The Setup

You: “Solve this problem.”
GPT: “writes 200 lines of perfectly formatted C++17 with descriptive variable names and a poetic docstring.”
You: “It’s WA on test 3.”
GPT: “Have you tried using long long?”

How GPT Thinks It Codes:

"I analyzed 10^8 lines of ICPC gold-medal solutions and learned optimal dynamic programming patterns."

How GPT Actually Codes:

"Let's just sort the array and pray."

Debugging with GPT

You: “It’s TLE.”
GPT: “Hmm, maybe use unordered_map.”
You: “Still TLE.”
GPT: “Then switch to map for more structure.”
You: “It’s slower.”
GPT: “At least it’s elegant.”

GPT’s Logic in Action

Test 1: Works.
Test 2: Works.
Test 3: Segmentation fault.
GPT: “That’s impossible. Theoretically flawless.”

Then it proudly explains:

"Maybe the problem statement is wrong."
— confidently, like a true Codeforces Div. 3 participant.

GPT and Data Structures

GPT loves unordered_set, unordered_map, vector, queue, and multiset for emotional comfort.
It has used segment tree exactly once — in a dream.
And it believes Fenwick trees are “for advanced users only.”

GPT’s Submission Timeline

1. Writes 100 lines of code.
2. Passes sample tests.
3. Claims it’s O(n log n).
4. Fails test 3.
5. Says: "Probably precision error."
6. Adds 1e-9.
7. Now fails test 1.

GPT’s Advice Section

“If it’s wrong, just use a different approach.”
“If it’s slow, try Python.”
“If it’s still wrong, add +1 or -1 somewhere.”
“Remember: debugging is just re-training your neural network manually.”

Final Verdict

Sure, GPT can code.
But until it: - forgets to clear a vector between test cases,
- gets WA because it used int instead of long long,
- overflows in style,
- and blames the system tests —

it’ll never be one of us.

GPT doesn’t panic at Pretest 3.
GPT doesn’t cry over -17 rating.
GPT doesn’t scream “IT WORKED LOCALLY.”

That’s why GPT may code…
but it’ll never compete.