Boe Jaker BoeJaker

## gitwiki.html
<!DOCTYPE html>
<html>
<head>
  <title>My Repositories Wiki</title>
  <style>
    body {
      margin: 0;
      padding: 0;
      height: 100vh;
      display: flex;

## resize_swap.sh
# Turn swap off
# This moves stuff in swap to the main memory and might take several minutes
sudo swapoff -a

# Create an empty swapfile
# Note that "1G" is basically just the unit and count is an integer.
# Together, they define the size. In this case 8GB.
sudo dd if=/dev/zero of=/swapfile bs=1G count=8

# Set the correct permissions

## index.py
import docker
import re
import yaml
from flask import Flask, render_template, redirect
import os, time

app = Flask(__name__)

stack_name="workbench"


## index.py
import docker
import re
import yaml
from flask import Flask, render_template, redirect
import os, time

app = Flask(__name__)

stack_name="workbench"


## openAssist.py
import gradio as gr
from transformers import pipeline
import time
import requests

model = "gpt2"
generator = pipeline("text-generation", model=model)
generator.model.config.temperature = 0.6
generator.model.config.repetition_penalty = 1.5
generator.model.config.length_penalty = 2

## expert_v2.py
from googlesearch import search
import requests
from bs4 import BeautifulSoup
from transformers import BertForQuestionAnswering, BertTokenizer

import requests
from bs4 import BeautifulSoup

def read_website_context(url):
    """

## expert.py
import random
import wikipediaapi
from transformers import BertForQuestionAnswering, BertTokenizer

# Load pre-trained BERT-QA model and tokenizer
model_name = 'bert-large-uncased-whole-word-masking-finetuned-squad'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForQuestionAnswering.from_pretrained(model_name)

# Example question

## Docker_Arg_Check.sh
#!/bin/bash

# Set the path to the directory containing the Dockerfiles and docker-compose.yml file
DOCKER_DIR="path/to/docker/files"

# Get a list of Dockerfiles in the directory
DOCKERFILES=$(ls ${DOCKER_DIR}/*.dockerfile)

# Read the environment variables and arguments defined in docker-compose.yml
COMPOSE_VARS=$(docker-compose -f ${DOCKER_DIR}/docker-compose.yml config | grep -E '(environment:|args:)')

## Ouroboros.py
class Ouroboros:
    def __init__(self, val):
        self.val = val
        self.tail = self

    def __repr__(self):
        return f'Ouroboros({self.val})'

    def eat_tail(self):
        print(f"Eating tail... before: {self.tail}")

## circular_network.py
import socket

# Define some constants
PORT = 9000
MAX_CONNECTIONS = 10

# Create a circular list of nodes
nodes = ['node1', 'node2', 'node3']
num_nodes = len(nodes)
for i, node in enumerate(nodes):
	<!DOCTYPE html>
	<html>
	<head>
	<title>My Repositories Wiki</title>
	<style>
	body {
	margin: 0;
	padding: 0;
	height: 100vh;
	display: flex;
	# Turn swap off
	# This moves stuff in swap to the main memory and might take several minutes
	sudo swapoff -a

	# Create an empty swapfile
	# Note that "1G" is basically just the unit and count is an integer.
	# Together, they define the size. In this case 8GB.
	sudo dd if=/dev/zero of=/swapfile bs=1G count=8

	# Set the correct permissions
	import docker
	import re
	import yaml
	from flask import Flask, render_template, redirect
	import os, time

	app = Flask(__name__)

	stack_name="workbench"
	import gradio as gr
	from transformers import pipeline
	import time
	import requests

	model = "gpt2"
	generator = pipeline("text-generation", model=model)
	generator.model.config.temperature = 0.6
	generator.model.config.repetition_penalty = 1.5
	generator.model.config.length_penalty = 2
	from googlesearch import search
	import requests
	from bs4 import BeautifulSoup
	from transformers import BertForQuestionAnswering, BertTokenizer

	import requests
	from bs4 import BeautifulSoup

	def read_website_context(url):
	"""
	import random
	import wikipediaapi
	from transformers import BertForQuestionAnswering, BertTokenizer

	# Load pre-trained BERT-QA model and tokenizer
	model_name = 'bert-large-uncased-whole-word-masking-finetuned-squad'
	tokenizer = BertTokenizer.from_pretrained(model_name)
	model = BertForQuestionAnswering.from_pretrained(model_name)

	# Example question
	#!/bin/bash

	# Set the path to the directory containing the Dockerfiles and docker-compose.yml file
	DOCKER_DIR="path/to/docker/files"

	# Get a list of Dockerfiles in the directory
	DOCKERFILES=$(ls ${DOCKER_DIR}/*.dockerfile)

	# Read the environment variables and arguments defined in docker-compose.yml
	COMPOSE_VARS=$(docker-compose -f ${DOCKER_DIR}/docker-compose.yml config \| grep -E '(environment:\|args:)')
	class Ouroboros:
	def __init__(self, val):
	self.val = val
	self.tail = self

	def __repr__(self):
	return f'Ouroboros({self.val})'

	def eat_tail(self):
	print(f"Eating tail... before: {self.tail}")
	import socket

	# Define some constants
	PORT = 9000
	MAX_CONNECTIONS = 10

	# Create a circular list of nodes
	nodes = ['node1', 'node2', 'node3']
	num_nodes = len(nodes)
	for i, node in enumerate(nodes):