queertypes

-- make psql really quiet while the next several commands are processed
\set QUIET 1

-- a colorful prompt that displays, in order:
-- * hostname (green)
-- * port number (yellow)
-- * user (blue)
-- * database (purple)
-- in the format: host:port user@db# 
\set PROMPT1 '%[%033[1;32m%]%M:%[%033[1;33m%]%> %[%033[1;34m%]%n@%[%033[1;35m%]%/%R%[%033[0m%]%# '

ftfarias

When are Python circular imports fatal?

In your Python package, you have:

• an __init__.py that designates this as a Python package
• a module_a.py, containing a function action_a() that references an attribute (like a function or variable) in module_b.py, and
• a module_b.py, containing a function action_b() that references an attribute (like a function or variable) in module_a.py.

This situation can introduce a circular import error: module_a attempts to import module_b, but can't, because module_b needs to import module_a, which is in the process of being interpreted.

But, sometimes Python is magic, and code that looks like it should cause this circular import error works just fine!

nrb

Installing

Disable SecureBoot for the time being.

For booting my Archlinux kernel (4.3 series on the USB drive I made), I needed the following options:

nomodeset intel_pstates=no_hwp

You can edit the parameters by hitting the e key on the ArchISO boot option

audiodude

File Edit Options Buffers Tools Python Help                                     
#!/usr/bin/python3                                                              
"""                                                                             
"You can't spell 'skateboarding' without d-a-t-a-g-r-o-k."                      
Usage: python3 cantspell.py WORD                                                
Prints all the words in its dictionary that contain at least all the            
letters given in WORD                                                           
"""
from collections import Counter

wwahammy

So let's consider a small business that runs a website that happens to run on GPL'd software. They don't know anything about the GPL but they paid a local web shop to set it up on their shared web host. They don't have to know that the GPL requires them to provide source code if they convey a copy because they don't convey

Let's say the same business runs a wwebsite that runs on AGPL software. They don't know anything about the AGPL but they paid a local web shop to set it up on their shared web host. What happens when someone requests a copy of the source? The answer will likely be "what's source code?"

The local business doesn't want to pay someone to get them the source. And we can't even tell them "give us the contents of directory holding the web site" because that might contain private configuration information which isn't covered by the AGPL.

This is why I don't normally recommend AGPL software to anyone unless they already understand licensing and source code.

vanstee

# Base dirs
SRC_DIR            = src
TMP_DIR            = tmp
DEV_DIR            = dev
SRC_PUBLIC_DIR    := $(SRC_DIR)/public
SRC_STYLES_DIR    := $(SRC_DIR)/styles
SRC_SCRIPTS_DIR   := $(SRC_DIR)/scripts
TMP_SCRIPTS_DIR   := $(TMP_DIR)/scripts
DEV_PUBLIC_DIR    := $(DEV_DIR)/public
DEV_STYLES_DIR    := $(DEV_DIR)/styles

alfredodeza

" I don't remember exactly how to make it not spit out the actual command you are running. Meh.
nnoremap u :call system("say -v whisper 'balls'")<CR>

mattvonrocketstein

#!/usr/bin/env python
""" pybcompgen

    Pybcompgen calculates context sensitive tab-completion data which is
    derived from environment bash system settings.  It doesn't need to know
    anything about whether you use /etc/completion or /etc/bash_completion.d,
    all that matters is whether *bash* knows about the completion.  The benefit
    of doing this are obvious: you get access to all the completion features
    that the system has installed without caring how the completion features
    work.  Note that this approach doesn't just work for things in the users

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

wolever

#!/bin/bash
# Reverts HEAD back to ORIG_HEAD, for example after a 'git pull' accidentally
# creates a merge. This is identical to running 'git reset --hard ORIG_HEAD',
# except that unpull prints some helpful information along the way.
# Useage:
#     $ git unpull
#          HEAD: a0ac0fd Merge branch 'master' of /tmp/foo
#                2284c9d some remote commit
#     ORIG_HEAD: 35431fd my local commit
#     Really reset HEAD to ORIG_HEAD? (y/n) y