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Against vibes: When is a generative model useful

williamjbowman.com

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Re: Against vibes: When is a generative model useful

#11

Post by mekael »

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…

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 with business partners.

Despite my ethical issues with AI, I am using it for a handful of personal projects so I am at least keeping up with what the frontier models are doing and I'm quite impressed with them for doing reverse engineering (they need a lot of hand holding, but I've been able to knock out months of trial and error pretty quickly).

That being said, I'm still perplexed when people state they're getting huge gains from them in terms of knocking out boilerplate, or helping them plan out the project. I was under the impression that the former was a solved problem, and the latter was a requirement of being a decent engineer.

Re: Against vibes: When is a generative model useful

#12

Post by janalsncm »

LLMs have significantly reduced the time I’ve spent chasing down cryptic errors on stack overflow, old github issues, or asking in random slack channels about it. Even if that’s all they did, they would be very valuable.

If that means I’m actually coding instead of figuring out why xyz random plugin isn’t doing its job right now, some subsystem that I need but don’t care to learn the internals of, then I am happy.

Re: Against vibes: When is a generative model useful

#13

Post by lukan »

andai wrote: post #8

> What is the cost of verifying the generated artifact meets requirements vs. a directly produced artifact? This is mostly a function of the task and the user, but also the generative model. So this is the fun one for programming. I let AI agents do some programming on my codebases, but then I had to spend more time catching up with their changes. So first I was bored waiting for them to finish, and then I was confus…

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. )

Re: Against vibes: When is a generative model useful

#14

Post by linkregister »

SOLAR_FIELDS wrote: post #5
qsera wrote: post #4

>The scientific version of these claims is “the total encoding cost (for some class of tasks) is lower than previous models” I wonder why? Can the new models read mind? > For example, I was recently trying to install a package whose name I forgot. I prompted the model to “install that x11 fake gui thing”, a trivial prompt. Yes, they are a better search. I would also add that there is also a subjective factor. If I en…

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…

I think that the skill of hand-writing software is still useful in 2026. The vast majority of programming is a module calling another API. This does not spark joy. Truly interesting classes of problems —application of algorithms or applying complex arcane knowledge— are often not handled well by LLMs. Also, what the author wrote really strikes a chord. We should write the exceptionally difficult sections ourselves so we understand how the software operates.

This reminds me of the observation that Anthropic's unsupervised LLM-generated Rust implementation of sqlite3 was correct for the subset of features they chose, but thousands of times slower (wall clock). Of course, performance will be the next skill to be targeted by expert-led RHLF, but this is a hard problem with many tradeoffs. It may prove to be time-consuming to improve.

Re: Against vibes: When is a generative model useful

#15

Post by charcircuit »

mekael wrote: post #11
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…

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, what race conditions exist, what pitfalls or other things you may run into saves a lot of time in regards to planning.

Re: Against vibes: When is a generative model useful

#16

Post by lukan »

neonstatic wrote: post #7
smilindave26 wrote: post #6

> For almost all software I write, I do care about the process. I’m typically designing software as part of research, and me doing the design and implementation work creates knowledge that I will then share. Similar here. For a lot of software I write, I don't really know what the essential "abstraction" I need is until I'm actively writing it. The answers, when I get them right, look obvious in retrospect. Sometimes…

I agree it's a different process. Personally, I do not enjoy it. If I get code wrong or the solution I came up with is clunky, I am okay to start over. At least I learned something valuable. With Claude, I get irritated, frustrated, and frankly just really tired. I feel like I've been burning hour after hour of my precious time trying to explain something to a machine, which just doesn't understand, cannot understand…

" 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.

Re: Against vibes: When is a generative model useful

#17

Post by webn3rd »

7777777phil wrote: post #2

I particularly like this framework: how hard is it to describe the task vs. how hard is it to check the output.

What's more, this can be made conditional on one's linguistic intelligence. Some people can simply convert their thoughts into written language much more effectively than others. They have a natural advantage when it comes to writing prompts that actually... work, whereas others might struggle with the results that their prompts produce. It may therefore be crucial to assess the usefulness of generative models relative to oneself, not to a group of people.

Re: Against vibes: When is a generative model useful

#18

Post by neonstatic »

lukan wrote: post #16
neonstatic wrote: post #7

Earlier quoted context omitted.

I agree it's a different process. Personally, I do not enjoy it. If I get code wrong or the solution I came up with is clunky, I am okay to start over. At least I learned something valuable. With Claude, I get irritated, frustrated, and frankly just really tired. I feel like I've been burning hour after hour of my precious time trying to explain something to a machine, which just doesn't understand, cannot understand…

" 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?

Re: Against vibes: When is a generative model useful

#19

Post by qsera »

SOLAR_FIELDS wrote: post #5
qsera wrote: post #4

>The scientific version of these claims is “the total encoding cost (for some class of tasks) is lower than previous models” I wonder why? Can the new models read mind? > For example, I was recently trying to install a package whose name I forgot. I prompted the model to “install that x11 fake gui thing”, a trivial prompt. Yes, they are a better search. I would also add that there is also a subjective factor. If I en…

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?

Re: Against vibes: When is a generative model useful

#20

Post by dreadnip »

lukan wrote: post #13
andai wrote: post #8

> What is the cost of verifying the generated artifact meets requirements vs. a directly produced artifact? This is mostly a function of the task and the user, but also the generative model. So this is the fun one for programming. I let AI agents do some programming on my codebases, but then I had to spend more time catching up with their changes. So first I was bored waiting for them to finish, and then I was confus…

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.
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