- 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
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/* 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} |
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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), |
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.gist { | |
color: #000; | |
} | |
.gist div { | |
padding: 0; | |
margin: 0; | |
} | |
.gist .gist-file { |
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""" | |
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 |
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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))) |
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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 |
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############################################################################################### | |
## ## | |
## Setup ## | |
## ## | |
############################################################################################### | |
# install.packages("Rfacebook") # from CRAN | |
# install.packages("Rook") # from CRAN | |
# install.packages("igraph") # from CRAN |
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.
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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) |
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