Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.
Avoid being a link dump. Try to provide only valuable well tuned information.
Neural network links before starting with transformers.
# ----------------------------------------------------------------------------- | |
# AI-powered Git Commit Function | |
# Copy paste this gist into your ~/.bashrc or ~/.zshrc to gain the `gcm` command. It: | |
# 1) gets the current staged changed diff | |
# 2) sends them to an LLM to write the git commit message | |
# 3) allows you to easily accept, edit, regenerate, cancel | |
# But - just read and edit the code however you like | |
# the `llm` CLI util is awesome, can get it here: https://llm.datasette.io/en/stable/ | |
gcm() { |
#!/usr/bin/python3 | |
# Quick and dirty ChatGPT from your command line, without any library dependencies (API key required) | |
# End your input with a line consisting of just --- | |
# Stop generating with ctrl-c | |
from urllib.request import Request, urlopen | |
import json | |
messages = [] |
log stdout | |
errors stdout | |
auto_https off | |
http://myapp.fly.dev { | |
reverse_proxy 100.120.108.62:8000 | |
} |
The set
lines
set -euxo pipefail
is short for:set -e
set -u
25/5/2020
Imagine a future where a user Alice has bitcoins and wants to send them with maximal privacy, so she creates a special kind of transaction. For anyone looking at the blockchain her transaction appears completely normal with her coins seemingly going from address A to address B. But in reality her coins end up in address Z which is entirely unconnected to either A or B.
Now imagine another user, Carol, who isn't too bothered by privacy and sends her bitcoin using a regular wallet which exists today. But because Carol's transaction looks exactly the same as Alice's, anybody analyzing the blockchain must now deal with the possibility that Carol's transaction actually sent her coins to a totally unconnected address. So Carol's privacy is improved even though she didn't change her behaviour, and perhaps had never even heard of this software.
#!/usr/bin/env python | |
import os, os.path, stat, sys, base64 | |
# TOTP lib inlined | |
import time, hmac, base64, hashlib, struct | |
def pack_counter(t): | |
return struct.pack(">Q", t) |
/* | |
Copy this into the console of any web page that is interactive and doesn't | |
do hard reloads. You will hear your DOM changes as different pitches of | |
audio. | |
I have found this interesting for debugging, but also fun to hear web pages | |
render like UIs do in movies. | |
*/ | |
const audioCtx = new (window.AudioContext || window.webkitAudioContext)() |
# test_camera.py | |
# | |
# Open an RTSP stream and feed image frames to 'openalpr' | |
# for real-time license plate recognition. | |
import numpy as np | |
import cv2 | |
from openalpr import Alpr | |