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#!/usr/bin/env python3
"""
nvtop.py - Terminal-based NVIDIA GPU monitor (htop-style).
Zero dependencies beyond Python 3 stdlib + nvidia-smi on PATH.

Controls:
  q / Esc   - Quit
  Up / Down - Select GPU (when multiple)
  p         - Toggle process list sort (pid / memory)
  h         - Toggle help overlay

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"""
Wake-on-LAN (WoL) Utility

This script sends a Wake-on-LAN magic packet to a target network device,
allowing it to be powered on remotely. The magic packet is constructed
using the device's MAC address and sent via UDP broadcast to the specified
IP address and port.

Features:
- Validates MAC address, IP address, and port inputs.

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#!/bin/bash

function encrypt {
    input=$1
    echo "Encrypting $input..."
    openssl enc -aes-256-cbc -pbkdf2 -iter 800000 -in "$input" -out cipher.bin
    echo "Encrypted file saved to: $(pwd)/cypher.bin"
}

function decrypt {

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Strong Opinions weakly held

Orginally posted on 07.26.02008 by Paul Saffo, at https://saffo.com

The point of forecasting is not to attempt illusory certainty, but to identify the full range of possible outcomes. Try as one might, when one looks into the future, there is no such thing as “complete” information, much less a “complete” forecast. As a consequence, I have found that the fastest way to an effective forecast is often through a sequence of lousy forecasts. Instead of withholding judgment until an exhaustive search for data is complete, I will force myself to make a tentative forecast based on the information available, and then systematically tear it apart, using the insights gained to guide my search for further indicators and information. Iterate the process a few times, and it is surprising how quickly one can get to a useful forecast.

Since the mid-1980s, my mantra for this process is “strong opinions, weakly held.” Allow your intuition to guide you to a conclusion, no matter how imperfec

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Intro

Lately, I've been building a Python platform that relies heavily on interactions with Github. Instead of re-inventing the wheel, I decided to go with PyGithub as a wrapper around Github v3 API.

The problem

This library implements the full API, which allows me to be very productive when I need to add a workflow in my platform that involves Github. However, it quickly became a real PITA to write unit tests. Even if the package comes with its own testing Framework, it is not documented yet and I didn't manage to crack it up in a decent amount of time.

I decided to hack the testing a little bit, using another very cool package, httpretty. Httpretty allows you to monkey patch the socket module during testing, so you can respond anything you want to any kind of network requests. Here's what I came up with, do not hesitate to give any feedback.

---

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# Following https://stackoverflow.com/questions/34028219/how-to-execute-commands-on-aws-instance-using-boto3
import boto3

ssm = boto3.client('ssm')

response = ssm.send_command(
    InstanceIds=['i-abcde12345...'],
    DocumentName='AWS-RunShellScript',
    Parameters={'commands': ['echo "This command ran on $(date)"']
)

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# Taken from https://docs.github.com/en/github/authenticating-to-github/removing-sensitive-data-from-a-repository

# Warning:
# --------
# All stashes may be lost!

PATH_TO_REMOVE=big-folder/commited/by/error

# 1. Force Git to process, but not check out, the entire history of every branch and tag
# 2. Remove the specified file, as well as any empty commits generated as a result

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(ns util.redis
  (:refer-clojure :exclude [memoize])
  (:require [taoensso.carmine :as car]))

;; boilerplate stuff that is not in Carmine

(def ^:dynamic ^:private *pool*)
(def ^:dynamic ^:private *spec*)

(defmacro with-redis

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# Usage: ./pw.sh Github.com

op get item $1 | jq -r '.details.fields[] | select(.designation=="password").value' | xclip -selection c