AlbertRapp

library(dplyr)
library(ggplot2)
library(patchwork)
library(ggiraph)

dat <- gapminder::gapminder |> 
  janitor::clean_names() |> 
  mutate(
    # ID that is shared for boxplots (this one uses factors, i.e. numbers, as ID instead of continents)

veekaybee

ChatGPT Resources

Context

ChatGPT appeared like an explosion on all my social media timelines in early December 2022. While I keep up with machine learning as an industry, I wasn't focused so much on this particular corner, and all the screenshots seemed like they came out of nowhere. What was this model? How did the chat prompting work? What was the context of OpenAI doing this work and collecting my prompts for training data?

I decided to do a quick investigation. Here's all the information I've found so far. I'm aggregating and synthesizing it as I go, so it's currently changing pretty frequently.

Model Architecture

chrisdiana

OP-Z Cheatsheet

General

• Play: Press Play
• Stop: Press Stop
• Sequence: Hold Trig + key
• Sequence v2: Hold Rec + keys
• Live Record: Hold Rec + Play + key/Play
• Stop Recording: Press Rec

epijim

###############################################################################################
##                                                                                           ##
##           Setup                                                                           ##
##                                                                                           ##
###############################################################################################

# install.packages("Rfacebook")  # from CRAN
# install.packages("Rook")  # from CRAN
# install.packages("igraph") # from CRAN

crowding

library(ggplot2)
library(plyr)

theme_set(theme_bw())
theme_update(panel.border=element_blank())

# A reaction time experiment works as follows: On each trial, an
# observer is asked to view a stimulus and categorize it into one of
# multiple values. For instance, it may be a noisy motion stimulus,
# and the observer is asked to classify the motion as "leftward" or

timriffe

TuftePyramid <- function(males, females, age, widths, gap = .05, 
        fill.args = list(), border.args = list(), grid.args = list(), 
        age.label.args = list(), x.label.args = list(), 
        grid = TRUE, labels = TRUE, add = FALSE){
    
    Total <- sum(males, females)
    males   <- males / Total
    females <- females / Total

    max.x <- max(abs(pretty(c(males, females), n = 25))) 

NorthIsUp

"""
Author: adam@northisup.com

to run you will need to install the following:
    pip install requests
    pip install simplejson

get the auth token and device id by sniffing the nike app syncing
with api.nike.com with charles

kkosuge

.gist {
  color: #000;
}

  .gist div {
    padding: 0;
    margin: 0;
  }

  .gist .gist-file {

bds

library(ggplot2)

sparkLinePlot <- function(df, plot.file) {

  highest <- subset(df, outcomes == max(outcomes))
  lowest <- subset(df, outcomes == min(outcomes))

  p <- ggplot(df, aes(x=date, y=outcomes)) +
       geom_line() +
       opts(panel.border = theme_rect(linetype = 0),

jnrbsn

/* Better styles for embedding GitHub Gists */
.gist{font-size:13px;line-height:18px;margin-bottom:20px;width:100%}
.gist pre{font-family:Menlo,Monaco,'Bitstream Vera Sans Mono','Courier New',monospace !important}
.gist-meta{font-family:Helvetica,Arial,sans-serif;font-size:13px !important}
.gist-meta a{color:#26a !important;text-decoration:none}
.gist-meta a:hover{color:#0e4071 !important}