ianb

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.

kellabyte

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

lsloan

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

shawwn

This was a response to a Hacker News comment asking me what I've been up to since 2010. I'm posting it here since HN rejects it with "that comment is too long." I suppose that's fair, since this ended up being something of an autobiography.

--

What happened after 2010?

katef

#!/usr/bin/awk -f

# This program is a copy of guff, a plot device. https://github.com/silentbicycle/guff
# My copy here is written in awk instead of C, has no compelling benefit.
# Public domain. @thingskatedid

# Run as awk -v x=xyz ... or env variables for stuff?
# Assumptions: the data is evenly spaced along the x-axis

# TODO: moving average

johnhamelink

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")

abishekmuthian

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

########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

W4ngatang

''' Script for downloading all GLUE data.

Note: for legal reasons, we are unable to host MRPC.
You can either use the version hosted by the SentEval team, which is already tokenized, 
or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually.
For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example).
You should then rename and place specific files in a folder (see below for an example).

mkdir MRPC
cabextract MSRParaphraseCorpus.msi -d MRPC

evertrol

Makefile cheat sheet

Mostly geared towards GNU make

I've used ->| to indicate a tab character, as it's clearer to read than ⇥

• Set a target, its dependencies and the commands to execute in order

target: [dependencies]
->|