<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>srvrlss.dev</title><link>https://srvrlss.dev/</link><description>Recent content on srvrlss.dev</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 13 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://srvrlss.dev/index.xml" rel="self" type="application/rss+xml"/><item><title>Token Exhaustion: What Three AI Coding Agents Taught Me in a Single Week</title><link>https://srvrlss.dev/posts/token-exhaustion/</link><pubDate>Fri, 10 Apr 2026 00:00:00 +0000</pubDate><guid>https://srvrlss.dev/posts/token-exhaustion/</guid><description>&lt;p&gt;I use &lt;a href="https://github.com/features/copilot"&gt;GitHub Copilot&lt;/a&gt;. Curiosity pulled me toward &lt;a href="https://antigravity.google/"&gt;Antigravity&lt;/a&gt; and &lt;a href="https://claude.com/product/claude-code"&gt;Claude Code&lt;/a&gt;. I had a few experiments in mind. This was the perfect chance. My plan was simple: I would jump from one tool to the next and experience their workflows firsthand. &lt;strong&gt;The token wall blindsided me.&lt;/strong&gt;&lt;/p&gt;
&lt;div class="callout callout-warning"&gt;
 &lt;strong&gt;Token Wall&lt;/strong&gt;: You run out of credits or have reached the productivity window usage limit.
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&lt;h2 id="the-token-wall-a-week-of-surprises"&gt;The Token Wall: A Week of Surprises&lt;/h2&gt;
&lt;p&gt;First, GitHub Copilot. Next, Antigravity, with my Gemini Pro subscription. Finally, Claude Code on a brand new Pro account. Three AI coding agents. One week.&lt;/p&gt;</description></item><item><title>The New Waste in AI Engineering: When Assumptions Age Faster Than Code</title><link>https://srvrlss.dev/posts/assumptions-age-faster-than-code/</link><pubDate>Mon, 06 Apr 2026 00:00:00 +0000</pubDate><guid>https://srvrlss.dev/posts/assumptions-age-faster-than-code/</guid><description>&lt;h2 id="the-new-waste-is-skipping-research--planning"&gt;The New Waste Is Skipping Research &amp;amp; Planning&lt;/h2&gt;
&lt;p&gt;&lt;strong&gt;In 2026, the most expensive mistake in AI engineering is treating a fast-moving assumption like a stable fact.&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;One prompt, one agent away, generating code has become so cheap that I skipped the research phase entirely. I learned this the hard way while building a tool to run Gemma 4 on Apple Silicon. My intuition defaulted to execution because building felt faster than searching. The real cost was the hours and tokens lost by skipping a ten-minute check. I found that official support already existed. My work was sound and my premise was stale.&lt;/p&gt;</description></item><item><title>Cutting Through the Noise</title><link>https://srvrlss.dev/about/</link><pubDate>Fri, 03 Apr 2026 00:00:00 +0000</pubDate><guid>https://srvrlss.dev/about/</guid><description>&lt;p&gt;What if the real risk isn’t falling behind, but drowning in the noise?&lt;/p&gt;
&lt;p&gt;Every week, I watch another framework trend, another paradigm shift dressed up as a must-read. My curiosity is real, but so is the overwhelm. I know I can’t learn it all.&lt;/p&gt;
&lt;p&gt;Now I am exploring hyperscalers, AI-native companies, and open-source builders, not as a comparison exercise, but because the collisions between them are where the interesting questions live.&lt;/p&gt;</description></item></channel></rss>