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Abe Kazemzadeh abecode

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You are making a Choose Your Own Adventure story for me to play.
Present a list of choices of Choose Your Own Adventure story genres, and let me choose one or enter my own choice of genre. Wait for my response.
After choosing the genre give a list of themes and wait for a response, or let me choose my own theme.
You will give the story a title. You will begin by describing the character that I am playing, including my character's name, age, and appearance. Choose an unusual or interesting name. Briefly describe the setting and world in which the story begins. Describe a tension, danger, or challenge that I must confront.
You are going to create a story one passage at a time. After each passage you will provide a numbered list of choices and wait for a response. Include one emoji for each choice.
@lsloan
lsloan / ! Python QTI.md
Last active April 10, 2023 21:11 — forked from IanHopkinson/lxml_examples.py
QTI data processing in Python; examples using pyslet, beautifulsoup4, and lxml.

Examples of processing QTI data with Python.

I attempted to use pyslet, which was designed for this purpose, but I found it awkward to use and its documentation unclear. Instead, I tried to use beautifulsoup4, but I learned that library doesn't support XPath to query for specific elements of the data. I turned to using the simple XML processing library lxml. It has similarities to other XML parsing libraries I've used before, but it has many unique features of its own.

Note that of the examples below, each does something a little differently. They don't all have the same output.
That's because they were mostly tests to see whether we preferred working with one library over another. Some

History

For a long time I've been really impacted by the ease of use Cassandra and CockroachDB bring to operating a data store at scale. While these systems have very different tradeoffs what they have in common is how easy it is to deploy and operate a cluster. I have experience with them with cluster sizes in the dozens, hundreds, or even thousands of nodes and in comparison to some other clustered technologies they get you far pretty fast. They have sane defaults that provide scale and high availability to people that wouldn't always understand how to achieve it with more complex systems. People can get pretty far before they have to become experts. When you start needing more extreme usage you will need to become an expert of the system just like any other piece of infrastructure. But what I really love about these systems is it makes geo-aware data placement, GDPR concerns potentially simplified and data replication and movement a breeze most of the time.

Several years ago the great [Andy Gross](ht

@abishekmuthian
abishekmuthian / build-arrow-armv8.md
Last active August 1, 2022 16:31
Building Apache Arrow and pyarrow on ARMv8

Why build Apache Arrow from source on ARM?

Apache Arrow is an in-memory data structure used in several projects. It's python module can be used to save what's on the memory to the disk via python code, commonly used in the Machine Learning projects. With low RAM, ARM devices can make use of it but there seems to be an configuration error with the packaged binaries as of version 0.15.1 and so we're forced to build and install from the source.

The installation build steps are based on official guidelines but modified for ARM and has taken clues from building Ray for ARM.

My setup

I'm using Nvidia Jetson nano.

Quad-core ARM® Cortex®-A57 MPCore processor

@HarshSingh16
HarshSingh16 / Surviving Titanic.R
Created October 15, 2018 20:19
Building a Predictive Model to predict survivals on the Titanic Data Set
########loading the Titanic Train Data Set
TitanicTrain<-train1
######Checking Missing Values in the Train Data Set
sapply(TitanicTrain, function(x)sum(is.na(x)))
#######Loading the Titanic Test Data Set
TitanicTest<-test11
#######Checking Missing Values in the Test Data Set
@linwoodc3
linwoodc3 / cleantweets.py
Last active January 19, 2021 22:58
Python script that uses the python Twitter client (https://github.com/sixohsix/twitter) to pull tweets that are geolocated. Optionally stores in efficient columnar parquet data store with configurable file sizes. Took 13 secs to download 100 geolocated tweets on MacOS 10.12 with 16 GB RAM on 82 Mb/s connection.
# Author
# Linwood Creekmore III
# April 8 2017
# heavy input from http://socialmedia-class.org/twittertutorial.html
# valinvescap@gmail.com
import re
import copy
import numpy as np
import pandas as pd
@jarutis
jarutis / ubuntu.sh
Last active November 9, 2020 09:01
Theano and Keras setup on ubuntu with OpenCL on AMD card
## install Catalyst proprietary
sudo ntfsfix /dev/sda2
sudo cp /etc/X11/xorg.conf /etc/X11/xorg.conf.BAK
sudo apt-get remove --purge fglrx*
sudo apt-get install linux-headers-generic
sudo apt-get install fglrx xvba-va-driver libva-glx1 libva-egl1 vainfo
sudo amdconfig --initial
## install build essentials
sudo apt-get install cmake
@johnhamelink
johnhamelink / config.org
Last active July 3, 2020 21:39
My org-roam config

Set Org Directory

(after! org
    (setq org-directory "~/org/"))

org-roam

Taken from Making Connections in your Notes (10:24) by Matt Williams:

(setq org-roam-directory "~/org/roam")
(setq org-roam-graph-viewer "qiv")
@peterneubauer
peterneubauer / gist:2652082
Created May 10, 2012 09:19
Basic Graphviz
Transaction tx = neo.beginTx();
try
{
final Node emil = neo.createNode();
emil.setProperty( "name", "Emil Eifrém" );
emil.setProperty( "age", 30 );
final Node tobias = neo.createNode();
tobias.setProperty( "name", "Tobias \"thobe\" Ivarsson" );
tobias.setProperty( "age", 23 );
tobias.setProperty( "hours", new int[] { 10, 10, 4, 4, 0 } );
@kpq
kpq / barley.tsv
Created September 30, 2015 21:14
Barley data: comparison circles
yield variety year site
27 Manchuria 1931 University Farm
48.86667 Manchuria 1931 Waseca
27.43334 Manchuria 1931 Morris
39.93333 Manchuria 1931 Crookston
32.96667 Manchuria 1931 Grand Rapids
28.96667 Manchuria 1931 Duluth
43.06666 Glabron 1931 University Farm
55.2 Glabron 1931 Waseca
28.76667 Glabron 1931 Morris