davidrichards

Waldemar Januszczak created a remarkable four-part series, The Impressionists: Painting and Revolution (available on YouTube), where he explores the unexpected role of technology in making Impressionism possible. He draws attention to innovations like ferrules (the metal bands that allowed brushes to be flat), portable metal paint tubes, lightweight easels, and steam trains—all of which enabled artists to paint on location and respond directly to light and atmosphere.

Later, in New Deal–era America, art instructors developed stepwise frameworks for visual education that echo what the Impressionists practiced intuitively. One such teacher, Victor D’Amico, wrote Experiments in Creative Art Teaching, which I believe includes this kind of framework—though my copy is in storage, so I can't confirm the exact sequence. These teachers shifted the focus from rendering photographic likeness to developing visual awareness—learning to see atmosphere, rhythm, form, and mood.

There’s a clear evolution here: as mechanized

davidrichards

davidrichards

system_defaults = {}
config_paths = [
    Path.home()/'.myconfig',
]
env_config = config_filter(os.environ)
user_config = get_config(*config_paths)
config = d_merge(env_config, user_config, system_defaults)

davidrichards

DROP VIEW IF EXISTS flat_links;
CREATE TEMPORARY VIEW flat_links AS
SELECT
  l.id as link_id, title, snippet, link, domain, rank,
  link_type, search_engine_name, page_number,
  requested_at, num_results_for_query,
  num_results, q
FROM link l
LEFT JOIN (
  SELECT

davidrichards

#!/usr/bin/env ruby

require 'json'
require 'yaml'

class AutoSuggest

  def self.call(*args)
    new(*args).call
  end

davidrichards

davidrichards

s = Sample.new(5,8) # Uses a default 95% confidence level, drawing 5,000 samples
s.call # Returns a histogram of 5 entries
s.mean # Returns the calculated mean between 5 and 8
s.sample_mean # Returns the mean of the sample data
s.sigma # Returns the standard deviation, calculated from the confidence interval
s1 = Sample.new(5, 8, confidence: 0.8) # Changes the confidence level to 80%

Sample.call(5,8) # Cuts to the chase, just returns the histogram

davidrichards

Try something twice, with a probability of failure at 10%, record wether the cummulative result was failure. Do that 1 million times, and form a posterior belief about this situation.

TruthSampling.call(n_samples: 1_000_000, trials: 2, probability: 0.1)

Or, here's a basketball example. If I've got a 1/6 free throw average, and I take 4 free throws, what is the chance I make at least one of those 4?

TruthSampling.call(trials: 4, probability: 1/6.0)
{false=>0.48256, true=>0.51744}

This gives me about a 52% chance of making at least one basket.

davidrichards

davidrichards