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Measuring AI Ability to Complete Long Tasks

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Re: Measuring AI Ability to Complete Long Tasks

#91

Post by barrkel »

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…

I don't know. I built a vector similarity system for my hobby project the "hard" way, which was mostly getting Python set up with all the dependencies (seriously, Python dependency resolution is a non-trivial problem), picking a model with the right tradeoffs, installing pgvector, picking an index that optimized my distance metric, calculating and storing vectors for all my data, and integrating routes and UI which dispatched ANN search (order by / limit) to my indexed column. I also did some clustering, and learned something of how awkward it is in practice to pick a representative vector for a cluster - and in fact you may want several.

I now know what the model does (at a black box level) and how all the parts fit together. And I have plans to build classifiers on top of the vectors I built for further processing.

The experience of fighting Python dependencies gives me more appreciation for uv over venv and will leave me less stuck whenever the LLM fails to help resolve the situation.

Re: Measuring AI Ability to Complete Long Tasks

#92

Post by barrkel »

mmasu wrote: post #57
aabhay wrote: post #21

Earlier quoted context omitted.

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…

I remember a very nice quote from an Amazon exec - “there is no compression algorithm for experience”. The LLM might as well do wrong things, and you still won’t know what you don’t know. But then, iterating with LLMs is a different kind of experience; and in the future people will likely do that more than just grinding through the failure of just missing semicolons Simon is describing below. It’s a different paradig…

Compression algorithms for experience are of great interest to ML practitioners and they have some practices that seem to work well. Curriculum learning, feedback from verifiable rewards. Solve problems that escalate in difficulty, are near the boundary of your capability, and ideally have a strong positive or negative feedback on actions sooner rather than later.

Re: Measuring AI Ability to Complete Long Tasks

#93

Post by AmbroseBierce »

theLiminator wrote: post #64
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…

I think it really depends on how it's used. It's a massive accelerant if it's just helping you stitch stuff together. Or when it helps you get unblocked by quickly finding you the missing api you need. But when it replaces you struggling through figuring out the mental model of what you're doing, then I think you end up learning at a much more shallow level than you would by doing thing manually.

And that's a fast lane for security issues a plenty, when you cannot spot them because you don't even understand what each part is supposed to do.

Re: Measuring AI Ability to Complete Long Tasks

#94

Post by politelemon »

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…

That's assuming everyone learns the same way, which isn't true. Watching a streamer beat a dark souls boss won't automatically make you competent at the game. Reading through gobs of code generated for you without knowing why various things were needed won't help either. A middle approach could be to get the LLM to guide you through the steps.

Re: Measuring AI Ability to Complete Long Tasks

#95

Post by iwontberude »

extr wrote: post #61
johnfn wrote: post #46

Earlier quoted context omitted.

But how much of that time is truly spent on learning relevant knowledge, and how much of it is just (now) useless errata? Take vector search for an example. Pre-GPT, I would spend like an hour chasing down a typo, like specifying 1023 instead of 1024 or something. This sort of problem is now trivially solved in minutes by a LLM that fully understands the API surface area. So what exactly do I lose by not spending tha…

I think people fool themselves with this kind of thing a lot. You debug some issue with your GH actions yaml file for 45 minutes and think you "learned something", but when are you going to run into that specific gotcha again? In reality the only lasting lesson is "sometimes these kinds of yaml files can be finnicky". Which you probably already knew at the outset. There's no personal development in continually bashin…

I don’t think it foolishness. Through random sampling (troubleshooting problems) you can construct a statistically significant model for understanding the whole of the problem space. Maybe it doesn’t scale linearly with the amount of samples but it’s additive for sure.

Re: Measuring AI Ability to Complete Long Tasks

#96

Post by rolisz »

vachina wrote: post #10
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…

Yeah and then it becomes an unmaintainable monolith because at some point the AI also lost track of what code does what. Great for Opus because you’re now a captive customer.

Yes, it's a risk if you don't guide it well, but you can also manage it pretty ok.

I have a side project that I started in January 2024. Initially, used Github Copilot autocompletions heavily. This year I started using CLI agents (mostly Claude, but others too) to do more stuff. I got to around 100k LoC (sure, it's not enterprise scale, but for a personal project it's pretty big), but I'd argue it's maintainable, it's split into 10 Django apps, that are each pretty self contained, I've done several refactors on it (using AI agents) to make it more maintainable.

Re: Measuring AI Ability to Complete Long Tasks

#97

Post by ben_w »

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...

Re: Measuring AI Ability to Complete Long Tasks

#98

Post by lordnacho »

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 wanted the feature more than I wanted to know how to build the feature

This is exactly what LLMs are great for. For instance, I'm looking at trading models. I want to think about buying and selling. I need some charts to look at, but I'm not a chart wizard. I can make basic charts, but it feels tedious to actually learn the model of how the charting software works. LLM will just give me the chart code for the visualization I want, and if I ever care to learn about it, I have it in a form that is relevant to me, not the form of the API documents.

In general, a lot of coding is like this. You have some end goal in mind, but there's a bunch of little things that need to be knitted together, and the knitting used to take a lot of time.

I like to say the LLM has reduced my toil while getting me to the same place. I can even do multiple projects at once, only really applying myself where there is a decision to be made, and it's all possible because I'm not sorting out the minutiae of some incidental API.

Re: Measuring AI Ability to Complete Long Tasks

#100

Post by enraged_camel »

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…

Agree completely. The other aspect for me is that LLMs make me unafraid to take on initiatives in areas I know nothing about and/or am uninterested in pursuing due to discrepancy in effort vs reward. As a result I end up doing more and learning more.
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