Comment by jgord

I did actually use an LLM to teach myself photoshop recently for a fantasy map building project. Mostly I'd just ask it things like "I have a mask with 50/50 gray and would like an elevation height map to be an embossed layer on it. Where do I find that in the menus?" those sorts of questions.

I finished the project out feeling pretty confident in my photoshop abilities. Then I did a similar project later and realized I had to open up the LLM again as there were still a ton of holes in my knowledge. After this second time though I'm feeling confident enough on my own that referring back to the LLM is increasingly less common, as it's just a waste of time if I already know what to do.

I don't think this is true. The LLM provides confidence that it won't be a tremendous waste of time to try something you don't already know how to do. But eventually it gets tedious to consult the LLM all the time so you stop doing so. That initial "couldn't hurt to try" boost is worth a lot.

The learning is at that level of abstraction, so yes taking away the abstraction prevents will stymy you.

Just like if you pressed a button to wipe out our main language stack at our job, yeah none of us would know how to do anything with assembly or machine code.

I recently spent a day coding by hand. I was able to build a project, about 200-300 lines in python. Took me half day. But I could still do it. LLM would have done this in less than half an hour.

After this I realized why I personally choose LLM to write code: it is simply more convenient.

As a parallel, once you learn how to drive you stop walking, as for the primary mode of transportation.

We are in the Matrix with the internet.

"Please download the ability to do X"

Just that there is still a big latency. Its smaller than finding the correct information on the web, but you can do so. Especially with MCP. The question is when this goes over into the physical world.

Watching Youtube videoas about anything that requires skill does the same.

I think it's like driving without navigation systems. Yes, you have to make an effort to do it, but then it's fine. You don't have to use LLMs every minute of the (work)day.

I have sat down and figured out how to program an Apple //e this year for fun. It is possible. It was quite instructive on how much has been added to the idea of what a computer is these days.

Take away all reference books/material/notes, from any engineer, in any field, and see how well they do.

Human memory is shite. Understanding, and knowing what you need to reference, is ludicrously more valuable than rote memorization.

> The problem is that if you take away the LLM, all of a sudden you can't do any of the things you "learned".

Even if that were true, LLMs aren't going away, so that isn't a problem.

> if you take away the LLM, all of a sudden you can't do any of the things you "learned"

What? That is absolutely not true. I've been using LLMs for language learning, math/physics, clarify home improvement best practices that I now regularly think about, etc. and that knowledge is not LLM-bound at all. Instead of acquiring from a regular textbook, I acquired it from a model.

My first project with it was in Jan 22, back when it was just sentence completion. Really hard to implement practically.

But in the past year the production boost is just insane. LLM assistance took away so many of the barriers that prevented me from executing on ideas.

I've also learned more than ever before, although it is admittedly getting harder to stay involved when the models are becoming more and more reliable.

What I have not figured out yet is why most developers around me do not share this effect. It's like it mostly works for a very specific subset of people, that do understand code, but it's not their main focus.

Just take a look at what is happening with the M-Wave FM1. Stating those devs would be lost without Ai won't stop the tide. It's not going to go away.

You can't remove LLMs. Once models are released as open weights, they cannot be removed. As long as the compute to run them exists, so will LLMs.

"LLM assistance is a huge dependence on something owned by few."

Open models are capable and owned by everyone who wants to.

> Remove LLM and 80 pct of people will have to jump to start.

Isn't the same true for every service and technology we start to rely on?

Remove GPS and people will have to figure out how to read maps again.

Remove search engines and people will have to figure out how domains work.

Remove Wikipedia and we have to go back to the library to check encyclopedias.

But thinking requires mental manipulation of the things that are in your memory. The less structures you have there, the lesser the ability to reason.

DynamoDb has a max item size of 400kb.

That was months ago. It often surfaces pertinent info which is memorable cos it relates to whatchu doin.

I mean, it depends.

Like, unless it is somehow relevant to your daily life knowing the capital of Nova Scotia is literally useless. Same with all the history I learned in middle school. Remembering takes repetition, and so retaining the information I learn needs a habit of review and processing. And a lack thereof (alongside the brain seeking to offload information, bc information is expensive) is why you forgot the capital city.

I find I remember things best when I jot them down, and personally I’ve been needing to journal more, writing down my ideas and what I learn when discussing my ideas with LLMs, though I’ve also noticed that using the LLM as a rubber duck has actively helped my memory due to more review and processing and needing to “teach” my ideas to the model.

"I don’t think most people use these tools to learn."

I often hear the phrase "use chatgpt" when common people don't know something but want to understand something new. So they use it for learning, even though most likely never think of checking the sources ever.

I push back on this idea hard. There's some things I consider not worth my time since an LLM will always be available to do it for me. But a whole lot of things you can learn far better with a LLM than almost anywhere else.

Been learning Linux, how to build a homelab, and quite a few other related topics since about chatgpt 3.5. If the LLM went away tomorrow I'd still know how to do most things in Linux, set up my opnsense router, reinstall xcp-ng, do everything in docker, and a boatload of other things that would've been so much more tedious to learn before an LLM. Simplicity of access to knowledge doesn't mean you don't learn it. A google search didn't make us learn slower than when we had to scour for books at the library, it increased the pace dramatically. Same goes for LLMs.

You can use LLMs and learn nothing. People who like to learn won't ever use it that way.

I think there are truths in there but depends on your view.

If you have a finite time and have a goal, is learning something that takes a longer time better towards that goal than being able to move fast and accumulate more capabilities that are on hand when you ‘forget’ them?

The answer engines and the context around my goals allow me to move at an unprecedented pace and move into places that were just not possible before.

But, the nuances learned concepts are indeed skipped over or Pareto’d

> but i also don't make any effort to write clearly anymore

This seems like a huge mistake considering that prompting AI clearly seems to yield better results.

You think reading fiction is supposed to be productive? I suspect even aphantasics will be using brain function when reading fiction.

Same but I am leaning towards it being sarcastic.

Palantir guy would say neurodivergence

probably assortive mating by education / income level which seems to be getting more and more common.

Wealth gaps and parental behaviors with respect to regulating screen time.

I think the goal comes from the user. Some users want to solve a problem, some users may want to learn something. I'm not sure that having AI widely available really changes the ratio of the two, but I think I see AI availability as a factor that could accelerate the learning curve of the ones that have a goal of learning.

Can we explore the digging holes analogy a bit more?

Digging by hand with a shovel involves

1. Assessing the terrain and checking any documented or obvious obstacles (subsurface water, gas, electricity etc)

2. Planning the approach to digging the hole, most efficient angle, and ensuring that collapse doesn't occur

3. Organising disposal

4. Digging the hole

5. Dealing with any unforeseen issues (old tree roots, old foundations etc)

This is exactly the same whether digging by hand or using an excavator. The excavator isn't automating stages 1, 2, 3, or 5. It's just speeding up the 4th stage and only if there aren't stage 5 issues present.

LLMs when employed in a manner on the extreme end of the vibe coding spectrum are automating every stage of the process. If the excavator was hooked up to an LLM and left to run all stages of digging (extreme vibe digging?), I'd argue that yes, the operator is becoming lazy, and is running a very dangerous operation.

Over in the "AI is not a tool" post there's this comment: https://news.ycombinator.com/item?id=50063732

Which says that it's a tool for querying and summarization, but not a tool for reading or writing, because it helps you to do something different from those.

(Yes I know, don't cross the streams. I just thought it was a shame for this nice distinction to languish over there when you need it over here.)

The problem is that while it is entirely possible in principle to use AI as a tool, not a surrogate, the actual interfaces to widely available AI models constantly push their users towards delegating their thinking to AI, rather than assisting their thinking. I constantly have to stop Claude from just doing the thing for me instead of presenting me with options or evidence and letting me choose how to proceed.

I guess my counterpoint is bluntly - no-one will not be using the tool at their day-job.

It requires so much extra discipline to think for yourself and not use it, you will be forced to use it in your job or be seen as a Luddite and your ability to think for yourself in the domain will diminish over time - your domain thinking muscle will gradually atrophy from lack of use.

Why would any of us bother thinking hard, when there is no financial incentive to doing so ?

Your analogy fails with shovels and excavators .. because General synthetic intelligence is not a single purpose narrow 'tool'. near-future AI is equivalent to an alien intelligent species competing for resources, or equivalent to a cheaper smarter non-human 'outsource worker'.

In the context of education, your analogy doesn't work. We still have physical education in schools because, even though people can now use automated tools for physical work rather than doing it by hand, it is still useful for people to be physically fit.

A wheelchair is also a tool! A very efficient one too! People claim they've 2x'd their energy efficiency by not ever walking or standing, and they are much more happy now that they don't need to suffer the pain of walking! I'm thinking of amputating my feet just to make footoids seethe more!

I don’t think that’s “the whole shtick” anymore than it is for something like cars. There are valid ways to use it as a tool to provide net new value, and inevitably a whole bunch of people will end up using it as a lazy machine anyway, especially if their country/society benefits from people using it that way (Americans in both cases).

This was literally George Jetson's job on The Jetsons: pushing a button on the computer RUDI while it did all the work. The joke being that he works 4 hours a day, 2 days a week pushing a button and complains about being tired when he gets home, because that's how the future will be: full of unimaginable leisure.

As alienating and stultifying as this work seemed, however, The Jetsons is still an optimistic future compared to the one we're getting: when George is fired, RUDI intervenes on his behalf and gets him rehired. I don't see Claude doing that for me; its raison d'être is ensuring I'm put out of work.

I use it as a hybrid of that I guess: I prompt some simple tool into existence, iterate a bit, then I look at the code, am very dismayed and spend more time than it might have taken me to build from scratch to refactor it.

It solves a huge motivation problem for me though, because having a working prototype of something I want is a lot. Especially when it's something I never did, where I cannot imagine the end result fully, or estimate my own ability to get there. But if I can see the thing in action, while I may look at the code (and my own skills) and realise it would take me months to make it something I could share with others, I at least know there is a destination.

Not to mention how it surfaces all sorts of issues I didn't think of, so I'm basically prototyping v2 before I ever fully built v1, and so on, until it kinda settles.

I guess I'm lucky I don't do it professionally :P

If the top students are doing better, especially if it is significantly better, I think that can be a net positive outcome. If you look at the distribution of contribution to society it's not like it's remotely even and there is an argument to primarily focusing on and pouring most of our resources into the top Nth percent instead of trying to slightly improve everyone.

I definitely don't disagree with screen or exposure or social media exposure being deleterious.

That said, I suspect this technology will allow us to study and mitigate those effects in ways we weren't previously capable of. That's in addition to the other broad swathe of things that should make us healthier and more intelligent (see some of my other posts here). I'm not particularly negative about our chances.

> But in just 20 years?

Ahh, I think this bit on my part may need some elaboration. Here's my main underlying belief:

We are systematizing biology and turning it into a computationally tractable domain. I think we're going to make more gains in our understanding of it in the next 20 years than we have in the entirety of human existence prior to this moment (that's mainly because our understanding until now has been remarkably poor).

The average score peaked in 2009 though.

https://www.oecd.org/en/data/dashboards/pisa-education-and-s...

Ie it's been dropping since 2009.

For mathematics and reading the large drop off is between 2018 and 2022.

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Peptide style drugs already have clear positive effects on executive function, it's not particularly hard to envision that we'll find other classes of drugs that have similar effects in other cognitive domains. Twenty years is a long time, and we're set up to make absurd gains in our understanding of biology over that time frame. If we additionally are able to mitigate or cure various intellectual disabilities, that's a massive baseline performance boost to the population as well.

If you want some examples in how AI could improve education:

* Alpha School (https://alpha.school/the-program/) reports 2.6× faster learning growth with just 2 hours of AI-driven instruction daily, with students scoring in the 99th percentile nationally.

* Mindspark (https://www.povertyactionlab.org/evaluation/scaling-personal...), deployed in Indian schools, demonstrated gains of +0.22 SD in math and +0.20 SD in language in a randomized trial involving 6,500 students.

* A Harvard study (https://pmc.ncbi.nlm.nih.gov/articles/PMC12179260/) found more than 2× the median learning gains using AI tutors.

The broader implications are clear: individualized, adaptive tutoring at scale is becoming accessible in ways that historically required expensive one-on-one instruction. This is also going to be possible in places that historically never had access to this quality of instruction, which again, is a massive boost to the human baseline.

I don't think the future looks like Wall-E. I think it looks like insanely healthy people able to pursue their passions with a level of rigour and capability that has never existed before.

Edit: fucked up link 2, fixed

What high quality engagement on your part. Clearly you're the mature party here.

I'm 35 and have worked in ML generally for a decade and NLP for six years. Any other questions about my bonafides?

Personally I see it in the tendency for solutions that are absolutely bonkers in their structure and form.

I don't even mean that from an aspect of "doesn't look nice from a technical standpoint" I mean

- need to track progress and/or defect rate for a component of the system, invent solution (we pay for jira). This was an executive - folks proposing rewrites of front ends both in design or technology stack because "Claude can do it in 3 days", quoting Claude for the "market research" (completely fabricated metrics and studies). Current tech stack is perfectly fine and there's no monetary or engineering pressure to changes - QA has Claude qa the code only, hands ticket back to dev with qa notes straight from Claude instead of.... Automating a way to have the llm do this in a reproducible manner

I genuinely am interested in what other people see as AI psychosis.. was not being sarcastic .. I have my own impressions, but want other field-reports.