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

sheriffff

Usage

run python jupyter_translate.py, and you will be asked for 3 inputs:

• path to notebook
• source language
• target language

A new notebook will be created in target language in the same path as notebook.
It will preserve original language cells too, for easier human debugging of the translation.

You need a DeepL API

MarinaGolivets

# as input, provide a vector of verbatim taxon names (preferably with authorship)
#               and a vector of existing local identifiers for those names

# load R packages
library(dplyr)
library(tidyr)

match_to_gbif.fn <- function(taxon_name, taxon_id, include_genus = FALSE) {
  
  # perform initial matching in parallel

andrewheiss

library(tidyverse)
library(analogsea)
library(furrr)

# R on local machine → DigitalOcean Linux image → Docker on that → R

# Install analogsea, log into DigitalOcean, generate an API key with write
# permissions, and add this line to ~/.Rprofile:
#
# Sys.setenv(DO_PAT = "KEY_GOES_HERE")

mcoster

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
This script takes a BibTeX .bib file and outputs a series of .md files for use
in the Academic theme for Hugo, a general-purpose, static-site generating web
framework. Each file incorporates the data for a single publication.

Written for and tested using python 3.6.1

crsh

#' Plot network of package dependencies
#'
#' @param pkg package description, can be path or package name. See \code{\link[devtools]{as.package}} for
#'    more information.
#'
#' @details The resulting plot visualizes the network of package dependencies. If you are trying to cut down
#'    on package dependencies look for big red dots that represent a lot of upstream but few downstream
#'    dependencies.
#' @import ggplot2
#' @export

hadley

msg <- function(..., prob = 0.25) {
  if (runif(1) > prob) {
    return(invisible())
  }
  
  messages <- c(...)
  message(sample(messages, 1))
}

encourage <- function() {

dgrtwo

# somewhat hackish solution to:
# https://twitter.com/EamonCaddigan/status/646759751242620928
# based mostly on copy/pasting from ggplot2 geom_violin source:
# https://github.com/hadley/ggplot2/blob/master/R/geom-violin.r

library(ggplot2)
library(dplyr)


"%||%" <- function(a, b) {

lucasfais

Sublime Text 2 – Useful Shortcuts (Mac OS X)

General

⌘T	go to file
⌘⌃P	go to project
⌘R	go to methods
⌃G	go to line
⌘KB	toggle side bar
⌘⇧P	command prompt