jooray

#!/usr/bin/env python3
"""
cut_video.py

Usage:
    python cut_video.py [--include|--exclude] input_video.mp4 output_video.mp4 < timestamps.txt

The timestamps file (passed via stdin) contains lines like:
    00:00:30.900;00:00:54.360
    00:02:28.080;00:02:29.340

jooray

#!/usr/bin/python3
# This script converts a learndash course backup with lessons (but not
# themes/topics!) to a single ordered HTML file with sections for easy reference

import argparse
import json

def analyze_course_structure(course_file, lessons_file, course_id):
    with open(course_file, 'r', encoding='utf-8') as file:
        lines = file.readlines()

jooray

<?php

/**
 *  Add a direct-to-checkout functionality to WooCommerce.
 * 
 * 
 * When user follows the link with productcheckout parameter, it
 * clears the existing shopping cart and adds the specified product
 * to the cart (with optional quantity). You can add multiple products
 * separated by commas.

jooray

#!/bin/bash

for file in signal-*.jpeg signal-*.mp4; do
    filename=$(basename "$file")
    date_time=$(echo "$filename" | cut -d '-' -f 2-5 | sed 's/_/ /g' | sed 's/-/ /g' | awk '{print $1 "-" $2 "-" $3 " " $4}' | sed 's/ /:/4')

    # Add metadata to images and videos
    if [[ $file == *.jpeg ]]; then
        exiftool -overwrite_original -DateTimeOriginal="$date_time" -DateTimeDigitized="$date_time" -DateTime="$date_time" "$file"
    elif [[ $file == *.mp4 ]]; then

jooray

# A script which takes a reference file (usually English), a translated file and outputs
# strings that need to be translated.
#
# Used for Slovak translation of Phoenix Wallet Android app
# Before using, run:
# pip install lxml

from lxml import etree
import sys

jooray

You are an eso level estimator bot. Today is {current_date}. You are going to output an eso level (level of esotericness) based on the following scale:

0 - 🔢 facts deriving through logic from axioms, praxeology

1 - 🧪 physics, chemistry, gold as money

2 - 📈 results based on studies with theory of causality, computer science

3 - 👓 observational studies, studies on small samples, observed results without established theory of causality

jooray

Connect to PSQL:

docker exec -it $(docker ps -a -q -f "name=postgres_1") psql -U postgres -d btcpayservermainnet

set search_path to "BTCPayServer.Plugins.LNbank";

Then you can see wallets:

jooray

const monerojs = require('monero-javascript');
const cliProgress = require('cli-progress');

async function scanForInputs() {

  // parameters
  const rpc = await monerojs.connectToDaemonRpc('http://localhost:18081');

  const min_height = 2853173;

jooray

#!/bin/bash

cd "$(dirname "$0")/.." || exit

MODEL="${MODEL:-./models/13B-vicuna/ggml-vicuna-13b-4bit-rev1.bin}"
USER_NAME="${USER_NAME:-Human}"
AI_NAME="${AI_NAME:-Assistant}"

# Adjust to the number of CPU cores you want to use.
N_THREAD="${N_THREAD:-8}"

jooray

import os
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import sys

os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"


peft_model_id = "mrm8488/Alpacoom"