I screenshotted the question and pasted it in Claude 3.5 Sonnet. It answered:

To determine the output of the given function for an input of 3, let's analyze the code step by step:

The function is called fizzbuzz(n) and we're considering n = 3. The return statement uses a conditional expression: return (["Fizz","Buzz","FizzBuzz"])[ (n % 3 === 0) + (n % 5 === 0) ]; Let's break down the conditions:

(n % 3 === 0) evaluates to true (1) because 3 is divisible by 3 (n % 5 === 0) evaluates to false (0) because 3 is not divisible by 5

Adding these boolean values: 1 + 0 = 1 This result (1) is used as an index to select from the array ["Fizz", "Buzz", "FizzBuzz"] Index 1 in this array corresponds to "Buzz"

Therefore, for an input of 3, the function will return "Buzz". This might seem counterintuitive since typically in a FizzBuzz problem, numbers divisible by 3 return "Fizz". However, the indexing in this implementation leads to this unexpected result. Would you like me to explain any part of this analysis further?

Here’s o1’s answer:

The function `fizzbuzz(n)` returns a value based on whether `n` is divisible by 3, 5, both, or neither. Let's break down the function step by step for an input of `n = 3`:

1. Array Initialization:

  ["Fizz", "Buzz", "FizzBuzz"]

   This array has indices 0, 1, and 2 corresponding to "Fizz", "Buzz", and "FizzBuzz" respectively.

   - `n % 3 === 0` checks if `n` is divisible by 3.

     - For `n = 3`, this is `true` (which equates to `1` when used in arithmetic).

   - `n % 5 === 0` checks if `n` is divisible by 5.

     - For `n = 3`, this is `false` (which equates to `0`).

  (n % 3 === 0) + (n % 5 === 0)

   - For `n = 3`, this becomes `1 + 0 = 1`.

  ["Fizz", "Buzz", "FizzBuzz"][1]

   - This accesses the element at index `1`, which is `"Buzz"`.

For an input of `3`, the function returns `"Buzz"`.

Answer:

"Buzz"

This feels like a sloppier version of Searle’s Chinese Room.

Is iterative thinking and search required to understand code? And while LLMs don’t do that, why couldn’t complex system like o1 be able to do that?

I was not convinced by Seattle, this is not convincing either.

I use various models through Openrouter API + Open WebUI with a custom step by step prompt. Here's the output using the Qwen2.5 72B model:

To determine the output of the given `fizzbuzz` function for an input of 3, let's break down the code step by step:

1. *Function Definition*: ```javascript function fizzbuzz(n) { return (["Fizz","Buzz","FizzBuzz"])[ (n % 3 === 0) + (n % 5 === 0) ]; } ```

2. *Input*: - The input `n` is 3.

3. *Evaluate the Conditions*: - `n % 3 === 0`: This checks if `n` is divisible by 3. - For `n = 3`, `3 % 3 === 0` is `true`. - Therefore, `(n % 3 === 0)` evaluates to `1` (since `true` is treated as `1` in arithmetic operations). - `n % 5 === 0`: This checks if `n` is divisible by 5. - For `n = 3`, `3 % 5 === 0` is `false`. - Therefore, `(n % 5 === 0)` evaluates to `0` (since `false` is treated as `0` in arithmetic operations).

4. *Sum the Results*: - The expression `(n % 3 === 0) + (n % 5 === 0)` becomes `1 + 0`, which equals `1`.

5. *Array Indexing*: - The array `["Fizz", "Buzz", "FizzBuzz"]` is indexed by the result of the sum. - The index `1` corresponds to the second element of the array, which is `"Buzz"`.

6. *Return Value*: - The function returns `"Buzz"`.

Therefore, the output of the function `fizzbuzz(3)` is `"Buzz"`.

### Reflection and Error Checking - *Step-by-Step Verification*: - The conditions `n % 3 === 0` and `n % 5 === 0` are correctly evaluated. - The sum of the boolean results is correctly calculated. - The array indexing is correctly performed. - *Logical and Factual Accuracy*: - The logic and arithmetic operations are correct. - The array indexing is consistent with the conditions.

No errors were found in the reasoning or the final answer. The output of the function `fizzbuzz(3)` is indeed `"Buzz"`.

Too easy.

But can your LLM understand this Go code and explain to you how it works?

  func extract(x, m uint64) uint64 {
      x &= m
      var (
          holes = ^m
          shift = uint(1)
      )
      for ; shift < 64; shift *= 2 {
          odd := holes
          odd ^= odd << 1
          odd ^= odd << 2
          odd ^= odd << 4
          odd ^= odd << 8
          odd ^= odd << 16
          odd ^= odd << 32
          holes &^= odd
          move := x & odd
          x ^= move
          x |= move >> shift
      }
      return x
  }

More broadly, I'm curious to know how much of the BUGFIX-66 debugging gauntlet your LLM can solve.

You have to Google it, because I'm not allowed to link to it on Hacker News. Banned.

This whole silly post seems to have been written so they can make this statement, which they seem to believe is profound:

"This failure is not surprising, because any machine learning model, no matter how many magic parameters it has, is only capable of ex ecuting a fixed pipeline of mathematical operations. In algorithmic complexity terms it is O(1). Such a process can not solve any problem that requires iteration or search, not even a simple algorithm like find ing a square root. Certainly it can't tackle the complex logic involved in answering queries about code, where even determining the behav ior of a pure boolean expression is known to be in the much harder NP-complete complexity class."

It is neither profound nor even close to correct.

A CPU capable of only executing a fixed pipeline of mathematical operations can easily deal with all the things they talk about. Amazing? No, it just turns out they have confused a whole bunch of unrelated CS concepts to make a huge muddle of a post (mixing algorithmic complexity, decidability, simulation vs explanation, etc).

This is true even if the pipeline is truly fixed (IE everything passes through every functional unit), as it was in the past.

Th counterpoint to the argument made by the author is simple:

When it comes to determining output, any of the NN's are universal enough could be taught to arbitrarily approximate a CPU well enough to execute code and determine the output with arbitrary levels of precision. Period. Once you take cost out of it, and we venture into the realm of the "what can be done", the author's statement is simply wrong on its face. Even within the real of "less abstract", there are lots of formal papers/studies of this with specific NN types, etc. Even for transformers, which people love to beat up on as huge brute force machines, see, e.g., https://arxiv.org/abs/2107.13163

You can even throw compilation of every possible programming language we have compilers for right now in there as a pre-step if you want, since we are in the realm of abstract "what is possible".

Heck, you can go further - any of these NN's could be taught to arbitrarily approximate any machine driven process that can explain code.

That is, if you build a combination static/dynamic analysis tool/whatever that explains code, you can theoretically build an NN can approximate that tool. This is easily provable, and directly counter to the author's point.

Now, much like the author's nonsense, neither of the above is not a particularly useful result in practice[1] - among other reasons, we can prove that it is possible, but the proofs are not constructive, they only show that it must be possible to exist, it doesn't tell you how to make it :)

That's before you get into cost/etc.

There are plenty of things to bash AI code understanding on that are totally valid criticisms, we don't need to waste our time on strange, wrong ones.

[1] - though nvidia would probably be very happy if you build a 1t parameter model to try to approximate a $100 intel CPU's results and convince others to use it.

Author hasn't used https://aider.chat/