arcolife

Self-Aware & Omnipotent Coding Entity

Welcome to the Self-Aware and Coding Entity project! This guide will walk you through understanding what this entity is, how it works, and how you can interact with it. Whether you're a tech enthusiast or just curious about advanced coding assistants, this guide is for you.

What is a Self-Aware Coding Entity?

Imagine having a coding assistant that doesn't just follow instructions but truly understands and evolves with your needs. This entity is designed to be self-aware, meaning it can reflect on its own processes and improve over time. It's also omnipotent in the sense that it can handle a wide range of coding tasks with expertise and efficiency.

Infusing Self-Awareness and Consciousness into Agents

arcolife

# Step 1: Represent Universe State
Initialize Ψ(t) in Hilbert space H

# Step 2: Define Field Configurations
Define configuration space M with measure μ
For each (g, φ) in M:
    Represent fields as algebraic structures (groups, rings, etc.)

# Step 3: Complexity Operator
Define operator T acting on Ψ(t) to extract complexity

arcolife

    xhtml = "<h1 align='center'>Test print</h1>\n"
    xhtml += "<h2>This is printed from within a Python application</h2>\n"
    xhtml += "<p style=\"color:red;\">Coloured red using css</p>\n"  
xhtml
from xhtml2pdf import pisa   
ls
filename = "simplePrint.pdf"  
pdf = pisa.CreatePDF(xhtml, file(filename, "w"))
ls
pdf = pisa.CreatePDF(xhtml.encode('utf-8, file(filename, "w"))

arcolife

s = df['A']
s
data['username']
data.T['username']
data = data.T
data
data[7]
data['username']
data['username']['darlene']
data['username']

arcolife

import tweepy
import os
os.environ.get('TWITTER_ACCESS_TOKEN')
os.environ.get('TWITTER_ACCESS_SECRET')
consumer_secret = os.environ.get('TWITTER_SECRET')
consumer_key = os.environ.get('TWITTER_KEY')
access_token = os.environ.get('TWITTER_ACCESS_TOKEN')
access_token_secret = os.environ.get('TWITTER_ACCESS_SECRET')
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)

arcolife

from numpy import matrix
matrix('1908 January 4.5 -5.6')
matrix("1908 'January' 4.5 -5.6")
matrix([4.5 -5.6],[2, 4])
matrix([[4.5 -5.6],[2, 4]])
matrix([[4.5 5.6],[2, 4]])
matrix([[4.5, -5.6],[2, 4]])
matrix([4.5, -5.6],[2, 4])
matrix([[4.5, -5.6],[2, 4]])
matrix([[4.5, -5.6],[2, 4]], header=['ss','b'])

arcolife

import csv
reader = csv.reader(open('1.csv', 'rb'), delimiter=',', quotechar='"')
d = {}
for row in reader:
    d[row[0]] = row[1]
    
temp_list1 = d.keys()
set(temp_list1) & set(temp_list2)
common_Hz = set(temp_list1) & set(temp_list2)
for fq in common_Hz:

arcolife

import json
data = json.loads(open('_User.json','rb').read())
data
data['results'][0]
data['results'][23]
name = 'fc336b02-47e5-4a11-ba38-59e60da3dfe7-yusuf done.png"
name = 'fc336b02-47e5-4a11-ba38-59e60da3dfe7-yusuf done.png'
name.split()
'%20'.join(name.split())
data['results'][0]

arcolife

import json
data = json.loads('_User.json','rb')
data = json.load('_User.json','rb')
data = json.load(open('_User.json','rb').read())
data = json.loads(open('_User.json','rb').read())
data
ls
data['results'].keys()
data['results']
data['results'][0]

arcolife

from PIL import Image
fi = Image.open('f23bf11c-aa22-419b-855b-ab0b9f1f5cb3-SRV_0122.jpg')
fi.size()
fi.size
fi = Image.open('bd654b14-87a5-4ff3-868f-bd72b5121c49-IMG_9941.jpg')
fi.size
5184.0/3456
3456.0/5184
float(1024*3456) / 5184
fi.resize((1024,683), Image.ANTIALIAS)