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Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches.
Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed.
Use <count> tags after each step to show the remaining budget. Stop when reaching 0.
Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress.
Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process.
Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach:

0.8+: Continue current approach
0.5-0.7: Consider minor adjustments
Below 0.5: Seriously consider backtracking and trying a different approach

darkseed

#!/bin/bash

# Function to display usage information
usage() {
    echo "Usage: $0 /path/to/input.mp4 [ /path/to/output_directory ]"
    exit 1
}

# Check if at least one argument (input file) is provided
if [ $# -lt 1 ]; then

darkseed

import os
from pathlib import Path
import logging
import argparse
from concurrent.futures import ThreadPoolExecutor, as_completed
import pathspec
from collections import defaultdict

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

darkseed

#
# LlamaIndex Starter Example with DBRX
#

# Based on the LlamaIndex Starter Example
# https://docs.llamaindex.ai/en/stable/getting_started/starter_example/
#
# Ensure you have installed both llama_index and Databricks integration
# e.g., pip install llama_index llama-index-llms-databricks
# 

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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)

darkseed

import openai
import asyncio
from typing import Any

async def dispatch_openai_requests(
    messages_list: list[list[dict[str,Any]]],
    model: str,
    temperature: float,
    max_tokens: int,
    top_p: float,

darkseed

Klipper mesh on print area only install guide

What this macro do

• This macro will dymanic change the bed_mesh area based on the size of the printed part. The fw will only probe on the area that the part will be printed (plus mesh_area_offset value)

Setup guide

• (1) Add the following macrro to your printer config, this will replace the default BED_MESH_CALIBRATE command.

darkseed

# coding=UTF-8
from __future__ import division
import nltk
import re
import requests

# Add your freebase key here
# If you don't have one, register at https://code.google.com/apis/console
FREEBASE_KEY = ""

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"""
An implementation of the `Local reparameterization trick`
from Kingma & Wellings and 
Bayesian RNN 
from Fortunato, Blundell & Vinyals
"""
import os
import time
import copy
from os.path import join as pjoin

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Elasticsearch CLI

Experimental CLI interface for the helpers in the `python library`_.

Main purpose is to expose the bulk functionality to enable rapid loading of data into an elasticsearch cluster. Combined with the scan command it can also be used to reindex data from elasticsearch into a different index or cluster.