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.
This is basically the right approached, framed as critique. Success with these models means engaging in detail with their work, persistently and at all scales. You need attention to detail, ability to evaluate (independent from the model), and mechanisms for enforcing all that. In a word: engineering.
But because people get all bent out of shape I prefer to call it vibe coding anyway.
>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 enjoy writing code a lot more than reviewing it, I am going to prefer NOT using it for writing and might just use it to review.
So "hardness" is also related to how much you like/dislike doing it.
> 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, starting with Claude Code, I can get there, but my mindset is that I'm using this tool to generate software that helps me immerse myself in the problem space. It's a different pace to the process - sometimes it speeds me up, sometimes I end up taking bad concepts a lot further than I normally would before getting to the better path
> 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 confused and frustrated making sense of the result.
Whereas, when I am asking AI small things like "edit this function so it does this instead", and accepting changes manually, my mental model stays synced the whole time. And I can stay active and in flow.
(Also for such fine grained tasks, small fast cheap models are actually superior because they allow realtime usage. Even small latency makes a big difference.)
>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 their code using agents. I had to double check the year on the post to see if this was actually posted in 2026.
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.
> 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.
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.
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[ 0.86 ms ] story [ 38.4 ms ] threadBut because people get all bent out of shape I prefer to call it vibe coding anyway.
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 enjoy writing code a lot more than reviewing it, I am going to prefer NOT using it for writing and might just use it to review.
So "hardness" is also related to how much you like/dislike doing it.
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, starting with Claude Code, I can get there, but my mindset is that I'm using this tool to generate software that helps me immerse myself in the problem space. It's a different pace to the process - sometimes it speeds me up, sometimes I end up taking bad concepts a lot further than I normally would before getting to the better path
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 confused and frustrated making sense of the result.
Whereas, when I am asking AI small things like "edit this function so it does this instead", and accepting changes manually, my mental model stays synced the whole time. And I can stay active and in flow.
(Also for such fine grained tasks, small fast cheap models are actually superior because they allow realtime usage. Even small latency makes a big difference.)
This position is untenable when from my perspective everyone writes all of their code using agents. I had to double check the year on the post to see if this was actually posted in 2026.
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.
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.
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.