504 karma · joined January 1, 2011
Before it needs to write the code and have an external program execute it. Here it can change its mind mid execution. Kinda like what was observed in the CoT’s ah ha moment
The puzzle assumes that the room temperature is greater than the cold milk's temperature. When I added that the room temperature is, say, -10 °C, Mercury fails to see the difference.
https://chromewebstore.google.com/detail/chatgpt-search/ejcf...
Months in different locales can be written as yyyy-MM-dd. It can also be a catalog/reference number. So, it seems right that their embedding similarity is not perfectly aligned.
So, it's not a tokenizer problem. The text meant different things according to the LLM.
Throw that away, setting aligned SMART goals would achieve a similar effect as aligned OKRs.
You do that by having the entire organization's O and KR roll up and cascade down. My Objectives directly roll up to my parent organization's Key Result. My parent org's Objectives rolls up to their parent's KR, and so on to the top. Then, you have the top-level check downwards if the sets of OKRs still make sense in their entirety.
Unfortunately, I have never seen any organization I was with ever do this...
`You take a random ball out of the urn—it’s red—and discard it.`
How normal people read it: Given this specific instance where you just discarded a red ball from this urn, what's the probability of the next ball?
How it expects you to read it: Given infinitely many random samples from the urn. For cases where you get red, remove it, then take a second sample. What's the probability of the next ball, given all the samplings?
Looking at Bill Gurley's 2,851 Mile talk (https://12mv2.com/2023/10/05/2851-miles-bill-gurley-transcri...)