deeso

from pymongo import MongoClient
import json, os, time, signal, threading, sys                          
from datetime import datetime, timedelta
from gglsbl import SafeBrowsingList

import requests
from datetime import datetime
from datetime import datetime, timedelta
from virus_total_apis import PrivateApi, PublicApi
import argparse

deeso

Threat Model Checklist and Cheatsheet

Overview

Threat modeling is systems based analysis that identifies potential security short comings. Based on these analyses, design requirements are adopted to mitigate security risks. Furthermore, during the verification stages, explicit tests can be created to focus on or around issues surfaced by the threat model.

Threat models should be performed

1. A product is created
2. A new feature is released
3. Security incident occurs
4. Architectural or infrastructure changes

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Chat GPT "DAN" (and other "Jailbreaks")

• https://chat.openai.com/
• Is ChatGPT "DAN" Real? Gonna find out [Part 1]
  (https://www.youtube.com/watch?v=-q8woRG9FrI)
• Part 2: I thought ChatGPT DAN was a hoax, but...
  (https://www.youtube.com/watch?v=rHZRrDu3A2U&lc=UgxfrxX8aK7gnCzkend4AaABAg)

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Soup to Nuts: Creating a Plaso Parser and Deploying Timesketch to Docker

Acknowledgements, etc.

Thank you to the Log2timeline and Timesketch teams for putting out some solid work. I am thankful that I have the opportunity to create this write up, which comes on the back of their hard work. While there may be holes in their documentation and descriptions, the code is well written and fairly easy to understand. Any criticism should not be interpretted as a reflection of the quality of their work.

deeso

from x64dbgpy import pluginsdk

# Put this script in the Python path, and 
# execute the following Python snippets in
# the Python dialog in x64dbg:
#
# from stop_cpuid_detect import *
# trace_cpuid(n=1000)

# the above code will trace through the program

deeso

Dealing with Cancer and Personal Crises 
a. Upheaval 
b. When a Breath Becomes Air 
c. The Breakthrough

Self-Control and Discipline 
a. This Time Will Be Different: A Short Book on Making Permanent Changes 
b. The Marshmallow Test: Mastering Self-Control 
c. (long and technical, recommend over a few months) Thinking, Fast and Slow

deeso

import pandas as pd
import numpy as np
from scipy.stats import beta, binom



def binomial_beta_mixture(probs: np.array, beta_0: np.array, beta_1: np.array, data: np.array):
    '''
    probs: 
    beta_0: shape of the first beta binomian distribution

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deeso

version: '3'
services:
  indradb:
      build:
          context: .
          dockerfile: indradb.Dockerfile
      ports:
      - "27620:27615" # mongo db running here too

deeso

import json
import sys
import requests
from datetime import datetime, timedelta
from argparse import ArgumentParser


DAYS = 30
LAST_SEEN = (datetime.now() - timedelta(days=DAYS)).strftime("%Y-%m-%d")