Just your typical internet guy with questionable humor

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Joined 2 years ago
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Cake day: June 22nd, 2023

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  • Nintendo and their lawyers can go suck an elephant dick.

    From part 8 of the newest doc

    the boarding character is selected among a plurality of characters the player character owns in association with providing a second operation input when the player character is in the air, cause the player character to board an air boarding character and bringing the character into a state where the player character can move in the air

    and while the player is aboard the air boarding character, move the player character, aboard the air boarding character, in the air based on a third operation input

    Plurality of mounts and “second operation when in the air” ain’t new, World of Warcraft had that in 2007 with Burning Crusade. I have no clue what “third operation input” means there.

    A lot of the other alterations seem to focus on “air boarding character”, probably because they realized that you can only change your mount in Palworld manually while on ground or water, which kept glider pals safe, as they were the only ones you could summon while in the air.




  • Anthropic made lots of intriguing discoveries using this approach, not least of which is why LLMs are so terrible at basic mathematics. “Ask Claude to add 36 and 59 and the model will go through a series of odd steps, including first adding a selection of approximate values (add 40ish and 60ish, add 57ish and 36ish). Towards the end of its process, it comes up with the value 92ish. Meanwhile, another sequence of steps focuses on the last digits, 6 and 9, and determines that the answer must end in a 5. Putting that together with 92ish gives the correct answer of 95,” the MIT article explains.

    But here’s the really funky bit. If you ask Claude how it got the correct answer of 95, it will apparently tell you, “I added the ones (6+9=15), carried the 1, then added the 10s (3+5+1=9), resulting in 95.” But that actually only reflects common answers in its training data as to how the sum might be completed, as opposed to what it actually did.

    Another very surprising outcome of the research is the discovery that these LLMs do not, as is widely assumed, operate by merely predicting the next word. By tracing how Claude generated rhyming couplets, Anthropic found that it chose the rhyming word at the end of verses first, then filled in the rest of the line.