141–150 of 196 posts
The Conclusions section is not for making a sales pitch for your article. It is for summarizing any new knowledge the article brings out.
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…
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…
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.
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This conversation isn't about building a vector database from scratch, it's about learning to integrate with an existing vector database.
The topic is basically irrelevant. I could just edit my post to change the two instances of "vector database" to "vector database integration" and nothing else would change about my point. I could change the post to be about learning word-working by watching a robot build a shelf and nothing would change.
Turns out there's science that backs me up here: https://en.wikipedia.org/wiki/Worked-example_effect - showing people "worked examples" can be more effective than making them solve the problem themselves.
That Wikipedia article is a little weak, this MIT page is better: https://tll.mit.edu/teaching-resources/how-people-learn/work...
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Fair enough. In my imagination, I can see people writing AI-first framework/architectures and a general trend for people to “migrate to such frameworks”, just like the push towards the microservices architectures in 2010s. A part of these frameworks would be “re-constructibility” by changing contracts in parts where it matters, and somehow the framework would make it easy for the LLM to discover such “parts”. Honestl…
My experience with LLM and agents has led to the opinion that a LLM-friendly codebase is actually a very human friendly code base.
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…
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…
The big issue is the 50%, if you switch to 80% it's much less. Now if you are in the wrong side of 50% given the task was 4hours. How much additional time to 4hours you need. repeat trying to get the task done 50%*50%->25% , 50%^4 -> 6.25%. the cost of bad luck is very high.
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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.
What examples can you give of "real world situations" where they fail?
Obviously I don't want to use them for whatever that is.
What's interesting is the 50% vs 80% reliability gap. At 50% success rate on a 4-hour task, you're essentially gambling. If it fails, you've potentially wasted the 4 hours plus the time debugging why it failed.
This is why I think the current "agent" paradigm needs human checkpoints at regular intervals. Let the AI work for 30 minutes, then review progress. Repeat. This way you catch drift early before it compounds.
The other thing missing from these benchmarks: recovery ability. When the AI gets stuck on hour 3 of a 4-hour task, can it recognize the problem and backtrack? Or does it confidently continue down the wrong path?