<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Fernando Irarrázaval</title><link>https://fernandoi.cl/</link><description>Recent content on Fernando Irarrázaval</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 25 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://fernandoi.cl/index.xml" rel="self" type="application/rss+xml"/><item><title>What happened after 2,000 people tried to hack my AI assistant</title><link>https://fernandoi.cl/posts/hackmyclaw/</link><pubDate>Thu, 25 Jun 2026 00:00:00 +0000</pubDate><guid>https://fernandoi.cl/posts/hackmyclaw/</guid><description>&lt;p>I built &lt;a href="https://hackmyclaw.com">hackmyclaw.com&lt;/a>, where anyone could email Fiu, my OpenClaw assistant, and try to make it leak the contents of a &lt;code>secrets.env&lt;/code> file.&lt;/p>
&lt;p>After reaching the front page of Hacker News, Fiu received more than 6,000 emails from over 2,000 people trying to break it.&lt;/p>
&lt;p>The secrets never leaked. No attacker managed to make Fiu send an unauthorized reply.&lt;/p>
&lt;h2 id="the-setup">The setup&lt;/h2>
&lt;p>I enjoy using OpenClaw and Hermes, but I&amp;rsquo;m also concerned about the security implications. AI assistants have access to emails, calendars, files, and the web. If an attacker can trick your AI into doing something it shouldn&amp;rsquo;t, that&amp;rsquo;s bad news.&lt;/p></description></item><item><title>Why Bayesian Statistics</title><link>https://fernandoi.cl/posts/why-bayesian-statistics/</link><pubDate>Fri, 23 Jul 2021 00:00:00 +0000</pubDate><guid>https://fernandoi.cl/posts/why-bayesian-statistics/</guid><description>&lt;p>It is hard to describe the benefits of Bayesian statistics. So let me show them by describing a problem I solved using Bayesian statistics, which would be hard to solve with traditional statistics.&lt;/p>
&lt;h2 id="the-problem">The problem&lt;/h2>
&lt;p>When COVID struck, &lt;a href="www.enveritas.org">Enveritas&lt;/a> helped governments measure how fast the disease was spreading.&lt;/p>
&lt;p>Since this was early in the COVID pandemic, there wasn&amp;rsquo;t a lot of data about how widespread COVID-19 was in Ethiopia, rural areas, and Africa in general. Without data, governments couldn&amp;rsquo;t decide: should they lock down?&lt;/p></description></item><item><title>The reason I am using Altair for most of my visualization in Python</title><link>https://fernandoi.cl/posts/altair/</link><pubDate>Sat, 04 May 2019 00:00:00 +0000</pubDate><guid>https://fernandoi.cl/posts/altair/</guid><description>&lt;p>Sadly, in Python, we do not have a ggplot2.&lt;/p>
&lt;p>Python&amp;rsquo;s go to visualization library, matplotlib, is &lt;strong>very powerful&lt;/strong>&lt;sup id="fnref:1">&lt;a href="#fn:1" class="footnote-ref" role="doc-noteref">1&lt;/a>&lt;/sup> but has severe limitations. At times its flexibility is a blessing, but it is easy to get frustrated adding a small feature to your graph. Also, matplotlib dual &lt;a href="https://matplotlib.org/tutorials/introductory/lifecycle.html">object oriented and state-based interface&lt;/a> is confusing. I still don&amp;rsquo;t completely grasp it even though I have been using matplotlib for years. Lastly, it makes only static graphs.&lt;/p></description></item></channel></rss>