After a (potentially long) session of investigation and planning _then_ you let it code. Even just being aware of this process is part of the skill involved (then you have to actually get used to doing it effectively).
You soon will. If not next year, then maybe the year after, or the year after that.
Whatever value you are providing will evaporate in a few years.
Are you planning on stopping eating in 3 years?
"No no no, when I said build twitter I meant without the character limit!"
but you will!! you absolutely will!
Exactly, you have to know what to ask, especially on large codebase. I still have to baby sit Astra and opus 5.5 into reusing existing structures/functions, not reinventing the wheels, telling them "yes this might be a legit race condition, but you added 5000 LOC and I'm pretty sure it will never ever be a problem in production given it would require 5 distinct catastrophic failures at once to trigger it, if it ever happens log it in sentry and we'll have a look by then".
Some people are really really bad and won't even think about asking stuff like "run a memory profile and see if we can decrease our footprint", "benchmark the top 3 options for this bug and keep the one that uses the less cpu but still respond under 150ms".
You still need to have a broad understanding of what a computer is and how it works, both are skills, which a lot of dev didn't have anyways, but now they can shit out a looooot more code
So I stand by my assertion that anyone can do this now, it's definitely not a hard to learn/master skill, and people who say it is are naively believing there is a moat around their career.
So people who choose to not use it are not losing out on building any skills- in fact if they are focusing on hand coding and learning and keeping their brain sharp they, in my opinion, will be in a much stronger place than the AI enthusiasts whose brains are rotting.
It's not that simple, you have to remember at the end of the day these things are just doing next token prediction. If you don't give it the proper tokens to attend to, then your outputs won't be satisfactory.
You can get stellar outputs from LLMs, but it really is a function of how well you manage your input tokens.
(the ironic thing is tho, people starting out with just llms will probably never progress to 'good dev' in the first place...)
I imagine there will be value in being able to understand every line of code for a while, because many (most?) things don't seem amenable to long term vibe coding. But how will that knowledge be obtained? Maybe there will be a much smaller pool of devs with the patience to actual learn bespoke software development.
Or, AI will actually get good enough that vibe coding is better in every case than mindful development. I guess the main factor is how long it takes for that to happen.
> Just a bunch of people who treat the codebase as a black box, pump out slop, and call it a day when the feature works?
my guess is yes, because that is what i am seeing today in front of me with new grads > Or, AI will actually get good enough that vibe coding is better in every case than mindful development. I guess the main factor is how long it takes for that to happen.
i think there is a limit to this; you could train an llm to never dereference a null pointer or something like that, and you could probably minimize other security related stuff, but in the end its a token predictor, so you stuck with probabilities... i wouldn't vibe anything that had any security/legal/user related things but thats just me.Like you I would not vibe code something important, but I think that's primarily because I want to have a deep understanding of a system before I accept responsibility for it. An LLM would still be useful during implementation and likely to increase the quality of the result.
It seems very likely that AIs will become capable enough that they can take complete responsibility for development, deployment and maintenance of the systems we're used to building. Further, it seems likely they'll end up much more capable than humans have been. I think that's an eventuality we need to be prepared for.
A well-utilised AI tool can probably already build a much safer and more robust security/legal/user related system than you or I can, in any given time budget. There's a question around what happens as humans are required less for the "well utilised" part, and who is responsible when humans are not really involved anymore.
As someone who's used more and more software made this way—either for hobby stuff or because I've had to for work—I just don't get it. You don't always get exactly what you ask for, and you often ask for something that is really sub-optimal in many ways.
The problem with waterfall wasn't ever solely "it takes a long time to write the code."
The trouble with this argument is that fundamentally this whole field has no sound theoretical foundation. It has no deterministic rules you can learn that will reliably get good results.
That also means there is no way to prove that what works well today will continue to work well tomorrow. Nor can you be sure that something you've tried and ruled out in the past because it didn't get satisfactory results won't be the new SOP next week. Any experience you gain in the field might be obsolete within hours.
An important consequence of this is that you can't train someone else to use LLM-based tools effectively either. At best you can train them to use specific tools and models that exist today effectively and hope the knowledge remains useful for a significant period of time. Even that assumes you have the knowledge yourself and aren't relying on something that worked well yesterday but could already be out of date after something new was released last night.
This looks like a fundamental problem that can never be fully solved as long as LLMs of the type that are popular today are behind the tools. People forget in all the hype and rapid change that the whole idea of agentic AI tools in mainstream development is barely a year old. We have no idea yet what the long-term consequences of so many people and organisations in the industry all but abandoning traditional programming skills in favour of AI agents will be. There already seems to be a lot of anecdotal evidence about programming skills atrophying, developers burning out, and the quality of the finished product dropping but it's probably too soon to have serious data to analyse or to draw any big conclusions about what is a good way forward from here.
You don’t have to look too far to see where we are going. But you do need to have your eyes open.
As I said - we have no idea yet what the long-term consequences of the recent rapid shift towards AI and agents will be. I prefer to make decisions based on evidence, not hearsay or wishful thinking.
I have been around long enough to see a lot of hype cycles in programming. They were all going to be revolutionary. They were all going to change the nature of programming forever. They were all going to result in dramatic increases to productivity (or other similar claims).
Many of those phases did turn out to have some good - sometimes excellent - ideas. A lot of those have since been widely adopted in the industry. They also had plenty of ideas that didn't really work out. In the end we have improved some areas to a useful degree but none of those phases resulted in orders of magnitude increases in useful productivity or quality.
So far LLMs and agents are looking like a familiar story. They're proving to be useful tools for some types of work. They're now quite good at producing code to the standard of an average developer doing a well understood task - and in many cases that is all you need! But the jury is still out on whether they'll ever be able to replace good programmers or build genuinely innovative products where there aren't lots of examples of good existing implementations to train the models. Those are the parts of the industry where I do most of my own work and I hear much more scepticism about these tools from those developers than in the average online discussion.
No, it's not. As evidence of intelligence atrophy, I offer up this word salad that barely makes any sense as evidence: someone barely able to write a coherent thought can apparently produce working software.
When was the last time you built something meaningful with it? I admit, LLMs do help and save tons of time and effort, yet building noteworthy software still remains a difficult task, with or without LLMs.
Funny, but your comment reminded me of my wife. Circa 2009, she was walking behind my back, she stopped to watch me work without me noticing. I was using Visual Studio (sluggish, temperamental mammoth, not its smaller cousin). She stared at my screen for a few minutes and suddenly exclaimed: "You're not even working, this thing is telling you what to do. I could probably do this shit too..." By "this thing" she obviously meant the intellisense completion that was giving me hints whenever I typed.
Sure, the skill of writing beautiful for loops and recursive binary search tree is basically worthless, but software is written for a purpose. That purpose is not gone. People will keep wanting POS system, accounting system, lawyers, manufacture stuff, personal assistant, etc etc
> Your job will be gone and so will mine.
I mean, that's probably true, but the person you are replying to is talking about the present.