mazzma12

#!/usr/bin/env bash
# Pyenv https://github.com/pyenv/pyenv/wiki#suggested-build-environment
PYENV_PACKAGE="\
	build-essential libssl-dev zlib1g-dev make \
libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm \
libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev \
"
GIS_PACKAGE="\
	libproj-dev proj-data proj-bin libgeos-dev \
	libspatialindex-dev \

mazzma12

# Find IP address from host
nslookup $hostanme
# https://superuser.com/a/1414856
nslookup $hostname | grep -i address | awk -F" " '{print $2}' | awk -F# '{print $1}' | tail -n 1

# DNS issues: better use dig as host will use the local cache of the machine 
# @ specifies another resolver than the local cache DNS 
dig google.com +short @1.1.1.1

## Docker 

mazzma12

import json
import os
from io import StringIO

import requests

GITHUB_API = "https://api.github.com"
API_TOKEN = os.getenv("GITHUB_API_TOKEN")

mazzma12

ChatGPT Resources

Context

ChatGPT appeared like an explosion on all my social media timelines in early December 2022. While I keep up with machine learning as an industry, I wasn't focused so much on this particular corner, and all the screenshots seemed like they came out of nowhere. What was this model? How did the chat prompting work? What was the context of OpenAI doing this work and collecting my prompts for training data?

I decided to do a quick investigation. Here's all the information I've found so far. I'm aggregating and synthesizing it as I go, so it's currently changing pretty frequently.

Model Architecture

mazzma12

import numpy as np
import pandas as pd

def default_json_encoder(value):
    # This should be built-in ...
    if isinstance(value, (datetime.datetime, pd.Timestamp)):
        return value.isoformat()
    elif isinstance(value, np.bool_):
        return bool(value)
    elif isinstance(value, np.floating):