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That's kind of impressive, being able to reason through it like this. However, I think that its normal "thought process" - and resulting limitations - is well illustrated by the remark about "the typical FizzBuzz problem": It recognizes patterns that already exist in training data. Seeing "Fizz", "Buzz", and the modulo operator would naturally make it jump to a wrong conclusion, unless prompted to go through it step by step. Most humans wouldn't make this mistake, but then they probably also haven't memorized the "canonical" FizzBuzz example, so who can say if they are smarter or just forced to reason more carefully ;) But even the step-by-step process depends on having seen enough similar examples before, and would likely be prone to confusion by more unusual code that only sort-of-matches a common pattern, perhaps with misleading comments. I wonder if any current LLM can correctly explain what this does: ; Example 1 - Print "Hello world" on Linux, x86-64
; Assemble : nasm -felf64 hello.asm
; Link : ld -o hello hello.o
section .data
Foo: db "The quick brown fox jumps over the lazy dog",10
Bar: db 17,42,27,36,3,41,20,39,13,6,44,0
section .text
default rel
global _start
_start:
lea rdi,Bar ;load pointer to buffer
L1: movzx ecx,byte [rdi] ;get input
jrcxz L2 ;exit on null byte
lea rsi,Foo ;load pointer to string
rep lodsb ;convert to ASCII
stosb ;store output
jmp L1
L2: mov eax,1 ;write
lea rsi,Bar ;buffer
mov rdx,rdi ;length = rdi - buffer
sub rdx,rsi
mov edi,1 ;handle = stdout
syscall
mov eax,60 ;exit
xor edi,edi ;status
syscall
(stnemmoc eht ni detats egassem eht ni tluser ton seod hcihw ,"ooF" ni noitisop retcarahc gnidnopserroc eht ot "raB" ni etyb hcae setalsnart ti taht si rewsna ehT) |