Comment by lucianbr
3 days ago
Lots of people seem to think that you don't need to learn how to [scan a network], all you need to learn in this brave new world is how to prompt the agent to [scan a network].
Replace the content in brackets with anything.
The more time LLMs are a hyped thing now the more I realize how immensely important human expertise is. I recently stopped all usage of LLMs due to this. Skill degradation hits hard, learning effect is zero and the outcome is not really something a person without adequate expertise can properly judge. I fear we will loose a lot of human expertise due to this marketing stunt of a technology.
People often claim learning is actually supercharged with LLMs but to me it's the opposite. I didn't learn anything within the past year.
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The irony here that if you ever do any kind of practical woodworking lessons or general hands on craft work, metal working, or any 3D, you will be encouraged to use hand-tools over bandsaws, etc. The reasoning being so you know the fundamentals of what you're trying to achieve with the more complex tools later on.
It's always held true: You'll never get the most out of advanced tools unless you can 'do it by hand' so to speak
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You're very close but to woodworking AI is more akin to a 3d printer than even a CNC let alone swas and planes.
Yes, a 3d printer and not even a CNC. That difference nicely illustrates the difference of what AI brings to the table for any domain of competence.
> Sorry, but to me an LLM is nothing but a tool. It is not a replacement for my expertise and it is definitely not something to outsource my thinking to.
Great on you, that's indeed how LLMs should be used, proper. But if anything, the article demonstrates someone is trying to outsource thinking to an AI agent.
The weird thing is that this is the utopia that the AI companies are chasing - this is the best case scenario where AI doesn’t kill us all. We become happy sheep relying on the AI to think and provide for us.
"It is well that we are so foolish, or what little freedom we have would be wasted on us. It is for this that Book of Cold Rain says one must never take the shortest path between two points."
https://croissanthology.com/earring
You don’t need to achieve it, you just need to make people think you have. For the general population, that’s already happened.
To be honest lots of developers think they don’t need to learn machine code. They just need to learn a language which once compiled will produce machine code.
I wonder if a probabilistic compiler would be fine for the people arguing this. One that sometimes produces machine code that does something else, and sometimes produces machine code that is just broken and does nothing useful. From the same source code.
What if your compiler could be fooled by some other developers into spending thousands of dollars, and still not produce the desired machine code in the end?
I've run into compiler bugs before.
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This is different.
Understanding assembly/machine code is optional but helpful. The programming language semantics are enough to reason about what the program is doing. Other tools also help, but are optional for learning how to program.
Using an AI, there is no semantic model that can be used to reason through. You're left without any mental model of the proglblem at all.
I've been arguing for years that is isn't optional and treating it like it is is how we ended up with Electron and 400MB JavaScript websites.
When you have no mental model of the machine running your code or what the physical implications of code mean, you fundamentally lack the ability to reason or care about performance. "Works on my machine" is the original vibecoding.
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LLMs these days seem to have no problem using language semantics to conceptualize what’s happening in a program. This is my favorite use of an LLM, “why is this library doing x” and then it digs through the library itself in my venv to find an answer.
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Compilers are deterministic and, luckily, not agentic.
But yes, it's not obvious (or perhaps even likely) that it just happens that current high-level languages are the "correct" optimal level of abstraction at which you can ignore the sausage-making details at the lower levels. Ultimately, of course, it depends on the use case. Something like Python is so far removed from machine instructions that knowing assembly hardly gives the programmer any additional value.
(Also, obligatory reminder that assembly and even numeric machine code are also abstractions, an "API" provided by the CPU. Instructions get split or fused into micro-ops, named registers are a backwards-compatible abstraction over a much larger register file, instructions get reordered and executed in parallel depending on their data dependencies, a large fraction of the total transistor budget is spent on multi-level caches and cache logic to maintain the illusion of fast access to a single, uniform memory space...)
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Developers can change their minds.
The catch is just that if you lack the capacity to estimate how much computing power [task in brackets] might need, and your agent can autonomously create AWS instances, that might have bad consequences for you (or your bank account).
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If it's a one off and needs no or minimal maintenance work afterwords, sure.
If it's intended to be actively maintained, then you probably should understand how things work, unless you want to wipe everything and start from scratch when the LLM creates such a mess that it can't be sorted out.
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If you look into large fully-vibecoded projects getting styling changes to work is a nightmare. The problem with agents is using them on large projects without manual review for consistency, guidelines and taste. Doesn't really matter the type of project.
Agents can't look at a large system holistically, guidelines on .md files only go so far.
This line of thinking is like suggesting people who would like to become structural engineers should learn to Google plans and copy them since in the future, all plans will be out there more or less, or something that insane.
I suggest people who need some structural engineering done may use an AI tool to do it, in the hypothetical scenario that it was within the AI's capabilities.
That's hardly insane. Not everyone is interested in learning something they want done.
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CSS keeps improving and models still train on legacy. So yes, knowing what’s possible and how is very much needed if you want to do something scalable and maintainable. Random blog or landing page, not so much.