<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Greg Cordover</title><link>https://gregcordover.com/</link><description>Recent content on Greg Cordover</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><copyright>© 2022 Greg Cordover</copyright><lastBuildDate>Wed, 10 Aug 2022 00:00:00 +0000</lastBuildDate><atom:link href="https://gregcordover.com/index.xml" rel="self" type="application/rss+xml"/><item><title>Converting American Lighting Neon LED Strips To WLED</title><link>https://gregcordover.com/posts/converting-led-neon-strip/</link><pubDate>Wed, 10 Aug 2022 00:00:00 +0000</pubDate><guid>https://gregcordover.com/posts/converting-led-neon-strip/</guid><description>Costco recently had the American Lighting Color Changing Neon Flex Light LED strips on sale for $15 each1. Given that you can&amp;rsquo;t find a bare strip of addressable LEDs on Amazon for that price, a set of that cost including diffusion, power supply, and remote was too good to be true.
After getting a chance to look over the strips in person, I was pleasantly surprised by how good of a value they were.</description></item><item><title>Reverse Image Searching</title><link>https://gregcordover.com/posts/image-hashes/</link><pubDate>Tue, 18 Feb 2020 00:00:00 +0000</pubDate><guid>https://gregcordover.com/posts/image-hashes/</guid><description>Checking if files are identical is a trivial task. You could directly compare all their bytes or perhaps compute a hash of each file and compare those. Trying to compare the similarity of files&amp;rsquo; content is an entirely more difficult task. What does similarity mean? How can a computer determine if two photos contain the same content after getting resized or compressed from being reposted on Reddit and Twitter?
Due to the lossy nature of most image compression algorithms, you cannot compare the bytes of images to determine if they have the same content.</description></item><item><title>Acetone Smoothing 3D Printed Parts</title><link>https://gregcordover.com/posts/acetone-smoothing/</link><pubDate>Tue, 01 Aug 2017 00:00:00 +0000</pubDate><guid>https://gregcordover.com/posts/acetone-smoothing/</guid><description>A major inconvenience of 3D prints is trying to make them look better after they&amp;rsquo;re printed.
Most consumer 3D printers work by melting and extruding filament into a thin line of plastic and moving the print head relative to a bed to create shapes. This process is known as FDM. While this process is relatively cheap and easy, the motion and layers often result in defects and often result in visible layers.</description></item><item><title>Finding .git Folders</title><link>https://gregcordover.com/posts/finding-git-folders/</link><pubDate>Fri, 08 Jan 2016 00:00:00 +0000</pubDate><guid>https://gregcordover.com/posts/finding-git-folders/</guid><description>The Git version control system stores all its data about current and past revisions of files in a hidden folder, called .git. By convention, one does not include information such as passwords in Git and instead should use environmental variables or other configuration methods. However, due to poor programming practices and the general laziness of developers in major software platforms, it is common for developers to include sensitive information.
Most sane web servers will block access to hidden directories or files.</description></item><item><title>Btrfs</title><link>https://gregcordover.com/posts/btrfs/</link><pubDate>Sun, 06 Dec 2015 00:00:00 +0000</pubDate><guid>https://gregcordover.com/posts/btrfs/</guid><description>Retrieving data from a storage device is fairly simple, assuming an you know which track and sector the data is stored in.
However, it is simply impossible for a person to remember all of these positions. A modern hard drive can store 8x10¹² individual bits of data!
An Intro To Filesystems To solve the issue of remembering where files are on a disk, we have filesystems. One of the original filesystem is named FAT, or File Allocation Table.</description></item><item><title>Availability of Short Twitter Handles</title><link>https://gregcordover.com/posts/twitter-handles/</link><pubDate>Fri, 07 Aug 2015 00:00:00 +0000</pubDate><guid>https://gregcordover.com/posts/twitter-handles/</guid><description>Somewhat recently, I wrote a post about signing up for Twitter accounts using their private API. I also talked about how there were endpoints for checking username availability. These endpoints have no rate limiting, which enables us to do many fun things. One such thing is checking over 2,000,000 handles to see if they were still available.
On Twitter, short handles are a coveted thing. There are not many of them, and most have been taken.</description></item><item><title>Registering Accounts Using the Twitter API</title><link>https://gregcordover.com/posts/twitter-signup-api/</link><pubDate>Mon, 15 Jun 2015 00:00:00 +0000</pubDate><guid>https://gregcordover.com/posts/twitter-signup-api/</guid><description>Twitter uses an API for everything. Many people have seen the limitations of it, such as third party clients not being able to get favorites on Tweets or only 100,000 clients allowed per token. This API has many hidden methods and functions that most developers will never see, even though they are very easy to find. Official 1st party tokens have access to many methods you can find using an endpoint designed to show ratelimiting information.</description></item><item><title>Twitter Visualizations, Again</title><link>https://gregcordover.com/posts/twitter-visualizations-again/</link><pubDate>Wed, 13 Aug 2014 00:00:00 +0000</pubDate><guid>https://gregcordover.com/posts/twitter-visualizations-again/</guid><description>A while back, I did some visualizations about Twitter&amp;rsquo;s grouping.
Recently, I went back and ran some updates.
This time, instead of calculating groups, I mapped out the connections between groups.
This was the initial result, generated the same was as last time.
This image doesn&amp;rsquo;t really look too much different.
I then limited the connections to users who had just more than two mentions.
Here&amp;rsquo;s where it got interesting! It seems that there&amp;rsquo;s some groups that just talked once or twice.</description></item><item><title>Twitter Visualizations</title><link>https://gregcordover.com/posts/twitter-visualizations/</link><pubDate>Fri, 11 Apr 2014 00:00:00 +0000</pubDate><guid>https://gregcordover.com/posts/twitter-visualizations/</guid><description>Twitter is an interesting website, especially considering how basic its premise is: 140 character &amp;ldquo;status updates&amp;rdquo;. With these, you can talk about something briefly, share something interesting, or mention other users. I&amp;rsquo;m going to talk a bit about mentioning users here today.
Twitter, from my experience, is extremely cliquey. There are certain groups of people who mostly mention or &amp;ldquo;subtweet&amp;rdquo; (referring to a user without actually mentioning them) each other very frequently, and only rarely mention someone outside of this group.</description></item><item><title>About</title><link>https://gregcordover.com/about/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://gregcordover.com/about/</guid><description>Hi there! I have a lot of interests such as programming, automation, data visualization, electronics, and 3D printing.
Currently, my favorite programming language is Rust. I also frequently use Python and Go depending on the nature of the project. I greatly enjoy the level of automation in my personal programming projects which goes all the way from testing to deployment using software like Drone and Docker. I&amp;rsquo;d love to get Kubernetes in my stack somewhere but I don&amp;rsquo;t have enough servers, projects, or load to give it a real try.</description></item></channel></rss>