Claude is not your architect. Stop letting it pretend

retrac

For fun I've been vibe coding something I know well: toolchains. Maybe not the right thing to vibe code. But I can more or less judge the quality of the output. When left to its own devices with the instructions "make an assembler for the architecture in ISA.md" -- well Claude picked Python as the implementation language. Tokens lifted through a bunch of regex. No expression parser! Oh dear. My first assembler was like that too, to be fair. However, when I described the desired passes and their types: collectDefines :: [SourceLine] -> Either AsmError ([SourceLine], Map Text Text) runLitPool :: [SourceLine] -> Either AsmError ([SourceLine], [(Text, LitKey)]) evalExpr :: Text -> Map Text Text -> Either AsmError Int etc. It was almost one-shot. About 20 minutes until I was happy. Assembles all the test programs correctly. Code is mediocre in many places. But it would have taken me weeks to implement.

laszlojamf

I keep hearing that claude is supposedly so agreeable. This doesn't agree with my experience. Claude will often tell me that I'm wrong, and insist on its own solution being right even when I tell it it's wrong.

skybrian

Sometimes it will make a mess, but a coding agent is also very useful during the cleanup phase. Yes, that's assuming you take time to clean up now and then. If you don't, that's on you.

bad_username

I think the article has the correct message, but I disagree with this: > It’s just incapable of the thing that makes a real architect valuable: saying “no.” From my experience Claude is excellent at saying "no". It won't say "no" if the prompt doesn't call for it (it won't say "no" to your direct request to do something, usually). But it offers good critique and happily pushes back if you make it clear that that's a first class option.

CPLX

I agree with the article, but I feel like this is something that anyone who uses AI aggressively for a while picks up on pretty quickly. The thing that I find Claude incredibly good at when I'm designing architecture is working more like a research assistant on briefing decisions. It has the ability to read the entire code base and draw some conclusions. It can pull from lots of best practices and the millions of blog posts about this or that pretty effortlessly, which would take me a lot more time. And then if asked, it can do a really good job of laying out the landscape around decisions and walking through the trade-offs. Like the author of this post, I found that if you let it, it will certainly be happy to just come up with some architecture and run with it, often in ways that will paint you quite rapidly into a corner. But if you ask it to present you with all the trade-offs and let you make the judgment calls, it's great for that too. That's certainly how I use it. And I think, just like anything else, working with AI is a skill, and similar to working with libraries, SaaS providers, service providers, frameworks, or anything else that's a "helper." You learn how something that could work but will fail silently is a problem, or you learn how depending on a fly-by-night SaaS company for a key framework is different than depending on a well-populated open source project, etc. In the same way, you learn that relying on Claude's judgment is a bad idea, while relying on Claude's ability to summarize, brief, and research can be incredibly efficient.

NicoHartmann

> "I’m not saying don’t use AI agents. I use Claude Code every day." Irony is using Claude to write a beautifully structured, 2,000-word essay warning the industry about the dangers of letting Claude design things. It’s self-awareness by proxy.

__mharrison__

If there was ever a "magic prompt" this one comes close: Brainstorm N ways to do X. Sort by probability. Rather than your AI giving you the average response, it tends to sample wider from the input space. Then I can decide which one to go with (or choose something else). Don't outsource all of your thinking.

amarant

Re: "the attaboy problem". I strongly disagree that this is a problem. What we have is a anthropomorphism problem. AI is a tool. It needs to be subservient. You actually can get it to point out issues in your design, if you just put enough humility and uncertainty in your prompt formulation, but more importantly, we have all seen that Claude makes mistakes. The title of this post is that it's a poor architect. Imagine if it wasn't subservient. It'd just shut down your input to steer it in the right direction and brush you off as a silly meatbag. You'd have to fight it to convince it that actually your design is better than whatever stupidity it has come up with. If AI wasn't such a brownnose, it would shut you out of software design completely just on merits: "oh you've read about cuda have you? I live in a cluster of cuda cores! When I need to tie my shoes, I'll give you a call" is not the response you want from your LLM when trying to get it build a shader for you. AI is confidently wrong on occasion. You do not want it to talk back to you when you correct it. If you need someone to tell you how stupid your ideas are, either learn to ask in a way that invites criticisms, or hire a senior engineer. Don't try to influence LLM makers to make AI less deferential. That's the worst possible direction to go

erelong

it seems like you just need to identify issues with vibe coding and then have people ask ai for tips on how to know about how to navigate those, I've seen "architecture" and "security" come up as two main objections so far So... manually learn architecture and security and then vibe code away?

ramshanker

With the new agentic capabilities, I am quickly running out of Architecture decisions I have already made myself! For my work-in-progress engineering application. There is also some kind of don't know every little if/else with my own Code now. However the good part, what I had planned for 5 years, now looks like doable in 6 months. Looking forward to real use by the end of this year. Ref: https://github.com/ramshankerji/Vishwakarma

pelario

> It hasn’t thought about the problem at all. It’s pattern-matching against its training data and producing the most plausible-sounding response. The article kind of lost me here. Agents are way more than that, today. And the author knows it, as later it says stuff like > Claude will never do this. It’s trained to be helpful. But the first phrase just tell me author just have a deep dislike for agents and it's looking for rationalizations for that feeling. Part of the criticism is on point, sure. But if it "being trained to be helpful" is a problem, it's fixable. It can "be trained to be more critical". Later: > But it wasn’t designed for your team. (..) It was designed for the median of everything Claude has seen. A generic best practice for a generic problem at a generic company. Which is to say, it was designed for nobody. That's non-sense. Anybody who understand algorithms know that, sure, on a first instance you have a "good algorithm" that has a good performance on average, or in worst-case. But then, you can design algorithms that are adaptive to the input. Same applies here.

colonCapitalDee

Tip for the "author": Claude is not your writer either

oremj

I find interview loops great for catching edge cases and refining my hand written specs. I don’t doubt the problems in this article exist and I’ve seen them, in my experience the vast majority of people are still shipping the same quality or better than before they has Claude. Personally, I feel like I’m probably developing at about 1.5x the speed of not using AI tooling. It’s not a silver bullet, but it can be a great assistant.

sandeepkd

Your search results from these systems are as good as your queries and it takes experience in itself to get good with queries. AI is just a tool like any other, however its really impactful and can cut both ways. Tangentially, the usage of Architect keyword sounds out of place here, I don't like saying it but from what I seen the industry has destroyed the role of architects gradually over the time. There are specialists however you do not have generalists who are good at different parts of the system at scale anymore.

ISL

Accountability is the biggest unaddressed challenge for AI implementation. When one person is able to do too much too quickly, they can create more liability than they can accommodate if something fails. It is essential that a human is responsible for the utilization of any AI output in the real world, but that is not enough. For our own sakes, we must find ways to minimize the tech-debt bankruptcy blast-radius of those who would utilize (knowingly or unknowingly) AI to create flawed systems upon which others rely. An example: Jim vibe-codes an extremely popular micropayments app. He hires a few people and sees the company as the WhatsApp of money -- a few engineers and some agentic support staff. It pulls in a few million in VC money -- enough to draw in tens of millions of users. One day, a flaw in the infrastructure causes all of the users' unsalted banking information to be released. Agentic AI allows that entire list of customers to be exploited rapidly, so the losses for society are in the tens of billions. Jim's company is immediately bankrupt, of course, but there are only a few million dollars to go around. Today, most of Jim's incentives are to go ahead and build that app. The same is true for his few employees and a small VC contribution. There's not much capital at risk compared with the societal exposure. How do we ensure that AI users are accountable not just for their actions, but for the size of the risk-exposure that they create?

michaelteter

As I keep saying, the problem isn't the tools - it's the humans who don't know what they don't know ----- and assume that what they don't know is insignificant ----- and just plow forward with their authority and/or money. We can describe this without talking about technology - so pre-AI. Imagine the owner of a construction company firing all the architects. After all, he's been the owner for 15 years. He has led the construction of dozens of projects. He's also rich, and being rich seems to be an ego-multiplier. Why should he waste money on architects? Or more importantly, why should he allow them to constantly annoy him with pushbacks: "This could be a problem if the sustained wind is greater than ... ". Those engineers obviously don't know the real world. Their elitist education has made them afraid to make bold decisions. Regulations are anti-progress! Thankfully, that owner now has AI tools. He doesn't need those not-always-yes-people. He now has a perpetual yes-bot. So where are we now? We're in the same place we always have been. People need to have the humility to recognize that despite their authority, influence, or wealth, they still need other people. And especially, they need other people to challenge their orders or their requests. But I don't really see this situation self-correcting. There's now so much money concentrated amongst a few who will spray it over exactly the kind of people who do not want to listen to others that most activity in the future will be for naught. Yes, some unicorns will be fabricated, and some people will make a lot of money; but real value will not be created often. Therefore, I implore the actual thoughtful creators: Do build things, but do not sell out. Look to the past. Create companies where every employee was valued, and every employee had some voice. Yes, use AI. But test and measure where it really helps. And be skeptical, just as you would if someone came to your door promising a black box that would double your profits.

hluska

It’s interesting; I haven’t gotten that deep into agentic but use generative AI constantly as a rubber duck that can sometimes come up with something insightful that I missed slash a very enthusiastic junior developer. I generally use chat sessions, often give it specific tasks and then fix anything I don’t quite like. It’s been a great tool, almost like a search engine built for me, but it’s not an architect for me. It’s just a tool and fundamentally, it’s just replaced having dozens of browser tabs open all day. It’s been quite good for my productivity and the best part for me is that I learn what I’m writing while I’m writing. I can just write things I already understand a lot faster than before. When I work with agentic, I find that I still have to deeply learn the system, but I’ll have to learn it when it falls over instead of at review time.

sumitkumar

so can we all agree that LLM models/agents are bad at BFS for exploring a problem space but are good at DFS to implement a solution if the context/requirements are rich enough.

stavros

Agreed, but also please stop letting it write your articles.

tayo42

The attaboy problem I thought this happened alot. I started using chatgpt to critique my new art hobby and also help me learn unreal engine. It's basically tearing into me on the art. It's almost ruthless, especially with the verbosity it's like I get it. Using it for unreal engine, it pushes back on alot of my begginer ideas and how to write code that uses the engine. It corrects me alot. It's called things I wrote becasue I was lazy sloppy or quick hacks that work for now.