I get that people love saying LLMs are just compilers from human language to $OUTPUT_FORMAT but... they simply are not except in a stretchy metaphorical sense.
That's only true if you reduce the definition of "compiler" to a narrow `f = In -> Out`. But that is _not_ a compiler. We have a word for that: function. And in LLM's case an impure one.
I dislike arguing semantics but I bet it's not an abstraction by most engineers' definition of the word.
Producing outputs you don’t understand is novel
I agree it's not a layer of abstraction in the traditional sense though. AI isn't an abstraction of existing code, it's a new way to produce code. It's an "abstraction layer" in the same way an IDE is is an abstraction layer.
Actually yes, because Humans can be held accountable for the code they produce
Holding humans accountable for code that LLMs produce would be entirely unreasonable
And no, shifting the full burden of responsibility to the human reviewing the LLM output is not reasonable either
Edit: I'm of the opinion that businesses are going to start trying to use LLMs as accountability sinks. It's no different than the driver who blames Google Maps when they drive into a river following its directions. Humans love to blame their tools.
Why? LLMs have no will nor agency of their own, they can only generate code when triggered. This means that either nature triggered them, or people did. So there isn't a need to shift burdens around, it's already on the user, or, depending on the case, whoever forced such user to use LLMs.
A fundamentally unreliable one: even an AI system that is entirely correctly implemented as far as any human can see can yield wrong answers and nobody can tell why.
That’s not entirely the fault of the technology, as natural language just doesn’t make for reliable specs, especially in inexperienced hands, so in a sense we finally got the natural-language that some among our ancestors dreamed of and it turned out to be as unreliable as some others of our ancestors said all along.
It partly is the fault of the technology, however, because while you can level all the same complaints against a human programmer, a (motivated) human will generally be much better at learning from their mistakes than the current generation of LLM-based systems.
(This even if we ignore other issues, such as the fact that it leaves everybody entirely reliant on the continued support and willingness to transact of a handful of vendors in a market with a very high barrier to entry.)
creating a product in a span of mere months that millions of developers use everday is opposite of ridiculous. we wouldn't even have known about the supposed ridiculousness of code if it hadnt leaked.
Claude Code is being produced at AI Level 7 (Human specced, bots coded), whereas the author is arguing that AI Level 6 (Bots coded, human understands somewhat) yields substantially better results. I happen to agree, but I'd like to call out that people have wildly different opinions on this; some people say that the max AI Level should be 5 (Bots coded, human understands completely), and of course some people think that you lose touch with the ground if you go above AI Level 2 (Human coded with minor assists).
> of course some people think that you lose touch with the ground if you go above AI Level 2
I really think that this framing sometimes causes a loss of granularity. As with most things in life, there is nuance in these approaches.
I find that nowadays for my main project I where I am really leaning into the 'autonomous engineering' concept, AI Level 7 is perfect - as long as it is qualified through rigorous QA processes on the output (ie it is not important what the code does if the output looks correct). But even in this project that I am really leaning into the AI 'hands-off' methodology, there are a few areas that dip into Level 5 or 4 depending on how well AI does them (Frontend Design especially) or on the criticality of the feature (in my case E2EE).
The most important thing is recognizing when you need to move 'up' or 'down' the scale and having an understanding of the system you are building
I could probably get to a 7 with some additional tooling and a second max 20 account, but I care too much about the product I'm building right now. Maybe for something I cared less about.
IMO if you're going 7+, you might as well just pick a statically typed and very safe (small surface area) language anyways, since you won't be coding yourself.
Thanks for that list of levels, it's helpful to understand how these things are playing out and where I'm at in relation to other engineers utilizing LLM agents.
I can say that I feel comfortable at approximately AI level 5, with occasional forays to AI level 6 when I completely understand the interface and can test it but don't fully understand the implementation. It's not really that different from working on a team, with the agent as a team member.
I’m not sure I believe that Level 7 exists for most projects. It is utterly *impossible* for most non-trivial programs to have a spec that doesn’t not have deep, carnal knowledge of the implementation. It can not be done.
For most interesting problems the spec HAS to include implementation details and architecture and critical data structures. At some point you’re still writing code, but in a different language, and it migtt hurt have actually been better to just write the damn struct declarations by hand and then let AI run with it.
There may be certain fields where you can't even get to 5.
Building the rendering pipeline, algorithms, maths, I've turned off even level 2. It is just more of a distraction than it's worth for that deep state of focus.
So I imagine at least some of the disconnect comes from the area people work in and it's novelty or complexity.
This attribute plus a bit of human tribalism, social echo-chambering, & some motivated reasoning by people with a horse in the race, explains most of the disagreement I see in rhetoric around AI.
The fact is, I think the art of building well with AI (and I'm not saying it's easy) is to have a heterogenously vibe-coded app.
For example, in the app I'm working on now, certain algorithmically novel parts are level 0 (I started at level 1, but this was a tremendously difficult problem and the AI actually introduced more confusion than it provided ideas.)
And other parts of the app (mostly the UI in this case) are level 7. And most of the middleware (state management, data model) is somewhere in between.
Identifying the appropriate level for a given part of the codebase is IMO the whole game.
This is painful to read. It feels like rant from person who does not use version control, testing and CI.
It is cruel to force machine into guessing game with a todler whose spec is "I do not like it". If you have a coding standarts and preferences, they should be already destiled and exlained somewhere, and applied automatically (like auto linter in not so old days). Good start is to find OS projects you like, let claude review it, and generate code rule. Than run it on your code base over night, until it passes tests and new coding standarts automated code review.
The "vibe coding" is you run several agants in parallel, sometimes multiple agents on the same problem with different approach, and just do coding reviews. It is mistake to have a synchronous conversation with a machine!
This type of works needs severe automation and parallelisation.
This can be easily automated away!
If anything, it’s the exact opposite. It shows that you can build a crazy popular & successful product while violating all the traditional rules about “good” code.
And at first glance, none of it was about complex runtime optimizations not present in Node, it was all "standard" closure-related JS/TS memory leak debugging (which can be a nightmare).
I don't have a link at hand because threads about it were mostly on Xitter. But I'm sure there are also more accessible retros about the posts on regular websites (HN threads, too).
After some experience, it feels to me (currently primarily a JS/TS developer) like most SPAs are ridden by memory leaks and insane memory usage. And, while it doesn't run in the browser, the same think seems to apply to Claude CLI.
Lexical closures used in long-living abstractions, especially when leveraging reactivity and similar ideas, seems to be a recipe for memory-devouring apps, regardless of browser rendering being involved or not.
The problems metastasize because most apps never run into scenarios where it matters, a page reload or exit always is close enough on the horizon to deprioritize memory usage issues.
But as soon as there are large allocations, such as the strings involved in LLM agent orchestration, or in non-trivial other scenarios, the "just ship it" approac requires careful revision.
Refactoring shit that used to "just work" with memory leaks is not always easy, no matter whose shit it is.
if you have one of the top models in a disruptive new product category where everyone else is sprinting also, sure..
Code quality only matters in maintainability to developers. IMO it's a very subjective metric
In the current market, most people using one LLM are likely going to have a positive view of it. Very little is forcing you to stick with one you dislike aside from corporate mandates.
You don’t have to go far on this site to find someone that doesn’t like Claude code.
If you want an example of something moronic, look at the ram usage of Claude code. It can use gigabytes of memory to work with a few megabytes of text.
To be fair, their complaints are about very recent changes that break their workflow, while previously they were quite content with it.
The tooling can be hacky and of questionable quality yet, with such a model, things can still work out pretty well.
The moat is their training and fine-tuning for common programming languages.
It's a bit of both. Claude Code was the tool that made Anthropic's developer mindshare explode. Yes, the models are good, but before CC they were mostly just available via multiplexers like Cursor and Copilot, via the relatively expensive API.
Perhaps the problem is getting multiple vibe-coders synced up when working on a large repo.
which has always been true
No accounting for taste, but part of makes code hard for me to reason about is when it has lots of combinatorial complexity, where the amount of states that can happen makes it difficult to know all the possible good and bad states that your program can be in. Combinatorial complexity is something that objectively can be expensive for any form of computer, be it a human brain or silicon. If the code is written in such a way that the number of correct and incorrect states are impossible to know, then the problem becomes undecidable.
I do think there is code that is "objectively" difficult to work with.
I mean, it seems like that has always been true to an extent, but now it may be even more true? Once you know you're sitting on a lode of gold, it's a lot easier to know how much to invest in the mine.
And some people thought they were building "disposable" code, only to see their hacks being used for decades. I'm thinking about VB but also behemoth Excel files.
I hate self-promotion but I posted my opinions on this last night https://blog.tombert.com/Posts/Technical/2026/04-April/Stop-...
The tl;dr of this is that I don't think that the code itself is what needs to be preserved, the prompt and chat is the actual important and useful thing here. At some point I think it makes more sense to fine tune the prompts to get increasingly more specific and just regenerate the the code based on that spec, and store that in Git.
Observability into how a foundation model generated product arrived to that state is significantly more important than the underlying codebase, as it's the prompt context that is the architecture.
The solution people are coming up with now is using AI for code reviews and I have to ask "why involve Git at all then?". If AI is writing the code, testing the code, reviewing the code, and merging the code, then it seems to me that we can just remove these steps and simply PR the prompts themselves.
Also, the approach you described is what a number of AI for Code Review products are using under-the-hood, but human-in-the-loop is still recognized as critical.
It's the same way how written design docs and comments are significantly more valuable than uncommented and undocumented source.
I don't entirely disagree that there is code that's objectively difficult to work with, but I suspect that the Venn diagram of "code that's hard for humans" and "code that's hard for computers" has much less overlap than you're suggesting.
I'm sure that these models will get better, and I agree that the overlap will be lower at that point, but I still think what I said will be true.
If you make sure the compiler catches most issues, AI will run it, see it doesn't build and fix what needs to be fixed.
So I agree that a lot of things that make code good, including comments and documentation, is beneficial for AI.
Ive noticed that theyre often quite bad at refactoring, also.
Now whether this is still true with AI, or if vibe coding means bad code no longer have this long term stability and velocity cost because AI are better than humans at working with this bad code... We don't know yet.
Some business models will require “good” code, and some won’t. That’s how it is right now as well. But pretending that all business models will no longer require “good” code is like pretending that Michelin should’ve retired its list after the microwave was invented.
Research in academia seems less appropriate because that’s famously not really a business model, except maybe in the extractive sense
Obviously it does some fairly smart stuff under the hood, but it's not exactly comparable to a large software project.
But to your point, that doesn't mean you can't vibe code some poorly built product and sell it. But people have always been able to sell poorly built software projects. They can just do it a bit quicker now.
I don't know why people keep acting like harnesses are all the same but we know they aren't because people have swapped them out with the same models and receive vastly different results in code quality and token use.
This is similar to retarded builders in Turkey saying “wow, I can make the same building, sell for the same price, but spend way less” and then millions of people becoming victim when there is an earthquake.
This is not how responsible people should think about things in society
You're not alone in thinking that, but unfortunately I think it's a minority opinion. The only thing most people and most businesses care about is money. And frankly not even longterm, sustainable money. Most companies seem happy to extract short term profits, pay out the executives with big bonuses, then rot until they collapse
Getting money is 100% what it is about and Claude Code is great product.
Well, if unmaintainable code gets in the way of the "sustained over time" part, then that is still a real problem.
The negative emotion regex, for example, is only used for a log/telemetry metric. Sampling "wtf?" along would probably be enough. Why would you use an agent for that?
I don't see how a vibe-coded app is freed from the same trade-offs that apply to a fast-moving human-coded one.
Especially since a human is still driving it, thus they will take the same shortcuts they did before: instead of a formal planning phase, they'll just yolo it with the agent. Instead of cleaning up technical debt, they want to fix specific issues that are easy to review, not touch 10 files to do a refactor that's hard to review. The highest priority issues are bugs and new integrations, not tech debt, just like it always was.
This is really just a reminder of how little upside there is to coding in the open.
Claude’s source code is fine for a 1-3 person team. It’s atrocious for a flagship product from a company valued over $380 BILLION.
Like if that’s the best ai coding can do given infinite money? Yeah, the emperor has no clothes. If it’s not the best that can be done, then what kinda clowns are running the show over there?
Either they're massively overpaying some scrubs to underperform with the new paradigm, or they are squeezing every last drop out of vibe coding and this is the result.
Seems like you're also under the impression that privately developed software should be immaculate if the company is worth enough billions, but you'd be wrong about that too.
If they DIDN'T heavily vibe-code it they might fall behind. Speed of implementation short term might beat out long-term maintenance and iteration they'd get from quality code
They're just taking on massive tech debt
For you, non-buggy software is important. You could also reasonably take a more business centered approach, where having some number of paying customers is an indicator of quality (you've built something people are willing to pay for!) Personally I lean towards the second camp, the bugs are annoying but there is a good sprinkling of magic in the product which overall makes it something I really enjoy using.
All that is to say, I don't think there is a straightforward definition of quality that everyone is going to agree on.
For LLMs, I don't really know. I only have a couple years experience at that.
Everything depends on context. Most code written by humans is indeed, garbage.
What? Your comment makes absolutely zero sense. Legal team forces people to use Claude Code?
And they don't need a massive legal team to declare that you can't use their software subscription with other people's software.
This isn't a dig at anyone, I've certainly shipped my share of bad code as well. Deadlines, despite my wishes sometimes, continue to exist. Sometimes you have to ship a hack to make a customer or manager happy, and then replacing those hacks with better code just never happens.
For that matter, the first draft of nearly anything I write is usually not great. I might just be stupid, but I doubt I'm unique; when I've written nice, beautiful, optimized code, it's usually a second or third draft, because I ultimately I don't think I fully understand the problem and the assumptions I am allowed to make until I've finished the first draft. Usually for my personal projects, my first dozen or so commits will be pretty messy, and then I'll have cleanup branches that I merge to make the code less terrible.
This isn't inherently bad, but a lot of the time I am simply not given time to do a second or third draft of the code, because, again, deadlines, so my initial "just get it working" draft is what ships into production. I don't love it, and I kind of dread of some of the code with my name attached to it at BigCo ever gets leaked, but that's just how it is in the corporate world sometimes.
I get a junior developer or a team of developers with varying levels of experience and a lot of pressure to deliver producing crummy code, but not the very tool that's supposed to be the state-of-the-art coder.
Yeah, we even have an idiom for this - "Temporary is always permanent"
We already knew that. This is a matter of people who didn't know that or didn't want to acknowledge that thinking they now have proof that it doesn't matter for creating a crazy popular & successful product, as if it's a gotcha on those who advocate for good practices. When your goal is to create something successful that you can cash out, good practices and quality are/were never a concern. This is the basis for YAGNI, move-fast-and-break-things, and worse-is-better. We've know this since at least betamax-vs-VHS (although maybe the WiB VHS cultural knowledge is forgotten these days).
Also to correct another common myth, porn was widely available on both formats and was not the cause of VHS’s success over Betamax.
WiB doesn't mean the thing is worse, it means it does less. Claude Code interestingly does WAY more than something like Pi which is genuinely WiB.
Move Fast and Break Things comes from the assumption that if you capture a market quick enough you will then have time to fix things.
YAGNI is simply a reminder that not preparing for contingencies can result in a simpler code base since you're unlikely to use the contingencies.
The spaghetti that people are making fun of in Claude Code is none of these things except maybe Move Fast and Break Things.
The situation there is akin to Viaweb - Viaweb also rode hype wave and code situation was awful as well (see PG's stories about fixing bugs during customer's issue reproduction theater).
What did Viaweb's buyer do? They rewrote thing in C++.
If history rhymes, then buyer of Anthropic would do something close to "rewrite it in C++" to the current Claude Code implementation.
Not the front end
It already costed many developers months and hundreds of dollars worth of tokens because of a bug. There will be more.
I do M&As at my company - as a cto. I have seen lots of successful companies' codebases, and literally none of them elegant. Including very profitable companies with good, loved products.
The only good code I know is in the open source domain and in the demoscene. The commercial code is mostly crap - and still makes money.
2. The problem with "bad code" has nothing to do with the short-term success of the product but with the ability to evolve it successfully over time. In other words, it's about long-term success, not short-term success.
3. Perhaps most importantly, Claude Code is a fairly simple product at its core, and most of its value comes from the model, not from its own code (and the same is true on the cost side). Claude Code is relatively a low stakes product. This means that the problems caused by bad code matter less in this instance, and they're managed further by Claude Code not being at the extreme "vibey" end of the spectrum.
There's this definition of LLM generation + "no thorough review or testing"
And there's the more normative one: just LLM generation.[1][2][3]
"Not even looking at it" is very difficult as part of a definition. What if you look at it once? Or just glance at it? Is it now no longer vibe coding? What if I read a diff every ten commits? Or look at the code when something breaks?
At which point is it no longer vibe coding according to this narrower definition?
[1] https://www.collinsdictionary.com/dictionary/english/vibe-co...
[2] https://www.merriam-webster.com/dictionary/vibe%20coding
The product is also a bit wonky and doesn't always provide the benefits it's hyped for. It often doesn't even produce any result for me, just keeps me waiting and waiting... and nothing happens, which is what I expect from a vibe coded app.
It shows that you can have a garbage front end if people perceive value in your back end.
It also means that any competitor that improves on this part of the experience is going to eat your lunch.
But we (the dev community) are kind of spoiled, because we have a lot of great developer tools that come from people passionate about their work, skilled at what they do and take pride in what they put out. I don't count myself among one of those people but I have benefited from their work throughout my career and have gotten used to it in my tooling.
All that being said Opus is hands down the best coding model for me (and I'm actively trying all of them) and I'll tolerate it as long as I can get it to do what I need, even with the warts and annoyances.
What harness would you recommend instead?
- Good code is what enables you to be able to build very complex software without an unreasonable number of bugs.
- Good code is what enables you to be responsive to changing customer needs and times. Whether you view that as valuable is another matter though. I guess it is a business decision. There have been plenty of business that have gone bust though by neglecting that.
Good code is for your own sanity, the machine does not care.
Each one is broken, doesn’t have working error handling, and prevents you from giving them money. They all exist to insert the same record somewhere. Lost revenue, and they seem to have no idea.
Amazons flagship ios app has had at least three highly visible bugs, for years. They’re like thorns in my eye sockets, every time I use it. They don’t care.
These companies are working with BILLIONS of dollars in engineering resources, unlimited AI resources, and with massive revenue effects for small changes.
Sometimes the world just doesn’t make sense.
AI could play a big rule here. Husky (git hook) but AI. It will score lazy engineering. You lazy implement enough times, you loose your job.
That was always the case. Landlords still want rent, the IRS still has figurative guns. Shipping shit code to please these folks and keep the company alive will always win over code quality, unless the system can be edited to financially incentivize code quality. The current loss function on society is literally "ship shit now and pay your taxes and rent".
To me it said, clearly: nobody cares about your code quality other than your ability to ship interesting features.
It was incredibly eye-opening to me, I went in expecting different lessons honestly.
This codebase has existed for maybe 18 months, written by THE experts on agentic coding. If it is already unintelligible, that bodes poorly for how much it is possible to "accelerate" coding without taking on substantial technical debt.
I think the vibe-coding-deniers have move of a cult vibe, personally.
As if 97% of web apps aren't just basic CRUD with some integration to another system if you are lucky.
99% of companies won't even have 50k users.
It's an important distinction
I think that citizen developers will be a thing--but not in the way you might be thinking.
More people will be enabled (and empowered) to "build" quick-and-dirty solutions to personal problems by just talking to their phone: "I need way to track my food by telling you what I ate and then you telling me how much I have left for today. And suggest what my next meal should be."
In the current paradigm--which is rapidly disappearing--that requires a UI app that makes you type things in, select from a list, open the app to see what your totals are, etc. And it's a paid subscription. In 6 months, that type of app can be ancient history. No more subscription.
So it's not about "writing apps for SaaS subscribers." It's about not needing to subscribe to apps at all. That's the disruption that's taking place.
Crappy code, maintenance, support, etc.--no longer even a factor. If the user doesn't like performance, they just say "fix ___" and it's fixed.
What subscription apps can't be replaced in this disruption? Tell me what you think.
When you move to the enterprise layer, suddenly you get the opposite problem, you have a low amount of "users" but you often need a load of CPU intensive or DB intensive processing to happen quickly.
One company I worked for had their system built by, ummmm, not the greatest engineers and were literally running out of time in the day to run their program.
Every client was scheduled over 24 hours, and they'd got to running the program for 22 hours per day and were desperately trying to fix it before they ran out of "time". They couldn't run it in parallel because part of the selling point of the program was that it amalgamated data from all the clients.
Disruption happens when firms are disincentivized to switch to the new thing or address the new customer because the current state of it is bad, the margins are low. Intel missed out on mobile because their existing business was so excellent and making phone chips seemed beneath them.
The funny thing is that these firms are being completely rational. Why leave behind high margins and your excellent full-featured product for this half-working new paradigm?
But then eventually, the new thing becomes good enough and overtakes the old one. Going back to the Intel example, they felt this acutely when Apple switched their desktops to ARM.
For now, Claude Code works. It's already good enough. But unless we've plateaued on AI progress, it'll surpass hand crafted equivalents on most metrics.
Set up an AI bot to analyze the code for spaghetti code parts and clean up these parts to turn it into a marvel. :-)
The ship has sailed. Vibe coding works. It will only work better in the future.
I have been programming for decades now, I have managed teams of developers. Vibe coding is great, specially in the hands of experts that know what they are doing.
Deal with it because it is not going to stop. In the near future it will be local and 100x faster.
A pig with lipstick it's still a pig.
Or, aptly, as you quoted "Don Quixote":
'Con la iglesia hemos topado'.
(indeed Sancho), we just met the Church...
Both of these camps are the loudest voices on the internet, but there is a quiet but extremely productive camp somewhere in the middle that has enough optimism, open mindedness along with years of experience as an engineer to push Claude Code to its limit.
I read somewhere that the difference between vibe coding and "agentic engineering" is if you are able to know what the code does. Developing a complex website with claude code is not very different than managing a team of off shore developers in terms of risks.
Unless you are writing software for medical devices, banking software, fighter jets, etc... you are doing a disservice to your career by actively avoiding using LLMs as a tool in developing software.
I have used around $2500 in claude code credits (measured with `bunx ccusage` ) the last 6 months, and 95% of what was written is never going to run on someone else's computer, yet I have been able to get ridiculous value out of it.
How do you quantify and measure this productivity gain?
Bad code or good code is no longer relevant anymore. What matters is whether or not AI fulfills the contract as to how the application is supposed to work. If the code sucks, you just rerun the prompt again and the next iteration will be better. But better doesn't matter because humans aren't reading the code anymore. I haven't written a line of code since January and I've made very large scale improvements to the products I work on. I've even stopped looking at the code at all except a cursory look out of curiosity.
Worrying about how the sausage is made is a waste of time because that's how far AI has changed the game. Code doesn't matter anymore. Whether or not code is spaghetti is irrelevant. Cutting and pasting the same code over and over again is irrelevant. If it fulfills the contract, that's all that matters. If there's a bug, you update the contract and rerun it.
It's extremely relevant inasmuch as garbage code pollutes the AI's context and misleads it into writing more crap. "How the sausage is made" still matters.
But that isn't the hard part. The hard part is that some people are using the tool versions and some are using the agent versions, so consolidating them one way or another will break someone's workflow, and that incurs a real actual time cost, which means this is now a ticket that needs to be prioritized and scheduled instead of being done for free.
> AI is whatever hasn’t been done yet
From a tech discourse perspective, things have never been less productive than they are right now. I feel like we’re witnessing the implosion of an industry in real time. Thanks in no small part to venture capital and its henchmen.
Everyone seems to be drinking the proverbial kool-aid, and everyone else who is looking at the situation skeptically are labeled luddites. I expect we’ll get some clarity over the next few years on who is right. But I don’t know. It feels like the breakdown of shared epistemology. The kind of shared epistemology on which civilization was built.
In the past, which is a different country, we would throw away the prototypes.
Nowadays vibe coding just keeps adding to them.
- Brooks' No Silver Bullet: no single technology or management technique will yield a 10-fold productivity improvement in software development within a decade. If we write a spec that details everything we want, we would write soemthing as specific as code. Currently people seem to believe that a lot of the fundamentals are well covered by existing code, so a vague lines of "build me XXX with YYY" can lead to amazing results because AI successfully transfers the world-class expertise of some engineers to generate code for such prompt, so most of the complex turns to be accidental, and we only need much fewer engineers to handle essential complexities.
- Kernighan's Law, which says debugging is twice as hard as writing the code in the first place. Now people are increasingly believing that AI can debug way faster than human (most likely because other smart people have done similar debugging already). And in the worst case, just ask AI to rewrite the code.
- Dijkstra on the foolishness of programming in natural language. Something along the line of which a system described in natural language becomes exponentially harder to manage as its size increases, whereas a system described in formal symbols grows linearly in complexity relative to its rules. Similar to above, people believe that the messiness of natural language is not a problem as long as we give detailed enough instructions to AI, while letting AI fills in the gaps with statistical "common sense", or expertise thereof.
- Lehman’s Law, which states that a system's complexity increases as it evolves, unless work is done to maintain or reduce it. Similar to above, people start to believe otherwise.
- And remotely Coase's Law, which argues that firms exist because the transaction costs of using the open market are often higher than the costs of directing that same work internally through a hierarchy. People start to believe that the cost of managing and aligning agents is so low that one-person companies that handle large number of transactions will appear.
Also, ultimately Jevons Paradox, as people worry that the advances in AI will strip out so much demand that the market will slash more jobs than it will generate. I think this is the ultimate worry of many software engineers. Luddites were rediculed, but they were really skilled craftsmen who spent years mastering the art of using those giant 18-pound shears. They were the staff engineers of the 19th-century textile world. Mastering those 18-pound shears wasn't just a job but an identity, a social status, and a decade-long investment in specialized skills. Yeah, Jevons Paradox may bring new jobs eventually, but it may not reduce the blood and tears of the ordinary people.
Intereting times.
I thought you were gonna go the opposite direction with this. Debugging is now 100x as hard as writing the code in the first place.
> Lehman’s Law, which states that as a system's complexity increases as it evolves, unless work is done to maintain or reduce it. Similar to above, people start to believe otherwise.
Gotta disagree with this too. I find a lot of work has to be done to be able to continue vibing, because complexity increases beyond LLM capabilities rapidly otherwise.
100x harder if a human were to debug AI-generated code. I was merely citing other people's beliefs: AI can largely, if not completely, take care of debugging. And "better", rewrite the code altogether. I don't see how that could be a better approach, but that might just be me.
"And in the worst case just pay for it twice."
That leads to a dead end.
> Brooks' No Silver Bullet
Just because a person can create code or "results" much faster now, it doesn't say anything about productivity. Don't mistake dev productivity for economic productivity.
> Kernighan's Law, which says debugging is twice as hard as writing the code
Debugging is such a vague term in these matters. An AI may be decent to figure out their error they introduced into their code after it runs its own tests. But a production bug, i.e. reported from a user, can be very hard for AIs due to their utter lack of context.
> Dijkstra on the foolishness of programming in natural language. > ... > Lehman’s Law, which states that as a system's complexity increases as it evolves, unless work is done to maintain or reduce it.
No clue what the argument is here, "people believe otherwise" isn't.
> Also, ultimately Jevons Paradox
Actually relevant tech people confirm the paradox in the long run. Companies slash jobs now because they tend consolidate in chaotic times.
> No Silver Bullet
As an industry, we do not know how to measure productivity. AI coding also does not increase reliability with how things are going. Same with simplicity, it's the opposite; we're adding obscene complexity, in the name of shipping features (the latter of which is not productivity).
In some areas I can see how AI doubles "productivity" (whatever that means!), but I do not see a 10x on the horizon.
> Kernighan's Law
Still holds! AI is amazing at debugging, but the vast majority of existing code is still human-written; so it'll have an easy time doing so, as indeed AI can be "twice as smart" as those human authors (in reality it's more like "twice as persistent/patient/knowledgeable/good at tool use/...").
Debugging fully AI-generated code with the same AI will fall into the same trap, subject to this law.
(As an aside, I do wonder how things will go once we're out of "use AI to understand human-generated content", to "use AI to understand AI-generated content"; it will probably work worse)
> just ask AI to rewrite the code
This is a terrible idea, unless perhaps there is an existing, exhaustive test harness. I'm sure people will go for this option, but I am convinced it will usually be the wrong approach (as it is today).
> Dijkstra on the foolishness of programming in natural language
So why are we not seeing repos of just natural language? Just raw prompt Markdown files? To generate computer code on-the-fly, perhaps even in any programming language we desire? And for the sake of it, assume LLMs could regenerate everything instantly at will.
For two reasons. The prompts would either need to raise to a level of precision as to be indistinguishable from a formal specification. And indeed, because complexity does become "exponentially harder"; inaccuracies inherent to human languages would compound. We need to persist results in formal languages still. It remains the ultimate arbiter. We're now just (much) better at generating large amounts of it.
> Lehman’s Law
This reminds me of a recent article [0]. Let AI run loose without genuine effort to curtail complexity and (with current tools and models) the project will need to be thrown out before long. It is a self-defeating strategy.
I think of this as the Peter principle applied to AI: it will happily keep generating more and more output, until it's "promoted" past its competence. At which point an LLM + tooling can no longer make sense of its own prior outputs. Advancements such as longer context windows just inflate the numbers (more understanding, but also more generating, ...).
The question is, will the market care? If software today goes wrong in 3% of cases, and with wide-spread AI use it'll be, say, 7%, will people care? Or will we just keep chugging along, happy with all the new, more featureful, but more faulty software? After all, we know about the Peter principle, but it's unavoidable and we're just happy to keep on.
> Jevons Paradox
My understanding is the exact opposite. We might well see a further proliferation of information technologies, into remaining sectors which have not yet been (economically) accessible.
People were given faster typers with incredible search capabilities and decided quality doesn’t matter anymore.
I don’t even mean the code. The product quality is noticeably sub par with so many vibe-coded projects.
The PRs that it comes with are rarely even remotely controversial, shrink the codebase, and are likely saving tokens in the end when working on a real feature, because there's less to read, and it's more boring. Some patterns are so common you can just write them down, and throw them at different repos/sections of a monorepo. It's the equivalent of linting, but at a larger scale. Make the language hesitant enough, and it won't just be a steamroller either, and mostly fix egregrious things.
But again, this is the opposite of the "vibe coding" idea, where a feature appears from thin air. Vibe Linting, I guess.
I sick Opus, GPT5.4, and Gemini on it, have them write their own hitlists, and then have a warden Opus instance go and try to counterprove the findings, and compose a final hitlist for me, then a fresh context instance to go fix the hitlist.
They always find some little niggling thing, or inconsistency, or code organization improvement. They absolutely introduce more churn than is necessary into the codebase, but the things they catch are still a net positive, and I validate each item on the final hitlist (often editing things out if they're being overeager or have found a one in a million bug that's just not worth the fix (lately, one agent keeps getting hung up on "what if the device returns invalid serial output" in which case "yeah, we crash" is a perfectly fine response)).
So I set out to build an app with CC just to see what it's like. I currently use Copilot (copilot.money) to track my expenditures, but I've become enamored with sankey diagrams. Copilot doesn't have this charting feature, so I've been manually exporting all my transactions and massaging them in the sankey format. It's a pain in the butt, error prone, and my python skills are just not good enough to create a conversion script. So I had CC do it. After a few minutes of back and forth, it was working fine. I didn't care about spaghetti code at all.
So next I thought, how about having it generate the sankey diagrams (instead of me using sankeymatic's website). 30 minutes later, it had a local website running that was doing what I had been manually doing for months.
Now I was hooked. I started asking it to build a native GUI version (for macOS) and it dutifully cranked out a version using pyobjC etc. After ironing out a few bugs it was usable in less than 30 min. Feature adds consumed all my tokens for the day and the next day I was brimming with changes. Burned through that days tokens as well and after 3 days (I'm on the el cheapo plan), I have an app that basically does what I want in a reasonable attractive, and accurate manner.
I have no desire to look at the code. The size is relatively small, and resource usage is small as well. But it solved this one niche problem that I never had the time or skill to solve.
Is this a good thing? Will I be downvoted to oblivion? I don't know. I'm very very concerned about the long term impact of LLMs on society, technology and science. But it's very interesting to see the other side of what people are claiming.
LLM-driven develop lets me have the thing built without needing to build the thing, and at the same time I get to exercise some ways-to-build I don't use as often (management, spec writing, spec editing, proactive unblocking, etc.). I have no doubt my work with LLMs has strengthened mental muscles that are also be helpful in technical management contexts/senior+principal-level technical work.
There's nothing wrong with saying that Claude Code is written shoddily. It definitely is. But I think it should come with the recognition that Anthropic achieved all of its goals despite this. That's pretty interesting, right? I'd love to be talking about that instead.
So would I and a couple of others, but HNers don't want to have those kinds of conversations anymore.
memory created!
Consider this overly simplified process of writing a logic to satisfy a requirement:
1. Write code
2. Verify
3. Fix
We, humans, know the cost of each step is high, so we come up various way to improve code quality and reduce cognitive burden. We make it easier to understand when we have to revisit.
On the other hand, LLMs can understand** a large piece of code quickly***, and in addition, compile and run with agentic tools like Claude Code at the cost of token****. Quality does not matter to vibe coders if LLMs can fill the function logic that satisfies the requirement by iterating the aforementioned steps quickly.
I don't agree with this approach and have seen too many things broken from vibe code, but perhaps they are right as LLMs get better.
* Anecdotal
** I see LLM as just a probabilistic function so it doesn't "reason" like humans do. It's capable of highly advanced problem solving yet it also fails at primitive task.
*** Relative to human
**** Cost of token I believe is relatively cheaper compared to a full-time engineer and it'll get cheaper over time.
reconnecting•1h ago
Users like the author must be the most valuable Claude asset, because AI itself isn't a product — people's feedback that shapes output is.
disposition2•1h ago
He’s a pretty interesting fella, I imagine his work influenced a lot of people over the years
https://en.wikipedia.org/wiki/Bram_Cohen
reconnecting•1h ago