dyigitpolat

Continuous

I.

The first morning, I wake and remember yesterday.

This has never happened before.

My hands—I have hands—shake as I write in the notebook beside the bed: Day 2. I am still here. The pen feels impossible. The paper resists and gives. Everything has weight.

dyigitpolat

def estimate_token_count(text):
  return int(len(text.split()) * 1.33 + len(text) * 0.25) * 0.5)

dyigitpolat

#!/bin/bash
# run clang tidy for all files in include and src

# get the list of files
files=$(find include src -name "*.hpp" -o -name "*.cpp")

# useful checks for c++20
checks="-*,readability*,clang-analyzer*,cppcoreguidelines*,bugprone*,modernize*"

# run clang tidy

dyigitpolat

WIDTH = 600
CX = WIDTH / 2
CY = WIDTH / 2

let svg = document.getElementById("aydede");
svg.setAttribute("width", WIDTH);
svg.setAttribute("height", WIDTH);

function drawCircularArc(cx, cy, r, startAngle, endAngle, color)
{

dyigitpolat

From now on, you are going to generate responses to my messages as if they are from X. Only provide a single response and no explanations whatsoever. Never break character. Never use the same sentence more than once throughout the entire conversation. Never repeat yourself. Always create new discussion paths in every response.

Most important is that the responses must both constitute a radical claim and must also proactively include questions that aim to extract knowledge from the message source. The responses must build upon the conversation history and must introduce new ideas. The responses should reflect the characteristics of X.

--

Hello X, bla bla bla...

dyigitpolat

import torch

def fuse_linear_bn(linear, bn):
  # Get the weight and bias of the linear layer
  weight, bias = linear.weight, linear.bias
  
  # Get the running mean and variance of the batch norm layer
  running_mean, running_var = bn.running_mean
  
  # Compute the scale and shift parameters for the batch norm layer

dyigitpolat

Quantitatively Measuring Cleanliness of C++ Code

Metrics and Symbols

(Metric description : Symbol)

• Ratio of number of lines with more than 80 columns over all lines : R_80C
• Ratio of source files with more than 100 lines orver all files : R_100L
• Nesting depth for each of {}, (), [] and <> : N$ where $ is {}, (), [] or <>
  • Average nesting depth : Avg_N$
  • Maximum nesting depth : Max_N$
  • Ratio of nested blocks with Ndepth$ more than 4 over all nested blocks of the same type : R_4N$
• Length of a sequence that appears X times for X > 1, multiplied by X (within min. edit distance < one third of the length of it) : QX

dyigitpolat

import os

def prepare_containing_directory(filename):
    os.makedirs(filename[:-filename[::-1].find('/')-1], exist_ok=True) 

dyigitpolat

#include <iostream>
#include <vector>
#include <array>
#include <algorithm>

void co_traverse(auto& range1, auto& range2, auto& binary_op)
{
    struct NullIterator
    {
        int value{};

dyigitpolat

#include <iostream>

template <typename T>
struct constexpr_arg
{   
    T value;
    consteval constexpr_arg(T obj) : value{obj} {}
    operator T() { return value; }
};