tommasoturchi

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 and experience preferred (super rare at this point).

My own notes from a few months back.

Background

• Survey of LLMS
• Self-attention and transformer networks

nickyvanurk

import { useEffect, useRef, useState } from 'react';
import { useFrame, useThree } from '@react-three/fiber';
import { MapControls, OrthographicCamera, PerspectiveCamera } from '@react-three/drei';
import type { OrbitControls as OrbitControlsImpl } from 'three-stdlib';

export function Camera() {
  const [oldType, setOldType] = useState('PerspectiveCamera');
  const [coords, setCoords] = useState({ x: 0, y: 0 });

  const gl = useThree((state) => state.gl);

rain-1

Does prompt injection matter to AutoGPT?

Executive summary: If you use AutoGPT, you need to be aware of prompt injection. This is a serious problem that can cause your AutoGPT agent to perform unexpected and unwanted tasks. Unfortunately, there isn't a perfect solution to this problem available yet.

Prompt injection can derail agents

If you set up an AutoGPT agent to perform task A, a prompt injection could 'derail' it into performing task B instead. Task B could be anything. Even something unwanted like deleting your personal files or sending all your bitcoins to some crooks wallet.

Docker helps limit the file system access that agents have. Measures like this are extremely useful. It's important to note that the agent can still be derailed.

rain-1

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

aileftech

$ grep -P "^[ABCDEFabcdefOoIi]{6,6}$" /usr/share/dict/words | tr 'OoIi' '0011' | tr '[:lower:]' '[:upper:]' | awk '{print "#" $0}' 
#ACAD1A
#B0BB1E
#DEBB1E
#AB1DED
#ACAC1A
#ACCEDE
#AC1D1C
#BAB1ED
#BA0BAB

hmans

import { useFrame } from "@react-three/fiber"
import { useRef } from "react"

/* TODO: Extract this into hmans/things or similar */

export function useFrameEffect<T>(
  dependencyCallback: () => T,
  callback: (args: T) => void,
  renderPriority = 0
) {

tylermorganwall

library(geojsonsf)
library(sf)
library(rayrender)

#Data source: https://github.com/telegeography/www.submarinecablemap.com
cables = geojson_sf("cable-geo.json")

cablescene = list()
counter = 1
for(i in 1:length(cables$geometry)) {

akihikodaki

Linux Desktop on Apple Silicon in Practice

I bought M1 MacBook Air. It is the fastest computer I have, and I have been a GNOME/GNU/Linux user for long time. It is obvious conclusion that I need practical Linux desktop environment on Apple Silicon.

Fortunately, Linux already works on Apple Silicon/M1. But how practical is it?

• Two native ports exist.

tobi

#!/usr/bin/env ruby

# gem install active_support
require 'active_support/inflector' 
require 'active_support/core_ext/string'

# gem install webrick (only ruby3) 
require 'webrick' 

# gem install mechanize

ityonemo

A half-hour to learn Zig

This is inspired by https://fasterthanli.me/blog/2020/a-half-hour-to-learn-rust/

Basics

the command zig run my_code.zig will compile and immediately run your Zig program. Each of these cells contains a zig program that you can try to run (some of them contain compile-time errors that you can comment out to play with)