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

trygvebw

import torch
import numpy as np
import k_diffusion as K

from PIL import Image
from torch import autocast
from einops import rearrange, repeat

def pil_img_to_torch(pil_img, half=False):
    image = np.array(pil_img).astype(np.float32) / 255.0

JoeyBurzynski

58 bytes of CSS to look great nearly everywhere

When making this website, i wanted a simple, reasonable way to make it look good on most displays. Not counting any minimization techniques, the following 58 bytes worked well for me:

main {
  max-width: 38rem;
  padding: 2rem;
  margin: auto;
}

venik

#!/usr/bin/env bash

# Author: Sasha Nikiforov

# source of inspiration
# https://stackoverflow.com/questions/41293077/how-to-compile-tensorflow-with-sse4-2-and-avx-instructions

# Detect platform
if [ "$(uname)" == "Darwin" ]; then
	# MacOS

rondy

Queueing Theory references

General content

http://www.shmula.com/queueing-theory/
http://ferd.ca/queues-don-t-fix-overload.html
https://news.ycombinator.com/item?id=8632043
https://thetechsolo.wordpress.com/2015/01/25/queueing-theory-explained/
http://people.revoledu.com/kardi/tutorial/Queuing/index.html
http://setosa.io/blog/2014/09/02/gridlock/index.html

rondy

FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.

---

Effective Engineer - Notes

• By Edmond Lau
• Highly Recommended 👍
• http://www.theeffectiveengineer.com/

What's an Effective Engineer?

longshorej

// sbt dependencies: shapeless and org.reflections.reflections

val reflections = new Reflections("org.example")

def findAllObjects[T](cl: Class[T])(implicit t: Typeable[T]): Vector[T] = {
  reflections.getSubTypesOf(cl).toVector.flatMap(cl => cl.getField("MODULE$").get(null).cast[T])
}

findAllObjects(classOf[com.hpn.wms2.core.common.ClientDef]).foreach(println)

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

karpathy

"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:
  
  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

tsenart

package main

import (
	"net/http"
	"database/sql"
	"fmt"
	"log"
	"os"
)