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

Hellisotherpeople

You probably don't know how to do Prompt Engineering

(This post could also be titled "Features missing from most LLM front-ends that should exist")

Apologies for the snarky title, but there has been a huge amount of discussion around so called "Prompt Engineering" these past few months on all kinds of platforms. Much of it is coming from individuals who are peddling around an awful lot of "Prompting" and very little "Engineering".

Most of these discussions are little more than users finding that writing more creative and complicated prompts can help them solve a task that a more simple prompt was unable to help with. I claim this is not Prompt Engineering. This is not to say that crafting good prompts is not a difficult task, but it does not involve doing any kind of sophisticated modifications to general "template" of a prompt.

Others, who I think do deserve to call themselves "Prompt Engineers" (and an awful lot more than that), have been writing about and utilizing the rich new eco-system

ines

function venv {
    default_envdir=".env"
    envdir=${1:-$default_envdir}

    if [ ! -d $envdir ]; then
        python -m venv $envdir
        pip install ipython black flake8
        echo -e "\x1b[38;5;2m✔ Created virtualenv $envdir\x1b[0m"
    fi
    source $envdir/bin/activate

vvanirudh

# Drawn from https://gist.github.com/rocknrollnerd/c5af642cf217971d93f499e8f70fcb72 (in Theano)
# This is implemented in PyTorch
# Author : Anirudh Vemula

import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np

from sklearn.datasets import fetch_mldata

sebp

from theano.printing import Print
import pymc3 as pm
import numpy as np
import theano.tensor as T


def covariance(sigma, rho):
    C = T.fill_diagonal(T.alloc(rho, 2, 2), 1.)
    S = T.diag(sigma)
    M = S.dot(C).dot(S)

benkaiser

# Just add the following lines to your i3 config file
# dynamic tagging feature
bindsym $mod+t exec ~/.i3/get_workspace_options.py | dmenu -b | ~/.i3/go_to_workspace.py
bindsym $mod+Shift+t exec ~/.i3/get_workspace_options.py | dmenu -b | ~/.i3/move_to_workspace.py

migurski

from sys import argv
from shapely.ops import polygonize
from shapely.geometry import asShape, LineString

import json

if __name__ == '__main__':

    input = argv[1]
    input = json.load(open(input))

netj

#!/usr/bin/env bash
# memusg -- Measure memory usage of processes
# Usage: memusg COMMAND [ARGS]...
#
# Author: Jaeho Shin <netj@sparcs.org>
# Created: 2010-08-16
############################################################################
# Copyright 2010 Jaeho Shin.                                               #
#                                                                          #
# Licensed under the Apache License, Version 2.0 (the "License");          #