I think it is interesting that, at least thus far, Apple has chosen not to raise the price of their comps despite presumably the price of RAM going up multiples.

Tipping point for me: It will be a pretty kickass media server for at least a decade.

I'm reminded of that scene in "Ghost in the Shell" where some guy ask the Major why he is on the team (full of cyborgs) and she responds something along the line of "Because you are basically un-enhanced (maybe without a ghost?) and are likely to respond differently then the rest of us; Overspecialization is death."

I think a diversity of opinion is important for society. I'm worried that LLM's are going to group-think us into thinking the same way, believing the same things, reacting the same way.

I wonder if future children will need to be taught how to purposely have their own opinions; being so used to always asking others before even considering things on their own? The LLM will likely reach a better conclusion than you would on your own, but there is value in diverging from the consensus and thinking your own thoughts.

https://stephencagle.dev/posts-output/2025-10-14-you-should-...

Beliefs: At this time, I do not actually believe that LLM's can innovate in any real way. I'm not even clear if they can abstract. I think the most creative thing they can do is act as digital "nudgers" on combinatorial deterministic problems; illustrated by their performance on very specific geometry and chemistry problems.

Anyway, my point is that I think they may still need human beings to actually provide novel solutions to problems. To handle the unexpected. To simplify. LLM's can execute once they have been trained, but they cannot train themselves.

In the past, the saying in silicon valley was often "ideas are cheap". And there was some truth to that. Execution was far more difficult then the idea itself. Execution was so much more difficult than "pure thought" that you could often publicize the algorithmic/process/whatever that you had and still offer a product/service/consultancy that made use of it. The execution was the valuable thing.

But LLM's execute at a cost that is fractional of human cost and multiples of human development speed. The idea hasn't increased in value, but the execution cost has decreased markedly. In this world, protecting the idea is far more valuable than it is in the previous world. You can't keep your competitors away by out executing them, but you can keep them away if you have some advantage that they do not understand.

And, I agree, that is quite worrisome. If people don't share knowledge then knowledge disseminate much more slowly as everyone has to independently learn things on their own. That is a frightening future.

- Infinite Scrolling?

- Play Next Video Automatically?

- Shorts?

- Matching to your peer group?

- Variable Reward?

- Social Reciprocity?

- Notifications?

- Gamification (Streaks)?

Was the case won on the argument that it is the aggregate of these things (and many more I am sure)? The power imbalance between the user and the company? Was it some particular subset of them that they rest their argument on? I'm just genuinely curious how you can win a very challenging case like this without inadvertently lassoing so many other industries that your arguments seem ludicrous?

tldr; Just like knowledge work, most trade stuff is probably mostly repeated (i.e. very trainable) task with a small amount of taste and discernment applied. The repeated will be trainable, the discernment may be trainable. I don't think the physical world is necessarily any safer than the knowledge world.

Basically, I want to be able to run completely unverified code off of the internet on my local machine, and know that the worst thing it can possibly due is trash its own container.

I feel like NixOS, is one path toward getting to that future.

I've seen that problem in an interview before, and I thought the solution I hit upon was pretty fun (if dumb).

  class Solution:
      def findMin(self, nums: List[int]) -> int:
          class RotatedList():
              def __init__(self, rotation):
                  self.rotation = rotation
              def __getitem__(self, index):
                  return nums[(index + self.rotation) % len(nums)]
  
          class RotatedListIsSorted():
              def __getitem__(self, index) -> bool:
                  rotated = RotatedList(index)
                  print(index, [rotated[i] for i in range(len(nums))])
                  return rotated[0] < rotated[len(nums) // 2]
              def __len__(self):
                  return len(nums)
  
          rotation = bisect_left(RotatedListIsSorted(), True)
          print('rotation =>', rotation)
          return RotatedList(rotation)[0]

Good luck on your second round!

My own experience...

I've tried approaching vibe coding in at least 3 different ways. At first I wrote a system that had specs (markdown files) where there is a 1 to 1 mapping between each spec to a matching python module. I only ever edited the spec, treating the code itself as an opaque thing that I ignore (though defined the intrefaces for). It kind of worked, though I realized how distinct the difference between a spec that communicates intent and a spec that specifies detail really is.

From this, I felt that maybe I need to stay closer to the code, but just use the LLM as a bicycle of the mind. So I tried "write the code itself, and integrate an LLM into emacs so that you can have a discussion with the LLM about individual code, but you use it for criticism and guidance, not to actually generate code". It also worked (though I never wrote anything more then small snippets of Elisp with it). I learned more doing things this way, though I have the nagging suspicion that I was actually moving slower than I theoretically could have. I think this is another valid way.

I'm currently experimenting with a 100% vibe coded project (https://boltread.com). I mostly just drive it through interaction on the terminal, with "specs" that kind of just act as intent (not specifications). I find the temptation to get out of the outside critic mode and into just looking at the code is quite strong. I have resisted it to date (I want to experiment with what it feels like to be a vibe coder who cannot program), to judge if I realistically need to be concerned about it. Just like LLM generated things in general, the project seems to get closer and closer to what I want, but it is like shaping mud, you can put detail into something, but it won't stay that way over time; its sharp detail will be reduced to smooth curves as you then switch to putting detail elsewhere. I am not 100% sure on how to deal with that issue.

My current thoughts is that we have failed to actually find a good way of switching from the "macro" (vibbed) to the "micro" (hand coded) view of LLM development. It's almost like we need modules (blast chambers?) for different parts of any software project. Where we can switch to doing things by hand (or at least with more intent) when necessary, and doing things by vibe when not. Striking the balance between those things that nets the greater output is quite challenging, and it may not even be that there is an optimal intersection, but simply that you are exchanging immediate change for future flexibility to the software?

I try not to run LLM's directly on my own host. The only exception I have is that I do use https://github.com/karthink/gptel on my own machine, because it is just too damn useful. I hope I don't self own myself with that someday.

I'm just making a slight point that walking is probably the simplest most effective thing you can do to improve almost every aspect of your life.

Like, somehow I could tell my agent that I have a $20 a month budget for entertainment and a $50 a month budget for news, and it would just figure out how to negotiate with the nytimes and netflix and spotify (or what would have been their equivalent), which is fine. But would also be able to negotiate with an individual band who wants to directly sell their music, or a indie game that does not want to pay the Steam tax.

I don't know, just a "histories that might have been" thought.

I have noticed that LLM's seem surprisingly good at translating from one (programming) language to another... I wonder if transforming a generic mathematical expression into an expression template is a similar sort of problem to them? No idea honestly.

tldr; It is great at search, not so much action.