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> - "Add these domains with these mailboxes to an SMTP server running on my homelab, forward all incoming and outgoing via mxroute" I laughed reading this. Hav

> - "Add these domains with these mailboxes to an SMTP server running on my homelab, forward all incoming and outgoing via mxroute" I laughed reading this. Have you actually tried it? I recently tried to get Claude (Opus) to give me a setup for a simple SMTP relay with Postfix. I had to constantly fight it not to include completely unnecessary components and configurations like Dovecot or just override my requirements. Even Claude is nothing but a semi-competent tutorial clicker. To someone who never did anything like this themselves the result might seem fantastic. They might not battle Claude on details like I did because "hey, it works, doesn't it". Don't get me wrong, I agree in general that LLMs are a significant milestone. But I would like to add, especially because of your tone, that perception of how much of a leap it is, is very likely a matter of individual competence. So your enthusiasm might say more about yourself than about the tool.

rsync 2mo ago
"To someone who never did anything like this themselves the result might seem fantastic ..." I've been "doing this" for over 30 years now and I consider these LLM results to be fantastic. If one has a deep, conceptual understanding of tools, systems and protocols and if one has the ability to carefully provide careful, concise specifications, these LLM tools are like being granted superpowers. In fact ... My next project upon which to bring LLM tools to bear is a fully local, solar-powered, self-hosted LLM toolchain. Not only do I intend to never work without these tools again, I intend to do so without the ongoing environmental externalities that they produce.
- goatlover 2mo ago
  But you do plan on continuing to work, not have the AI bootstrap itself into a machine god and do all possible work for humanity instead.
- oblio 2mo ago
  > If one has a deep, conceptual understanding of tools, systems and protocols and if one has the ability to carefully provide careful, concise specifications Most people already don't have the ability to do this and their use of LLMs atrophies the little they had. "AI natives" will be completely doomed.
- jazzyjackson 2mo ago
  Umm, hate to break it to you, but by the time you load up an open weights model, the environmental externality has already happened
  - rsync 2mo ago
    This is a fair point - and well taken. However, I never asked for LLMs nor did my consumer nor professional behavior send market signals that I would use them. They were thrust upon me apropos of nothing. Now that I find them useful, the least I can do is stop making the problem worse.
    - jazzyjackson 2mo ago
      As a rule of thumb the energy use to produce a silicon wafer is roughly equal to the energy it will use in its lifetime. For GPU and VRAM this is considerable. To equip yourself with the capability to run an LLM locally is making an upfront investment in the production of extremely toxic and resource intensive products. Why buy a personal EV with 600 mile range that sits idle 95% of the time when you can take a bus (aka the cloud) that maximizes the resource utilization?
keeganpoppen 2mo ago
[flagged]
- annzabelle 2mo ago
  I mostly view AI as raising me up one more level of abstraction in coding. It still requires babysitting and verification if you want anything vaguely maintainable that's not pulling in a ton of irrelevant libraries, but now I am able to give a clear spec for a ~50-100 line task and it'll get it 90% right, and it's a small enough chunk I can validate it well. Claude though is still slow enough that I think it works out to maybe a 50% improvement at my time to do a task, but sacrificing deep knowledge and learning. I worry that I'll never develop the same skills and intuition that senior engineers developed a decade ago because I'm outsourcing a lot of the planning and thinking.
- nemosaltat 2mo ago
  that’s perhaps a bit harsh, and I’m late (intentionally) to all this as I’ve worried about personal atrophy, security, and feeding the shoggoth but also with my instructions it will churn on prompts for hours. It makes use of my local rags by domain (which it also helped me build and maintain). Most recent example: it’s been on my list to set up headscale for like 5 years now. I know how to do it, I’ve read the docs and kept putting it off, partially cuz Tailscale works and has a generous free tier, and partially because I didn’t want to strand assets that I’d somewhat shortsightedly hardened to only accept ssh from my Tailnet. claude did it all in 3 hours, while I worked on other stuff, prompting for `op` access when it needed keys, and logging each usage, with minimal involvement from me. All with more documentation, roll back points, and “learnings” propagated to the appropriate rags than I could have created in a weekend session. It made mistakes (some that I likely would have made, like region settings) then reasoned about it, corrected and tested, all transparently, all git backed, all posted about on my Mattermost server as it went, so I can easily search it the way I would of an extremely detailed teammate did it. I don’t know what to make of it all, but letting it churn through my backlog has been eye-opening.
  - keeganpoppen 2mo ago
    surely this is closer to the common experience with LLMs... i don't know how people don't see it for the miracle it is. it's like if God himself came down from the heavens and said "no one will ever die of hunger ever again, but you have to run 5 miles a week" and we all got bogged down in how hard it is to run 5 miles... god, that analogy was awful. sorry. xD

rsync 2mo ago

"To someone who never did anything like this themselves the result might seem fantastic ..." I've been "doing this" for over 30 years now and I consider these LLM results to be fantastic. If one has a deep, conceptual understanding of tools, systems and protocols and if one has the ability to carefully provide careful, concise specifications, these LLM tools are like being granted superpowers. In fact ... My next project upon which to bring LLM tools to bear is a fully local, solar-powered, self-hosted LLM toolchain. Not only do I intend to never work without these tools again, I intend to do so without the ongoing environmental externalities that they produce.

goatlover 2mo ago
But you do plan on continuing to work, not have the AI bootstrap itself into a machine god and do all possible work for humanity instead.
oblio 2mo ago
> If one has a deep, conceptual understanding of tools, systems and protocols and if one has the ability to carefully provide careful, concise specifications Most people already don't have the ability to do this and their use of LLMs atrophies the little they had. "AI natives" will be completely doomed.
jazzyjackson 2mo ago
Umm, hate to break it to you, but by the time you load up an open weights model, the environmental externality has already happened
- rsync 2mo ago
  This is a fair point - and well taken. However, I never asked for LLMs nor did my consumer nor professional behavior send market signals that I would use them. They were thrust upon me apropos of nothing. Now that I find them useful, the least I can do is stop making the problem worse.
  - jazzyjackson 2mo ago
    As a rule of thumb the energy use to produce a silicon wafer is roughly equal to the energy it will use in its lifetime. For GPU and VRAM this is considerable. To equip yourself with the capability to run an LLM locally is making an upfront investment in the production of extremely toxic and resource intensive products. Why buy a personal EV with 600 mile range that sits idle 95% of the time when you can take a bus (aka the cloud) that maximizes the resource utilization?

keeganpoppen 2mo ago

[flagged]

annzabelle 2mo ago
I mostly view AI as raising me up one more level of abstraction in coding. It still requires babysitting and verification if you want anything vaguely maintainable that's not pulling in a ton of irrelevant libraries, but now I am able to give a clear spec for a ~50-100 line task and it'll get it 90% right, and it's a small enough chunk I can validate it well. Claude though is still slow enough that I think it works out to maybe a 50% improvement at my time to do a task, but sacrificing deep knowledge and learning. I worry that I'll never develop the same skills and intuition that senior engineers developed a decade ago because I'm outsourcing a lot of the planning and thinking.
nemosaltat 2mo ago
that’s perhaps a bit harsh, and I’m late (intentionally) to all this as I’ve worried about personal atrophy, security, and feeding the shoggoth but also with my instructions it will churn on prompts for hours. It makes use of my local rags by domain (which it also helped me build and maintain). Most recent example: it’s been on my list to set up headscale for like 5 years now. I know how to do it, I’ve read the docs and kept putting it off, partially cuz Tailscale works and has a generous free tier, and partially because I didn’t want to strand assets that I’d somewhat shortsightedly hardened to only accept ssh from my Tailnet. claude did it all in 3 hours, while I worked on other stuff, prompting for `op` access when it needed keys, and logging each usage, with minimal involvement from me. All with more documentation, roll back points, and “learnings” propagated to the appropriate rags than I could have created in a weekend session. It made mistakes (some that I likely would have made, like region settings) then reasoned about it, corrected and tested, all transparently, all git backed, all posted about on my Mattermost server as it went, so I can easily search it the way I would of an extremely detailed teammate did it. I don’t know what to make of it all, but letting it churn through my backlog has been eye-opening.
- keeganpoppen 2mo ago
  surely this is closer to the common experience with LLMs... i don't know how people don't see it for the miracle it is. it's like if God himself came down from the heavens and said "no one will ever die of hunger ever again, but you have to run 5 miles a week" and we all got bogged down in how hard it is to run 5 miles... god, that analogy was awful. sorry. xD