The evidence for reduced persistence is kinda weak.
The strongest experimental result in the paper is: if people use AI to help solve a series of problems, and then the AI is suddenly taken away, they perform worse on subsequent problems than people who hadn't been using AI for the earlier questions.
But:
- They're looking at really really short-term effects, like seconds or minutes. That's not really interesting if we care about long term effects (which is what the abstract says is important).
- What they found is totally expected and isn't something I'd worry about: it's kind of expected that if one guy warms up with 10 questions done himself, and the other guy warms up with 10 questions delegated to AI, then the second guy is going to do worse on the final 10 questions (because he didn't warm up).
And the IQs of those born after 1975 via the reverse Flynn Affect, those who had these devices of consumption and easy answers such as TV, and later Social Media, AI, etc has been decreasing.
I can only imagine a further and speedier decline will happen, and is happening.
Put a person from 1870s London in current-day London. At first they will be amazed by the technological advancements. Then they will be amazed by how dumb the average person has become. Idiocracy is a documentary, not a comedy.
Is it the domestication of humankind? The average person is no longer able to hunt for themselves. Like ant colonies, people are no longer capable of living alone.
Where would they do it? There is not remotely enough available land near where most people live that isn't privately controlled.
What would they eat? Getting enough calories from hunting for it to become a frequent part of everyone's life is currently a thermodynamic impossibility. On top of that, food webs are actively collapsing or have already.
This has not been true in my experience. The bar of what is possible has been moved dramatically. So my daily quest is not to continue to achieve our old objectives, but rethink worthy objectives for our new tools.
The blasé attitude with which people talk about “moving up the stack” as if it’s a foregone conclusion that it’s the right thing to do or even an accurate description of what’s happening is easily one of the most disheartening aspects of this whole movement.
> The blasé attitude with which people talk about “moving up the stack” as if it’s a foregone conclusion that it’s the right thing to do or even an accurate description of what’s happening is easily one of the most disheartening aspects of this whole movement
They need to walk the walk. Or accept that they themselves haven't stopped to think before they "moved up the stack".
The article is not about the output but on the process. Sure you can produce more and more quickly with AI, but at the price of losing the ability to do the same without it. And that's been true in my experience
The average human is no longer able to hunt animals for food anymore either. I think it's just the domestication of humankind as it has been ongoing for thousands of years
> The average human is no longer able to hunt animals for food anymore either.
The average human may no longer desire or have a need to hunt animals for food. But re-introduce that need and they will do fine. Kids and adults continue to be able to pick up fishing for the first time as well as handling a .30-30 or 20-gauge.
Using my brain to solve abstract problems and getting money that I can exchange for food, in the comfort of an office or a home, is already way past hunter-gatherer. Now if I don't even get to use my brain that's more a dumbification than domestication.
10 minutes of AI doesn’t ruin someone from programming or solving fractions either, it just lowers the ability short term. Likely not by making someone dumber but instead impacting motivation. Give someone a calculator and then take it away and you would get a similar result
Unfortunately there is no way for us to try this. “yes” might very well be the answer. My hunch is that although it would seem pointless, you would still be able to go through the motions since the work is largely physical. For knowledge work, concentration and perseverance is half the work itself.
> The average human is no longer able to hunt animals for food anymore either.
And that is not a good thing. We shouldn’t celebrate or ignore the loss of basic living skills, especially when what we get in return is a nightmare scape of a society whose whole purpose is to dominate your thoughts and fill you with anxiety to suck you dry.
"Basic living skills" are relative to the environment and change by culture, location, access to technology and so on. In our environment, we don't need to hunt. It has been fully superseded by supermarkets, and that can reasonably be expected to continue into the future. That's literally the only reality we have ever known, and the only reality towards which adapting is worthwhile.
The new hunting-equivalent basic living skill would be the ability to go to a store, orient yourself, pick reasonably priced items, ideally choose healthy ones and avoid the temptations of junk food, check quality and macros (calories, protein etc)...
I think I get your point. I think what you're getting at is that hunting etc is a fundamental skill, below this abstraction we have built, and if you would be dropped into a forest and left to your devices, you'd still be able to handle yourself. But I just think that it's an interesting mental exercise, but nothing more. I really don't see it as a tragedy.
Tl;Dr "Basic skills" are relative. Hunting is now an outdated, impractical, unusable skill to hone, and a vastly inferior source of food.
If anything, I would claim the opposite ( and agree with you )
Not only has AI allowed me to start projects without knowing all the steps ahead of time, it has helped me proceed through each of those steps to completion.
"For the first experiment with 354 participants, one group was tasked with solving 15 basic fraction problems without help, while another had ChatGPT open on the side to use for assistance and to even ask for the answer. The group with AI access started off more accurate than the group without it. Then, after 12 problems, researchers took away the AI tool. Almost immediately, the people in that group stopped solving questions accurately."
Looking at the design of the experiment, it seems everyones brain was primed to think "I have AI at my disposal" ... and then had the framework shattered when it was taken away, which could have affected the test.
Perhaps this study exposes "conversational mindset" versus "mathematical mindset" switching times ?
Theres only one thing I'm certain of ... Claude loves me. :D
>Honestly I don’t expect these kind of low quality comment in HN. Where’s your thinking?
A comment that opens a discussion is not low quality just because you disagree with it Mr 7 karma. There is always a degree of irony with such statements.
You choosing the toppings for your pizza and whether you want cheese in your crust does not mean you made the pizza. You did not make it, you merely ordered it.
A more apt comparison would be the producer role (not the executive type, I mean the Project Manager type of roles). And to be honest, I've seen some amazing music A&R being the reason an album is a masterpiece without ever pushing a button or playing an instrument. But none of them would call themselves musicians.
Or is it more like creating machine code but using a tool called a compiler? If you've ever made anything with AI you know it by default generates things that aren't ideal. You still have to go in and debug, make the UI better, think of more elegant ways to present things. The fact that you're focused on those issues instead of tediously finding the block of code and maybe tediously looking up ways to fix / make it better is probably an overall good thing in the long run. We've lost the ability to code in assembly I'm not sure that's made software or life worse even if coders as a group are in all likelihood less intelligent these days.
In your analogy maybe it's more like creating a pizza except you didn't have to make the cheese, grind up the flour, go find the water in a stream somewhere, grow the tomatoes yourself, butcher the animal for the meat, etc.
You can throw away the compiler and the developer who created the software will still understand how it works. You can't say the same about someone who used an LLM who would need an LLM to continue working on the project and understand how it works. If the tool understands the code better than you, you have essentially given up ownership.
The only caveat with my statement is that there are different degrees in which you can involve an LLM in your project but I mean if you are using it to write the majority of your code.
It is the same way a football fan thinks he helped his team win by watching on the sidelines while the players do the work and give you the illusion of participation.
Shouting racist slurs at the opposition, making monkey noises and throwing bananas is surely why my team won the match! Couldn’t have done it without us.
There is some evidence of that, where the closer the crowd is to the field the more likely the referees are to call in the home team's favour. There was a study on this, probably close to a decade ago now.
I think this still leaves much of the concern the article points towards open. If we come across an objective that remains tedious or annoying, does AI create pressure towards thinking that it must not be a good objective and some AI-enabled objective would be better?
If it would be possible to walk to get groceries (and also exercise at the same time), do people choose to drive instead because it's simply easier and faster? They then claim that the goal of exercise is simply not worth it any more given driving is faster (and easier)?
I would say it more depends how the environment is designed and what transport options you have at your disposal.
In suburbia where the grocer is surrounded by a parking lot and there's busy roads to navigate nearby then of course people would drive rather than walk, and thus also end up getting a week's worth of groceries at a time.
In a dense mixed use environment where it is easy enough to navigate on foot then the urge to drive lessens and all grocery shopping can be done more frequently and on foot or by public transport.
I've experienced this personally in Australia, where living in suburbia I went grocery shopping once a week with a car and brought back many bags worth at a time, and also in a more dense neighbourhood where I could get groceries on the walk back from work more frequently and barely used my car even once a month. In Europe I only ever walk or get public transport for groceries as I live in a dense area and don't need to own a car.
Yes, absolutely they do. That's a huge contributing factor to why the obesity rate is so high in the US. I grew up about 10 minutes walking distance from a grocery store with a big parking lot and most of us in my neighborhood drove there.
- I (https://compile-xc.org) built a compiler for a language I call ‘xc’, similar to Objective-C but designed to be cross-platform and have less square brackets :). It can run on any of {Windows, Mac, Linux, Zynq} and produce code for any of {Windows, Mac, Linux, Zynq, WASM, iOS, Android, m68k, 6502}. It needs no host-platform tools to compile code, so for example I have a signed binary on my iPhone, written and signed entirely on Linux. The compiler currently produces code that is between 2x and 0.5x the speed of clang, which is pretty good for a nascent compiler. It also compiles the same language for execution on a GPU, and makes it trivial to designate parts of your code as data-parallel (https://compile-xc.org/compiler/language/par/). One statement turns a code block into a GPU kernel
- I built (more accurately: am building) a (https://compile-xc.org/compiler/api/uxkit) cross-platform UI framework, so you can write code once, even Table-views, Outline-views, Collection-views, etc., etc and your application will bind to the platform’s native toolkit through a common programming API. The next step here is an “Interface Builder” like application I call RoCkS (for historical reasons), which lets you design the UI for {desktop, tablet, phone} to bind to the same core code, dragging connections between that core code and the UI elements for the different environments
- I built (https://atari-xt.com/os) XTOS for the Zynq - an OS that started with FreeRTOS and added loadable processes, dynamic linking, shared memory, paging, a boot environment that can be scripted like Linux, networking, a graphical UI based on GEM, with a hardware blitter, and HDMI output with Audio islands mixed in. The blitter owns the video memory and uses shared-memory pools to give each “virtual workstation” (GEM-speak for “view of the display”) its own retained-mode and composited graphics memory. The whole OS boots from power-on to desktop in a couple of seconds, which is the reason for making it, rather than just using Linux, I wanted that “instant-on” feeling. It runs on a Z-turn board from MyIR.
- I built an Atari (6502, ANTIC, POKEY, various other chips) (https://atari-xt.com/hardware/x/)emulator, which passes the (https://forums.atariage.com/topic/171296-acid800-an-atari-te...) Acid 800 emulation torture test, and can run games such as Ballblazer, Elektraglide and (https://0x0000ff.co.uk/mov/xt/xtos-aug-11.mov)Despatch Rider. It’s integrated into XTOS, so the OS boots into the GEM-based Desktop.app (written in xc) and you can double-click an icon to launch a game. The plan is to extend that to the ST once I get past the current task, which is …
- I built a Reddit-like website that I plan to launch in a couple of months, written entirely in xc, both the Linux-based server and the client WASM code; the server-side is just xc code, no Apache, no scripting language, just lean-and-mean xc code handling everything from the TLS handshake to the Postgres database back-end. The main difference between (https://blewit.net/)blewit.net and Reddit is that I don’t collect any personal information, don’t track your habits and sell your data, don’t use adverts (tracking by another name) or any of the other dark patterns social media is infested with these days. Blewit is so named because I think they did. One of the other differentiators is that I plan to support the groups with specialist helpers. You can see a selection of them at (https://blewit.net/sandbox) the blewit sandbox - things like rendered music staves from ABC notation, 3d interactive molecules structure from SMILES notation, mathml with an editor to create it, etc, etc.
- One last purposely-separated “helper” from the others because of its scale, is the electronic circuit editor/simulator. I introduced it [url=https://www.eevblog.com/forum/projects/circuit-simulation-fo...]in the EEVBlog projects forum[/url], but no-one seemed to care [grin]. That’s a shame because I think it’s pretty cool, draw the circuit in the editor, you can create your own parts, even store them to a library, add digital logic to pins, import SPICE models, and simulate with ideal or oscilloscope-type probes. It understands transmission lines, tolerances, can run monte-carlo simulations, produce overlaid graphs for {R=1k, 4.7k, 10k} etc., I could go on. The full docs to it are in the (https://blewit.net/post/93083024324820992) Blewit editor-information pages
Overall, looking back at what using an LLM has helped me code over the last half-year or so, it is unbelievable what you can code with AI in 2026. I always used to say that I had too many ideas and not enough time to implement them, well that’s still true, but to a significantly lesser extent…
For all the above “I” is short for “I, with the help of Claude”
Maybe more like an architect saying "I built" when actually construction workers did all the building. Even that's stretching it though because construction workers tend to follow the plans fairly well. AI has you constantly tweaking the beams and cement yourself mid-construction to get a usable product.
Yes, it’s very similar to that, except the ic has no imagination, vision, or drive.
I wrote my first compiler about 35 years ago, and my first OS around the same time, well before LLMs were on the scene. I know exactly just how much work is involved with doing it well, which is why I’d never normally take on a project like this now I’m retired.
But we do not live in normal times. The language “I” have created (and it’s not as though I just said to Claude “hey, write me a compiler”) is (even if I say so myself) pretty cool. Here’s the full source of a Mandelbrot program:
#import "Stdio.xc"
#import "Par.xc"
#import "../src/include/bench_time.xc"
#define W 2048
#define SIZE (W * W)
i32 main(void)
{
// Eight runs: the first carries one-off costs (a GPU builds its kernel
// then), so the figure is the best run; the first is printed too. Auto
// decides after its fourth (two on each device), and uses the rest.
u32 total = 0;
i64 best = 0;
i64 first = 0;
for (u32 rep in 0..8)
{
total = 0;
i64 t0 = bench_now_us();
par mandel :reduce(+ total)
{
for (u32 i in 0..SIZE)
{
float cx = (float)(i % W) * (3.0f / 2048.0f) - 2.0f;
float cy = (float)(i / W) * (3.0f / 2048.0f) - 1.5f;
float x = 0.0f;
float y = 0.0f;
u32 k = 0;
while (k < 256 && x * x + y * y < 4.0f)
{
float xt = x * x - y * y + cx;
y = 2.0f * x * y + cy;
x = xt;
k = k + 1;
}
total = total + k;
}
}
i64 t1 = bench_now_us();
if (rep == 0)
first = t1 - t0;
if (rep == 0 || t1 - t0 < best)
best = t1 - t0;
}
Stdio.printf("%u %lld %lld\n", (total + 500000) / 1000000, best, first);
return 0;
}
Just one line in that source code is what turns it from a bog-standard C-like program into a GPU kernel, automatically tracked, scheduled, and all data hazards transparently managed.
On my M4 max, that one line of code takes it from 481ms to 1.6ms. All ‘par’ does is say “this next block of code, call it “mandel”, is data-parallel, and for the ‘total’ variable all execution units should combine their output to that variable using +
No huge GPU boilerplate, no stream management for data transport from cpu to gpu and back, no memory barriers, no hassle. Just “hey, do this on the gpu”.
GPU blocks are bound at runtime using the best design for the hardware on the machine, obtained by measuring it if it hasn’t been measured before. If it has, the cache is read. The user can always override, which is why ‘par’ blocks can be named.
Next up is an overlapping scheduler, which keeps 3 work units in play {pending, being-processed, being-transferred-once-done} for a processing device (right now, just a local gpu), which is also the multi-gpu story, and even the networked-machines story.
I think this is great work; these are strange times, i personally don't feel like i'm building stuff anymore, it is more like thinking it into existence
Schrödinger's AI: can't come up with ideas on its own and we'll all be doomed once we come to rely on it, but we shouldn't credit you with what you use it for; it's all just AI slop. Neat work.
:D added reddit clone to personal watch list. You are braver than me sharing this stuff on HN ( they can be ruthless when it comes to this stuff ). Nice work.
I’m satisfied that my actual engineering is up to snuff, done some pretty cool things over my career. Now I’m just having fun, and LLMs make it easier to have said fun :)
When faced with AI some companies are looking for employees to fire to achieve same goals cheaper. Other companies are looking how to uplift their employees to expand their capability and excelence.
Maybe the same thing happens at the level of individual persons. I think the difference is whether you felt limited or competent. If you felt competent you shrink. If you felt limited, you grow.
Exactly! The sheer effort of having AI produce good results, from writing, to planning, to coding is immense. I have consistently gather all my energies and focus to guide agents to the ideal, maintainable, and usable solution. Often it is me that makes the crucial design contribution.
Yes, I could be lazy and just vibe piles of slop. But anyone who spends any meaningful amount of time using AI (more than the articles 10 minutes), knows that quite extreme concentration and effort is required to build ambitiously with agents.
I feel like I work harder and and with more difficult problems than before AI, just at a much faster pace and scope.
Yes, thank you! This is exactly my experience. It requires immense effort and concentration, maybe more than before AI, where
- the next ideal step progressively manifested itself to me during coding
- I actually had perfect knowledge of the codebase and didn't have to work as hard to reconstruct it in my head each time
- coding itself is half a mechanical job. AI does the mechanical job itself well, leaving you only the hard thinking.
- AI prose isn't easy to parse, at least to ne.
- Prompting AI until it gets the optimal solution / implementation takes time and revisions.
Something like that.
I keep thinking that it's an issue of my approach, though. I've been trying to improve my AI workflow as much as possible for months, every day, but still, I'm not content with it. I might have gotten stuck on a local optimum. Maybe I'm writing prompts needlessly detailed. Maybe I should read more of the code, because I don't, really, making me not know the actual codebase and how things work.
It's not easy, this whole thing. And honestly, sometimes I wonder - after accounting for the total time, including revisions, mental reconstructions etc - if it wouldn't have been done faster by me in the pre-AI world.
I find that this is noticeable in situations where you're working on something a bit more complex and are initially using an LLM as code assistant to make progress. Once it hits a wall and you find yourself needing to just figure it out yourself, man does the debt get paid with interest. It's not a permanent cognitive impairment though. But there is a bit of a recovery period needed. On the whole I think there are some productivity gains, in spite of setbacks (assuming you know how to solve the problems yourself, to begin with)
That's exactly the problem. You need to religiously contain AI work to the parts of your codebase that you don't actually care about. Put it in separate repositories and limit work to working directories that do not check out the sources that you value. And be prepared to toss it at any point.
The risk if you ignore that is that you're on a nicely greased slope all the way down into the mess that a typical AI will leave behind.
I have a project right now that needs a lot of scaffolding to be worked on. I'm fine with the scaffolding and the mocks to be AI generated, they have no value to me at all. But the core gets written line-by-line by hand and I am in absolute control of what is going on there. The number of times that the AI has tried to gain control over that part of the codebase that it can't see but infers must be around somewhere is really interesting, it just does not want to stay in its lane. It will gaslight itself into believing that the root cause of whatever problem it is dealing with must be located in this magical invisible body of code and it will do what it can to try to find it, including attempts at reverse engineering that code from libraries and searching all over the accessible parts of the file system to see if there are any copies lying around.
Since I don't work at OpenAI I've found that containing the bot is rather easy, you just make sure it does not have network access, but that won't stop it from trying.
Genuine curiosity here, just to ruminate a bit: is there a reason you disallow the model from seeing it at all? I can totally accept not wanting any edits, and that's more than reasonable. But even close source binaries end up with useful patches once someone knows that a particular loop does something weird or a process implementation behind a protocol is actually a different interpretation than the protocol definition itself belies. Seems like being aware of it is at least helpful, but again, curious!
Yes, because I've noticed that anything at all that is in the input stream gets used to waste tokens on various rabbit holes. Less information -> more efficiency and better output as long as it is sufficient to get the job done. For a human distinguishing important stuff from unimportant stuff is hard for an LLM it is next to impossible (at least, from what I've seen so far). There is no distinction between 'obscure thing that I once saw in passing 30 turns ago versus 'the thing right in front of my nose'. So you get all of these wild goose chases that lead nowhere and that will easily burn 50 to 100K tokens before finally being abandoned, more often than not after doing considerable littering and/or actual damage. So I am purposefully limiting what goes into the LLM and I find that by doing this it gets to the point much faster and produces better output. 'Lead it not into temptation' or something to that effect ;)
This behavior is not very surprising to me, and I think the conclusion is a bit off. Here is an analogous study:
Participants are asked to drive 20 nails into a plank of wood. Group A is given a hammer and an unripe banana. Group B is given only a banana. Halfway through the experiment, the hammers are taken away.
Hammers erode your ability to persist at hard things…
My point being that AI is a new tool that allows us to work on harder things at a higher level. It’s like having a team of 100 skilled workers with nail guns (and all manner of other tools) that never tire, and you get to direct them. Once you experience that, it’s a bit disheartening to go back to building alone with your bare hands… even if you lament the decline in your hand-eye coordination.
for those that have spent 20 years of their career, perfecting the art of coding / writing / research / all forms of knowledge work, it sure is depressing to see the career moats we tried to shore up just diminish practically overnight. The ability to operate at a higher level of abstraction is real, but so is the sudden devaluation of skills that previously required years to acquire.
You hit it right on the nail! You do lose hand-eye coordination, but I’d argue you’d get an even better skill, the ability to orchestrate.
Before AI, when building anything, you’d have to have a lot of context from different parts of the app. And then work with that knowledge to build the feature out in those places. Keeping that context in your head was tough and coming back the next day required you to “fill your internal RAM” with the context and then start.
Now we can use our “internal RAM” thinking about the bigger picture and bigger projects. We’ve just gone an abstraction layer deeper.
>Once you experience that, it’s a bit disheartening to go back to building alone with your bare hands.
Art provides a counterpoint. The invention of cameras allowed you to capture reality in ways never seen before, and yet this lead to art having a _really weird_ few decades/century where artists rejected realism and instead embraced abstraction.
Wouldn't it have been more useful/conclusive if the study had another control group or two with non-AI based assistance (access to math books, access to calculator, access to human experts, etc)?
This is exactly the reason the studios garbage, and was clearly tailored to prove a point and sell books. He literally should have asked got5 or Claude how to design a useful study of the problem.
FWIW, well designed study probably would still support his premise. He at least needed a group with no help, a group with no help but books and a calculator (tools), a group with human assistance, and a group with AI assistance, and a group that does an unrelated activity during the priming period.
This is just our energy conservation system in practice. It happens every time we experience and easier/better way of doing something. Another version of this headline could have been "Experiencing VIP treatment for just 10 minutes erodes your ability to tolerate the normal shit you have to put up with"
I can’t tell if you’re defending this as good or just hypothesizing about the underlying cause but “appeal to nature” is one of the many freshman-level logical fallacies that seem ubiquitous in AI hype circles. Just because it’s what our animal brains do, doesn’t mean it’s going to be good or advantageous to our current evolutionary context.
Our energy conservation system also encourages us to eat as much high fat, high sugar food as we can get down our gullets, but that turns out to be a big problem for modern humans.
They need a better control or comparison. For the math problem, introduce a standard calculator and one that that sometimes gives the wrong answer. Pretty sure he’ll find the same issue with persistence and focus.
Using a table saw for just 10 minutes eroded my ability to persist at hand sawing lumber.
The time difference was measured in hours, not minutes, and and effort required to hand-saw is enormous. Also, the accuracy difference is ridiculous, even with a lot of practice, and maintaining that accuracy continues to be a struggle with hand-sawing.
I now own 6 different electric saws for different specific purposes. Table saw, chop saw, circular saw, track saw, jig saw, scroll saw... I actually probably have more. I got rid of my chainsaw because it lost its chain in a way that really scared me one day. I might get another some day, but not that one again. Does a Dremel count? I'm going to say 'no' so I don't have to count 2 of those.
And yet, I still own a 2 Japanese pull saws and a normal American hand saw. I don't use them as much, but some times they're still worth the effort.
When using table saw, you still need to make a lot of decision: The depth of cut, the angle, the length. If you don't make them, your project will not progress.
With AI, you can stop decide, tell the chat bot to do whatever they want and make any decision they like. Your ability to analyze problem and make decision is not practiced anymore and begin corroding.
I'm very capable of getting sloppy cuts to fit into a project, whether I made them by hand or with power tools, or whether I paid someone else to make those cuts for me using whatever tools they use.
I mean: In cabinetry, for instance, that's the kind of slop that dado joints help to hide and nobody ever knows about it after it's glued up.
And I'm not Norm Abram over here: I can can cheat even harder than that. I can get a sloppy six-sided wooden box to look nice and square with neat joints after basic assembly is done, using just a few minutes of time and a router with an appropriate bit.
It won't pass muster in even the most simple of metrological examinations, but I don't have to care about that. (And my work doesn't detract from that of Norm, either.)
First, you know it's sloppy and can be improved and you know what to improve. An AI source code can look very high quality, until you finally realize it's not after diving deeper.
Second, you make decision to be sloppy. That means you can also make decision to do it right. With AI, no matter how much "make no mistake" you scream toward it, no guarantee whatever it produces is high quality.
No. I know it's sloppy, and I don't care to improve it. Some of the sins that the dado hides are truly hidden and can't be discovered without destructive disassembly.
I made a decision to do it fast-er instead of perfect-er. I understand and embrace the results of this decision.
That's exactly my point here, you know it's sloppy. To know it's sloppy you must know what a good joint look like. Many vibecode projects the author even can't tell if their code is good or sloppy. Is it important, the ability to judge and decide something, depends on each individual, personally it's very important to me.
No. It's sloppy, and it looks identical to a not-sloppy actually-snug joint. I think I've mentioned this a few times so far. (Many readers of comments can't tell what the comment in front of them actually spells out, I guess, no matter how plainly or often it gets repeated.) :)
---
Code. Despite this talk about woodworking (which I actually can accomplish with precision using a variety of tools when I elect to do that), I really can't write code for shit. I've been working on that for several decades and it's still just shit.
And I just don't care anymore.
I can feed stuff into the bot at a high level. And I can read code well-enough after all these decades of failure, so I can be hyper-critical of the results and produce precise demands for corrections. I can get resolutely-tight performance from it when it's important.
I once spent a lot of time working with the bot on RP2040 PIO assembly, where I was working with continuous high-speed signals. The RP2040 PIO has only a single-digit instruction count, a few registers, and just 32 instructions worth of memory. Individual clock cycles need counted, and slop doesn't work there at all. I accomplished what I needed to accomplish with perfection (as if it could ever be any other way). I'm pleased with the result, and I couldn't even have ever gotten started on that without the bot.
Some other times, I think that it's OK that it is slow or wasteful as long as the inputs eventually produce the right outputs. Like an intern who, when given a task to make a thing that implements a process comes back with something that's messy-but-working. It's not ideal, but it can be good enough some of the time.
I'm not fast at producing code with the bot and that doesn't matter to me: I'm getting things done (finished!) that I previously spent decades beating myself up about being unable to do. There are hardware+software things that are rolling right now and behaving themselves seemingly-perfectly that I wrote no code for at all; all I did was propose ideas, review the code where useful (because sometimes it's all fucked up), then discuss and implement changes.
Anyone can think of me as being stupider, somehow, than I used to be because of the addition of the bot into my life -- but I implore them to address that concern to me directly. I'm right here.
It reduces tolerance for dealing with bullshit.
Who wants to start a stackoverflow humiliation ritual and breadcrumb following
random websites to find reliable stuff(90% of which is AI generated),
when prompting gets you directly to the answer?
Before that, the "Google rots your brain" articles prophesied the fall of humanity to
search engines: now its mark of intellect to able to manually
curate and search within websites for relevant info.
https://en.wikipedia.org/wiki/Is_Google_Making_Us_Stupid%3F
Two things can be true at the same time. Google rotted our brain and AI will rot our brains even further. Google made remembering unnecessary. AI makes thinking unnecessary.
I see AI as allowing me to delegate details I'm aware of but don't care to think about to AI, while thinking about the larger picture and the details AI missed.
I'm still thinking, just about different things at different levels.
Same thing we argued about Google, that now we don't spend time learning stuff to remember, but we can just look it up and use that time to do more, spend it on more important problems than remembering. Yet it made us stupider.
Prior to Google, I kept a thesaurus and a dictionary on my desk so I could find new stuff and avoid having to remember everything. There was also a huge set of encyclopedias in the living room, along with a globe.
Did those tools make me stupid, too? (Can we do libraries next?)
I've sort of come around to the other side, a few months ago I was pumping out as many project in my lab as I could manage thinking I was suddenly "able to do all the things I never got around to", now, idk. Give me a terminal, Ubuntu 26, and the simplest text editor in the world. I'm not making money off producing slop.
This all reminds me of keruigs for some reason, like a quick easy way to get coffee that produces a lot of waste for a forgettable drink. I'm sort of advocating digital pour overs.
> The findings backed up what anecdotes and surveys have increasingly found in recent years: An overreliance on AI for answers erodes our cognitive fitness.
What is our cognitive fitness without our tools? How capable would we be at intellectual tasks if you remove computers, search engines and other things we now take for granted?
It's clear to me our overall output is better with those tools, and better with AI. So the comparison the paper is trying to make is disingenuous, because it tries to compare a baseline without any modern tools (ie, solving fractions by hand) to a result where the best tools are suddenly removed. But these tools are here to stay, and my cognitive fitness has adapted to their continued existence.
How well would these people doing fractions by hand perform if math notation could not be used? Or if the most common method for solving fractions on paper was suddenly disallowed?
I honestly don't care anymore. No matter how hard I try nothing ever gets better. If AI eases the burden even a little I'll take it. Persistenting at hard things hasn't made my life any better yet.
I don't understand this. What ever was the purpose of your life in the first place? What are you measuring to say "hasn't made my life better"?
Everything interesting enough to be worth doing is hard, and persisting at it is my most rewarding way to spend time.
If you took it away and made me loaf on a beach, I would enjoy it for about a day.
And then my life is empty and worthless and definitely not better. Even on that beach I would shortly be trying to make something interesting out of the palm leaves.
And then someon would say we should have a machine to do that because it's hard and "persisting at hard things hasn't made my life any better".
It's the most depressing sentiment I can imagine to say that.
Writers disagree on what effect word processing will have on the quality of our written language.
Some writers are concerned that computer assistance may promote dry bland writing.
Fear-mongering clickbait title signals complete nonsense.
Since I started using AI seriously in my (mathematics) research about 6 weeks ago I've advanced more and been more productive than in years. I'm more motivated and persistent than before because I have at my disposal a tool that helps me get past technical obstacles that previously took up a lot of my time.
I remember when people would say that if a person took LSD 10 times or more that they would go insane. After about my 30th or 40th trip I knew it was nonsense.
This feels similar. 10 minutes? So, after sever thousand hours of using AI to build projects, I can't solve hard problems? More nonsense.
I have to go now, the nurse here at the psychiatric ward is about to strap me down and give me my meds.
Learned Helplessness. Happens alot all around. I see it a lot in disability care. SO much that there is a saying: "Suplemental disability caregiver" (Zusatzbehinderung Eltern). Might be a schock to the more socially oriented people. But the fact is, the more help you allow to be provided to you, the more you forget/unlearn how to handle things on your own. Which in the end, will make you hyperdependant on others.
The evidence for reduced persistence is kinda weak.
The strongest experimental result in the paper is: if people use AI to help solve a series of problems, and then the AI is suddenly taken away, they perform worse on subsequent problems than people who hadn't been using AI for the earlier questions.
But:
- They're looking at really really short-term effects, like seconds or minutes. That's not really interesting if we care about long term effects (which is what the abstract says is important).
- What they found is totally expected and isn't something I'd worry about: it's kind of expected that if one guy warms up with 10 questions done himself, and the other guy warms up with 10 questions delegated to AI, then the second guy is going to do worse on the final 10 questions (because he didn't warm up).
Eventually you think you deserve to have the answer just handed to you without having done any of the work.
https://www.stuartmcmillen.com/comic/town-without-television...
And the IQs of those born after 1975 via the reverse Flynn Affect, those who had these devices of consumption and easy answers such as TV, and later Social Media, AI, etc has been decreasing.
I can only imagine a further and speedier decline will happen, and is happening.
The intelligence doesn't shine out of all the faces.
What would they eat? Getting enough calories from hunting for it to become a frequent part of everyone's life is currently a thermodynamic impossibility. On top of that, food webs are actively collapsing or have already.
We aren't all dead yet. (And we ain't just hunter-gatherers no more, neither!)
> The blasé attitude with which people talk about “moving up the stack” as if it’s a foregone conclusion that it’s the right thing to do or even an accurate description of what’s happening is easily one of the most disheartening aspects of this whole movement
They need to walk the walk. Or accept that they themselves haven't stopped to think before they "moved up the stack".
The average human may no longer desire or have a need to hunt animals for food. But re-introduce that need and they will do fine. Kids and adults continue to be able to pick up fishing for the first time as well as handling a .30-30 or 20-gauge.
Would you become unable to hunt?
And that is not a good thing. We shouldn’t celebrate or ignore the loss of basic living skills, especially when what we get in return is a nightmare scape of a society whose whole purpose is to dominate your thoughts and fill you with anxiety to suck you dry.
The new hunting-equivalent basic living skill would be the ability to go to a store, orient yourself, pick reasonably priced items, ideally choose healthy ones and avoid the temptations of junk food, check quality and macros (calories, protein etc)...
I think I get your point. I think what you're getting at is that hunting etc is a fundamental skill, below this abstraction we have built, and if you would be dropped into a forest and left to your devices, you'd still be able to handle yourself. But I just think that it's an interesting mental exercise, but nothing more. I really don't see it as a tragedy.
Tl;Dr "Basic skills" are relative. Hunting is now an outdated, impractical, unusable skill to hone, and a vastly inferior source of food.
turn every supermarkt visit into a scavenger hunt?
oh, wait ...
Not only has AI allowed me to start projects without knowing all the steps ahead of time, it has helped me proceed through each of those steps to completion.
"For the first experiment with 354 participants, one group was tasked with solving 15 basic fraction problems without help, while another had ChatGPT open on the side to use for assistance and to even ask for the answer. The group with AI access started off more accurate than the group without it. Then, after 12 problems, researchers took away the AI tool. Almost immediately, the people in that group stopped solving questions accurately."
Looking at the design of the experiment, it seems everyones brain was primed to think "I have AI at my disposal" ... and then had the framework shattered when it was taken away, which could have affected the test.
Perhaps this study exposes "conversational mindset" versus "mathematical mindset" switching times ?
Theres only one thing I'm certain of ... Claude loves me. :D
If someone used AI and created something illegal(deepfake or whatever), can they claim it’s not their creation?
A comment that opens a discussion is not low quality just because you disagree with it Mr 7 karma. There is always a degree of irony with such statements.
You choosing the toppings for your pizza and whether you want cheese in your crust does not mean you made the pizza. You did not make it, you merely ordered it.
In your analogy maybe it's more like creating a pizza except you didn't have to make the cheese, grind up the flour, go find the water in a stream somewhere, grow the tomatoes yourself, butcher the animal for the meat, etc.
The only caveat with my statement is that there are different degrees in which you can involve an LLM in your project but I mean if you are using it to write the majority of your code.
Thats what $200 for a ticket to any N*L sunday football event buys you.
T-shirts are extra, and if we win, we sell more of them.
The hard working, professional, polite members of society benefit from the release I think.
If it would be possible to walk to get groceries (and also exercise at the same time), do people choose to drive instead because it's simply easier and faster? They then claim that the goal of exercise is simply not worth it any more given driving is faster (and easier)?
In suburbia where the grocer is surrounded by a parking lot and there's busy roads to navigate nearby then of course people would drive rather than walk, and thus also end up getting a week's worth of groceries at a time.
In a dense mixed use environment where it is easy enough to navigate on foot then the urge to drive lessens and all grocery shopping can be done more frequently and on foot or by public transport.
I've experienced this personally in Australia, where living in suburbia I went grocery shopping once a week with a car and brought back many bags worth at a time, and also in a more dense neighbourhood where I could get groceries on the walk back from work more frequently and barely used my car even once a month. In Europe I only ever walk or get public transport for groceries as I live in a dense area and don't need to own a car.
> If it would be possible to walk to get groceries
It is.
> do people choose to drive instead because it's simply easier and faster?
Some. You also have to factor in the cost of gas (quite high) vs distance vs how much you plan to buy and possibly other factors (like discounts,...).
> They then claim that the goal of exercise is simply not worth it any more given driving is faster (and easier)?
I would think the goal is to get groceries, but yeah. People are quite lazy. The possibility of taking a car or a bus is always there.
- I (https://compile-xc.org) built a compiler for a language I call ‘xc’, similar to Objective-C but designed to be cross-platform and have less square brackets :). It can run on any of {Windows, Mac, Linux, Zynq} and produce code for any of {Windows, Mac, Linux, Zynq, WASM, iOS, Android, m68k, 6502}. It needs no host-platform tools to compile code, so for example I have a signed binary on my iPhone, written and signed entirely on Linux. The compiler currently produces code that is between 2x and 0.5x the speed of clang, which is pretty good for a nascent compiler. It also compiles the same language for execution on a GPU, and makes it trivial to designate parts of your code as data-parallel (https://compile-xc.org/compiler/language/par/). One statement turns a code block into a GPU kernel
- I built (more accurately: am building) a (https://compile-xc.org/compiler/api/uxkit) cross-platform UI framework, so you can write code once, even Table-views, Outline-views, Collection-views, etc., etc and your application will bind to the platform’s native toolkit through a common programming API. The next step here is an “Interface Builder” like application I call RoCkS (for historical reasons), which lets you design the UI for {desktop, tablet, phone} to bind to the same core code, dragging connections between that core code and the UI elements for the different environments
- I built (https://atari-xt.com/os) XTOS for the Zynq - an OS that started with FreeRTOS and added loadable processes, dynamic linking, shared memory, paging, a boot environment that can be scripted like Linux, networking, a graphical UI based on GEM, with a hardware blitter, and HDMI output with Audio islands mixed in. The blitter owns the video memory and uses shared-memory pools to give each “virtual workstation” (GEM-speak for “view of the display”) its own retained-mode and composited graphics memory. The whole OS boots from power-on to desktop in a couple of seconds, which is the reason for making it, rather than just using Linux, I wanted that “instant-on” feeling. It runs on a Z-turn board from MyIR.
- I built an Atari (6502, ANTIC, POKEY, various other chips) (https://atari-xt.com/hardware/x/)emulator, which passes the (https://forums.atariage.com/topic/171296-acid800-an-atari-te...) Acid 800 emulation torture test, and can run games such as Ballblazer, Elektraglide and (https://0x0000ff.co.uk/mov/xt/xtos-aug-11.mov)Despatch Rider. It’s integrated into XTOS, so the OS boots into the GEM-based Desktop.app (written in xc) and you can double-click an icon to launch a game. The plan is to extend that to the ST once I get past the current task, which is …
- I built a Reddit-like website that I plan to launch in a couple of months, written entirely in xc, both the Linux-based server and the client WASM code; the server-side is just xc code, no Apache, no scripting language, just lean-and-mean xc code handling everything from the TLS handshake to the Postgres database back-end. The main difference between (https://blewit.net/)blewit.net and Reddit is that I don’t collect any personal information, don’t track your habits and sell your data, don’t use adverts (tracking by another name) or any of the other dark patterns social media is infested with these days. Blewit is so named because I think they did. One of the other differentiators is that I plan to support the groups with specialist helpers. You can see a selection of them at (https://blewit.net/sandbox) the blewit sandbox - things like rendered music staves from ABC notation, 3d interactive molecules structure from SMILES notation, mathml with an editor to create it, etc, etc.
- One last purposely-separated “helper” from the others because of its scale, is the electronic circuit editor/simulator. I introduced it [url=https://www.eevblog.com/forum/projects/circuit-simulation-fo...]in the EEVBlog projects forum[/url], but no-one seemed to care [grin]. That’s a shame because I think it’s pretty cool, draw the circuit in the editor, you can create your own parts, even store them to a library, add digital logic to pins, import SPICE models, and simulate with ideal or oscilloscope-type probes. It understands transmission lines, tolerances, can run monte-carlo simulations, produce overlaid graphs for {R=1k, 4.7k, 10k} etc., I could go on. The full docs to it are in the (https://blewit.net/post/93083024324820992) Blewit editor-information pages
Overall, looking back at what using an LLM has helped me code over the last half-year or so, it is unbelievable what you can code with AI in 2026. I always used to say that I had too many ideas and not enough time to implement them, well that’s still true, but to a significantly lesser extent…
For all the above “I” is short for “I, with the help of Claude”
I wrote my first compiler about 35 years ago, and my first OS around the same time, well before LLMs were on the scene. I know exactly just how much work is involved with doing it well, which is why I’d never normally take on a project like this now I’m retired.
But we do not live in normal times. The language “I” have created (and it’s not as though I just said to Claude “hey, write me a compiler”) is (even if I say so myself) pretty cool. Here’s the full source of a Mandelbrot program:
Just one line in that source code is what turns it from a bog-standard C-like program into a GPU kernel, automatically tracked, scheduled, and all data hazards transparently managed.On my M4 max, that one line of code takes it from 481ms to 1.6ms. All ‘par’ does is say “this next block of code, call it “mandel”, is data-parallel, and for the ‘total’ variable all execution units should combine their output to that variable using +
No huge GPU boilerplate, no stream management for data transport from cpu to gpu and back, no memory barriers, no hassle. Just “hey, do this on the gpu”.
GPU blocks are bound at runtime using the best design for the hardware on the machine, obtained by measuring it if it hasn’t been measured before. If it has, the cache is read. The user can always override, which is why ‘par’ blocks can be named.
Next up is an overlapping scheduler, which keeps 3 work units in play {pending, being-processed, being-transferred-once-done} for a processing device (right now, just a local gpu), which is also the multi-gpu story, and even the networked-machines story.
I’m satisfied that my actual engineering is up to snuff, done some pretty cool things over my career. Now I’m just having fun, and LLMs make it easier to have said fun :)
E.g.: I painted the Mona Lisa.
“I” meaning “I with the help of a JPEG on a computer and a color printer”.
Maybe the same thing happens at the level of individual persons. I think the difference is whether you felt limited or competent. If you felt competent you shrink. If you felt limited, you grow.
Yes, I could be lazy and just vibe piles of slop. But anyone who spends any meaningful amount of time using AI (more than the articles 10 minutes), knows that quite extreme concentration and effort is required to build ambitiously with agents.
I feel like I work harder and and with more difficult problems than before AI, just at a much faster pace and scope.
- the next ideal step progressively manifested itself to me during coding
- I actually had perfect knowledge of the codebase and didn't have to work as hard to reconstruct it in my head each time
- coding itself is half a mechanical job. AI does the mechanical job itself well, leaving you only the hard thinking.
- AI prose isn't easy to parse, at least to ne.
- Prompting AI until it gets the optimal solution / implementation takes time and revisions.
Something like that.
I keep thinking that it's an issue of my approach, though. I've been trying to improve my AI workflow as much as possible for months, every day, but still, I'm not content with it. I might have gotten stuck on a local optimum. Maybe I'm writing prompts needlessly detailed. Maybe I should read more of the code, because I don't, really, making me not know the actual codebase and how things work.
It's not easy, this whole thing. And honestly, sometimes I wonder - after accounting for the total time, including revisions, mental reconstructions etc - if it wouldn't have been done faster by me in the pre-AI world.
Or when your weekly limit hit. More fun if it's Tuesday. Say goodbye to the rest of week.
The risk if you ignore that is that you're on a nicely greased slope all the way down into the mess that a typical AI will leave behind.
I have a project right now that needs a lot of scaffolding to be worked on. I'm fine with the scaffolding and the mocks to be AI generated, they have no value to me at all. But the core gets written line-by-line by hand and I am in absolute control of what is going on there. The number of times that the AI has tried to gain control over that part of the codebase that it can't see but infers must be around somewhere is really interesting, it just does not want to stay in its lane. It will gaslight itself into believing that the root cause of whatever problem it is dealing with must be located in this magical invisible body of code and it will do what it can to try to find it, including attempts at reverse engineering that code from libraries and searching all over the accessible parts of the file system to see if there are any copies lying around.
Since I don't work at OpenAI I've found that containing the bot is rather easy, you just make sure it does not have network access, but that won't stop it from trying.
Participants are asked to drive 20 nails into a plank of wood. Group A is given a hammer and an unripe banana. Group B is given only a banana. Halfway through the experiment, the hammers are taken away.
Hammers erode your ability to persist at hard things…
My point being that AI is a new tool that allows us to work on harder things at a higher level. It’s like having a team of 100 skilled workers with nail guns (and all manner of other tools) that never tire, and you get to direct them. Once you experience that, it’s a bit disheartening to go back to building alone with your bare hands… even if you lament the decline in your hand-eye coordination.
Before AI, when building anything, you’d have to have a lot of context from different parts of the app. And then work with that knowledge to build the feature out in those places. Keeping that context in your head was tough and coming back the next day required you to “fill your internal RAM” with the context and then start.
Now we can use our “internal RAM” thinking about the bigger picture and bigger projects. We’ve just gone an abstraction layer deeper.
Art provides a counterpoint. The invention of cameras allowed you to capture reality in ways never seen before, and yet this lead to art having a _really weird_ few decades/century where artists rejected realism and instead embraced abstraction.
FWIW, well designed study probably would still support his premise. He at least needed a group with no help, a group with no help but books and a calculator (tools), a group with human assistance, and a group with AI assistance, and a group that does an unrelated activity during the priming period.
Our energy conservation system also encourages us to eat as much high fat, high sugar food as we can get down our gullets, but that turns out to be a big problem for modern humans.
Extremely Weak study.
The time difference was measured in hours, not minutes, and and effort required to hand-saw is enormous. Also, the accuracy difference is ridiculous, even with a lot of practice, and maintaining that accuracy continues to be a struggle with hand-sawing.
I now own 6 different electric saws for different specific purposes. Table saw, chop saw, circular saw, track saw, jig saw, scroll saw... I actually probably have more. I got rid of my chainsaw because it lost its chain in a way that really scared me one day. I might get another some day, but not that one again. Does a Dremel count? I'm going to say 'no' so I don't have to count 2 of those.
And yet, I still own a 2 Japanese pull saws and a normal American hand saw. I don't use them as much, but some times they're still worth the effort.
When using table saw, you still need to make a lot of decision: The depth of cut, the angle, the length. If you don't make them, your project will not progress.
With AI, you can stop decide, tell the chat bot to do whatever they want and make any decision they like. Your ability to analyze problem and make decision is not practiced anymore and begin corroding.
I mean: In cabinetry, for instance, that's the kind of slop that dado joints help to hide and nobody ever knows about it after it's glued up.
And I'm not Norm Abram over here: I can can cheat even harder than that. I can get a sloppy six-sided wooden box to look nice and square with neat joints after basic assembly is done, using just a few minutes of time and a router with an appropriate bit.
It won't pass muster in even the most simple of metrological examinations, but I don't have to care about that. (And my work doesn't detract from that of Norm, either.)
Second, you make decision to be sloppy. That means you can also make decision to do it right. With AI, no matter how much "make no mistake" you scream toward it, no guarantee whatever it produces is high quality.
I made a decision to do it fast-er instead of perfect-er. I understand and embrace the results of this decision.
That's exactly my point here, you know it's sloppy. To know it's sloppy you must know what a good joint look like. Many vibecode projects the author even can't tell if their code is good or sloppy. Is it important, the ability to judge and decide something, depends on each individual, personally it's very important to me.
---
Code. Despite this talk about woodworking (which I actually can accomplish with precision using a variety of tools when I elect to do that), I really can't write code for shit. I've been working on that for several decades and it's still just shit.
And I just don't care anymore.
I can feed stuff into the bot at a high level. And I can read code well-enough after all these decades of failure, so I can be hyper-critical of the results and produce precise demands for corrections. I can get resolutely-tight performance from it when it's important.
I once spent a lot of time working with the bot on RP2040 PIO assembly, where I was working with continuous high-speed signals. The RP2040 PIO has only a single-digit instruction count, a few registers, and just 32 instructions worth of memory. Individual clock cycles need counted, and slop doesn't work there at all. I accomplished what I needed to accomplish with perfection (as if it could ever be any other way). I'm pleased with the result, and I couldn't even have ever gotten started on that without the bot.
Some other times, I think that it's OK that it is slow or wasteful as long as the inputs eventually produce the right outputs. Like an intern who, when given a task to make a thing that implements a process comes back with something that's messy-but-working. It's not ideal, but it can be good enough some of the time.
I'm not fast at producing code with the bot and that doesn't matter to me: I'm getting things done (finished!) that I previously spent decades beating myself up about being unable to do. There are hardware+software things that are rolling right now and behaving themselves seemingly-perfectly that I wrote no code for at all; all I did was propose ideas, review the code where useful (because sometimes it's all fucked up), then discuss and implement changes.
Anyone can think of me as being stupider, somehow, than I used to be because of the addition of the bot into my life -- but I implore them to address that concern to me directly. I'm right here.
I'm still thinking, just about different things at different levels.
Did those tools make me stupid, too? (Can we do libraries next?)
This all reminds me of keruigs for some reason, like a quick easy way to get coffee that produces a lot of waste for a forgettable drink. I'm sort of advocating digital pour overs.
What is our cognitive fitness without our tools? How capable would we be at intellectual tasks if you remove computers, search engines and other things we now take for granted?
It's clear to me our overall output is better with those tools, and better with AI. So the comparison the paper is trying to make is disingenuous, because it tries to compare a baseline without any modern tools (ie, solving fractions by hand) to a result where the best tools are suddenly removed. But these tools are here to stay, and my cognitive fitness has adapted to their continued existence.
How well would these people doing fractions by hand perform if math notation could not be used? Or if the most common method for solving fractions on paper was suddenly disallowed?
Everything interesting enough to be worth doing is hard, and persisting at it is my most rewarding way to spend time.
If you took it away and made me loaf on a beach, I would enjoy it for about a day.
And then my life is empty and worthless and definitely not better. Even on that beach I would shortly be trying to make something interesting out of the palm leaves.
And then someon would say we should have a machine to do that because it's hard and "persisting at hard things hasn't made my life any better".
It's the most depressing sentiment I can imagine to say that.
How do you know trying hard isn’t stopping things from getting worse?
> If AI eases the burden even a little I'll take it.
It does ease the burden for a little bit but then makes the burden much harder when you don’t have it. That’s the point.
> Persistenting at hard things hasn't made my life any better yet.
Again, how do you know it’s not stopping it from getting worse? Also, are you sure you’re worrying about the right hard things?
https://www.youtube.com/watch?v=Jt0OoXluC8g @ 4:08:
That's from 1983Since I started using AI seriously in my (mathematics) research about 6 weeks ago I've advanced more and been more productive than in years. I'm more motivated and persistent than before because I have at my disposal a tool that helps me get past technical obstacles that previously took up a lot of my time.
Eg. I’m glad AI built my startups landing page in (almost) one prompt, because it’s allowed me to do the hard things that matter and I enjoy.
This feels similar. 10 minutes? So, after sever thousand hours of using AI to build projects, I can't solve hard problems? More nonsense.
I have to go now, the nurse here at the psychiatric ward is about to strap me down and give me my meds.