<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning on Pocket Dev</title><link>/tags/learning/</link><description>Recent content in Learning on Pocket Dev</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 01 May 2025 20:14:24 +0000</lastBuildDate><atom:link href="/tags/learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Level Up Your Tech Skills: Recreating Old Projects in New Frameworks</title><link>/posts/level-up-tech-skills-recreating-projects/</link><pubDate>Thu, 01 May 2025 20:14:24 +0000</pubDate><guid>/posts/level-up-tech-skills-recreating-projects/</guid><description>&lt;p&gt;One of the biggest challenges as a developer is staying ahead of the curve with new frameworks and languages. Technology evolves quickly, and yesterday&amp;rsquo;s hot framework can feel like today&amp;rsquo;s relic. If you&amp;rsquo;ve ever struggled with learning new tech, let me share a technique that has worked wonders for me: &lt;strong&gt;recreating old projects in new frameworks&lt;/strong&gt;. This method combines familiarity with your past work and the opportunity to expand your skill set.&lt;/p&gt;</description></item><item><title>Navigating Linked Lists: Sequential Flexibility Explained</title><link>/posts/navigating-linked-lists-sequential-flexibility-explained/</link><pubDate>Wed, 19 Mar 2025 19:23:00 +0000</pubDate><guid>/posts/navigating-linked-lists-sequential-flexibility-explained/</guid><description>&lt;h2 id="understanding-data-structures-through-big-o-notation"&gt;Understanding Data Structures Through Big O Notation&lt;/h2&gt;
&lt;p&gt;In our previous discussion on &lt;strong&gt;Big O notation&lt;/strong&gt;, we explored how it helps us measure the efficiency of algorithms. Now, let’s take the next logical step: &lt;strong&gt;understanding data structures in the context of Big O.&lt;/strong&gt;&lt;/p&gt;</description></item><item><title>Mastering the Basics: Arrays from the Ground Up</title><link>/posts/data-structures-series-arrays/</link><pubDate>Thu, 13 Mar 2025 02:29:45 +0000</pubDate><guid>/posts/data-structures-series-arrays/</guid><description>&lt;h2 id="understanding-data-structures-through-big-o-notation"&gt;&lt;strong&gt;Understanding Data Structures Through Big O Notation&lt;/strong&gt;&lt;/h2&gt;
&lt;p&gt;In our previous discussion on &lt;strong&gt;Big O notation&lt;/strong&gt;, we explored how it helps us measure the efficiency of algorithms. Now, let’s take the next logical step: &lt;strong&gt;understanding data structures in the context of Big O.&lt;/strong&gt;&lt;/p&gt;</description></item><item><title>Big O Notation Explained: A Simple Guide to Algorithm Complexity</title><link>/posts/big-o-notation-explained-algorithm-complexity/</link><pubDate>Wed, 05 Feb 2025 13:56:00 +0000</pubDate><guid>/posts/big-o-notation-explained-algorithm-complexity/</guid><description>&lt;h1 id="understanding-big-o-notation-a-simple-comparison"&gt;&lt;strong&gt;Understanding Big O Notation: A Simple Comparison&lt;/strong&gt;&lt;/h1&gt;
&lt;p&gt;This post is part of my &lt;strong&gt;data structures and algorithms refresher series&lt;/strong&gt;, where I revisit essential concepts that every developer should understand. One of the most important is &lt;strong&gt;Big O notation&lt;/strong&gt;—a way to measure how an algorithm&amp;rsquo;s efficiency scales as input size increases.&lt;/p&gt;</description></item></channel></rss>