staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

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This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

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willurd

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

plentz

# to generate your dhparam.pem file, run in the terminal
openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048

tyhoff

from tqdm import tqdm
from tqdm.utils import CallbackIOWrapper

file_path = os.path.abspath(__file__)
upload_url = https://some-bucket.s3.amazonaws.com

file_size = os.stat(file_path).st_size
with open(file_path, "rb") as f:
    with tqdm(total=file_size, unit="B", unit_scale=True, unit_divisor=1024) as t:
        wrapped_file = CallbackIOWrapper(t.update, f, "read")

hgfischer

Benchmarking Nginx with Go

There are a lot of ways to serve a Go HTTP application. The best choices depend on each use case. Currently nginx looks to be the standard web server for every new project even though there are other great web servers as well. However, how much is the overhead of serving a Go application behind an nginx server? Do we need some nginx features (vhosts, load balancing, cache, etc) or can you serve directly from Go? If you need nginx, what is the fastest connection mechanism? This are the kind of questions I'm intended to answer here. The purpose of this benchmark is not to tell that Go is faster or slower than nginx. That would be stupid.

So, these are the different settings we are going to compare:

• Go HTTP standalone (as the control group)
• Nginx proxy to Go HTTP
• Nginx fastcgi to Go TCP FastCGI
• Nginx fastcgi to Go Unix Socket FastCGI

Nathan-Furnal

;;; init.el --- Fun stuff all around -*- lexical-binding: t; -*-

;;; Commentary:
;; This is a simple init.el which offers a Python configuration.  Each package
;; usage is annotated with the how and why of its use.  `use-package' is used to
;; manage the configuration as it provides lots of facilities to load modes,
;; define custom variables and key-maps, etc.

;;; Code:

sgillies

Author: Sean Gillies Version: 1.0

Abstract

This document describes a GeoJSON-like protocol for geo-spatial (GIS) vector data.

Introduction

tylerneylon

-- Two dashes start a one-line comment.

--[[
     Adding two ['s and ]'s makes it a
     multi-line comment.
--]]

----------------------------------------------------
-- 1. Variables and flow control.
----------------------------------------------------

chaitanyagupta

Reader Macros in Common Lisp

This post also appears on lisper.in.

Reader macros are perhaps not as famous as ordinary macros. While macros are a great way to create your own DSL, reader macros provide even greater flexibility by allowing you to create entirely new syntax on top of Lisp.

Paul Graham explains them very well in [On Lisp][] (Chapter 17, Read-Macros):

The three big moments in a Lisp expression's life are read-time, compile-time, and runtime. Functions are in control at runtime. Macros give us a chance to perform transformations on programs at compile-time. ...read-macros... do their work at read-time.

celoyd

Pansharpening notes, mid-2021

First posted in August 2021. This is basically a snapshot of my thinking about pansharpening at that time; I’m not making any substantial updates. Last typo and clarity fixes in February 2023.

Preface

This is a collection of notes on how I’ve been approaching convolutional neural networks for pansharpening. It’s an edited version of an e-mail to a friend who had asked about this tweet, so it’s informal and somewhat silly; it’s not as polished as, say, a blog post would be. It’s basically the advice I would give to an image processing hobbyist before they started working on pansharpening.

If you want a more serious introduction, start with the literature review in Learning deep multiresolution representations for pansharpening. Most of the academic work I would recommend is mentioned there.