Isn't this more a problem created by them doing garbage automations over anything really solved. Wow Disney could solve fast pass feeling. It's not a problem it's a feature.

*i would have come up with a similar list but i dont trust my own judgment here. maybe i'd sub in claude code vs codex but jury is a bit out still on that

It's already possible to achieve tasks on a time horizon of multiple days if you put the LLM into a sufficiently structured workflow (where you have a separate program that smartly manages its context). E.g. a standards-compliant HTTP 2.0 server where the code is 100% written by Gemini Pro (over 40k lines of code total, including unit tests, in around 120 hours of API time): https://open.substack.com/pub/outervationai/p/building-a-100...

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The only thing that will slow AI down is massive universal international regulation. Human intelligence really isn’t the be all end all to intelligence in general, it’s just a stepping stone. I feel many on this site don’t want to accept this because their intelligence has been such a valuable tool and source of personal pride/identity for them for so long.

Just like o1 Pro, was the same way. Took some minutes at least to get an answer, so only threw at it what others couldn't solve. o1 Pro is AFAIK the best model for solving things others can't. I kind of feel like you were paying $200/month before but wasn't using O1 Pro at that point, you were kind of missing out :P

Remains to be seen if O3 Pro is better, still evaluating it myself, but definitely true that it shouldn't be used in the same way as faster models.

I’d say “in an eval” when talking on Slack with very close coworkers, but _never_ on a public post.

Still, I don’t want to make accusations based on writing style, because like I said, I have my own idiosyncrasies that common models coincidentally share.

I don't know if o3 Pro would solve my task, but I feel we're still pretty far from the state where I'd struggle to give it a challenging enough problem.

I find these minutes-long iterations so painful that in practice I always go for the fast non-reasoning models.

Or tell it what you know about non-programming subject X, and ask it to explain it to you such that you understand it better.

Or for coding: ask it to look at your code, and suggest large scale architecture changes.

For these kinds of tasks, the models are still lacking.

If the LLM can complete the task using tools, then it's a pass.

Apples team went out of their way to select tests that LLMs would struggle with and then take away tools -- and then have the audacity to write that they're surprised at the outcome. Who would be surprised? No one using AI since GPT-4 expects them to be calculators or algorithm executors.

You want the LLM to be smart enough to realize "I can't do this without tools", grab the tool, use it correctly, and give you the actual correct answer. Preventing LLMs from using tools or writing and executing code -- then you're intentionally crippling them.

I don't have a Claude in front of me -- if you just give it the algorithm to produce the answer and ask it to give you the huge output for n=20, will it even do that?

Adding a "think about whether your answer actually depends on Y" fixed it, and the response was pretty well thought out. My only gripe is the current persona seems to be tech-bro-informal. It opened with "TL;DR" and included a lot of developer slang that reads like it's trying to be cool in a way that masks overconfidence.

I'm trying to understand what you mean by meta promoting

I had it analyze different related libraries, and it zeroed in on SDL and Wine codebases, and it found the exact lines of code related to the logic error in Winebus.

It really helps me dig deep for certain hard to track bugs.

Can you write a version of Chorin's projection method for the Navier-Stokes equations that is both explicit and second order in time?

Ideally the model should not need a more detailed prompt than this. A first-year grad student in numerical analysis certainly would not.

An example from Sonnet 4 'thinking':

Thread

* https://news.ycombinator.com/item?id=44259838

Result (prompt: Critique each view from this hacker news thread and give an overall summary and judgement. Do not waffle)

* https://imgur.com/aFl9uiA

This is just a trivial way to illustrate some capability, it is not meant to be deep or insightful or an end-task in itself.

Let me chatgpt that for you:

When someone “gets ratioed,” it means their post has more negative feedback than positive engagement. The number of comments or replies outweighs the number of likes or retweets, indicating the content is controversial.

When someone says they "got ratioed by <someone>", it usually means that the reply or response from that person received significantly more engagement (likes, retweets, etc.) than the original post.

It very often happens when a popular account disagrees with your take. When someone with a big following replies to your post, their audience quickly sees it, and if their response is witty, harsh, or widely agreed with, it can snowball into a ratio very fast. Their followers will usually like and boost their reply, sometimes swarming your original post with disagreement or mockery.

  When a reply to a tweet gets more likes than the tweet it was replying to, usually indicating the unpopularity or stupidity of the original tweet

https://www.urbandictionary.com/define.php?term=Ratioed