It’s like weightlifting: sure you can use a forklift to do it, but if the goal is to build up your own strength, using the forklift isn’t going to get you there.
This is the ultimate problem with AI in academia. We all inherently know that “no pain no gain” is true for physical tasks, but the same is true for learning. Struggling through the new concepts is essentially the point of it, not just the end result.
Of course this becomes a different thing outside of learning, where delivering results is more important in a workplace context. But even then you still need someone who does the high level thinking.
I think this is a pretty solid analogy but I look at the metaphor this way - people used to get strong naturally because they had to do physical labor. Because we invented things like the forklift we had to invent things like weightlifting to get strong instead. You can still get strong, you just need to be more deliberate about it. It doesn't mean shouldn't also use a forklift, which is its own distinct skill you also need to learn.
It's not a perfect analogy though because in this case it's more like automated driving - you should still learn to drive because the autodriver isn't perfect and you need to be ready to take the wheel, but that means deliberate, separate practice at learning to drive.
> people used to get strong naturally because they had to do physical labor
I think that's a bit of a myth. The Greeks and Romans had weightlifting and boxing gyms, but no forklifts. Many of the most renowned Romans in the original form of the Olympics and in Boxing were Roman Senators with the wealth and free time to lift weights and box and wrestle. One of the things that we know about the famous philosopher Plato was that Plato was essentially a nickname from wrestling (meaning "Broad") as a first career (somewhat like Dwayne "The Rock" Johnson, which adds a fun twist to reading Socratic Dialogs or thinking about relationships as "platonic").
Arguably the "meritocratic ideal" of the Gladiator arena was that even "blue collar" Romans could compete and maybe survive. But even the stories that survive of that, few did.
There may be a lesson in that myth, too, that the people that succeed in some sports often aren't the people doing physical labor because they must do physical labor (for a job), they are the ones intentionally practicing it in the ways to do well in sports.
I can’t attest to the entire past, but my ancestors on both sides were farmers or construction workers. They were fit. Heck, my dad has a beer gut at 65 but still has arm muscles that’ll put me to shame — someone lifting weights once a week. I’ve had to do construction for a summer and everyone there was in good shape.
They don’t go to the gym, they don’t have the energy; the job shapes you. More or less the same for the farmers in the family.
Perhaps this was less so in the industrial era because of poor nutrition (source: Bill Bryson, hopefully well researched). Hunter gatherer cultures that we still study today have tremendous fitness (Daniel Lieberman).
My dad was a machinist, apprenticed in Germany after WW2. Always somewhat overweight (5'9", 225 lbs during his "peak" years), but he could lift guys up by their belt with one arm, and pick up and move 200+ lb metal billets when he got too impatient to wheel the crane over. Even at 85 now, he's probably stronger in his arms than most 60 year olds. But I'm also not saying ALL of his co-workers were that strong, either.
Takes mass to move mass. Most of the strongest people in the world look "fat" and usually have a hefty gut. Strong and jacked are orthogonal characteristics.
I know what you mean, but from a physics perspective, no, it just takes force to move mass. More mass will generate more downward force due to gravity, and more force in other directions due to momentum once it’s moving, but there’s more to generating force than just mass. I’m not a kinesiologist but I would think how much force muscles generate depends on the amount and size of the fibers (mass) but also on their contractive efficiency and the amount of energy they can obtain and employ to contract (not necessarily proportional to mass, involves cardiovascular fitness)
The fact that Greeks and Romans had weightlifting and boxing gyms for their athletes in no way makes it a "bit of a myth" that people used to get strong naturally by doing physical labor. For example, average grip strength of people under age 30 in the US has declined markedly just since 1985.
Why do you think that? It's definitely true. You can observe it today if you want to visit a country where peasants are still common.
From Bret Devereaux's recent series on Greek hoplites:
> Now traditionally, the zeugitai were regarded as the ‘hoplite class’ and that is sometimes supposed to be the source of their name
> but what van Wees is working out is that although the zeugitai are supposed to be the core of the citizen polity (the thetes have limited political participation) there simply cannot be that many of them because the minimum farm necessary to produce 200 medimnoi of grain is going to be around 7.5 ha or roughly 18 acres which is – by peasant standards – an enormous farm, well into ‘rich peasant’ territory.
> Of course with such large farms there can’t be all that many zeugitai and indeed there don’t seem to have been. In van Wees’ model, the zeugitai-and-up classes never supply even half of the number of hoplites we see Athens deploy
> Instead, under most conditions the majority of hoplites are thetes, pulled from the wealthiest stratum of that class (van Wees figures these fellows probably have farms in the range of ~3 ha or so, so c. 7.5 acres). Those thetes make up the majority of hoplites on the field but do not enjoy the political privileges of the ‘hoplite class.’
> And pushing against the ‘polis-of-rentier-elites’ model, we often also find Greek sources remarking that these fellows, “wiry and sunburnt” (Plato Republic 556cd, trans. van Wees), make the best soldiers because they’re more physically fit and more inured to hardship – because unlike the wealthy hoplites they actually have to work.
I think he was saying upper classes that didn't do much physical labor have existed since at least classical era and needed to do some kind of physical training to maintain strength?
> > Many of the most renowned Romans in the original form of the Olympics and in Boxing were Roman Senators
> In the original form of the Olympics, a Roman senator would have been ineligible to compete, since the Olympics was open only to Greeks.
I did debate how to word that mixing of Greek and Roman things in the same sentence. I had emotional context I wanted to convey and considered a word like Decathlon there as more technically correct, but then fought the modern context that of the people that even know what the Decathlon is they know it in the context of it being a smaller event in the modern Olympics, from which perspective Olympics remains more technically correct as the modern English word for both.
As to the text you are quoting, I think it as much supports my claims as you think it doesn't. Ignoring the subject change from "weightlifting" (and sports more generally) to farming and soldiering, it mostly describes the general state of armies and feudalism in general through much of time: you have the rank and file from blue collar classes, and you have the officer corps from white collar classes. The wealthier class is fewer, but given more charge and importance. The lower class does more of the grunt work. The Romans had rich Officers and blue collar "enlisted".
The myth that I was referring to was that weightlifting is somehow a new invention because no one labors physically anymore. There have always been leisure classes that needed to lift weights as a hobby to get good at sports (and that class was also more often awarded medals in sports or important commands in armies, if we want to also connect to the blog post you quoted). As far as I'm aware there was never a period in recorded history where "everyone" was equally fit from physical labor and there was no such thing as training and gyms and needing leisure time to do that.
[Further tangent: Even "pre-history" and the modern (mis)conception of the "paleo ideal" idea of tribes of equally buff hunter-gatherers starts to fall apart when you ask questions about family units or what they think the "gatherer" side of the equation meant (and manage to divorce it from modern ideas of agriculture being highly intense labor) or what those societies would look like if more people lived to old age or how those societies survived things like the Ice Age (fattier and more hibernatory, because we are a mammalian species, we cannot escape that).]
> The ability of skinny old ladies to carry huge loads is phenomenal. Studies have shown that an ant can carry one hundred times its own weight, but there is no known limit to the lifting power of the average tiny eighty-year-old Spanish peasant grandmother.
Weightlifting and weight training was invented long before forklifts. Even levers were not properly understood back then.
My favorite historic example of typical modern hypertrophy-specific training is the training of Milo of Croton [1]. By legend, his father gifted him with the calf and asked daily "what is your calf, how does it do? bring it here to look at him" which Milo did. As calf's weight grew, so did Milo's strength.
This is application of external resistance (calf) and progressive overload (growing calf) principles at work.
"He was taken as a prisoner of war four times, but managed to escape each time. As a prisoner, he pushed and pulled his cell bars as part of strength training, which was cited as an example of the effectiveness of isometrics. At least one of his escapes involved him 'breaking chains and bending bars'."
If you do a single set of half of exercises you need to train each day of the week, rotating these halves, you get 3 and a half sets of each exercise per week.
Training volume of Bulgarian Method is not much bigger than that of regular training splits like Sheiko or something like that, if bigger at all. What is more frequent is the stimulation of muscles and nervous system paths and BM adapts to that - one does high percentage of one's current max, essentially, one is training with what is available to one's body at the time.
> people used to get strong naturally because they had to do physical labor.
People used to get strong because they had to survive. They stopped needing strength to survive, so it became optional.
So what does this mean about intelligence? Do we no longer need it to survive so it's optional? Yes/No informs on how much young and developing minds should be exposed to AI.
>if the goal is to build up your own strength
I think you missed this line. If the goal is just to move weights or lift the most - forklift away. If you want to learn to use a forklift, drive on and best of luck. But if you're trying to get stronger the forklift will not help that goal.
Like many educational tests the outcome is not the point - doing the work to get there is. If you're asked to code fizz buzz it's not because the teacher needs you to solve fizz buzz for them, it's because you will learn things while you make it. Ai, copying stack overflow, using someone's code from last year, it all solves the problem while missing the purpose of the exercise. You're not learning - and presumably that is your goal.
I like this analogy along with the idea that "it's not an autonomous robot, it's a mech suit."
Here's the thing -- I don't care about "getting stronger." I want to make things, and now I can make bigger things WAY faster because I have a mech suit.
edit: and to stretch the analogy, I don't believe much is lost "intellectually" by my use of a mech suit, as long as I observe carefully. Me doing things by hand is probably overrated.
The point of going to school is to learn all the details of what goes into making things, so when you actually make a thing, you understand how it’s supposed to come together, including important details like correct design that can support the goal, etc. That’s the “getting stronger” part that you can’t skip if you expect to be successful. Only after you’ve done the work and understand the details can you be successful using the power tools to make things.
The point of school for me was to get a degree. 99% of the time at school was useless. The internet was a much better learning resources. Even more so now that AI exists.
I graduated about 15 years ago. In that time, I’ve formed the opposite opinion. My degree - the piece of paper - has been mostly useless. But the ways of thinking I learned at university have been invaluable. That and the friends I made along the way.
I’ve worked with plenty of self taught programmers over the years. Lots of smart people. But there’s always blind spots in how they approach problems. Many fixate on tools and approaches without really seeing how those tools fit into a wider ecosystem. Some just have no idea how to make software reliable.
I’m sure this stuff can be learned. But there is a certain kind of deep, slow understanding you just don’t get from watching back-to-back 15 minute YouTube videos on a topic.
>I’ve worked with plenty of self taught programmers over the years. Lots of smart people. But there’s always blind spots in how they approach problems.
I've worked with PhDs on projects (I'm self-taught), and those guys absolutely have blind spots in how they approach problems, plenty of them. Everyone does. What we produce together is better because our blind spots don't typically overlap. I know their weaknesses, and they know mine. I've also worked with college grads that overthink everything to the point they made an over-abstracted mess. YMMV.
>you just don’t get from watching back-to-back 15 minute YouTube videos on a topic.
This is not "self taught". I mean maybe it's one kind of modern-ish concept of "self taught" in an internet comment forum, but it really isn't. I watch a ton of sailing videos all day long, but I've never been on a sailboat, nor do I think I know how to sail. Everyone competent has to pay their dues and learn hard lessons the hard way before they get good at anything, even the PhDs.
I think it depends on how they were self taught. If they just went through a few tutorials on YouTube and learned how to make a CRUD app using the shiny tool of the week, then sure. (I acknowledge this is a reduction in self-teaching — I myself am self-taught).
But if they actually spent time trying to learn architecture and how to build stuff well, either by reading books or via good mentorship on the job, then they can often be better than the folks who went to school. Sometimes even they don't know how to make software reliable.
I'm firmly in the middle. Out of the 6 engineers I work with on a daily basis (including my CTO), only one of us has a degree in CS, and he's not the one in an architecture role.
I do agree that learning how to think and learn is its own valuable skill set, and many folks learn how to do that in different ways.
> But if they actually spent time trying to learn architecture and how to build stuff well, either by reading books or via good mentorship on the job, then they can often be better than the folks who went to school.
Yeah I just haven’t seen this happen. I’ve seen plenty of people graduate who were pretty useless. But … I think every self taught programmer I’ve worked with had meaningful gaps in their knowledge.
They’d spend a week in JavaScript to save them from 5 minutes with C or bash. Or they’d write incredibly slow code because they didn’t know the appropriate algorithms and data structures. They wouldn’t know how to profile their program to learn where the time is being spent. (Or that that’s even a thing). Some would have terrible intuitions around how the computer actually runs a program, so they can’t guess what would be fast or slow. I’ve seen wild abstractions to work around misunderstandings of the operating system. Hundreds of lines to deal with a case that can’t actually ever happen, or because someone missed the memo on a syscall that solves their exact problem. There’s also hairball nests of code because someone doesn’t know what a state machine is. Or how to factorise their problem in other ways. One guy I worked with thought the react team invented functional programming. Someone else doesn’t understand how you could write programs without OO inheritance. And I’ve seen so many bugs. Months of bugs, that could be prevented with the right design and tests.
I’ve worked with incredibly smart self taught programmers. Some of the smartest people I’ve ever worked with. But the thing about blind spots is you don’t know you have them. You say you’re self taught, and self taught people can be better than people who went to school. In limited domains, yeah. Smart matters a lot. But you don’t know what you don’t know. You don’t know what you missed out on. And you don’t know what problems in the workplace you could have easily solved if you knew how.
Yeah, I agree, but not knowing what you don't know applies to almost everyone in every skill, not just programming. I acknowledge I have gaps in my knowledge. But it's because of those gaps that I am always trying to supplement my knowledge by studying different data structures, different patterns for solving problems, different algorithms. I don't aim for complete mastery. I aim for basically "what can I add to my bag of problem solving tools." I concede that because the barrier to entry is low, stories similar to your anecdotes are probably quite common in most self-taught programmers. I think this just speaks to the necessity of rigor during the interview process. Like, does the candidate just know how to build features, or do they know how to design fail-proof systems?
Also, to clarify, I'm not arguing that self-taught vs CS grad is mutually exclusive to smart/not smart. There are plenty of not-smart self-taught engineers and plenty of smart grads.
> In limited domains
I'd argue that many, if not most, teams operate in limited domains.
> I think this just speaks to the necessity of rigor during the interview process.
That gets expensive, fast. There's just so much to cover already, between communication skills, programming skills, debugging skills, architecture / "whiteboarding problems", data structures and algorithms, general problem solving ("interview problems"). A job interview can never be a fully rigorous test of someone's actual skills. Most don't cover even a fraction of that stuff already.
> I'd argue that many, if not most, teams operate in limited domains.
It depends what you consider yourself responsible for. If you think of your job (or your team's job) as shipping features X, Y and Z within this react based web app, then sure - you operate in a limited domain. But if your job is "solve the user's actual problems" then things can get pretty broad, pretty fast. Sometimes you write code. Sometimes you're debugging a hard problem. Or talking to the users. Or identifying and tracking down a performance regression. Or writing an issue for a bug in 3rd party code. Or trawling through MDN to figure out a workaround to some browser nonsense. Or writing reliable tests, or CI/CD systems. And so on.
Its only really junior engineers who have the luxury of a limited scope.
I haven't heard of self taught programmers binging 15 minute YT videos. I can't recall the last time I did myself.. aside from conference talks and such its probably been at least 5 years since I watched something explaining things in the realm of programming.
For a motivated learner with access to good materials, schools provide two important things besides that very important piece of paper:
1. contacts - these come in the form of peers who are interested in the same things and in the form of experts in their fields of study. Talking to these people and developing relationships will help you learn faster, and teach you how to have professional collegial relationships. These people can open doors for you long after graduation.
2. facilities - ever want to play with an electron microscope or work with dangerous chemicals safely? Different schools have different facilities available for students in different fields. If you want to study nuclear physics, you might want to go to a school with a research reactor; it's not a good idea to build your own.
To extend 2. facilities, my experience had a - somewhat older and smaller - supercomputer that we got to run some stuff on.
And I'd argue for:
3. Realisation of the scope of computing.
IE Computers are not just phones/laptop/desktop/server with networking - all hail the wonders of the web... There are embedded devices, robots, supercomputers. (Recent articles on HN describe the computing power in a disposable vape!)
There are issues at all levels with all of these with algorithms, design, fabrication, security, energy, societal influence, etc etc - what tradeoffs to make where. (Why is there computing power in a disposable vape?!?)
I went in thinking I knew 20% and I would learn the other 80% of IT. I came out knowing 5 times as much but realising I knew a much smaller percentage of IT... It was both enabling and humbling.
But you can also meet experts at a company and get access to a company's machinery. To top it off the company pays you instead of you paying the school.
> Everyone knows that debugging is twice as hard as writing a program in the first place. So if you're as clever as you can be when you write it, how will you ever debug it? — The Elements of Programming Style, 2nd edition, chapter 2
If you weren't even "clever enough" to write the program yourself (or, more precisely, if you never cultivated a sufficiently deep knowledge of the tools & domain you were working with), how do you expect to fix it when things go wrong? Chatbots can do a lot, but they're ultimately just bots, and they get stuck & give up in ways that professionals cannot afford to. You do still need to develop domain knowledge and "get stronger" to keep pace with your product.
Big codebases decay and become difficult to work with very easily. In the hands-off vibe-coded projects I've seen, that rate of decay was extremely accelerated. I think it will prove easy for people to get over their skis with coding agents in the long run.
I think this goes for many different kinds of projects. Take React, for example, or jQuery, or a multitude of other frameworks and libraries. They abstract out a lot of stuff and make it easier to build stuff! But we've also seen that with ease of building also comes ease of slop (I've seen many sloppily coded React code even before LLMs). Then react introduced hooks to hopefully reduce the slop and then somehow it got sloppy in other ways.
That's kinda how I see vibe coding. It's extremely easy to get stuff done but also extremely easy to write slop. Except now 10x more code is being generated thus 10x more slop.
Learning how to get quality robust code is part of the learning curve of AI. It really is an emergent field, changing every day.
Yeah I think that's an interesting point of comparison. There's definitely a phenomenon where people can take their abstractions for granted and back themselves into corners because they have no deeper understanding of what their framework does under the hood.
The key difference with LLMs is that React was written very intentionally by smart engineers who provided a wealth of documentation to help people who need to peek under the hood of their framwork. If your LLM has written something you don't understand, though, chances are nobody does, and there's nowhere you can turn to.
If (as Peter Naur famously argued) programming is theory building, then an abstraction like a framework lets you borrow someone else's theory. You skip developing an understanding of the underlying code and hope that you'll either never need to touch the underlying code or that, if you do, you can internalize the required theory later, as needed. LLM-generated code has no theory; you either need to supervise it closely enough to impose your own, or treat it as disposable.
> LLM-generated code has no theory; you either need to supervise it closely enough to impose your own, or treat it as disposable.
Agreed! And I think that's what I'm getting at. Adding what they're now calling "skills," or writing your own, is becoming crucial to LLM-assisted development. If the LLM is writing too much slop, then there probably wasn't sufficient guidance to ensure that slop wouldn't be written.
The first step of course is to actually check that the generated code is indeed slop, which is where many people miss the mark.
No, it's not a mech suit. A mech suit doesn't fire its canister rifle at friendly units and then say "You're absolutely right! I should have done an IFF before attacking that unit." (And if it did the engineer responsible should be drawn and quartered.) Mech-suit programming AI would look like something that reads your brainwaves and transduces them into text, letting you think your code into the machine. I'd totally use that if I had it.
This analogy works pretty well. Too much time doing everything in it and your muscles will atrophy. Some edge cases will be better if you jump out and use your hands.
There's also plenty of mech tales where the mech pilots need to spend as much time out of the suits making sure their muscles (and/or mental health) are in good strength precisely because the mechs are a "force multiplier" and are only as strong as their pilot. That's a somewhat common thread in such worlds.
Yes. Also, it's a fairly common trope that if you want to pilot a mech suit, you need to be someone like Tony Stark. He's a tinkerer and an expert. What he does is not a commodity. And when he loses his suit and access to his money? His big plot arc is that he is Iron Man. He built it in a cave out of a box of scraps, etc.
There are other fictional variants: the giant mech with the enormous support team, or Heinlein's "mobile infantry." And virtually every variantion on the Heinlein trope has a scene of drop commandos doing extensive pre-drop checks on their armor.
The actual reality is it isn't too had for a competent engineer to pair with Claude Code, if they're willing to read the diffs. But if you try to increase the ratio of agents to humans, dealing with their current limitations quickly starts to feel like you need to be Tony Stark.
Funny, because I was thinking of Evangelion's predecessor, Gunbuster, in which cadets are shown undergoing grueling physical training both in and out of their mechs to prepare for space combat.
I like the electric bike as a metaphor. You can go further faster, but you quickly find yourself miles from home and out of juice, and you ain't in shape enough to get that heavy bugger back.
As long as we're beating the metaphor... so don't do that? Make sure you charge the battery and that it has enough range to get you home, and bring the charger with you. Or in the LLMs case, make sure it's not generating a ball of mud (code). Refactor often, into discrete classes, and distinct areas of functionality, so that you're never miles from home and out of juice.
> to stretch the analogy, I don't believe much is lost "intellectually" by my use of a mech suit, as long as I observe carefully.
With all respect, that's nonsense.
Absolutely no one gains more than a superficial grasp of a skill just by observing.
And even with a good grasp of skills, human boredom is going to atrophy any ability you have to intervene.
It's why the SDCs (Tesla, I think) that required the driver to stay alert to take control while the car drove itself were such a danger - after 20+ hours of not having to to anything, the very first time a normal reaction time to an emergency is required, the driver is too slow to take over.
If you think you are learning something reviewing the LLM agent's output, try this: choose a new project in a language and framework you have never used, do your usual workflow of reviewing the LLMs PRs, and then the next day try to do a simple project in that new language and framework (that's the test of how much you learned).
Compare that result to doing a small project in a new language, and then the next day doing a different small project in that same language.
If you're at all honest with yourself, or care whether you atrophy or not, you'd actually run that experiment and control and objectively judge the results.
I'd agree, if my goal was "to be a great and complete coder."
I don't. I want just enough to build cool things.
Now, that's just me.
That being said, I'd also venture to say that your attitude here might be a tad dinosaurish. I like it too, but also, know that to a large extent, especially in the market -- this "quality" that you're striving for here may just not happen.
OK, it’s a mech suit. The question under discussion is, do you need to learn to walk first, before you climb into it? My life experience has shown me you can’t learn things by “observing”, only by doing.
Yes, you can learn to walk in the mech suit. Let’s put one leg forward, then the next, good. You are now 100% production ready at walking. Let’s run a load test. You’re running now. Now you’re running into the ocean. “I want to swim now.” You’re absolutely right! You should be swimming. Since we don’t have a full implementation of swimming let me try flailing the arms while increasing leg speed. That doesn’t seem to work. The user is upside down on the ocean floor burrowing themselves into the silt. Task Complete. Summary: the user has learned to walk.
>Here's the thing -- I don't care about "getting stronger."
Let's not mince words here, what you mean is that you don't care to learn about a craft. You just want to get to the end result, and you are using the shiny new tool that promises to take you from 0 to 100% with little to no effort.
In this way, I'd argue what you are doing is not "creating", but engaging in a new form of consumption. It used to be you relied on algorithms to present to you content that you found fun, but the problem was that algorithm required other humans to create that content for you to later consume. Now with LLMs, you remove the other humans from the loop, and you can prompt the AI directly with exactly what you wish to see in that moment, down to the fine grained details of the thing, and after enough prompts, the AI gives you something that might be what you asked for.
This strikes me as extreme cope from the other end. There may be some truth to that, but it also kind of reminds me of "how can you possibly create a new kind of tractor unless you know exactly how to build a combustion engine yourself?"
If all I know is the mech suit, I’ll struggle with tasks that I can’t use it for. Maybe even get stuck completely. Now it’s a skill issue because I never got my 10k hours in and I don’t even know what to observe or how to explain the outcome I want.
In true HN fashion of trading analogies, it’s like starting out full powered in a game and then having it all taken away after the tutorial. You get full powered again at the end but not after being challenged along the way.
This makes the mech suit attractive to newcomers and non-programmers, but only because they see product in massively simplified terms. Because they don’t know what they don’t know.
The mech suit works well until you need to maintain stateful systems. I've found that while initial output is faster, the AI tends to introduce subtle concurrency bugs between Redis and Postgres that are a nightmare to debug later. You get the speed up front but end up paying for it with a fragile architecture.
If observing was as good as doing, experience would mean nothing.
Thinking through the issue, instead of having the solve presented to you, is the part where you exercise your mental muscles. A good parallel is martial arts.
You can watch it all you want, but you'll never be skilled unless you actually do it.
Misusing a forklift might injure the driver and a few others; but it is unlikely to bring down an entire electric grid, expose millions to fraud and theft, put innocent people in prison, or jeopardize the institutions of government.
There is more than one kind of leverage at play here.
> Misusing a forklift might injure the driver and a few others; but it is unlikely to bring down an entire electric grid
That's the job of the backhoe.
(this is a joke about how diggers have caused quite a lot of local internet outages by hitting cables, sometimes supposedly "redundant" cables that were routed in the same conduit. Hitting power infrastructure is rare but does happen)
At my last job we had the power taken out by a backhoe. It was loaded onto a trailer and either the operator forgot to lower the bucket, or the driver drove away before he had time to lower it.
Regardless of whose fault it was, the end result was the bucket snagged the power lines going into the datacentre and caused an outage.
From an exercise standpoint, sure, but with sports there is more to it than just maximizing exercise.
If you practice judo you're definitely exercising but the goal is defeating your opponent. When biking or running you're definitely exercising but the goal is going faster or further.
From an an exercise optimization perspective you should be sitting on a spinner with a customized profile, or maybe do some entirely different motion.
If sitting on a carbon fiber bike, shaving off half a second off your multi-hour time, is what brings you joy and motivation then I say screw it to further justification. You do you. Just be mindful of others, as the path you ride isn't your property.
A use case I’ve been working through is learning a language (not programming). You can use LLMs to translate and write for you in another language but you will not be able to say, I know that language, no matter how much you use the LLM.
Now compare this to using the LLM with a grammar book and real world study mechanisms. This creates friction which actually causes your mind to learn. The LLM can serve as a tool to get specialized insight into the grammar book and accelerate physical processes (like generating all forms of a word for writing flashcards). At the end of day, you need to make an intelligent separation where the LLM ends and your learning begins.
I really like this contrast because it highlights the gap between using an LLM and actually learning. You may be able to use the LLM to pass college level courses in learning the language but unless you create friction, you actually won’t learn anything! There is definitely more nuance here but it’s food for thought
I've been showing my students this video of a robot lifting weights to illustrate why they shouldn't use AI to do their homework. It's obvious to them the robot lifting weights won't make them stronger.
I think forklifts probably carry more weight over longer distances than people do (though I could be wrong, 8 billion humans carrying small weights might add up).
Certainly forklifts have more weight * distance when you restrict to objects that are over 100 pounds, and that seems like a good decision.
I think it's a good analogy. A forklift is a useful tool and objectively better than humans for some tasks, but if you've never developed your muscles because you use the forklift every time you go to the gym, then when you need to carry a couch up the stairs you'll find that you can't do it and the forklift can't either.
So the idea is that you should learn to do things by hand first, and then use the powerful tools once you're knowledgeable enough to know when they make sense. If you start out with the powerful tools, then you'll never learn enough to take over when they fail.
A forklift can do things no human can. I've used a forklift for things that no group of humans could - you can't physically get enough humans around that size object to lift it. (of course levers would change this)
Yeah, it's a great analogy. Pushing it even further: a forklift is superhuman, but only in specific environments that are designed for it. As soon as you're off of pavement a forklift can't do much. As soon as an object doesn't have somewhere to stick the forks you need to get a bunch of other equipment to get the forklift to lift it.
You're making the analogy work: because the point of weightlifting as a sport or exercise is to not to actually move the weights, but condition your body such that it can move the weights.
Indeed, usually after doing weightlifting, you return the weights to the place where you originally took them from, so I suppose that means you did no work at in the first place..
That's true of exercise in general. It's bullshit make-work we do to stay fit, because we've decoupled individual survival from hard physical labor, so it doesn't happen "by itself" anymore. A blessing and a curse.
I think a better analogy is a marathon. If you're training for a marathon, you have to run. It won't help if you take the car. You will reach the finish line with minimal effort, but you won't gain any necessary muscles.
But if your goal is to get from A to B, car is more efficient.
It's the whole "journey vs destination" thing.
Currently AI seems to be the rocket you strap to your back as you put on VR glasses and enjoy the entertainment. You'll get there fast or blow up in the middle.
The True Artisanal Coders are the ones running the whole way, enjoying the scenery and the physical conditioning they get.
And there are people in between with bikes, cars etc. (different stages of AI use)
> This is the ultimate problem with AI in academia. We all inherently know that “no pain no gain” is true for physical tasks, but the same is true for learning. Struggling through the new concepts is essentially the point of it, not just the end result.
OK but then why even use Python, or C, or anything but Assembly? Isn't AI just another layer of value-add?
No, because AI is not deterministic. All those other tools are intentionally isomorphic with machine code, even if there's a lot of optimization going on under the hood. AI may generate code that's isomorphic with your prompt, but it also may not. And you have no way of telling the difference besides reading and understanding the code.
The real challenge will be that people almost always pick the easier path.
We have a decent sized piece of land and raise some animals. People think we're crazy for not having a tractor, but at the end of the day I would rather do it the hard way and stay in shape while also keeping a bit of a cap on how much I can change or tear up around here.
Wondering why the obvious solution isn’t applied here - instead of giving already well known problems that have been solved thousand times give students open research opportunities- stuff which is on the edge of being possible, no way to cheat with Ai. And if Ai is able to solve those - give harder tasks
The same reason we give beginner math students addition and subtraction problems, not Fermat’s last theorem?
There has to be a base of knowledge available before the student can even comprehend many/most open research questions, let alone begin to solve them. And if they were understandable to a beginner, then I’d posit the LLM models available today would also be capable of doing meaningful work.
Coincidentally, this is why Duolingo doesn’t work. They need to make it easy/low friction to keep you engaged but if it’s not hard you won’t be learning much.
Yes but the goal of school is to lift heavy things, basically. You're trying to do things that are difficult (for you) but don't produce anything useful for anyone else. That's how you gain the ability to do useful things.
Let's just accept that this weight lifting metaphor is leaky, like any other, and brings us to absurds like forklift operators need to lift dumbbells to keep relevant in their jobs.
Forklift operators need to do something to exercise. They sit in the seat all day. At least as a programmer I have a standing desk. This isn't relevant to the job though.
> At least as a programmer I have a standing desk.
When I stand still for hours at a time, I end up with aching knees, even though I'd have no problem walking for that same amount of time. Do you experience anything like that?
I kinda get the point, but why is that? The goal of school is to teach something that's applicable in industry or academia.
Forklift operators don't lift things in their training. Even CS students start with pretty high level of abstraction, very few start from x86 asm instructions.
We need to make them implement ALU's on logical gates and wires if we want them to lift heavy things.
We begin teaching math by having students solve problems that are trivial for a calculator.
Though I also wonder what advanced CS classes should look like. If they agent can code nearly anything, what project would challenge student+agent and teach the student how to accomplish CS fundamentals with modern tools.
In one of my college classes, after you submitted your project you'd have a short meeting with a TA and/or the professor to talk through your solution. For a smaller advanced class I think this kind of thing is feasible and can help prevent blind copy/pasting. If you wrote your code with an LLM but you're still able to have a knowledgeable conversation about it, then great, that's what you're going to do in the real world too. If you can't answer any questions about it and it seems like you don't understand your own code, then you don't get a good grade even if it works.
As an added bonus, being able to discuss your code with another engineer that wasn't involved in writing it is an important skill that might not otherwise be trained in college.
> Even CS students start with pretty high level of abstraction, very few start from x86 asm instructions.
> We need to make them implement ALU's on logical gates and wires
Things must have certainly changed since I was a CS student :-/ We did an assembler course in second year, and implemented a basic adder in circuitry in a different course.
This was in the mid-90s, when there was definitely little need for assembly programmers outside of EE (I was CS).
> the main goal of our jobs is not to lift heavy things, but develop a product that adds value to its users.
Well, whether we like it or not, we are all eventually going to find out if "developing a product that adds value to its users" can be done when you have no more skill than aforementioned users.
This is the ultimate problem with AI in academia. We all inherently know that “no pain no gain” is true for physical tasks, but the same is true for learning. Struggling through the new concepts is essentially the point of it, not just the end result.
Of course this becomes a different thing outside of learning, where delivering results is more important in a workplace context. But even then you still need someone who does the high level thinking.