I think the problem is that people:
* see the hype;
* try to replicate the hype;
* it fails miserably;
* they throw everything away;
I'm on call this week on my job, one of the issues was adding a quick validation (verifying the length of a thing was exactly 15). I could have sat and done that but I just spun an agent, told it where it was, told it how to add the change (we always add feature flags to do that), read the code, prompted it to fix a thing and boom, PR is ready. I wrote 3 paragraphs, didn't have to sit and wait for CI or any of the other bullshit to get it done, focused on more important stuff but still got the fix out.
Don't believe the hype but also don't completely discount the tools, they are incredible help and while they will not boost your productivity by 500%, they're amazing.
Instead my productivity would be optimised in service of my employer, while I still had to work on other things, the more important work you cite. It's not like I get to finish work early and have more leisure time.
And that's not to mention, as discussed in the video, what happens if the code turns out to be buggy later. The AI gets the credit, I get the blame.
You should be aiming to use AI in a way that the work it does gives you more time to work on these things.
I can see how people could end up in an environment where management expects AI use is expected to simply increase the speed of exactly what you do right now. That's when people expect the automobile to behave like a faster horse. I do not envy people placed in that position. I don't think that is how AI should be used though.
I have been working on test projects using AI. These are projects where there is essentially no penalty for failure, and I can explore the bounds of what they offer. They are no panacea, people will be writing code for a long while yet, but the bounds of their capability are certainly growing. Working on ideas with them I have been able to think more deeply about what the code was doing and what it was should do. Quite often a lot of the deep thinking in programming is gaining a greater understanding of what the problem really is. You can gain a benefit from using AI to ask for a quick solution simply to get a better understanding of why a naive implementation will not work. You don't need to use any of that code at all, but it can easily show you why something is not as simple as it seems at first glance.
I might post a show HN in a bit of a test project I started over the Christmas break. It's a good example of what I mean. I did it in Claude Artifacts instead of using Claude Code just to see how well I can develop something non-trivial in this manner. There have been certainly been periods of frustration trying to get Claude to understand particular points, but some of those areas of confusion came from my presumptions of what the problem was and how it differed to what the problem actually was. That is exactly the insight that you refer to as the tasty bits.
I think there is some adaptation needed to how you feel about the process of working on a solution. When you are stuck on a problem and are trying things that should make it work, the work can absorb you in the process. AI can diminish this, but I think some of that is precisely because it is giving you more time to think about the hard stuff, and that hard stuff is, well, hard.
In a sense, AI coding is like using a 3D printer. The machine outputs the final object but you absolutely decides how it will look like, how it will work.
Yes.
I use LLMs for coding in the exact opposite way as described in the video. The video says that most people start big, then the LLM fails, then they reduce the scope more and more until they're actually doing most of the work while thinking it's all the machine's work.
I use AI in two ways. With Python I ask it to write micro functions and I do all of the general architecture. This saves a lot of time, but I could do without AI if needed be.
But recently I also started making small C utilities that each do exactly one thing and for those, the LLMs write most if not all of the code. I start very small with a tiny proof of concept and iterate over it, adding functionalities here and there until I'm satisfied. I still inspect the code and suggest refactorizations, or putting things into independent, reusable modules for static linking, etc.
But I'm not a C coder and I couldn't make any of these apps without AI.
Since the beginning of the year, I made four of them. The code is probably subpar but they all work great! and never crash, and I use them every day.
Different outlets tilt different directions. On HN and some other tech websites it's common to find declarations that LLMs are useless from people who tried the free models on ChatGPT (which isn't the coding model) and jumped to conclusions after the first few issues. On LinkedIn it's common to find influencers who used ChatGPT for a couple things at work and are ready to proclaim it's going to handle everything in the future (including writing the text of their LinkedIn post)
The most useful, accurate, and honest LLM information I've gathered comes from spaces where neither extreme prevails. You have to find people who have put in the time and are realistic about what can and cannot be accomplished. That's when you start learning the techniques for using these tools for maximum effect and where to apply them.
Do you have any pointers to good (public) spaces like this? Your take sounds reasonable, and so I'm curious to see that middle-ground expression and discussion.
This requires the level of professionalism that 97.56% of SWEs do not have
> * see the hype;
> * try to replicate the hype;
> * it fails miserably;
> * they throw everything away;
I'm sure doing two years of vibecoding is is a considerably more sincere attempt than "trying to replicate the hype and failing at it".
>There's a new kind of coding I call "vibe coding", where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.
Just under one year ago.
You need to pick and choose when to use them, it largely depends on the task.
I use AI a lot now for programming and spend more time code reviewing, sometimes though, I use my hands.
My nimber one rule is, don't try to do too much in one prompt.
This comment took 15s, typing can be very fast.
They literally are.
After that it’s the “ask” capability when I need to get oriented in unfamiliar and/or poorly documented code. I can often use the autocomplete pretty effectively once I understand the patterns and naming conventions.
Similarly, agents are good for a first pass triage and plan when troubleshooting tricky bugs.
Still haven’t had a good candidate for going full vibe code. Maybe that’s because I don’t do a lot of greenfield coding outside of work, which seems to be where it shines.
Just my experience. It’s new set of tools in the toolbox, but not always the right one for a given task.
I agree with this take... for now. I wouldn't be surprised if the AI agents improved exponentially (in the next few years) to the point where his statement is no longer true.
I truly don’t know how this is going to play out. Will the software industry just be a total mess until agents can actually replace developers? Or will companies come to their senses and learn that they still need to hire humans - just humans that know how to use agents to augment their work?
If AI can't replace developers, companies can't replace developers with it. They can try — and then they'll be met with the reality. Good or bad
Bingo. And it’s causing the careers of a majority of juniors to experience fatal delays. Juniors need to leap into their careers and build up a good head of steam by demonstrating acquired experience, or they will wander off into other industries and fail to acquire said experience. But when no-one is hiring, this “failure to launch” will cause a massive developer shortage in the next 5-15 years, to the point where I believe entire governments will have this as a policy pain point.
After all, when companies are loathe to actually conduct any kind of on-the-job training, and demand 2-5 years of experience in an whole IT department’s worth of skills for “entry level” jobs, an entire generation of potential applicants with a fraction of that (or none at all) will cause the industry to have figurative kittens.
I mean, it will be the industry’s own footgun that has hurt them so badly. I would posit it may even become a leggun. The schadenfreude will be copious and well-deserved. But it’s going to produce massive amounts of economic pain.
The 80/20 rule is a painful lesson to internalize but it’s damn near a universal constant now. That last exponential improvement that takes LLMs over the finish line will take a lot longer than we think.
And what do they run on? Information. The production of which is throttled by the technology itself, in part because the salespeople claim it can (and should) "replace" workers and thinkers, in part because many people have really low standards for entertainment and accept so called slop instead of cheap tropes manually stitched together.
So it would seem unlikely that they'll get the required information fed into them that would be needed for them to outpace the public internet och and widely pirated books and so on.
I am not worried about losing my programming role to AI.
I am worried about hiring employees and contractors. I haven’t had to hire anyone i office since, but I have specially avoided Upwork and new contractors. It’s too hard to tell if anyone knows anything anymore.
Everyone has the right or right enough answers for an interview or test.
The bar to detect bullshit has been moved deeper into the non-detectable range. It’s like everyone has open-book testing for interviews.
Even if I can sus out who is full of shit in a video or phone interview… the number of people I need to sort through is too large to be effective.
For Upwork specifically, this was an issue for years already. With people buying US accounts and lying about their location or subcontracting to cheaper foreign labor.
So, is vibe coding something I want to hire? Absolutely not. But, I don’t see being able to avoid it or at least not suffering from someone cutting corners.
I also like the comments on how developers should be frequently reading the entire code and not just the diffs. But again there is probably pressure to speed up and then that practice gets sacrificed.
this is true with or without AI
From what I've seen, companies using AI well are shipping better code because of all the artifacts (supporting context like Architectural Design Records, project-specific skills and agents, etc.) and tests needed to support that. I understand that many are not using AI well.
But that also doesn't mean they're useless. Giving comparatively tedious background tasks to the agents that I check in on once or twice an hour does feel genuinely useful to me today.
There's a balance to be found that's probably going to shift slowly over time.
Just for fun, once I had played a bit with it like that, I just told it to finish the application with some vague Jira-epic level instructions on what I wanted in it and then fed it the errors it got.
It eventually managed to get something working but... Let's just say it's a good thing this was a toy project I did specifically to try out Claude, and not something anyone is going to use, much less maintain!
So far, LLM generated code hasn't lived up to my standards. I'll use it for things that aren't critical as-is, but mostly I use it as a reference, an example, a starting point. Essentially, where in the past I'd find a code base that does things and I'd try to do something similar, now I let the LLM generate the code base. There are to questions it helps me answer:
1) What are the possible ways of solving problems?
2) What are the pros and cons of each approach?
That said, there are people successfully deploying apps that are entirely vibe coded. How many fail or succeed, that I don't know. But there are enough, and you can't deny the evidence.
I posted this comment using only curl by the way. Don't bother replying because I only engage in bidirectional conversation via SSH tunnel and netcat. I doubt that you could figure it out.
You're gonna be out of work in the next month or two if you don't jump on this train immediately.
With the power of Claude one engineer can do the work it used to take 30-40 employees to do.
Artisanal engineering is dead.
Human Language is the new code.
There's a time and place for refactoring, but just fixing an isolated bug isn't it. But I've seen that often AI can't help itself from making changes you didn't ask for.
Before AI, "now" was seen as the wrong place for a refactor because of the time and effort involved, but if the hacky patch of a fix is roughly the same amount of wall clock time as a 500 line proper fix that makes the problem go away forever, what's the argument that the 10 line hack is actually better?
The tooling is going to change how we do development no doubt, but people are going to find their comfortable spot, and be productive.
I am vibe coding, if I needed x, I lay that out task with any degree of specificity, and ask for the whole result. Maybe it’s good, I gave the LLM a lot of rope to hang me.
I am using an LLM for assistance if I need something like this file renamed, and all its functions renamed to match, and all the project meta to change, and every comment that mentions the old name to be updated. There is an objectively correct result.
It’s a matter of scope.
By this I mean give it precise instructions and ensure the instructions are always included in the context window. You’re responsible for managing the agents effective working memory, if that analogy makes sense. If you don’t tell it the way you want it to act, it’s effectively a blank slate.
Who is going to be responsible for the code? AI is definitely not responsible.
I mean I am left with two thoughts:
1. programming language skill issue. Some languages are simply much better at composition than others. I find that yes, this happens, but actually on the order of a day, and once the code is "good", it doesn't really change that much in the grand scheme of things?
2. Even for languages where composition is better, this is exactly what happens with human development too?
I think the mistake is assuming you get high quality work with an agent regardless of the driver’s skill level. That doesn’t appear to be the case, but it’s the value prop that is being sold.
It's the same junk produced by fast-growing startups. It was always here, now there's more of it.
I'm convinced that people will continue to use it. Therefore, I need to be able to deal with it, maybe even leverage it somehow (there should be coding skills that I possess that people in that realm of fast-growing startups do not bother to learn).
Honestly, without judgment, I think this is just a fundamental difference in how people approach their craft. You either want to be capable yourself or you just want the results.
This is almost the only thing I'm against when it comes to LLMs. You have no ability to figure out if it is right, and you will be overly impressed by garbage because you aren't qualified to judge. Has anybody come up with a pithy way to describe Dunning-Kruger for evaluating the output of LLMs, or are people too busy still denying that Dunning and Kruger noticed anything?
When it comes to implementing math, the main problem is that the tiniest difference can make the entire thing wrong, often to the degree of inverting it. I wouldn't be in any way comfortable in shipping something I didn't even understand. The LLM certainly didn't understand it; somebody should.
The video is spot on for codebases of products that are critical systems: payment, erp etc -> single source of truth.
Simple Crud apps/ Frontends for ecom that abstracted away the critical functionality to backend APIs (ERP, shop system, payment etc) benefit from vibe slop vs no shipping cadence
I've been trying Google antigravity. Despite VS code being total dogshit compared to Intellij I'm pretty sure I won't be renewing any more (after 15 years, sorry jetbrains).
And some dinosaurs even remember riding the CPU with mov rcx, 5.
We were just shifting our focus byte by byte from the nits and bolts towards the actual problem we are solving. In other words going less "hard"-ware, more and more "soft"-ware.
AI is just continuing this evolution, adding another abstraction layer in soft dev process.