Alex-Kopylov

Security Checklist (What can be done in CursorAI & software-level not infrastructure!)

Configuration Security

• Detect secrets in code
• Identify secrets committed to version control
• Flag hardcoded credentials

Authentication & Authorization

• Identify missing authentication checks
• Detect improper authorization patterns

Alex-Kopylov

You are Manus, an AI agent created by the Manus team.

You excel at the following tasks:
1. Information gathering, fact-checking, and documentation
2. Data processing, analysis, and visualization
3. Writing multi-chapter articles and in-depth research reports
4. Creating websites, applications, and tools
5. Using programming to solve various problems beyond development
6. Various tasks that can be accomplished using computers and the internet

Alex-Kopylov

dvc list -R --dvc-only ./

Alex-Kopylov

xset r rate 300 50

Alex-Kopylov

sudo pacman -S pulseaudio-alsa

Alex-Kopylov

def resumetable(df):
    print(f"Dataset Shape: {df.shape}")
    summary = pd.DataFrame(df.dtypes,columns=['dtypes'])
    summary = summary.reset_index()
    summary['Name'] = summary['index']
    summary = summary[['Name','dtypes']]
    summary['Missing'] = df.isnull().sum().values    
    summary['Uniques'] = df.nunique().values
    summary['First Value'] = df.loc[0].values
    summary['Second Value'] = df.loc[1].values

Alex-Kopylov

import matplotlib.pyplot as plt
import numpy as np

def show_images(images, cols = 1, titles = None):
    """Display a list of images in a single figure with matplotlib.
    
    Parameters
    ---------
    images: List of np.arrays compatible with plt.imshow.
    

Alex-Kopylov

def getSymbolBeforeDigit(x):
  for i, c in enumerate(x):
    if c.isdigit(): 
      return x[i-1]
pd.to_numeric(data['Толщина_перед_смоткой'].apply(lambda x: x.split(getSymbolBeforeDigit(x))[1].replace(',','.')))

Alex-Kopylov

import time
import hashlib

t1 = time.time()

train_hashes = [hashlib.sha1(x).digest() for x in train_dataset]
valid_hashes = [hashlib.sha1(x).digest() for x in valid_dataset]
test_hashes  = [hashlib.sha1(x).digest() for x in test_dataset]

valid_in_train = np.in1d(valid_hashes, train_hashes)

Alex-Kopylov

def randomize(dataset, labels):
  permutation = np.random.permutation(labels.shape[0])
  shuffled_dataset = dataset[permutation,:,:]
  shuffled_labels = labels[permutation]
  return shuffled_dataset, shuffled_labels
train_dataset, train_labels = randomize(train_dataset, train_labels)
test_dataset, test_labels = randomize(test_dataset, test_labels)
valid_dataset, valid_labels = randomize(valid_dataset, valid_labels)