<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>command line interface on Micah Yong</title><link>https://www.micahyong.com/tags/command-line-interface/</link><description>Recent content in command line interface on Micah Yong</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><copyright>Copyright © 2023, Micah Yong; all rights reserved.</copyright><lastBuildDate>Tue, 20 Jul 2021 22:00:26 -0700</lastBuildDate><atom:link href="https://www.micahyong.com/tags/command-line-interface/index.xml" rel="self" type="application/rss+xml"/><item><title>A distributed programmer's chief assistant</title><link>https://www.micahyong.com/post/cli-for-distributed-systems/</link><pubDate>Tue, 20 Jul 2021 22:00:26 -0700</pubDate><guid>https://www.micahyong.com/post/cli-for-distributed-systems/</guid><description>In this piece, I&amp;rsquo;ll talk about the unique challenges of debugging distributed programs and how we can build a command line interface (CLI) to be a distributed programmer&amp;rsquo;s chief assistant.
Infinite scaling, impossible debugging Suppose I would like to build a memory-intensive 2D array in Python with a single CPU:
def f(x): return [i for i in range(x)] lists = [f(i) for i in range(100000000)] print(lists) Upon running, I get a MemoryError.</description></item></channel></rss>