Measuring AI Ability to Complete Long Tasks • Hacker News Board

Re: Measuring AI Ability to Complete Long Tasks

#121

Post by PessimalDecimal » Sun, Dec 21, 2025, 3:12 PM UTC

aabhay wrote: post #21

simonw wrote: post #18

Earlier quoted context omitted.

I don't think building it the long way is necessarily a more effective way to learn. You could spend 4 hours (that you don't have) building that feature. Or... you could have the coding agent build it in the background for you in 15 minutes, then spend 30 minutes reading through what it did, tweaking it yourself and peppering it with questions about how it all works. My hunch is that the 30 minutes of focused learnin…

This feels like the exactly wrong way to think about it IMO. For me “knowledge” is not the explicit recitation of the correct solution, it’s all the implicit working knowledge I gain from trying different things, having initial assumptions fail, seeing what was off, dealing with deployment headaches, etc. As I work, I carefully pay attention to the outputs of all tools and try to mentally document what paths I didn’t…

Forgetting LLMs and coding agents for a second, what OP describes is like watching a Youtube video on how to make a small repair around the house. You can watch that and "know" what needs to be done afterwards. But it is a very different thing to do it yourself.

Ultimately it comes to whether gaining the know how through experience is worth it or not.

Re: Measuring AI Ability to Complete Long Tasks

#122

Post by simonw » Sun, Dec 21, 2025, 3:17 PM UTC

habinero wrote: post #119

simonw wrote: post #40

Earlier quoted context omitted.

That's fair. Something I'm really interested right now is the balance in terms of the struggle required to learn something. I firmly believe that there are things where the only way to learn how to do them is to go through the struggle. Writing essays for example - I don't think you can shortcut learning to write well by having an LLM do that for you, even though actually learning to write is a painful and tiresome p…

That's not learning, that's building. It's like trying to learn how to draw via paint by numbers. Do you end up with something you could hang on the wall? Sure. Could you have fun doing it? Sure. Is there anything wrong with just doing that as a hobby? Of course not. Is it a substitute for actually learning how to look at objects and break them down into shapes and color and value? No. You gotta put in the work if yo…

I love that Ira Glass quote. I've thought about it a lot!

I still think paint by numbers is a valid early step along the path to learning to draw.

Re: Measuring AI Ability to Complete Long Tasks

#123

Post by Herring » Sun, Dec 21, 2025, 3:20 PM UTC

habinero wrote: post #119

simonw wrote: post #40

Earlier quoted context omitted.

That's fair. Something I'm really interested right now is the balance in terms of the struggle required to learn something. I firmly believe that there are things where the only way to learn how to do them is to go through the struggle. Writing essays for example - I don't think you can shortcut learning to write well by having an LLM do that for you, even though actually learning to write is a painful and tiresome p…

That's not learning, that's building. It's like trying to learn how to draw via paint by numbers. Do you end up with something you could hang on the wall? Sure. Could you have fun doing it? Sure. Is there anything wrong with just doing that as a hobby? Of course not. Is it a substitute for actually learning how to look at objects and break them down into shapes and color and value? No. You gotta put in the work if yo…

I like the sentiment, I really do, but nobody (outside a phd program) pays you to learn. That's just not how society is set up. If FAANG companies could get away with hiring high school kids at min wage to prompt all day they would. We'll figure that out real quick as that exponential rises. If you don't like it, build a better society. While you still can.

Re: Measuring AI Ability to Complete Long Tasks

#124

Post by [deleted] » Sun, Dec 21, 2025, 3:34 PM UTC

Arainach wrote: post #52

simonw wrote: post #42

Earlier quoted context omitted.

I think it's very easy to harm your learning by leaning into LLMs. What I don't believe is that it HAS to be like this. Maybe it's my natural optimism showing through here, but I'm confident it's possible to accelerate rather than slow down your learning progress with LLMs, if you're thoughtful about how you apply them. An open question for me is how feasible it is to teach people how to teach themselves effectively…

> An open question for me is how feasible it is to teach people how to teach themselves effectively using this new technology. It's not really an open question. We've had a huge amount of content on the internet including documentation, tutorials, example code, and actual online courses available for years and in the end most people don't learn effectively when presented with that information and left to themselves.…

[deleted]

Re: Measuring AI Ability to Complete Long Tasks

#125

Post by simonw » Sun, Dec 21, 2025, 3:35 PM UTC

simonw wrote: post #113

csomar wrote: post #105

Earlier quoted context omitted.

The struggle is how you learn. I think that’s pretty much established scientifically by now?

If it is I'd very much like to learn more about the science. I find it hard to believe that wasting hours hunting for a missing semicolon (at the very real risk of quitting entirely) is essential for learning. Does that mean every student who asks a TA or fellow-student to help them find that semicolon is hurting themselves when they do that? If not, what's different about asking an LLM?

I had Claude go dig up some science for me: https://claude.ai/share/2dc95280-ff92-4b13-816f-24f5993d8fc7

The most relevant concepts appear to be:

- Desirable Difficulties - https://en.wikipedia.org/wiki/Desirable_difficulty - "A desirable difficulty is a learning task that requires a considerable but desirable amount of effort, thereby improving long-term performance. [...] The task must be able to be accomplished. Too difficult a task may dissuade the learner and prevent full processing."

- Worked-example effect - https://en.wikipedia.org/wiki/Worked-example_effect - "Specifically, it refers to improved learning observed when worked examples are used as part of instruction, compared to other instructional techniques such as problem-solving. [...] However, it is important to note that studying [worked examples] loses its effectiveness with increasing expertise"

- Expertise reversal effect - https://en.wikipedia.org/wiki/Expertise_reversal_effect - "The expertise reversal effect refers to the reversal of the effectiveness of instructional techniques on learners with differing levels of prior knowledge."

- "Generation effect" - https://en.wikipedia.org/wiki/Generation_effect - "The generation effect is a phenomenon whereby information is better remembered if it is generated from one's own mind rather than simply read."

Re: Measuring AI Ability to Complete Long Tasks

#126

Post by dns_snek » Sun, Dec 21, 2025, 3:38 PM UTC

simonw wrote: post #113

csomar wrote: post #105

Earlier quoted context omitted.

The struggle is how you learn. I think that’s pretty much established scientifically by now?

If it is I'd very much like to learn more about the science. I find it hard to believe that wasting hours hunting for a missing semicolon (at the very real risk of quitting entirely) is essential for learning. Does that mean every student who asks a TA or fellow-student to help them find that semicolon is hurting themselves when they do that? If not, what's different about asking an LLM?

Well that's a gross oversimplification of the process. Hunting for a missing semicolon is a basic mechanical task that doesn't require much thought.

Engaging with an intellectual problem, trying to solve it one way, failing, reasoning through the process and the requirements, trying to discover a better way of solving something, going down some wrong paths, backtracking, merging diverging ideas and ultimately finding a solution is going to yield an infinitely deeper understanding of the problem, what works, what doesn't, and improve your general intuition and problem-solving skills.

Deep engagement builds deep understanding, shallow engagement builds shallow understanding. There's no substitute for doing the hard work yourself - I've tutored classmates in school and I find this rather obvious. A tutor (human or LLM) can try to find a way to explain something in a way that you understand but if you don't do most of the hard work yourself it's never going to stick. I noticed that when I would spoon-feed answers to people it would always just lead them into a false sense of confidence.

Re: Measuring AI Ability to Complete Long Tasks

#127

Post by macrolime » Sun, Dec 21, 2025, 3:53 PM UTC

tacitusarc wrote: post #39

simonw wrote: post #11

I didn't really understand the "long task" thing until I actually experienced it. The problem is finding a task you can set an agent that justifies working for that long. I finally hit one when I tried porting that Python HTML5 parser to JavaScript by pointing Codex CLI at the 9,200 html5lib-tests test suite: https://simonwillison.net/2025/Dec/15/porting-justhtml/ It's pretty amazing to watch tools-in-a-loop crunch a…

My problem with the OpenAI models (GPT5.2 in particular) recently is an extreme aversion to doing more than the smallest step in a task before asking for using input. Even if I explicitly instruct it to continue without input until the task is complete, it ignores the instruction. I cannot imagine GPT5.2 working on a task for more than 2 minutes, let alone 4 hours. I’m curious if you’ve run into this and figured out…

I find that surprising. GPT 5.2 is the model I've had working the longest. It frequently works more than 4 hours nonstop, while earlier models would stop to ask if they should continue every 10 minutes. 5.1 and earlier ignores it if I ask it to continue until a task is done, but 5.2 will usually finish it.

Re: Measuring AI Ability to Complete Long Tasks

#128

Post by throwaway613745 » Sun, Dec 21, 2025, 4:00 PM UTC

simonw wrote: post #18

eleventen wrote: post #5

I recently asked Opus to just “Add vector search” to my current hobby project, a topic I know very little about. It set up manticore, pulled an embedding model, wrote a migration tool for my old keyword indices, and built the front end. I’m not exaggerating much either: the prompt was the length of a tweet. I think it would easily have taken me 4+ hours to do that. It ran in 15 minutes while I played Kirby Air Riders…

I don't think building it the long way is necessarily a more effective way to learn. You could spend 4 hours (that you don't have) building that feature. Or... you could have the coding agent build it in the background for you in 15 minutes, then spend 30 minutes reading through what it did, tweaking it yourself and peppering it with questions about how it all works. My hunch is that the 30 minutes of focused learnin…

Just speaking from personal experience but the struggle is what creates the learning.

I learned refactoring patterns from Fowler's book. But when I tried to actually use them I still struggled. I didn't fully understand how the patterns worked until I actually tried (and failed) to use them a few times.

You don't really internalize things until you understand what doesn't work just as much as what does. You don't learn nearly as much from success as you do from failure. I would say the ratio of truly internalized knowledge is much higher for failure.

The notion that you can get a bot to just vomit out a vector database and then you can just "read the code" and you'll understand how a vector database works is just ludicrous.

Re: Measuring AI Ability to Complete Long Tasks

#129

Post by iLoveOncall » Sun, Dec 21, 2025, 4:02 PM UTC

ben_w wrote: post #97

iLoveOncall wrote: post #81

> current models have almost 100% success rate on tasks taking humans less than 4 minutes The contrary is easily verifiable by everyone individually. It's nowhere near 100%, or even 50% for few minutes tasks even with the best models in real world situations.

I've only noticed that combination (failure of short everyday tasks from SOTA models) on image comprehension, not text. So some model will misclassify my American black nightshade * weeds as a tomato, but I get consistently OK results for text out from good models unless it's a trick question. * I recon, at least; looked like this to me: https://en.wikipedia.org/wiki/Solanum_americanum#/media/File...

The research from Metr, and my comment, is exclusively related to software development tasks.

Re: Measuring AI Ability to Complete Long Tasks

#130

Post by Jcampuzano2 » Sun, Dec 21, 2025, 4:07 PM UTC

simonw wrote: post #11

I didn't really understand the "long task" thing until I actually experienced it. The problem is finding a task you can set an agent that justifies working for that long. I finally hit one when I tried porting that Python HTML5 parser to JavaScript by pointing Codex CLI at the 9,200 html5lib-tests test suite: https://simonwillison.net/2025/Dec/15/porting-justhtml/ It's pretty amazing to watch tools-in-a-loop crunch a…

How are you guys even doing long tasks with plain Codex or Claude code?

I use Claude code and I get hit with a permissions prompt every 2 seconds for anything I try to do.

Sure I can turn off all dangerous permissions but it'd probably honestly stop and claim it's finished well before it actually is in most cases from my experience.

To be fair I haven't tried codex so maybe it's better at this but I'm my experience almost every model stops at some point and claims victory or stops and tells me something like "next we'll continue on with XYZ" at which point I have to prompt it to continue.