diegslva

function transfer
    if test (count $argv) -eq 0
        echo "No arguments specified. Usage:\necho transfer /tmp/test.md\ncat /tmp/test.md | transfer test.md"
        return 1
    end

    ## get temporarily filename, output is written to this file show progress can be showed
    set tmpfile ( mktemp -t transferXXX )

    ## upload stdin or file

diegslva

<#
    
  Author: Casey Smith @subTee

  License: BSD3-Clause
	
  .SYNOPSIS
  
  Simple Reverse Shell over HTTP. Execute Commands on Client.  
  

diegslva

#!/bin/bash
IPT="/sbin/iptables"

# Server IP
SERVER_IP="$(ip addr show eth0 | grep 'inet ' | cut -f2 | awk '{ print $2}')"

# Your DNS servers you use: cat /etc/resolv.conf
DNS_SERVER="8.8.4.4 8.8.8.8"

# Allow connections to this package servers

diegslva

# Outbound UDP Flood protection in a user defined chain.
iptables -N udp-flood
iptables -A OUTPUT -p udp -j udp-flood
iptables -A udp-flood -p udp -m limit --limit 50/s -j RETURN
iptables -A udp-flood -j LOG --log-level 4 --log-prefix 'UDP-flood attempt: '
iptables -A udp-flood -j DROP

diegslva

#!/bin/bash
#
# --------------------------------------------------------------------
# This is a free shell script under GNU GPL version 3.0 or above
# Copyright (C) 2005 ReFlectiv project.
# Feedback/comment/suggestions : http://www.reflectiv.net/
# -------------------------------------------------------------------------
#
# This script automatically set up a new *Debian* server (IMPORTANT : Debian!), by doing these actions :
#

diegslva

Keybase proof

I hereby claim:

• I am diegslva on github.
• I am diegslva (https://keybase.io/diegslva) on keybase.
• I have a public key whose fingerprint is 8D40 D3B8 9CF0 26EF B853 3114 BDC2 D0F4 2780 23FD

To claim this, I am signing this object:

diegslva

import numpy as np
import pandas as pd


def compute_market_prices(prices):
    """Compute market prices according to the trading competition recipe.

    Parameters
    ----------
    prices : DataFrame

diegslva

# Time Series Testing 
import keras.callbacks
from keras.models import Sequential  
from keras.layers.core import Dense, Activation, Dense, Dropout
from keras.layers.recurrent import LSTM

# Call back to capture losses 
class LossHistory(keras.callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.losses = []

diegslva

# Code for Jupyter/IPython Notebook environment

from keras.models import Sequential  
from keras.layers.core import TimeDistributedDense, Activation, Dropout  
from keras.layers.recurrent import GRU
import numpy as np
from keras.utils.layer_utils import print_layer_shapes

%matplotlib inline
import matplotlib.pyplot as plt

diegslva

Code for Keras plays catch blog post

Train

python qlearn.py

Test

1. Generate figures