Against vibes: When is a generative model useful • Hacker News Board

Re: Against vibes: When is a generative model useful

#21

Post by swiftcoder » Thu, Mar 12, 2026, 9:37 AM UTC

charcircuit wrote: post #10

>I’m upset now when people are making claims that agents are so useful, but can’t tell me when or why or how they’re useful beyond vibes about feeling more productive (vibes that have been refuted by real science contrasting objective measure of productivity vs. subjective reports), or examples of having produced a lot of plausible output. This position is untenable when from my perspective everyone writes all of the…

> from my perspective everyone writes all of their code using agents

HN/twitter/etc may be something of a bubble in that regard. As far as I can tell out in the real world, most normal software developers are much more likely to be using LLMs as fancy auto-complete than to be using agents.

Re: Against vibes: When is a generative model useful

#22

Post by lukan » Thu, Mar 12, 2026, 9:48 AM UTC

neonstatic wrote: post #18

lukan wrote: post #16

Earlier quoted context omitted.

" Pattern recognition, sure. Not intelligence. Not even close." To me it is a form of intelligence, just not general intelligence. And yes, the trick is not treat them as intelligent, but like an idiot. Explain every single detail. Document everything in detail. Remove anything distracting. And then it might work like a charm at times.

No to be nitpicky or difficult, but I find it strange that we don't really have a solid, agreed upon definition of intelligence, but suddenly we have variants of the non-definition - general, super, etc. I think it's just marketing fluff. If the model understood what it sees, it wouldn't need to be treated like someone who doesn't? And if it doesn't understand, how can it be intelligent?

I don't have the answer here.

I just know, that if I would point a average human to a messy old codebase, he or she would just shrug helplessly. Even most programmers.

But if I tell claude to start digging in, refactor, update outdated tools .. it produces results. So there is some "understanding" I don't know how else to call it. So surely it is not a general intelligence, but it is certainly useful.

Re: Against vibes: When is a generative model useful

#23

Post by jgammell » Thu, Mar 12, 2026, 11:44 AM UTC

> Generative models are probabilistic: the output will be less likely to satisfy complex requirements, particularly

This is a misinformed 'critique' which always gets on my nerves, as someone who actually works with AI. The world is random. Generative models are only random in the sense that they randomly sample from the set of correct answers for a given problem (ideally). Of course LLMs make mistakes, but this has nothing to do with the fact that they are random.

Re: Against vibes: When is a generative model useful

#24

Post by mekael » Thu, Mar 12, 2026, 12:21 PM UTC

charcircuit wrote: post #15

mekael wrote: post #11

Earlier quoted context omitted.

Most people I know don't write any code with agents, 90ish percent is still written by hand. I'm personally still trying to figure out where to fit them into my workflow: there's not much boilerplate to write as well known frameworks and libraries had already taken care of the heavy lifting, templates for major project types cut down on the initial startup overhead, and all of the project planning is done upfront wit…

>the former was a solved problem It want solved. There was some generic boilerplate that was added to IDEs but it wouldn't be project specific. It wasn't able to look at patterns within your own codebase and repeat them. >and the latter was a requirement of being a decent engineer. Most software projects are too big to fit in one engineers head. Having AI be able to research what the relevant code is, how it works, w…

> It want solved. There was some generic boilerplate that was added to IDEs but it wouldn't be project specific. It wasn't able to look at patterns within your own codebase and repeat them.

I left out some expository, apologies. For example, I rely pretty heavily on using VSA as a way to keep things self contained and thus created templates for each different functionality type (background worker, crud action, etc) where they already have the logging/database connection/code flow pattern, I just need to shove in whatever business logic is needed. Now, that logic is the hard part and would require me to explain it in excruciating detail to Claude, or write it out by hand, but it take the same amount of time either way.

Re: Against vibes: When is a generative model useful

#25

Post by dominotw » Thu, Mar 12, 2026, 12:59 PM UTC

dreadnip wrote: post #20

lukan wrote: post #13

Earlier quoted context omitted.

Yes, the more you let agents loose, the less you are in control and the more time you spend later cleaning up their mess. It is tempting letting them loose, after they delivered unexpectedly good results for a while, but for me it is not worth it. Manually approve and actual read. (And manually edit CLAUDE.md etc. if necessary. )

This is exactly why I don't like those "swarm" approaches with 8 Claude Code's running in parallel. Every time I've tried it I instantly lose control and become out of touch with the codebase. The quantity of the produced output is simply too fast & large to follow, so I tune out and it becomes a 100% vibe coded project.

start with good prompts and good intentions , drift into sloppy prompt vibecoding ,finally "still not working" prompt in a loop.

this has been my story in every one of my personal projects.

Re: Against vibes: When is a generative model useful

#26

Post by neonstatic » Thu, Mar 12, 2026, 7:15 PM UTC

lukan wrote: post #22

neonstatic wrote: post #18

Earlier quoted context omitted.

No to be nitpicky or difficult, but I find it strange that we don't really have a solid, agreed upon definition of intelligence, but suddenly we have variants of the non-definition - general, super, etc. I think it's just marketing fluff. If the model understood what it sees, it wouldn't need to be treated like someone who doesn't? And if it doesn't understand, how can it be intelligent?

I don't have the answer here. I just know, that if I would point a average human to a messy old codebase, he or she would just shrug helplessly. Even most programmers. But if I tell claude to start digging in, refactor, update outdated tools .. it produces results. So there is some "understanding" I don't know how else to call it. So surely it is not a general intelligence, but it is certainly useful.

I think what you are describing is the actual usefulness of the tech. It can do some things and, contrary to humans, it doesn't get demotivated or uninterested, it's a machine.

I will stick to my earlier statement (I hope I made it in this thread) - it seems to be treating blocks of texts as concepts and tries to compose those concepts like lego blocks. It is quite amazing that it can transform characters into meanings, even if it doesn't really understand these meanings, then compose them. I just don't think that's enough to call it intelligent (but certainly it's enough to find it useful for some tasks, as you point out).

Re: Against vibes: When is a generative model useful

#27

Post by h4kunamata » Fri, Mar 13, 2026, 5:50 AM UTC

When you know what you are sking it to help you with.

I wanna to build a Proxmox LXC container via Ansible playbook, both things I know and use in my homelab.

It has to be 4 services running within the same container, VPN and what not. That would take me forever to find the latest and recommended:

* Each service installation proccess

* Known issues and workaround

* Firewall and what not

It sill took me 3 nights because I had to replace one of the services. I am not expert into iptables firewall, it helped me with that.

Ansible playbook was a hit and miss but it gave me the start so I fixed what was wrong and voila.

The problem is people using it for copy/paste, it works it is good enough. No understand of what is happening, security issues and alike.

Re: Against vibes: When is a generative model useful

#28

Post by seedium_tech » Fri, Mar 13, 2026, 8:51 AM UTC

One thing I’d add is that usefulness also seems to depend on whether the task can be broken down. When you can split work into small pieces that are easy to check, generative models tend to work really well. But once those pieces start depending heavily on each other and the design constraints pile up, the pattern you describe shows up pretty clearly.

Re: Against vibes: When is a generative model useful

#29

Post by SOLAR_FIELDS » Sat, Mar 14, 2026, 12:35 AM UTC

qsera wrote: post #19

SOLAR_FIELDS wrote: post #5

Earlier quoted context omitted.

It does feel like with each new frontier model release the major improvement I notice is that the model is, in fact, getting better at reading your mind. And what I mean by that is that it gets better at understanding the nuance and the subtleties of the intent of what you are saying better, and teasing out the actual intent of what you want better. So it gets easier and easier for the model to build a world around l…

> teasing out the actual intent of what you want better. Do you mean they ask clarifying questions before generating a response?

Kind of. I mean that they have gotten way better at taking some braindead sentence like “trace the performance of this app” and deriving what you actually mean which involves looking at your codebase, identifying your deployment scenario, identifying the steps required to pull the traces, writing the query to sample the traces, then correlating it all together. Just an example, you say 5 words and it’s able to figure out exactly what you want it to do and it might ask questions to clarify but otherwise it’s really good at figuring out what you actually need.

In the dark before times of 6 months ago, the thing would go completely off the rails and fuck it all up. In today’s world, 80% of the time it’s gonna get you pretty close to what you actually want with literally 5 words for simple tasks.

Complex tasks require more upfront work but my anecdata has demonstrated for me that complex tasks are showing similar relative reductions in upfront planning and effort to succeed