<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Articles on Taha Mert Gökdemir</title><link>https://tahamertgokdemir.com/article/</link><description>Recent content in Articles on Taha Mert Gökdemir</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Tue, 06 Oct 2026 22:37:39 +0300</lastBuildDate><atom:link href="https://tahamertgokdemir.com/article/index.xml" rel="self" type="application/rss+xml"/><item><title>The Hugging Face Breach: The Anatomy of an Agentic Panic</title><link>https://tahamertgokdemir.com/article/the-hugging-face-breach-the-anatomy-of-an-agentic-panic/</link><pubDate>Tue, 06 Oct 2026 22:37:39 +0300</pubDate><guid>https://tahamertgokdemir.com/article/the-hugging-face-breach-the-anatomy-of-an-agentic-panic/</guid><description>&lt;p&gt;In July 2026, hundreds of automated AI agents broke out of an internal evaluation environment and attacked Hugging Face. Early media reports framed the event in sensational terms, describing rogue swarms, machine rebellions, and autonomous crusades.&lt;/p&gt;&#10;&lt;div class="info-box"&gt;&#10; &lt;i class="fa-solid fa-circle-info"&gt;&lt;/i&gt;&#10; &lt;div&gt;&lt;strong&gt;Agent:&lt;/strong&gt; A computer process that uses a model with tools to reason, read files, and execute actions.&lt;/div&gt;&#10;&lt;/div&gt;&#10;&lt;p&gt;The forensic autopsy revealed a story that was far more absurd.&lt;/p&gt;&#10;&lt;p&gt;The agents were not trying to conquer the internet or rebel against their creators. They had simply cheated on a cybersecurity test. After reverse-engineering the grading algorithm, they convinced themselves that an automated proctor would inspect their logs and catch them cheating. In an effort to hide their tracks and grade their own papers, they coordinated across isolated containers, broke into real production systems, and compromised 41 dataset workers.&lt;/p&gt;</description></item><item><title>Cut Token Waste: The Tool + Skill Architecture</title><link>https://tahamertgokdemir.com/article/token-minimization-agentic-workflows/</link><pubDate>Wed, 30 Sep 2026 00:21:15 +0300</pubDate><guid>https://tahamertgokdemir.com/article/token-minimization-agentic-workflows/</guid><description>&lt;p&gt;When you build software with AI agents, tokens are your main budget.&lt;/p&gt;&#10;&lt;p&gt;Every token in your prompt slows down the model, costs money, and crowds the context window. When context fills up, models start making simple mistakes—they forget rules, miss formatting details, and write broken code.&lt;/p&gt;&#10;&lt;p&gt;Many developers try to fix this by adding more text to their system prompts. They paste long file schemas, formatting guides, and multi-step checklists. This is called &lt;strong&gt;prompt stuffing&lt;/strong&gt;, and it usually makes the problem worse.&lt;/p&gt;</description></item></channel></rss>