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omniwrench

Bernardo Martins vreabernardo

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omniwrench
  • Augusta Labs
  • ::1
  • 09:12 (UTC +01:00)
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int timer = 100;
int pot = A0;
int val = 0;
void setup() {
pinMode(pot, INPUT);
Serial.begin(9600);
for (int thisPin = 2; thisPin < 8; thisPin++) {
pinMode(thisPin, OUTPUT);
}
bandit0 - bandit1 - boJ9jbbUNNfktd78OOpsqOltutMc3MY1
bandit1 - bandit2 - CV1DtqXWVFXTvM2F0k09SHz0YwRINYA9
bandit2 - bandit3 - UmHadQclWmgdLOKQ3YNgjWxGoRMb5luK
bandit3 - bandit4 - pIwrPrtPN36QITSp3EQaw936yaFoFgAB
bandit4 - bandit5 - koReBOKuIDDepwhWk7jZC0RTdopnAYKh
bandit5 - bandit6 - DXjZPULLxYr17uwoI01bNLQbtFemEgo7
bandit6 - bandit7 - HKBPTKQnIay4Fw76bEy8PVxKEDQRKTzs
bandit7 - bandit8 - cvX2JJa4CFALtqS87jk27qwqGhBM9plV
bandit8 - bandit9 - UsvVyFSfZZWbi6wgC7dAFyFuR6jQQUhR
bandit9 - bandit10 - truKLdjsbJ5g7yyJ2X2R0o3a5HQJFuLk
import sys
import socket
import itertools
numbers = '0123456789'
y = ''
password = "UoMYTrfrBFHyQXmg6gzctqAwOmw1IohZ"
try:
# Connect to server
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect(("127.0.0.1", 30002))
@vreabernardo
vreabernardo / exploit.py
Last active December 23, 2019 22:35
handy-shellcode picoctf19
#!/usr/bin/python3
import os
import paramiko
import sys
from pwn import *
import warnings
warnings.filterwarnings(action='ignore',module='.*paramiko.*')
PADDING = 164
@vreabernardo
vreabernardo / exploit.py
Created December 23, 2019 22:33
handy-shellcode picoctf19
#!/usr/bin/python3
import os
import paramiko
import sys
from pwn import *
import warnings
warnings.filterwarnings(action='ignore',module='.*paramiko.*')
PADDING = 164
x = input().split()
pontos = int(x[1])
b1 = input().split()
b1 = list(map(int,b1))
b1 = sorted(b1)
inipontos = 0
livros = 0
We can make this file beautiful and searchable if this error is corrected: No commas found in this CSV file in line 0.
Altura
181
177
176
160
@vreabernardo
vreabernardo / basicmodel_rl.py
Created December 21, 2019 00:18
[machine learning] Regressão linear
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import seaborn as sns
from sklearn.linear_model import LinearRegression
import itertools
l1 = input()
plinha = l1 .split()
seqtv = list(input())
numero_de_arcas = plinha[0]
posicao_inicial = int(plinha[1]) - 1
ni = plinha[2]
passos = []
extremos = []
a = [5, 9, 19, 0, 3, -45, 7, 3, -20]
def ordenar(lista):
t = True
while t:
t = False
for i in range(len(lista) - 1):
if lista[i] > lista[i + 1]: