301–310 of 411 posts
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Another level of abstraction that has its real world cost made invisible, from job displacement to environmental damage.
I never saw anyone on HN bemoan the “environmental damage” of data centers until LLMs started popping up. As if all other software and internet tech is necessary and “worth it”?
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> That's not true. Yes, you can get more effective at something with more effort, but you can get even more effective at it if you find a way to get results without actually doing the work yourself in the first place. This has been going on for decades. It is called outsourcing development. We've just previously passed the work to people in countries with lower wages and conditions, now people are increasingly passin…
> Everyone seems to think we've magically jumped from ML into _real_ AI But we have: - LLMs pass the Turing test more convincingly than anything before - LLMs are vastly more popular than any AI/ML methods before it - "But they don't reason!" -- we're advancing chain of thought LLMs are AI (behaving artificially human) LLMs are also ML (using statistical methods)
Being able to converse (or convince someone the conversation is natural) is not the same as being able to perform a technical task well. The same applies to humans as well, of course, which perhaps brings us back to the outsourcing comparison — how often does that work out great or not?
> LLMs are vastly more popular than any AI/ML methods before it
Popular is not what I'm looking for. I've seen the decisions the general public make elsewhere, I'm not letting them chose how I do my job :)
> "But they don't reason!" -- we're advancing chain of thought
Call me back when we've advanced chain of thought much more than is currently apparent, and again: for being correct in technical matters, not conversation.
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The purpose of the least resistance instinct is to conserve resources of the organism due to scarcity of food. Consequently in absence of scarcity of food this instinct is suboptimal.
Time is always scarce.
Instead, those people are actually now much poorer and work much more. What was a respectable job is grunt work, and they’re sweat shop warm bodies.
The gains of their productivity was, predictably, siphoned upwards.
This isn’t just about individual laziness—it’s a systemic arms race towards intellectual decay.[0] With programming, the same basic tension exists as with the more effective smarter AI-enhanced approaches to conceptual learning: effectiveness is a function of effort, and the whole reason for the "AI epidemic" is that people are avoiding effort like the plague. So the problem seems to boil down to, how can we convince…
For me the question is different- you can ask "how to become better coder" for which the answer might be - to write more code. But the question I'm asking is what's the job of system/software developer. And to that the answer is very different. At least for me it is"building solutions to business problems, in the most efficient way". So in that case more/better code does not necessarily bring you closer to your goal.
* for some it is a form of art or craftsmanship, they take pride of their work and view it not just as a means to an end, they hone their skills just like any craftsman will, they believe that the quality of the work and the goal or purpose it serves are intrinsically linked and can never be separated
* for others its a means to an end, the process or object it produces is irrelevant EXCEPT for the purpose it serves, only the end goal matters, things like software quality are irrelevant distractions that must be optimized away since they serve no inherent purpose
There is no way to reconcile this, these are just radically different perspectives.
If you only care about the goal then of course raising questions about the quality of the output of current state of generative AI will be of no concern to you.
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_Kinda sorta?_ There's actually two things going on when I'm coding at work: 1) I'm writing the business code my company needs to enable a feature/fix a bug/etc. 2) I'm getting better as a programmer and as someone who understands our system, so that #1 happens faster and is more reliable next time. Using AI codegen can (arguably, the jury is still out on this if we include total costs, not just the costs of this one…
> In fact, the closest parallel for #2 I can think of us plagiarism in an academic setting. You didn't do the work, which means you didn't actually learn the material, which means this isn't actually progress (assuming we continue to value #2 (...) Plagiarism is a great analogy. It's great because what we call plagiarism in academic setting, in the real world work we call collaboration, cooperation, standing on the s…
In academia, if a result is quoted, this is called called "scholarship". If, on the other hand, you simply copy-paste something without giving a proper reference, it's "plagiarism".
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> In fact, the closest parallel for #2 I can think of us plagiarism in an academic setting. You didn't do the work, which means you didn't actually learn the material, which means this isn't actually progress (assuming we continue to value #2 (...) Plagiarism is a great analogy. It's great because what we call plagiarism in academic setting, in the real world work we call collaboration, cooperation, standing on the s…
The CTO asked the CEO what happens if we train these people and they decide to leave? The CEO asked in reply what happens if we don't train the people and they decide to stay? Any reasonably smart company will invest in its employees. They should absolutely be training you to be better programmers while you are on the job. Only shit companies would refuse to do so.
I rather see the problem in US culture: in many other countries switching the company every few years is considered to be a sign of low loyalty to the company, thus a red flag in a job application.
This isn’t just about individual laziness—it’s a systemic arms race towards intellectual decay.[0] With programming, the same basic tension exists as with the more effective smarter AI-enhanced approaches to conceptual learning: effectiveness is a function of effort, and the whole reason for the "AI epidemic" is that people are avoiding effort like the plague. So the problem seems to boil down to, how can we convince…
For me the question is different- you can ask "how to become better coder" for which the answer might be - to write more code. But the question I'm asking is what's the job of system/software developer. And to that the answer is very different. At least for me it is"building solutions to business problems, in the most efficient way". So in that case more/better code does not necessarily bring you closer to your goal.
The problem with this answer is that there is little to learn from a lot of code that is written in a corporate environment. You typically only learn from brutally hard bleeding-edge challenges that you set to yourself, which you will barely ever find at work.
This isn’t just about individual laziness—it’s a systemic arms race towards intellectual decay.[0] With programming, the same basic tension exists as with the more effective smarter AI-enhanced approaches to conceptual learning: effectiveness is a function of effort, and the whole reason for the "AI epidemic" is that people are avoiding effort like the plague. So the problem seems to boil down to, how can we convince…
> With programming, the same basic tension exists as with the more effective smarter AI-enhanced approaches to conceptual learning: effectiveness is a function of effort, and the whole reason for the "AI epidemic" is that people are avoiding effort like the plague. That's not true. Yes, you can get more effective at something with more effort, but you can get even more effective at it if you find a way to get results…
The progress comes from amplification of effot, which comes from leverage (output comes from input), not magic (output comes from nowhere) or theft (output comes from someone else).
The biggest problem what I have with using AI for software engineering is that it is absolutely amazing for generating the skeleton of your code, boilerplate really and it sucks for anything creative. I have tried to use the reasoning models as well but all of them give you subpar solutions when it comes to handling a creative challenge. For example: what would be the best strategy to download 1000s of URLs using asy…
Earlier quoted context omitted.
> With programming, the same basic tension exists as with the more effective smarter AI-enhanced approaches to conceptual learning: effectiveness is a function of effort, and the whole reason for the "AI epidemic" is that people are avoiding effort like the plague. That's not true. Yes, you can get more effective at something with more effort, but you can get even more effective at it if you find a way to get results…
It's ironic to see people say this type of things and not think about old software engineer practices that are now obsolete because overtime we have created more and more tools to simplify the craft. This is yet another step in that evolution. We are no longer using punch cards or writing assembly code, and we might not write actual code in the future anymore and just instruct ais to achieve goals. This is progress
The value of most of us as coders will drop to zero but their skills will remain valuable for the foreseeable future. LLMs can't parrot out what's not in their training set.