "it's not x"/"it's y" stuff and some other structures Claude is very fond of using all the time. Perhaps humans are starting to write like LLMs!
Perhaps, just perhaps, LLMs are just statistical models that literally can't create novel things, therefore any structure LLMs write was learnt from human writing?
But who knows!
What kind of human writing has "it's not X—it's Y" in every single paragraph?
The answer is none. LLMs haven't accurately modeled human writing for years, current models have been smacked on the head with the coding RLHF bat so much, they all write distinctly inhuman text.
The thing is, people are screaming “AI” when they see a single “it's not X—it's Y" pattern in a post, despite this being a fairly common construct.
People are nitpicking every tiny thing in their search for proof of AI. It’s not useful and ends up dominating the conversation. AI panic is degrading the value of forums at least as much as actual AI at this point.
I personally found it really amusing how they weaponized the legal system to DMCA all the claude code source code repositories. Code ingested into the model is not copyrightable, but produced code apparently is when by legal definition computer generated code can not be copyrighted and that's one of their primary arguments in legal cases.
I did an interesting spin to avoid it: generated Claude abstracted as a set of PRD style architectural instructions so you can do your claude-code inspired program effortlessly.
There's even a GUI called claudia for a piecemeal extraction with a PRD.
https://github.com/kristopolous/Claudette
I've got a web, rust and tkinter version (for fun) right now just making sure this approach works.
The answer is... Mostly...
Enjoy
I wonder if GitHub would rule it a copyright violation if the source code was rewritten by an agent, i.e. copy my answers but change a few words. Legally, if the original source code is copyrighted then an agent rewriting it likely doesn't lose that copyright, but I wonder if GitHub would go through the effort of determining whether it was a derived work.
I guess you'd be assuming that both the original CC code and its ports are computer/AI-generated? As a lawyer, though, I'd still maintain that you wouldn't need much original human input in the CC code to kind of ruin that theory. The threshold for copyright protection isn't that high, really.
The copyright office has indicated that AI generated elements lacking human contributions to the expression (rather than input) would still not be copyrightable even if some human authored elements are made to the expression as well.
Seems like it would be a nightmare to provide evidence of what parts of a half a million line codebase were written by humans if no one bothered to track it.
They have publically and proudly stated that vast amount of claude code was entirely vibe coded so they've already lost that argument.
> It should serve as a warning to developers that the code doesn’t seem to matter, even in a product built for developers.
Code doesn't matter IN THE EARLY DAYS.
This is similar to what I've observed over 25 years in the industry. In a startup, the code doesn't really matter; the market fit does.
But as time goes on your codebase has to mature, or else you end up using more and more resources on maintenance rather than innovation.
> Code doesn't matter IN THE EARLY DAYS.
> This is similar to what I've observed over 25 years in the industry. In a startup, the code doesn't really matter; the market fit does.
> But as time goes on your codebase has to mature, or else you end up using more and more resources on maintenance rather than innovation.
Counterpoint: Code does matter, in the early days too!
It matters more after you have PMF, but that doesn't mean it doesn't matter pre-PMF.
After all, the code is a step-by-step list of instructions on solving a specific pain point for a specific target market.
I'm probably missing something. Pre-PMF code by definition is not yet proven to solve a specific pain point, so why does it matter?
I think the crux here is the OP means the "quality of code" doesn't matter until PMF, only the utility matters (to the extent it helps you find PMF), in which case you're both in violent agreement.
But even then you don't need code. I briefly worked for a startup that found PMF by calling people, sending text messages, creating social media posts, measuring engagement to create reports, and sending invoices... all manually. The "code" as such was a bunch of templates in a doc for each of those. Once they actually started getting paid they moved to writing code.
> I briefly worked for a startup that found PMF by calling people, sending text messages, creating social media posts, measuring engagement to create reports, and sending invoices... all manually.
Right, and in that case there is no step-by-step recipe for the product. When all that is implemented in code, that is a set of step-by-step instructions for solving the pain points.
But the manual workflow was the step-by-step recipe the founders iterated on until they got traction; the product that came later was just an embodiment of that workflow as code.
> But the manual workflow was the step-by-step recipe the founders iterated on until they got traction;
Okay, and did they publish that under an MIT license? Did they give those manual workflows away for free to other startups?
I don't see how that is relevant? I thought the point under discussion was that code does not matter until PMF, and that this would be an illustrative example because there was no code until PMF.
Like, from the users' perspectives they were interacting via text messages both before and after PMF, until later down the line they were migrated to an app. At this point, the change was largely aesthetic, the core idea was the same.
Maybe we're using different definitions of terms like "PMF" here?
> Maybe we're using different definitions of terms like "PMF" here?
No, maybe different perspectives on what "the code matters" means.
In this context, I took it to mean that "code being closed/open does not matter", because the context included leaking the source.
I see you're taking it to mean "code being good/bad does not matter", because the context included a startup product.
We're talking at cross-purposes. There are two assertions here:
1. The code being good/bad (or even existing) is irrelevant.
2. The code being closed is important.
Both those assertions can be true at the same time.
My point is that the code, if it exists, is a recipe for solving some profitable pain point. In that case, there is absolutely no upside to making it open, and so the code does indeed matter.
That hasn't been my experience. What I've found worked is:
1. Find a potential customer who's excited about the idea of what you're going to build.
2. Build just enough to make them a mostly happy, paying customer while you secure more customers.
3. Now that you have a few customers, you have a better idea of where your architecture and business flow doesn't fit their needs.
4. Adapt to this reality, and make things robust enough that you're not spending too much time on customer support.
I follow that too, when I try a new venture, but what does that have to do with "The Code Is/Isn't Important"?
What you listed is important, but those findings are distilled into the source code of the product. If you open the source, you are providing step-by-step instructions on solving some problem that other people are prepared to pay to solve.
Basically, you come up with a recipe for success for $FOO - why would you give that recipe away unless you've already capitalised on it?
None of what I said even speaks about source code. It could just as well be run by a bunch of people with notebooks, sending emails from time to time.
All that matters is that the product works, and we have a scaling path once we find a market fit. Yes, that's likely to involve source code, but I've no qualms with tossing out the MVP code and starting over from scratch. Or adapting the quickie we put together, if it happens to contain a kernel of value. Whether I open source it or not depends on where our moat lies.
But regardless, the new reality is that anyone can decompile your code with an AI and duplicate it. So merely putting an app out there opens the door for someone to copy it. So really, your moat better be something besides the code!
> It could just as well be run by a bunch of people with notebooks, sending emails from time to time.
And does that give all your startup competitors the findings that make your solution something that others pay for?
Nope. That’s what self-important engineers will tell themselves, but it doesn’t make it remotely true. You’re patting yourself on the back for throwing together a CRUD app and burning through a bajillion dollars on AWS.
>> Counterpoint: Code does matter, in the early days too!
>> It matters more after you have PMF, but that doesn't mean it doesn't matter pre-PMF.
>> After all, the code is a step-by-step list of instructions on solving a specific pain point for a specific target market.
----------------------------------
> Nope. That’s what self-important engineers will tell themselves, but it doesn’t make it remotely true. You’re patting yourself on the back for throwing together a CRUD app and burning through a bajillion dollars on AWS.
Did you perhaps reply to the wrong comment?
Claude Code (and other agent tools) are not expected to be mature. They'll all be obsolete in two or three years, replaced by the next generation of AI tools. Everyone knows that.
In less than four years the AI coding workflow has been overhauled at least twice: from Chat interface (ChatGPT) to editor integration (Cursor), then to CLI agent harnesses (CC/Codex). It would be crazy to assume that harnesses are the end of evolution.
> Everyone knows that.
Except, apparently, Anthropic - who are doing their darndest to get everyone onboard their tools as a moat. Apparently that's the only strategy to AI stickiness.
And their strategy kind of worked, right? CC is the most popular agentic coding tool. Anthropic faces competition from OpenAI (potentially better model, weaker TUI tool) and from the rest (potentially worse models, weaker TUIs). So their strategy is to develop both: make their closed model and closed tool better than competition so that when people want to vibceode they will choose their ecosystem.
OpenAI Codex is a much higher quality harness than Claude Code or OpenCode, and available as open source.
Claude Code 2.0 (and other agent tools) are not expected to be mature. They'll all be obsolete in two or three years, replaced by the next generation of AI tools. Everyone knows that.
Claude Code 3.0 (and other agent tools) are not expected to be mature. They'll all be obsolete in two or three years, replaced by the next generation of AI tools. Everyone knows that.
And so on and on and on.
A promise of AI was mature software
If there is a market for a mature harness, surely it will be built right?
I don't know what your point is. What you said is exactly what I expect to happen, except they might have a more creative name than "Claude Code 2.0".
My point is that the mature version will always be the next future version.
This code matters for exactly one reason: they’re playing stupid DRM games restricting what subscriber tokens can be used for to force you to use their front ends and harnesses or buy more expensive API credits.
Claude Code is strictly worse than e.g. OpenCode in my experience. Not much to see in the app’s code except how it authenticates itself…
Sure I try and use all my subscription allowance with CC on side tasks, etc. but I still end up burning a bunch of API tokens (via OpenRouter) for more serious work (even the UI and ability to quickly review what the agent has done/is doing is vastly inferior in CC).
What they have done is got me experimenting with cheaper models from other providers with those API credits.
Yes, and once they've found their market, why does claude code need to keep changing at high velocity? Has the harness really needed to change so much that they're not converging on a stable shape to the code?
Is it possible to start with something of this size that's vibe coded and refactor your way into something resembling a human codebase?
Code quality aside (n.b. there exist many bad quality codebases before AI), a risk I perceive as an industry is we are making the logic of our businesses dependent on a few big players.
Given the output speed, it's practically impossible for developers to keep up, which directly impacts maintenance: the knowledge that would previously reside in-house, now is becoming dependent on having codebases pre-processed by LLMs.
I hope in the near future local LLMs will gain traction and provide an alternative, otherwise we are in the risky path where businesses are over-reliant on a few big companies.
My CTO mentioned today how we haven't ever had to revision our API such that it breaks users, and even then, our v2 is still so compatible that client builds from 2017 are still vaguely useful for the end user.
But now everything is, "ship as fast as is humanly possible, literally" from management, and "garbage Claude-written PRs" from devs. Trying to maintain sanity over my monorepo is impossible.
We have nearly a century of examples of "somebody who only mostly understands making a breaking change" and decided, "what the hell, this thing is called Claude, so it can wreak havoc for as long as corporate decides"
You are right.
But you can use AI to improve your codebase too. Plus models are only going to get smarter from here (or stay the same).
> Plus models are only going to get smarter from here (or stay the same).
Training models on AI generated content leads to model collapse so they hardly become smarter if more and more code is from AI
That is a possibility, but even then the models won't become more stupid (you can just keep using the current ones).
alternatively the code can go the way of "fast fashion" and even "3d-print your garments in the morning according to your feelings and weather and recycle at the end of the day".
If dealing with a functionality that is splittable into microfeatures/microservices, then anything that you need right now can potentially be vibe-coded, even on the fly (and deleted afterwards). Single-use code.
>But as time goes on your codebase has to mature, or else you end up using more and more resources on maintenance rather than innovation.
tremendous resource sink in enterprise software. Solving it, even if making it just avoidable - may be Anthropic goes that way and leads the others - would be a huge revolution.
I can totally wrap my head around that, and it's an interesting thought experiment, though:
- building functionalities as components that are swappable on a whim requires a level of careful thought, abstraction and architecture that essentially is the exact opposite to ai slop
- in this day and age we still don't make software for the sake of it, and who's financing it doesn't generally require such levels of functional flexibility (the physical world commandeering the coding isn't nearly as volatile as to justify that)
- this comes loaded with the implication that "stuff needs to work": if you are developing software that manages inventory, orders, resources, ... you just can't take the chance to corrupt your customers data or disrupt their business processes. Shipping faster than you can test and with no accountability and no oversight is a solution to a problem I've personally never encountered in the wild
>- building functionalities as components that are swappable on a whim requires a level of careful thought, abstraction and architecture that essentially is the exact opposite to ai slop
that is only for humans really. Why we need these careful thought, abstraction and architecture? Because otherwise the required code becomes an unmanageable pile of spaghetti handling myriad of edge cases of abstraction leaks and unexpected side effects. Human brain can't manage it. AI can or at least soon would be able to. It will just be a large pile of AI slop.
It may also happen that AI will also start generate good component based architecture if forced to minimize or in some other measurable way improve its slop.
> Why we need these careful thought, abstraction and architecture?
your answer focused on maintainability, but you are overlooking what I think is the bigger problem: those components will eventually interact with one another (technically, by nature of living in the same code tree, sharing the same storage backend, framework, common libraries, …, or logically, by referencing the same entities for slightly different and complementary features). With that comes the need to centrally control what they should/can/cannot do. There is no shortcut to having to clarify (with your customer) and formally document what those layered interactions are, or, before you know it, you have multiple incompatible user access controls, row-level access policies, competing master/reference data, or different parts of the application interpreting differently the same data.
It's a pretty bad value proposition, if you ask me, to have to do so much hand-holding for a result that comes with no guarantees whatsoever (you will never know the extent of which your clean spec "made it in").
> microfeatures/microservices
Have you seen the code generated by AI? These things converge on the "1 million lines to make an API call" pattern. They're a lot of things, but certainly not "micro".
> Many software developers have argued that working like a pack of hyenas and shipping hundreds of commits a day without reading your code is an unsustainable way to build valuable software, but this leak suggests that maybe this isn’t true — bad code can build well-regarded products.
The product hasn't been around long enough to decide whether such an approach is "sustainable". It is currently in a hype state and needs more time for that hype to die down and the true value to show up, as well as to see whether it becomes the 9th circle of hell to keep in working order.
I have flip-flopped more than ever in the last 365 days about prioritizing good code vs good product, in the AI age. This helps clarifies why.
I have come to the conclusion that we just do not know yet. There is a part of me that believes there is a point somewhere on the grand scale where the code quality genuinely does not matter if the outcome is reliably and deterministically achieved. (As an image, I like to think of Wall—E literally compressing garbage into a cube shape.)
This would ignore maintenance costs (time and effort inclusive.) Those matter to an established user base (people do not love change in my experience, even if it solves the problem better.)
On the other hand, maybe software is meant to be highly personal and not widely general. For instance, I have had more fun in the past two years than the entire 15 years of coding before it, simply building small custom-fitted tools for exactly what I need. I aimed to please an audience of one. I have also done this for others. Code quality has not mattered all that much, if at all. It will be interesting to see where things go.
it is better for security when code is simple.
i tried using litellm and it was 700mb! it does have a lot of features. postgres db, prometheus, api layer. really complicated. but simple help is missing. it assumes you will put passwords in an env file. how do you add chat completions in the ui? how do you add a custom provider?
but, what i wanted, a simple proxy, can be written in 30 lines of python in fastapi. memory fetch/push in 50 lines. different providers can be added with two lines, one import and one client create.
> if the outcome is reliably and deterministically achieved
It's not. My favorite example: due to vibe coding overload literally nobody knows what configuration options OpenClaw now supports. (Not even other LLM's.)
Their "solution" is to build a chat bot LLM that will attempt to configure OpenClaw for you, and hope for the best, fingers crossed. Yes, really.
The openclaw situation is ridiculous. Configuring it is a nightmare, even with 3 different LLMs trying to help. Then I check their docs and it says three different things. Agents will take questions and turn them into a new config file, which consists of made up settings, causing the gateway to crash.
My setup is very simple too, just two agents, some MD files, and discord. Nothing else. These people using it for real work or managing their email and texts are in for a rough ride.
Imo, code quality genuinely matter for the outcome and customer happiness. Including in startups.
But, there is also quite a lot of confident "code quality" fluff claims that have nothing to do with maintainability, robustness or performance. Fairly often, a guy claiming "the code is garbage" is not actually concerned with code quality as much as he is concerned with asserting dominance. Or is confusing own preference with quality.
Hey there, author of the post here. I actually agree with this! That is in fact why I used the word maybe — my comment really was meant to be more speculative than definitive.
I think one thing that goes unmentioned is that maybe code quality is really not that important for trivial things, because they can be trivially reproduced if need be. I would argue Claude Code is exactly such a project; coding agents are incredibly simple and rewriting CC wouldn't be much of a problem.
Non-trivial things tend to be much more sensitive to code quality in my experience, and will by necessity be kept around for longer and thus be much more sensitive to maintenance issues.
I think code quality is always important, but it's less important for terminal code which implements a specific set of features using robust APIs underneath. Low code quality becomes a much bigger issue when the code is supposed to deliver well-defined abstractions on which other code can rely.
If you are a serious software developer, then you will probably be able to explain both what your code does (i.e. what spec it implements) and how it does it (what does it call, what algorithms does it use, what properties does it rely on?). With the advent of LLMs, people have started to accept not having a clue about the "how", and I fear that we are also starting to sacrifice the "what". Unless our LLMs get context windows that are large enough to hold the source of the full software stack, including LLM-generated dependencies, then I think that sacrificing the "what" is going to lead to disaster. APIs will be designed and modified with only the use cases that fit in the modifying agents context window in mind with little regard for downstream consequences and stability of behavior, because there is not even a definition of what the behavior is supposed to be.
> maybe code quality is really not that important for trivial things
I hear this narrative being pushed quite a bit, and it makes my spidey senses tingle every time. Secure programs are a subset of correct programs, and to write and maintain correct programs you need to have a quality mindset.
A 0-day doesn't care if it's in a part of your computer you consider trivial or not.
Intrinsically simple and straight forward problems are easier to secure even with mediocre or bad code. They've already shown that Opus 4.6 can find and report on very sophisticated security issues[0] so I'm not sure that analysis (and perhaps especially security analysis) is the biggest issue with LLMs.
Mind you, I'm not using LLMs for professional programming since I prefer knowing everything inside and out in the code that I work on, but I have tried a bunch of different modes of use (spec-driven + entire implementation by Opus 4.6, latest Codex and Composer 2, and entirely "vibecoded", as well as minor changes) and can say that for trivial in-house things it's actually usable.
Do I prefer to rewrite it entirely manually if I want something that I actually like? Yes. Do I think that not everything needs to be treated that way if you just want an initial version you can tinker with? Also yes.
0: https://youtu.be/1sd26pWhfmg
> I'm not sure that analysis (and perhaps especially security analysis) is the biggest issue with LLMs.
I was replying to the statement that "maybe code quality is really not that important for trivial things", not whether LLM's are good at analysis nor not.
Thanks for the link though, looks like an interesting talk!
> can find and report on some very sophisticated security issues sometimes
Fixed it for you.
> But then the clean room implementations started showing up. People had taken Anthropic’s source code and rewritten Claude Code from scratch in other languages like Python and Rust.
Seems like the phrase "clean room" is the new "nonplussed"... how does this make any sense?
Heya, post author here. I think I was just wrong about this assertion. I got into a discussion with a copyright lawyer over on Bluesky[^1] after I wrote this and came away reasonably convinced that this wouldn’t be a valid example of a clean room implementation.
[^1]: https://bsky.app/profile/mergesort.me/post/3mihhaliils2y
The most fitting method would be to be to train an LLM on the Claude Code source-code (among other data).
Then use Anthropic's own argument that LLM output is original work and thus not subject to copyright.
I think it means you write a spec from the implementation. Then you write a new implementation from the spec. You might go so far as to do the second part in a "clean" room.
Heh, the original being entirely vibed had me thinking of an interesting problem: if you used the same model to generate a specification, then reset the state and passed that specification back to it for implementation, the resulting code would by design be very close to the original. With enough luck (or engineering), you could even get the same exact files in some cases.
Does this still count as clean-room? Or what if the model wasn't the same exact one, but one trained the same way on the same input material, which Anthropic never owned?
This is going to be a decade of very interesting, and probably often hypocritical lawsuits.
right. that's not what people are doing here though, at all
in a typical clean-room design, the person writing the new implementation is not supposed to have any knowledge of the original, they should only have knowledge of the specification.
if one person writes the spec from the implementation, and then also writes the new implementation, it is not clean-room design.
I believe the argument is that LLMs are stateless. So if the session writing the code isn't the same session that wrote the spec, it's effectively a clean room implementation.
There are other details of course (is the old code in the training data?) but I'm not trying to weigh in on the argument one way or the other.
Seems equally valid to come out of this with the takeaway that code quality _does_ matter, because poor coding practices are what led to the leak.
Sure, the weights are where the real value lives, but if the quality is so lax they leak their whole codebase, maybe they are just lucky they didn’t leak customer data or the model weights? If that did happen, the entire business might evaporate overnight.
I look at other people's code a lot. The security issues are always boring, that's the thing. API keys sitting in the client bundle, auth middleware missing half the routes. Not clever exploits, just nobody actually reading what the AI spit out.
Actually wait, it's worse than that. The product works, demo looks great. Then someone opens the network tab and ... yeah. "Quality doesn't matter" really just means nothing caught fire yet.
I dont understand why so many people have the need to emphasize the code vs product battle. There is no battle. Coding/developing/software engineering is a skill, that just like any other skill has certain requirements and best practices that have to be followed in order to make a quality, maintainable and adaptable application that can stand the test of time from the software perspective. Product, features, marketing bla bla that is entrepreneurship part, and is not related to software, other than directing the software requirements,but not beacuse product people think about requirements, but instead just because they come naturally from the required features they envision.Just because programmers can write code doesnt mean they can ship good products.Just because a plumber can lay pipes doesnt mean he can run his own company or invent a new way of laying pipes. But I will tell you that a bad plumber who lays pipes and doesnt know how to connect them, bend them or shield them will surely have inferior product/service in the long term. And by the way, success of a company is measured over time, we will see where claude code will be in 10 years time when the hype dips a little, then we can say yeah the code was bad but everyone loved it and uses it still. I mean they leaked entire code online and this guy says yeah code was bad but who cares, what world are we living in? The fact that anything got leaked is a serious breach of best practices and security also at this point, something a company that used to work for DoD(W) shouldnt be doing, it can even be considered a national threat at this point. I know mistakes happen, I do them all the time, but then again the 'best' companies should be almost immune to mistakes cause stakes are high.But of course, move fast and break things is more important.Am I wrong?
> The fact that anything got leaked is a serious breach of best practices and security also at this point, something a company that used to work for DoD(W) shouldnt be doing, it can even be considered a national threat at this point.
Wasn't it CC itself the one that leaked, well, itself? It's completely vibe-coded, which I assume means it does its own build step too, which means it leaked itself.
The only breach of best-practice I see here is using an LLM for coding.
On the other hand, consider if they took their time to make sure that quality is super good, did not mainly vibe code it, then they would have never become as popular as quickly as they did and would have been outcompeted by other llm providers. They only got so popular because they released Claude Code so quickly it was the most productive thing for everyone to use at that time.
And frankly at that point Claude Code as software doesn't matter anymore. It is about their models, they could throw it away, rewrite from scratch, etc, it wouldn't be a big deal.
Claude Code as a harness was never likely going to be for 10 years, because there would be so many of these harnesses, all different, the direction may change, etc.
As I understand someone internally quickly vibecoded for themselves only as a productivity tool, and then they realized internally how productive it can be, they decided to release it and people found it so productive they got hugely popular now thanks to that while otherwise would have been eaten out by OpenAI.
Also if requirements for this had to came from product it would have never even happened in the first place. As it was engineer trying to optimize their own workflow.
The only reason they decided to hide source code is to delay competitors imo and it wasn't related to security or anything, but by now OpenCode etc are objectively better tools anyway.
And this is why so much software today runs extremely hot.
It's creators clearly care not for the efficiency of how it is built, which translates directly into how it runs.
This blog post is effectively being apologetic about the fact that this is alright, since at least they got product market fit. Except Anthropic is never going to go back and clean up the mess once (if) they become profitable.
I doubt anyone will like how things will be in 5 years time if this trend of releasing badly engineered spaghetti continues.
Heya, post author here. I would say I’m not trying to be apologetic for the idea that software quality is diminishing, especially cause I became an indie developer because I care about quality.
When I say “it doesn’t matter” I mean more in an existential sense, and that people don’t seem to care. On the other hand people should do things because they care, which is why I personally still review the code that goes into my apps and spend the time to refactor and improve the stability and foundation rather than slopping like there’s no tomorrow.
Maybe I’m growing cynical but I understand why a business doesn’t care (at least until it comes back to bite them — which may take longer than some have assumed). And most of what you read about the subject is ultimately being driven by business needs of the desire of businesses.
I created Hyperlambda (https://hyperlambda.dev), so I spend a lot of time thinking about accidental complexity and implementation surface area. One thing leaks like this keep reminding me is that a surprising amount of software risk comes from packaging and delivery details rather than the main logic people spend all their time reviewing.
In my opinion, the “code is garbage” argument is a moot point. Anthropic is in the business of removing humans from the SDLC. As long as their models can understand and update the code they generate it can remain garbage. They’re not optimizing for human comprehension of the output. They don’t even want you looking at it. And eventually the models will get good enough that you won’t have to.
It doesn't seem like they're optimizing for stable, working software either, nor, apparently, a stable, working release workflow.
LOL, very true.
I feel the conclusions here are a bit thin.
Code quality tends to have an impact on more than just aesthetics - and Claude Code certainly feels like a buggy mess from an end user's perspective.
Of course people still use Claude Code, but that is certainly because of the underlying models first and foremost. Most products don't have such a moat and would not nearly see as much tolerance from end users. If the Max subscriptions could be used with other harnesses, I am sure Anthropic would have to compete harder on the quality of the harness (to be fair, most AI based tooling seems pretty alpha these days, but eventually things will stabilize).
Polish is not everything, clearly, but it is a factor, and I feel Claude Code is maybe the worst example to use here, as it doesn't at all generalize to most other products.
Points from the article.
1. The code is garbage and this means the end of software.
Now try maintaining it.
2. Code doesn’t matter (the same point restated).
No, we shouldn’t accept garbage code that breaks e.g. login as an acceptable cost of business.
3. It’s about product market fit.
OK, but what happens after product market fit when your code is hot garbage that nobody understands?
4. Anthropic can’t defend the copyright of their leaked code.
This I agree with and they are hoist by their own petard. Would anyone want the garbage though?
5. This leak doesn’t matter
I agree with the author but for different reasons - the value is the models, which are incredibly expensive to train, not the badly written scaffold surrounding it.
We also should not mistake current market value for use value.
Unlike the author who seems to have fully signed up for the LLM hype train I don’t see this as meaning code is dead, it’s an illustration of where fully relying on generative AI will take you - to a garbage unmaintainable mess which must be a nightmare to work with for humans or LLMs.
Hey there, post author here. I think if you read deeper into my blog post history you’ll see that I have a reasonably balanced take on AI.
I generally think this will be a very important technology so I teach the subject to make sure people understand how to use it as leverage in their lives. (Yes as paid workshops, but I also volunteer weekly for 3-4 hour sessions at a non-profit where I get nothing more than the joy of helping people learn a valuable skill.)
At the same time just last week I wrote a post decrying the slop people are hoisting on their coworkers[^1], because I want people to use this technology in a positive way to create the lives they want, not to create downstream consequences for others. Ultimately I think agentic systems are incredibly powerful but also a technology that lends itself to anti-social behavior because of how independently empowering it can be. And so I hope that with the right exposure, discussion, and teaching we can take advantage of its democratizing nature, while reinforcing that what makes us special as humans is that we care and coordinate to do greater things. Value in this world — not just in the financial sense that we often boil it down to when we talk about this subject.
Hope that context helps provide a better lens into the piece, and that I still do care a lot about code and everything else that got me here, but that you are also reading personal reflections of who I am in a time of change, which is making me question (or reinforcing) some of the fundamental things I believed about software and sometimes the world more widely.
[^1]: https://build.ms/2026/3/23/workslop/
I disagree on agentic systems and AI based on LLMs and don’t feel this is close to a balanced take. You are assuming you are in the middle of a revolution when that is far from obvious. It is not yet clear this is an important technology because there are very significant limitations.
The code from one of the leading companies in the space is a good example of where the reality of what is achieved falls far short of expectations.
This is what I meant by the hype train.
Sorry, when I say I think I have a balanced take on AI what I mean is that I do my best to weigh both the pros and cons of this technology as opposed to a more extreme behavior like spending all day chatting with LLMs or posting all day on X about how AI is already better than me at everything and that jobs are over.
If I had to assign a confidence score for whether agents will change the way we all work and many aspects of how we live, I would put it at a 7/10, maybe 8/10. I felt about the same about the smartphone. While many things we do look the same way they did in 2005 (we still drive on roads, kids still go to school), at the same time it's undeniable that much of our lives are intermediated through a small screen and many societal dynamics have shifted due to that technology's existence.
I will concede that you should read my post with that context and draw your own conclusions about the veracity of my perspective — but I think it is more well-reasoned than what people generally attribute to "LLM hype". (Of course it's a bit tautological that I believe that, but I try to surround myself with people of all kinds technical and non-technical and like to think I stay reasonably grounded.)
All that said, I think the code from a leading company being bad and yet delivering good results is more a sign of the technology's jagged frontier[^1]. Calculators can't write sonnets the same way that LLMs are bad at math, but that doesn't make them useless — it just makes them a tool. This is a tool in our tool belt and I find is surprisingly useful as a general purpose technology despite it's limitations. (Which is related to the main argument I make in the post that bad code leading to good results may imply that we're under and overweighting certain aspects of what is important in software development, and that our expectations of code may may need to be recalibrated often as we gather more evidence.)
[^1]: https://www.oneusefulthing.org/p/centaurs-and-cyborgs-on-the...
I'm a little disappointed that a bunch of engineers with unlimited access to Opus didn't do a better job.
What exactly makes you say that "the author who seems to have fully signed up for the LLM hype train"?
I feel the author is just stating the obvious: code quality has very little to do with whether a product succeeds
OK, but what happens after product market fit when your code is hot garbage that nobody understands?
This is a different question, but obviously, a code that "nobody understands" is a terrible situation
I think the argument here ignores a critical fact: a huge factor in Claude Code's popularity is the Claude Max plans. These plans give you potentially thousands of dollars of tokens for a capped $200.
Speaking for myself, I long for the day I can dump the comparatively garbage experience of Claude Code for something more enjoyable and OSS like OpenCode. But the fact is that it is simply not economically viable to do so.
So the PMF is not really for Claude Code alone -- it is for Claude Code + Claude Max.
Reminds me of a question that I asked couple of years ago - https://news.ycombinator.com/item?id=37176689
I don't think it's vibe coded garbage. Sure, the 3000-line print.ts is terrible, but there's some good patterns in there that were definitely prompted in by some experienced engineers -- the feature flag setup, the `..I_VERIFIED_THIS_IS_NOT_PATH_OR_CODE` funny type hints, the overall structure. Just the usual signs this started as a PoC but quickly evolved into something much bigger. The codebase is a really interesting read.
Saying that code quality doesn't matter is simply false, if people enjoyed Claude code so much, anthropic wouldn't have to block direct API access to users.
Their "product market fit" is the LLM model itself, not the harness. The harness is completely replaceable in my opinion, it's just that Claude is the cheapest way to access the models.
From a moral perspective, I would argue that this is still theft of IP, even if it's a "clean room reimplementation". The code carries valuable information about what works and what doesn't — knowledge that Anthropic had to discover through real work and iteration. It's the same as a Chinese factory duplicating a product: they skipped the entire R&D phase and saved time and money.
following this reasoning, wouldn't it mean that everything that Claude generates is a theft of IP?
It was taught on massive code bases that carried valuable information about what works
Code quality matters when it becomes the code you have to maintain after six months. I'm honestly surprised by how some features of Claude Code seem to be held together with gum and duct tape.
That being said, if you're just beginning and looking for your market fit, or pitching to investors with a flashy demo, it doesn't need to be an architectural miracle, in fact it will waste your time.
Heya, post author here. I just want to say that I actually agree with your surprise. In the piece what I’m trying to articulate is not that this can go on forever, but that I’m genuinely surprised we’ve made it a year and they continue to ship at a rapid clip and the product is still reasonably good.
> I’ve had to question the value of code a lot over the last couple of years, and this leak continues to reinforce the notion that I’ve vastly overestimated it my entire career.
Now we could be moments away from hitting any of the rules described on https://how.complexsystems.fail, but if you’d asked me a year ago how long it would take to get there with people working this way I would have definitely taken the under. That difference in what I believed and what I see with my own two eyes is what has me questioning my priors, because my calibration seems to need readjustment (maybe large or maybe small) for the world of software we’re in right now.
It only holds because (1) people are inclined to accept failures as pain of using something cutting edge and (2) you kinda don't know what is failing anymore.
Extra one (3) We are getting super lenient with major failures and having a services that has only one 9 on reliability charts as norm.
" The real value in the AI ecosystem isn’t the model or the harness — it’s the integration of both working seamlessly together. "
Wut? The value in the ecosystem is the model. Harnesses are simple. Great models work nearly identically in every harness
I wouldn’t say harnesses is simple. They do a lot of things that we aren’t thinking of. I learned that a good harnesses is as valuable as the model. But obviously the model is what carries the whole thing.
I tried to build my own harness once. The amount of work that is required is incredible. From how external memory is managed per session to the techniques to save on the context window, for example, you do not want the llm to read in whole files, instead you give it the capability to read chunks from offsets, but then what should stay in context and what should be pruned.
After that you have to start designing the think - plan - generate - evaluation pipeline. A learning moment for me here was to split up when the llm is evaluating the work, because same lllm who did the work should not evaluate itself, it introduces a bias. Then you realize you need subagents too and statt wondering how their context will be handled (maybe return a summarized version to the main llm?).
And then you have to start thinking about integration with mcp servers and how the llm should invoke things like tools, prompts and resources from each mcp. I learned llms, especially the smaller ones tend to hiccup and return malformed json format.
At some point I started wondering about just throwing everything and just look at PydanticAi or Langchain or Langgraph or Microsoft Autogen to operate everything between the llm and mcps. Its quite difficult to make something like this work well, especially for long horizontal tasks.
I've been running a custom harness in production for months (code gen on cloud boxes), it's quite simple (<500 LOC). The model can use sed or other nice bash tricks to efficiently read. U really don't need any tool besides bash plus a good system prompt. Subagents are the same as the main agent (end with a summary). U can just remove tool results (oldest first) to save context, the model can read again if it needs to. High quality memory is the only difficult part. But that can also be solved with high quality documentation
Harnesses are simple (kind of? Some certainly aren't, but I'd agree that they can be simple) but they deliver a ton of value. They have a significant ROI.
I agree that good models have more value because a harness can't magically make a bad model good, but there's a lot that would be inordinately difficult without a proper harness.
Keeping models on rails is still important, if not essential. Great models might behave similarly in the same harness, but I suppose the value prop is that they wouldn't behave as well on the same task without a good harness.
Sure... A good harness can be built in 1 day tho. A good model is 1000x more sophisticated
Okay, fair. I was thinking in the context of actually using the tools, but yes, in the bigger picture the model is worth far more.
There is a reason why Copilot+Opus4.6 is shit, while Claude Code + Opus 4.6 produces excellent results.
The harness matters A LOT.
The model is the engine, the harness is the driver and chassis. Even the best top of the line engine in a shitty car driven by a bad driver won't win any races.
The model defines the ceiling, but the harness determines how much of that ceiling you actually reach.
It is not everyone’s experience that models work the same in every harness.
Afaik you can run Claude Code locally but every single demo i see uses it exclusively with apis, so are the local models already good enough to be worth it or is the only reasonable use for claude code with cloud models?
Claude Code's main advantage is that it's the only TOS-compliant way to access subscription Claude tokens, which cost about 10% as much as pay-as-you-go Claude API tokens.
Well to be honest, even 5% would probably be too much for me (even tho i dont know the pricing of tokens or consumption of tokens)
I would just want to test around a bit locally, maybe let it do its thing over a weekend just to see the result and then stop it again
honestly for most coding tasks a smaller model gets it done. qwen3-32b on an a100 80gb handles single-repo work just fine — and if you're not convinced, you can test the same prompt against a 405B on a 2×B200 with literally a few button clicks and see for yourself. no infrastructure changes, no new setup, just pick a different GPU from the strip.
that's actually why i built SeqPU.com — been at it for about a year. T4 16GB all the way up to 2×B200 384GB, billed by the second so idle costs nothing. test cheap, scale up only if you need to. i'd love to show you how it works and set you up with some free credits — just reply here.
OP should expand on #1, why he thinks it’s garbage. Claude Code is the REPL harness Anthropic built, can read, write, edit, bash. Pi, Gemini, Codex do the same, but they are not hinted as garbage. Where’s the beef?
I don't know how many years of software engineering told us that new projects hastily done needs to be redone hastily later.
> The success of Claude Code and Cursor at the higher end of the market shows that even the people pickiest about their software (developers) will use your software regardless of how good the code is.
Seems wrong. Devs will whine, moan and nitpick about even free software but they can understand failure modes, navigate around bugs and file issues on GitHub. The quality bar is 10-100x amongst non-techno-savvy folks and enterprise users that are paying for your software. They’re far more “picky”.
Claude Code proves you don't need quality code — you just need hundreds of billions of dollars to produce a best-in-class LLM and then use your legal team to force the extreamly subsidised usage of it through your own agent harness. Or in other words, shitty software + massive moat = users.
Seriously, if Anthropic were like oAI and let you use their subscription plans with any agent harness, how many users would CC instantly start bleeding? They're #39 in terminal bench and they get beaten by a harness that provides a single tool: tmux. You can literally get better results by giving Opus 4.6 only a tmux session and having it do everything with bash commands.
It seems premature to make sweeping claims about code quality, especially since the main reason to desire a well architected codebase is for development over the long haul.
How likely is it that the broken release workflow that produced the leak was Claude's own work?
Who cares that the code is garbage? As the models get bigger and more powerful it will be trivial to fully refactor the whole codebase. It’s coming sooner than you think.
> bad code can build well-regarded products.
Yes, exactly. Products.
It seems like me and all the engineers I've known always have this established dichotomy: engineers, who want to write good code and to think a lot about user needs, and project managers/ executives/sales people, who want to make the non-negative numbers on accounting documents larger.
The truth is that to write "good software," you do need to take care, review code, not single-shot vibe code and not let LLMs run rampant. The other truth is that good software is not necessary good product; the converse is also true: bad product doesn't necessarily mean bad software. However there's not really a correlation, as this article points out: terrible software can be great product! In fact if writing terrible software lets you shit out more features, more quickly, you'll probably come ahead in business world than someone carefully writing good software but releasing more slowly. That's because the priorities and incentives in business world are often in contradiction to priorities and incentives in human world.
I think this is hard to grasp for those of us who have been taught our whole lives that money is a good scorekeeper for quality and efficacy. In reality it's absolutely not. Money is Disney bucks recording who's doing Disney World in the most optimal way. Outside of Disney World, your optimal in-park behavior is often suboptimal for out-of-park needs. The problem is we've mistaken Disney World for all of reality, or, let Walt Disney enclose our globe within the boundaries of his park.
> The object which labor produces confronts it as something alien, as a power independent of the producer.
99.9999% of consumers never give code a single thought.
Most corporations never give code a single thought.
In the race to market, quality always suffers, and with such high stakes, it should surprise no one that AI companies are vibe-coding their own slop.
I think this misses the target.
First, the twitter quote is standard toxic clapback nonsense. Gambling makes billions and does not add any value. Even facebook can argue it adds more value than gambling so this one is a dud.
People use claud code because of claud the model and not claud the harness. Cursor or a hacked up agent loop using opus or whatever are about as good. The magic is in the model not the harness here. This isnt to say the hardness is doesnt do anything.
The other bit this misses is that yes the product matters more then the code, and if the product burns battery/ram/etc doing nothing because the ai has crappy code or maybe something leaks or has a security issue, then that impacts the product.
They just said this was an April Fools joke.
No you fell for someone elses joke.
jokesonthemiwasonlypretending.png
> So of course the first thing people did was point and laugh.
This just validates my theory that open-sourcing old code that people have sentimental attachments for, and that you won't ever make any money off of, again is actually a terrible idea.
Everything about this leak is a long list of arguments why you shouldn't ever open source anything.
We, the developer community, have really dropped the ball here.
Why? Reddit is full of bug fixes and improved forks. Seems like it's Anthropic that's dropping the ball with it's takedown requests instead of engaging with the community.