Matt Pettis mpettis

Resources I recommend to learn Ruby and Rails

New? Start here

I like jumping into tutorials first.

Agile Web Development with Rails, I learned Rails with an older edition of this book
The Rails Tutorial, The most popular Rails tutorial before it became a paid resource, "The Hartl Tutorial"

"How to write Ruby" Books

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

(taken from Hard Sayings of the Bible)

The Rate for the Job? (Mt 20:14-15)

One of the complaints that right-living and religious people made about Jesus arose from his treatment of the more disreputable members of society. They might have agreed that such persons should not be entirely excluded from the mercy of the all-loving God. Even for them there was hope, if they showed by practical repentance and unquestionable amendment of life that they were not beyond redemption. But not until such evidence had been given could they begin to be accepted as friends and neighbors. Jesus, however, accepted them immediately; he did not wait to see the outcome before he committed himself to them. This was disturbing; it was even more disturbing that he seemed to think more highly of them than of those who had never blotted their public copybook. He gave the impression that he actually preferred the company of the rejects of society; he not only made them feel at home in his company, so that they felt free to take l

library(tidyverse)

lag_multiple <- function(x, n_vec){
  map(n_vec, lag, x = x) %>% 
    set_names(paste0("lag", n_vec)) %>% 
    as_tibble()
}

tibble(x = 1:30) %&gt;%

	``` r
	library(ggplot2)
	library(dplyr)
	library(magick)
	library(patchwork)
	library(gt)
	library(ggtext)

	mtcars %>%
	head() %>%

	library(tidyverse)
	library(broom)

	US <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us.csv") %>%
	mutate(new_deaths = deaths - lag(deaths)) %>%
	filter(date >= "2020-02-26")

	models <- tibble(degrees = 2:4) %>%
	mutate(model = map(degrees, ~ lm(log(new_deaths + 1) ~ poly(date, .), data = US)))

	import numpy as np
	import tensorflow as tf


	def dense(x, weights, bias, activation=tf.identity, **activation_kwargs):
	"""Dense layer."""
	z = tf.matmul(x, weights) + bias
	return activation(z, **activationn_kwargs)

	library(ggplot2)
	library(dplyr)
	library(tibble)
	library(tidyr)

	GeomVector <- ggproto("GeomVector", Geom,

	required_aes = c("x", "y", "direction", "length"),

	default_aes = aes(

	(require '[clara.rules :as r])

	;;;; Define 3 rules, where the "priority" order is r1, r2, r3, where the highest priority is first
	;;;; and the rest is in descending order of priority.

	;;;; :type :rule/result "syntetic" fact is used to hold the final changes that can be queried out
	;;;; from a session after `r/fire-rules` via `r/query` on the `find-results` query.
	;;;; A namespace qualified keyword is used to avoid collision with externally given :type of
	;;;; "real" facts.