I have no hard data but I have a strong feeling this morning that something's wrong with Fable 5 compared to Friday evening.
Just an hour ago I had Fable correctly identify an unused method that could be deleted. I then immediately get a diff for an exact duplicate method, and then Fable outputting, "I accidentally duplicated <method> instead of deleting it. Removing both copies now."
The remaining morning complaints that makes it feel like something's off is that it will do a lot of "thinking" for simple things that previously took very little time. And it got very lost and completely mixed up DE-91M predicate names and implementations. Just absolute disaster code that I had over the past months come to generally expect it to do without issue.
Glad I carefully review everything. I think what I need is reliability and consistency. But it feels like picking a model from the list doesn't guarantee that: that the models' "brain" is open on the table and they're screwing with it.
If you follow reddit forums for claude code, its common to see people, on the same day, claiming that Opus/Fable is especially smart today, and especially dumb today.
I think people are still not used to non deterministic tools like this, and human perception is absolutely horrible at evaluating trends like this no matter how smart, clever, and experienced you are.
If you have a bank of rigorously tested benchmarks that you run every few days, with enough trials to know what your standard deviation is, and you are getting significant trends over time with those, that would be interesting.
But "I have a feeling" and "Seems like" really isn't a reliable signal at all, humans just can't handle perceiving these things reliably. On top of that changes in your work environment can easily pollute LLMs and change quality of results. Are things getting added to your memory or claude.md files that you don't realize? Is your project growing in size and thus claude is performing worse as more context is needed to work with it? etc etc
> and human perception is absolutely horrible at evaluating trends like this
The need to have a mental measure of competence for your fellow man is, most likely, a pre-human skill, probably with a dedicated bit of neurons for it. I think the problem is that those instincts were co-evolved with our fellow man, and, as you say, don't apply at all to a more non-deterministic system that, fundamentally, lacks some logic faculties that even small children have (simple riddle modifications, car wash question, etc).
> I think people are still not used to non deterministic tools like this, and human perception is absolutely horrible at evaluating trends like this no matter how smart, clever, and experienced you are.
The implication is that humans are unreliable and shouldn't be trusted.
Or humans have certain shorthands when they complain on reddit, but their diagnoses are accurate for the specific context? If my AI does something stupid, am I not allowed to call it out? A NS-solving AI is still capable of not satisfying the abstract thing called the user experience. People have intelligent thoughts without compiling to lean.
OK, you say. Then let's get an aggregate benchmark for "intelligence". That doesn't prove that AI didn't flounder a specific use case that the user requested.
Classic moves: Humans are unreliable, converge to some "objective" benchmark that necessarily will quotient out the special cases, etc. Wonder how we'll be solving these issues in the AGI era - well, if you have an AGI that just replicates itself, dominates everybody because it's a machine and humans are soft fleshy creatures, and agrees with itself, fine. But part of the beauty of human experience is the messy part, and providing value is in the messy part.
In other industries of chance we have regulators that ensure compliance and that the providers aren't cheating.
At the end of the day the highest quality of benchmark tells you nothing if the man behind the curtain is constantly changing variables on you. You have no idea if you're really testing the same thing at all. So when you run your test at the top level on their system you're seeing lets say a 30% difference in quality most of the time, you have no idea if you should really only see a 5% difference in quality if you were running a local model with stable settings.
Waterluvian · · focus · HN ↗
Just an hour ago I had Fable correctly identify an unused method that could be deleted. I then immediately get a diff for an exact duplicate method, and then Fable outputting, "I accidentally duplicated <method> instead of deleting it. Removing both copies now."
The remaining morning complaints that makes it feel like something's off is that it will do a lot of "thinking" for simple things that previously took very little time. And it got very lost and completely mixed up DE-91M predicate names and implementations. Just absolute disaster code that I had over the past months come to generally expect it to do without issue.
Glad I carefully review everything. I think what I need is reliability and consistency. But it feels like picking a model from the list doesn't guarantee that: that the models' "brain" is open on the table and they're screwing with it.
JMKH42 · · focus · HN ↗
I think people are still not used to non deterministic tools like this, and human perception is absolutely horrible at evaluating trends like this no matter how smart, clever, and experienced you are.
If you have a bank of rigorously tested benchmarks that you run every few days, with enough trials to know what your standard deviation is, and you are getting significant trends over time with those, that would be interesting.
But "I have a feeling" and "Seems like" really isn't a reliable signal at all, humans just can't handle perceiving these things reliably. On top of that changes in your work environment can easily pollute LLMs and change quality of results. Are things getting added to your memory or claude.md files that you don't realize? Is your project growing in size and thus claude is performing worse as more context is needed to work with it? etc etc
nomel · · focus · HN ↗
The need to have a mental measure of competence for your fellow man is, most likely, a pre-human skill, probably with a dedicated bit of neurons for it. I think the problem is that those instincts were co-evolved with our fellow man, and, as you say, don't apply at all to a more non-deterministic system that, fundamentally, lacks some logic faculties that even small children have (simple riddle modifications, car wash question, etc).
sigbottle · · focus · HN ↗
The implication is that humans are unreliable and shouldn't be trusted.
Or humans have certain shorthands when they complain on reddit, but their diagnoses are accurate for the specific context? If my AI does something stupid, am I not allowed to call it out? A NS-solving AI is still capable of not satisfying the abstract thing called the user experience. People have intelligent thoughts without compiling to lean.
OK, you say. Then let's get an aggregate benchmark for "intelligence". That doesn't prove that AI didn't flounder a specific use case that the user requested.
Classic moves: Humans are unreliable, converge to some "objective" benchmark that necessarily will quotient out the special cases, etc. Wonder how we'll be solving these issues in the AGI era - well, if you have an AGI that just replicates itself, dominates everybody because it's a machine and humans are soft fleshy creatures, and agrees with itself, fine. But part of the beauty of human experience is the messy part, and providing value is in the messy part.
pixl97 · · focus · HN ↗
At the end of the day the highest quality of benchmark tells you nothing if the man behind the curtain is constantly changing variables on you. You have no idea if you're really testing the same thing at all. So when you run your test at the top level on their system you're seeing lets say a 30% difference in quality most of the time, you have no idea if you should really only see a 5% difference in quality if you were running a local model with stable settings.