Roger Jungemann rjungemann

repl time

$ boot repl

boot.user=> (require :reload '[pipeline :refer [engine]]
                             '[mount.core :as mount])
"|| mounting... #'pipeline/engine"

starting it

Racket `#lang`s and the REPL

The Racket platform provides a robust set of tools for developing languages, mostly centered around the macro system and the #lang protocol, which allows providing arbitrary readers to convert text input to syntax objects representing Racket modules. The REPL is a bit more complicated, however, because it has different rules—each expression needs to be dynamically read, assigned lexical context, expanded, compiled, evaluated, and printed, all in real time. (In that sense, perhaps the phrase “REPL” oversimplifies what Racket is doing, but that’s a separate conversation.)

So how does Racket accommodate this in the face of completely arbitrary user-defined languages, some of which may not even support interactive evaluation in a traditional sense? Racket mostly solves this problem by having a related but distinct set of protocols for managing runtime interactions that operates alongside #lang.

Modules and the top level

Racket’s evaluation model divides pretty much ev

Python3 Virtualenv Setup

Requirements

Python 3
Pip 3

$ brew install python3

using Ansible command line:

ansible-playbook --connection=local 127.0.0.1 playbook.yml

using inventory:

127.0.0.1 ansible_connection=local

	<div id="app">
	{{ message }}
	<button v-on:click="increment">+</button>
	<button v-on:click="decrement">-</button>
	</div>

	Retraining (ie https://www.tensorflow.org/versions/r0.11/how_tos/image_retraining/index.html ) doesnt really go into
	nuances about what types of labels you should choose based on your model.

	Since InceptionV3 is an object recognition task, and the penultimate layer (pool 3) contains some 2048 vector length descriptions that
	somehow infer various 'objectness' traits, its far better to say:

	train for labels that tend toward objectness (lamp, lampshade, chandelier, standing lamp, desk lamp)
	than train for labels that then to abstract image features like composition: chaotic, patterned, symmetric, asymmetric, mirrored, circular, diagonal, natural (photographic) , synthetic)

	If I were interested in the latter labeling (ie, meta-features), is it more sensible to:

	(*
	Some time ago I found this tutorial about writing a small VM in c.
	http://blog.felixangell.com/blog/virtual-machine-in-c
	Here's something similar in OCaml. Nothing super fancy and a lot of room for improvements.
	But it's a start :)
	*)
	open Core.Std

	type registers = {
	mutable a: int;

	(ns clj-spec-playground
	(:require [clojure.string :as str]
	[clojure.spec :as s]
	[clojure.test.check.generators :as gen]))

	;;; examples of clojure.spec being used like a gradual/dependently typed system.

	(defn make-user
	"Create a map of inputs after splitting name."
	([name email]

	local alert = require("hs.alert")
	local timer = require("hs.timer")
	local eventtap = require("hs.eventtap")

	local events = eventtap.event.types

	local module = {}

	-- Save this in your Hammerspoon configuration directiorn (~/.hammerspoon/)
	-- You either override timeFrame and action here or after including this file from another, e.g.

	let getRandomBytes = (
	(typeof self !== 'undefined' && (self.crypto \|\| self.msCrypto))
	? function() { // Browsers
	var crypto = (self.crypto \|\| self.msCrypto), QUOTA = 65536;
	return function(n) {
	var a = new Uint8Array(n);
	for (var i = 0; i < n; i += QUOTA) {
	crypto.getRandomValues(a.subarray(i, i + Math.min(n - i, QUOTA)));
	}
	return a;