<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Further Reading on Freenet</title><link>https://freenet.org/build/manual/further-reading/</link><description>Recent content in Further Reading on Freenet</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 30 Nov 2024 00:00:00 +0000</lastBuildDate><atom:link href="https://freenet.org/build/manual/further-reading/feed.xml" rel="self" type="application/rss+xml"/><item><title>Understanding Freenet's Delta-Sync</title><link>https://freenet.org/build/manual/further-reading/delta-sync/</link><pubDate>Sat, 30 Nov 2024 00:00:00 +0000</pubDate><guid>https://freenet.org/build/manual/further-reading/delta-sync/</guid><description>&lt;h3 id="the-challenge-of-consistency-in-distributed-systems">The Challenge of Consistency in Distributed Systems&lt;/h3>
&lt;p>Achieving consistency across distributed systems is a notoriously difficult problem. The key reason
is that, in a distributed environment, multiple nodes can independently make changes to the same
piece of data. When different nodes hold different versions of this data, deciding how to reconcile
these differences without losing valuable updates or introducing conflicts becomes a complex
challenge.&lt;/p>
&lt;p>Traditional approaches often require coordination mechanisms, such as &lt;strong>consensus algorithms&lt;/strong> (like
Paxos or Raft), to ensure consistency. However, these methods can be resource-intensive, require
high communication overhead, and often struggle with scalability, especially when dealing with
frequent updates across many nodes. The famous &lt;strong>CAP theorem&lt;/strong> even states that distributed systems
can only guarantee two of three properties (&lt;strong>Consistency, Availability, and Partition Tolerance&lt;/strong>) at
any given time, making it hard to achieve strong consistency while keeping a system always available
and partition-tolerant.&lt;/p></description></item><item><title>Understanding Small World Networks</title><link>https://freenet.org/build/manual/further-reading/small-world-networks/</link><pubDate>Mon, 25 Nov 2024 00:00:00 +0000</pubDate><guid>https://freenet.org/build/manual/further-reading/small-world-networks/</guid><description>&lt;div style="float: right; margin-left: 20px; margin-bottom: 10px; max-width: 300px; width: 100%;">
 &lt;img src="https://freenet.org/img/handing-letter-sw.webp" alt="Handing a Letter" style="width: 100%; border: 1px solid #ccc; border-radius: 5px; box-shadow: 2px 2px 10px rgba(0,0,0,0.1);">
&lt;/div>
&lt;p>In the 1960s psychologist Stanley Milgram conducted an influential experiment that revealed
something amazing about human relationships. Milgram chose people at random in cities like Kansas
and gave each a letter with the address of someone they didn&amp;rsquo;t know in Boston, Massachusetts. They
were instructed to get the letter to that person but only by sending it to someone they know
personally, who would send it to someone they know personally - and so on. Milgram repeated this
letter-sending experiment nearly 200 times. On average, these letters reached their target in just
six steps, this is where we get the term &amp;lsquo;six degrees of separation.&amp;rsquo; Milgram&amp;rsquo;s findings
demonstrated that despite the vastness of the world, most individuals are only a few links away from
each other, highlighting the surprisingly small number of intermediaries connecting us all.&lt;/p></description></item></channel></rss>