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I don’t want to pay for top notch AI, just like I don’t pay for top notch kernels (i.e., linux), top notch version systems (i.e., git) and so on.
I personally do not use LLMs (knowingly) at all, largely for environmental reasons. The improvements are, to me, not worth the attrocious energy use. I will happily pay 20% or whatever more in both money and time. I also have no interest in technology that impedes my skill development. I do not want to use anything that makes me a worse writer over time. YMMV, I am answering the OP not evangelizing. Counter arguments…
You don't owe anyone a back and forth discussion anyway, so the last line comes off as redundant.
Not at all. I'm an attorney. Fast law is bad law. If you're using LLMs (stop calling it AI ffs -- only morons parrot the marketing hype) to do law, you're just asking toget slapped sooner or later. Like the moron that is My Pillow Mike Lindell's lawyer. See here: https://ia801706.us.archive.org/34/items/gov.uscourts.cod.21...
> LLMs (stop calling it AI ffs -- only morons parrot the marketing hype) Researchers have called much less intelligent things AI since 1956. Before there were GPTs, there were RNNs and CNNs. AI is the field of study.
Good logic.
The main reason I’m slowly using less and less AI is price. I’m relying on the free tiers but I know that’s not gonna last forever. I don’t want to pay for top notch AI, just like I don’t pay for top notch kernels (i.e., linux), top notch version systems (i.e., git) and so on.
I'd also disagree Linux is the top notch kernel. It might be the most universal one because of drivers and licensing and that makes it my personal favorite because it "just works" with pretty much no fuss (technical or social) but there are a number of kernels out there with better features.
This has largely taken me out of the loop. I give it detailed task like I would a junior engineer, and we discuss approaches and align on direction, priorities, and goals, and it then goes off for literally hours iterating on the code.
I have done about 3 months worth of extremely complex engineering work in about a week since I started doing this.
It is a step change from trying to use the chat interface and copy/pasting snippets.
Once it’s going it writes code like a staff engineer.
There are some obscure bugs it can run into that it needs my 20 years of experience to unblock or unwind when it goes down a rabbit hole.
But it has accelerated my development 500x and while it’s iterating I’m not filling my mind with code and syntax and so on, I’m more like a tech lead or manager now. I’m in another room playing with my dog.
I installed claude code (an agentic ai that runs on your machine and can browse and edit code, run the build, and use unix commands) This has largely taken me out of the loop. I give it detailed task like I would a junior engineer, and we discuss approaches and align on direction, priorities, and goals, and it then goes off for literally hours iterating on the code. I have done about 3 months worth of extremely compl…
I have started testing Copilot for fun, my wife needs a web-based project that is not maintained for a while now and written in PHP.
I asked Copilot (Agent mode) to translate it to rust just for the fun of it, seeing how far it would come - I expected nothing out of it. I broke down the tasks into manageable chunks, directed it in design choices, and asked it to use some specific frameworks.
So far it wrote 40k lines of rust on its own, and keeps track of what functionality is missing compared to the original project. It was impressive seeing it iterate alone for 30m+ at a time.
I'm no programmer, more systems/cloud engineer, so a rewrite like this would have likely costed me >2 years of work and still ending up useless for all intents and purposes. I'm pretty sure that the end result won't work at first try, and I'll need to fix stuff manually or direct Copilot to fix it, but after two weeks of 1-2h iterating at night, I have 90% of something that would have required someone full time for at least a couple years.
The two things I found most valuable (also in other things, like shorter bash and python scripts):
1. Syntax boilerplate: if your task is specific enough, it normally gets it right 99.99% of the time, and my brain can look at the actual logic rather than the commas, brackets and (in python's case) spaces
2. Documentation: I spend 95% less time looking at documentation for something, I don't need to comb the entire language/package/class for the specific things, it normally gets it right, and worst case I can ask it to do a refactor with the most modern standards for specific library version X
I stopped using AI for code, everything it gives me requires manual checking and editing to work but now I have the overhead of not having the deep understanding I would otherwise have had if I figured out things myself from the start. I also stopped using AI as a sort of replacement for quick google searches in general, but I didn't go back to using google because now I feel those results are too shallow. I been try…
I don't use AI at all, primarily because I believe its harmful and I am quite mindful of things. I've observed colleagues who have used it extensively, I've often been a late adopter for things that carry unspecified risk; and AI was already on par with Pandora's box in my estimation when the weights were first released; I am usually perceptually pretty far ahead of the curve naturally (and accurately so). Objectivel…
The core question is not “are we degrading,” but rather: are we thinking better with better tools? Personally, I use AI only to reduce boilerplate and explore alternatives — the decision-making and abstraction stays on me.
If someone starts thinking less because of tools, the problem isn't the tool — it's how it's used.
AI is like a calculator. We don't degrade because we use it. We just spend more time thinking, not doing routine calculations.