<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet href="/rss/styles.xsl" type="text/xsl"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Alec Scollon</title><description>The personal website and blog of Alec Scollon.</description><link>https://www.alecscollon.com/</link><item><title>I Think I Have LLM Burnout</title><link>https://www.alecscollon.com/blog/llm-burnout/</link><guid isPermaLink="true">https://www.alecscollon.com/blog/llm-burnout/</guid><description>I didn&apos;t expect to get so tired of reading LLM output.</description><pubDate>Thu, 09 Jul 2026 01:00:00 GMT</pubDate><content:encoded>&lt;p&gt;I use LLMs a lot. By current dev standards, my usage rate is probably average,
and my methods are probably primitive. I work on one task at a time and discuss
it with Claude Code (at work) or Codex (at home, for now). Sometimes, I let the
assistant write code, but I read the output thoroughly, understand it, and
revise it. I&apos;m not in the deep end of autonomous agents or agent orchestration.
Still, I spend hours each day interacting with LLMs across work and home.
That&apos;s a hell of a lot more than I did a few years ago, and I probably don&apos;t go
a day without reading AI-generated text.&lt;/p&gt;
&lt;p&gt;My job has changed from designing and writing code to designing code,
describing the design to an LLM, reviewing code the LLM produces, and then
finally writing code. The LLM steps expose me to approaches I might not have
considered or been aware of. I also feel more comfortable in areas where I
don&apos;t have deep knowledge.&lt;/p&gt;
&lt;p&gt;My main project right now is to establish a framework for large-scale,
unsupervised code generation in our codebase. When I&apos;m not working with Claude
to create tooling, I&apos;m sifting through the unsupervised agent&apos;s (Qwen&apos;s)
output. Either way, I&apos;m reading LLM content.&lt;/p&gt;
&lt;p&gt;If I want to know something, I&apos;ll probably ask ChatGPT or read Gemini&apos;s
overview unless I know what sites I want to check. I still have to fall back to
browsing when the LLM&apos;s answer is wrong, but it&apos;s good enough for many casual
queries, especially when useless AI-generated articles clutter the search
results.&lt;/p&gt;
&lt;p&gt;It&apos;s been this way for about a year, and I don&apos;t see myself stopping. I feel
more productive with LLMs, and I think continually learning how to use them
effectively is valuable. However, my disposition has changed a bit in the last
few months. Some small part of me has started to dread reading LLM output
because I know what I&apos;m going to find. False assumptions and hallucinations.
Emphatic, staccato fragments. ✨ Excessive emojis 🚀. It&apos;s not
just me—these are real patterns (🤮).&lt;/p&gt;
&lt;p&gt;On their own, none of these annoyances gets to me. Together, though, they&apos;ve
gotten me sick of LLM writing in a hurry.&lt;/p&gt;
&lt;p&gt;I&apos;m not trying to condemn LLMs. Humans are fallible, too—we can be just
as unreliable or annoying. The problem is repetition. LLMs write in the same
style, and they make the same kinds of mistakes. Dealing with the same thing
over and over is wearing me out. I can use personalization features if the
interface offers them, but some idiosyncrasies seep through. And of course, I
don&apos;t control the style of content generated by other people.&lt;/p&gt;
&lt;p&gt;I don&apos;t know how to deal with this feeling yet. I didn&apos;t expect to be so
bothered by it. Frustration at a flaky tool is understandable, but the writing
patterns grind my gears, too. For now, I&apos;ll grit my teeth and hope I don&apos;t lose
my lunch.&lt;/p&gt;
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