5 ms·
You say you can have increasingly nuanced discussions with stronger models. What I say is, when I asked Claude why he applied a certain change I didn't underst
by sixtram 3mo ago
You say you can have increasingly nuanced discussions with stronger models.
What I say is, when I asked Claude why he applied a certain change I didn't understand, and boy, it was a small change, he said he "reasoned from first principles" based on the code paths. But it didn't work, and when I asked, "Okay, describe the steps of your reasoning from first principles," it literally answered that it had just made it up.
So, nuanced discussions with models, I don't buy it.
- solenoid0937 3mo agoPosts like this are meaningless without more context - the model you're using, the harness, the initial prompt and context. Fable is better than most staff engineers at my FAANG.
- hrmon 3mo agoBut staff engineers take "responsibility"
- sn0n 3mo agoMaybe I’m missing something, but he talks about charm and tasks (repos on his GitHub). Charm being his harness, and tasks being one of his skills. Idk, maybe I’m mistaken from reading the article… https://github.com/taoeffect https://github.com/taoeffect
- maccard 3mo ago> Fable is better than most staff engineers at my FAANG. While this wouldn’t entirely surprise me, my experience is just not that. Using Claude and fable, it regularly (poorly) recreates features that exist inside our codebase. Sure, I could give way more initial context but at a certain point I’ve given so much context that I would have been faster writing the code myself, or I could have literally handed it to even a fresh graduate to write.
- well_ackshually 3mo agoFable will definitely be the one on call when it inevitably breaks down from the pile of shit slop it wrote at 5AM, don't worry <3
- solenoid0937 3mo agoWe already use AI for oncall and it works better than our humans most of the time.
- OtomotO 3mo agoIncluding you?
- Toutouxc 3mo ago> Fable is better than most staff engineers at my FAANG. That’s genuinely disturbing.
- semilin 3mo ago"Nuanced discussion" doesn't necessarily mean the sort one would have with a human. Statistical apologies are never going to be meaningful. One could edit nonsense into the context window and the model would attempt to rationalize it. The models are smart but you need to use them in a way that makes sense for what they are.
- doctoboggan 3mo agoYou can never ask why a model did a certain thing, or what it was "thinking" when it said something - just like you can't ask a human which neurons were firing when they had a certain thought. The information just isn't available at that level. You absolutely can have deep nuanced discussions with LLMs however, you just need to better understand their strengths and weaknesses.
- Shitty-kitty 3mo agoA human won't respond with "Neuron 10-100 of the frontal cortex" (jokes aside) with deceptively convincing confidence.
- youdont 3mo agoThe human will quite convincingly be able to construct a post-hoc reasoning on an action that may or may not be related at all to what was actually going through their head or the actual instinctual reasons that led to a decision.
- Shitty-kitty 3mo agoThat's exactly what the LLM seems to have done as well. The problem is that we want and even expect the A.I to be truthful.
- sn0n 3mo agoIsn’t that part of what the think blocks are for? Yea, don’t inject them back into the context, but do log them for review of that train of thought… no?
- NitpickLawyer 3mo agoYou don't get access to the thinking traces. Might work with local models tho, but the current <thinking/> meta isn't particularly suited for this either, as it's a big blob of rambling surfaced by RL, with the "only" objective being that the thinking blob somehow leads to a better final answer. Something more detailed, using templates akin to oAI's harmony could work, provided there's also a step that teaches the models to reflect on the various thinking channels, and maybe surface bits and pieces to include in "skills" or "learnings".
- sothatsit 3mo ago"Nuanced discussions" is more about describing a design to a model, asking the model to critique your design and ask you for clarifications, and then you providing those clarifications and the model "getting it" and proceeding to additional levels of detail before implementation. In particular the models being able to highlight concerns you have not yet thought about is a pretty good sign of this. Fable is noticeably better at this compared to Opus. I was not talking about models making mistakes. Mistakes, and then models making up justifications for those mistakes, is a failure mode of any LLM, and Fable is no different in that regard. Newer models might make less mistakes, or at least make less egregious mistakes, but they still make mistakes.
- dolebirchwood 3mo ago> he :/
- cpursley 3mo ago[flagged]
- atq2119 3mo agoWe can point out mistakes that feel rather grating without assuming intent behind them. I agree that their use of "he" is likely because they're not a native speaker, especially because they're arguing against the capabilities of LLMs. That doesn't make it inherently wrong to point out the mistake when it's so intertwined with the deeper discussion here, especially given the fact that some (hopefully few) people do build relationships with LLMs.
- weakfish 3mo ago> turd bucket autist I’d be more willing to engage with your argument in good faith without inflammatory language like this. Try and meet people where they are and these conversations become easier.
- recroad 3mo agoThat may be true but it’s still capable of nuanced discussions.