101–110 of 334 posts
I’ve observed exactly these patterns with Opus and Fable as well - for example, forgetting that they can edit files and instead use python scripts as a patching tool…
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Sounds like you completely lack all reading comprehension ability LLMs sometimes like to execute one-off Python scripts to make edits to files rather than just calling the edit tool directly. Both are tool calls so saying that you should have it write code instead of doing tool calls makes no sense because writing code is a tool call for it...
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In SWE I've found gpt-6-astra (high) inconsistent and oddly focused on overtly taking responsibility for mistakes it made rather than prioritizing concrete steps to rectify problems. Such steps once elicited are often either incomplete or beyond the scope.
From an alignment perspective I’ve got no idea who it’s aligned to but it isn’t me, the meat proxy, who just wants to know why it crashed.
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Why would you use a constrained edit tool when you are also allowed to use the complete power of python?
Why even offer the edit tool in that case? Also, what kind of editing could they possible do what wouldn't be possible with POSIX ed?
This matches my experience with Astra so far too. > I think I’m suspecting something is going “wrong” in the training process. The model is greatly rewarded for succeeding on long-horizon tasks, but presumably there is very little punishing going on for “shitty code.” My suspicion is that both OpenAI and Anthropic moved their RL agendas from "being rated as useful according to human feedback" to "succeeds at long hor…
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Most would applaud that as Sol has quite a reputation for over engineering. Not every software needs to go to the moon.
So your experience is that Astra doesn't over engineer? For more than twice the price of Sol I think most people will take the over engineering.
But yeah, it's really expensive, at least in relative terms.
These machines are doing some crazy things to get to the result. That said, I can't help but feel like this is the compilers argument all over again. Are the methods used to get to the result good? No. Is the code that it generates good? No. Does it achieve the goal. Yes. Is it likely to get better with time. Also yes. In my use cases, jobs that would have taken weeks to months are being done in minutes to hours. Inv…
What is the "compilers argument"?
> I’m more and more convinced that all of AI engineering is Neijuan (内卷, meaning curl inwards). In China it describes a system that demands ever more effort and competition without improving output. The way in which it sometimes shows up in the West is the 996 nonsense. The English term for Neijuan is “Involution” from the book Agricultural Involution. Agricultural involution describes the intensification of farming…
But doesn't this reduce the required amount of farmland?
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Sounds like you completely lack all reading comprehension ability LLMs sometimes like to execute one-off Python scripts to make edits to files rather than just calling the edit tool directly. Both are tool calls so saying that you should have it write code instead of doing tool calls makes no sense because writing code is a tool call for it...
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I have noticed two things - new features take at a minimum at least half the time it took me previously and bugs are much less frequent. Even faster when bug fixing.
My takeaway is that you need solid requirements, clear context and thoughtful human oversight primarily during planning but also during verification