rhaps0dy

import json
from pathlib import Path

cves_dir = Path('cves/2025')
severity_count = {'CRITICAL': 0, 'HIGH': 0, 'MEDIUM': 0, 'LOW': 0, 'UNKNOWN': 0}
total = 0

for json_file in cves_dir.rglob('*.json'):
    total += 1
    try:

rhaps0dy

import math
import numpy as np


def logsumexp(a, b):
    """Stable log(exp(a) + exp(b))"""
    m = np.maximum(a, b)
    return m + np.log(np.exp(a - m) + np.exp(b - m))

rhaps0dy

;;; packages.el --- latexsub layer packages file for Spacemacs.
;;
;; Copyright (c) 2012-2018 Sylvain Benner & Contributors
;;
;; Author: Adrià Garriga Alonso <adria@k9>
;;
;; This file is not part of GNU Emacs.
;;
;;; License: GPLv3

rhaps0dy

#!/usr/bin/env python3

import abc

import torch

from gpytorch.distributions import MultivariateNormal
from gpytorch.lazy import CholLazyTensor
from gpytorch.variational._variational_distribution import \
    _VariationalDistribution

rhaps0dy

fdsf

rhaps0dy

<mujoco model="inverted pendulum">
  <compiler inertiafromgeom="true"/>
  <default>
	<joint pos="0 0 0" armature="0" damping="0" frictionloss="0" limited="true"/>
	<geom pos="0 0 0" contype="0" conaffinity="0" friction="0 0 0"
		  rgba="0.7 0.7 0 1"/>
	<tendon/>
	<motor ctrlrange="-1 1" ctrllimited="true"/>
  </default>
  <option gravity="0 0 -9.81" integrator="RK4" timestep="0.02"/>

rhaps0dy

#How to use this?

1. Go to a charity's page, such as the AMF or the GFI.

2. Click. one of the videos and vote, normally. This is so you complete the captcha thing.

3. Go back to the charity page.

4. Open the browser console.

rhaps0dy

#include<stdio.h>
void f(char*b){while(*b){if(*b=='"'||*b=='\\')putchar('\\');if(*b=='\n') printf("\\n"),b++; else putchar(*b++);}}
void g(char *c){printf("%s", c); putchar('"'); f(c); printf("\");}\n");}
int main(){g("#include<stdio.h>\nvoid f(char*b){while(*b){if(*b=='\"'||*b=='\\\\')putchar('\\\\');if(*b=='\\n') printf(\"\\\\n\"),b++; else putchar(*b++);}}\nvoid g(char *c){printf(\"%s\", c); putchar('\"'); f(c); printf(\"\\\");}\\n\");}\nint main(){g(");}

rhaps0dy

#!/usr/bin/env python2
import tensorflow as tf
import numpy as np
import time

# Hyperparameters
learning_rate = 1e-1
n_hidden = 100
seq_length = 25
n_show = 2500 // seq_length

rhaps0dy

(setv test

(let [[a 5]
       [b 7]]
  (print a b)))

(print (type test))