veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

coolaj86

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)

androidfred

This "progressively more detail" image loading algorithm can help you do and learn anything

Summary, TLDR

When doing or learning pretty much anything, the strategy where you tackle the whole thing all at once, but starting with a rough outline and then incrementally revisiting everything to go into increasingly more detail, is often more effective than doing things perfectly little by little and missing the big picture. (pun intended)

Longer version

Back in the days of dial up internet, which was very limited in speed and capacity compared to modern internet connections, images on a web page could take very long to load. There were different strategies for dealing with that problem, and if you look hard enough (pun intended again), they can reveal some things about how to do and learn stuff.

cchen140

# This file is located at /boot/wpa_supplicant.conf
country=GB
ctrl_interface=DIR=/var/run/wpa_supplicant GROUP=netdev
update_config=1

network={
    #ssid="EML33T2"
    #psk="emrooftop"
    #psk=c150059ac2d9df589127a8ee0a1cc475099c2d6e60ea48644c21684cb7ee6b23
    ssid="2.4 26S6-U1" 

myounus96

Convert PascalVOC Annotations to YOLO

This script reads PascalVOC xml files, and converts them to YOLO txt files.

Note: This script was written and tested on Ubuntu. YMMV on other OS's.

Disclaimer: This code is a modified version of Joseph Redmon's voc_label.py

Instructions:

1. Place the convert_voc_to_yolo.py file into your data folder.

sdoshi579

import os
import pandas as pd
from scipy.misc import imread
import math
import numpy as np
import cv2
import keras
import seaborn as sns
from keras.layers import Dense, Dropout, Flatten, Input
from keras.layers import Conv2D, MaxPooling2D

disulfidebond

Overview

This gist details how to create or restore a disk image in Mac OSX. There are three methods that are described: Carbon Copy Cloner, Disk Utility, and CommandLine.

• Disclaimer:
  • I have no financial incentives to https://bombich.com or Apple.
  • Always make a backup of your data, and make 2 separate backups before trying something new.
  • The following steps have been tested and are a summary of my personal recommendations, but should be used at your own risk.
  • If there is a chance of imminent data loss, contact a professional for assistance, and do not rely on a random person from the Internet for help.

Method 1: Carbon Copy Cloner (CCC)

lotusirous

import logging

from flask import Flask
from werkzeug.utils import find_modules, import_string


def configure_logging():
    # register root logging
    logging.basicConfig(level=logging.DEBUG)
    logging.getLogger('werkzeug').setLevel(logging.INFO)

cldwalker

I like to learn, index and retrieve information a lot. I know a lot of others do as well. We share data but I don't think we share our information and rarely our ontologies (a.k.a. our mental models). If we shared our ontologies, I think we could learn more from each other. With this hope in mind, I'm looking for a tool that provides these features:

• Shares my ontology publicly
• Shares my bookmarks publicly
• Provides easy entry, extension and querying of my ontology
• Provides easy entry and querying of my bookmarks
• Shares interesting snapshots of my bookmarks
• Encourages discovery of information that is new and interesting to others

Since I have not found such a tool, I have built a tool that:

• publishes my ontology using https://schema.org/ as a foundation.

pharzan

#listens to the above run to
#FLASK_APP=server.py FLASK_DEBUG=1 python3.5 -m flask run -h 192.168.1.124 -p 8999:
#run this on the local server to listen to socket communication 

from flask import Flask, render_template, jsonify
from flask import request as query

app = Flask(__name__)