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<category>Marketing</category>
<category>SEO</category>
<category>Online Publishing</category>
<category>LLM Optimization</category>
<category>AEO</category>
<category>Software Application</category>
<ss:platform>YouTube</ss:platform>
<ss:platform>Reddit</ss:platform>

<category>Artificial Intelligence</category>
<category>State of AI</category>
<category>Hallucinations</category>
<category>Hybrid Search</category>
<category>RAG</category>
<category>Mixture of Experts</category>
<category>LLM efficiency</category>


<item>

<link>https://solution-smith.com/seo-practice/headlines/</link>
<title>Headlines and sub-headlines for AI-aware SEO</title>
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update 8-13-26
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<pubDate>Fri, 14 Aug 2026 02:33:00 GMT</pubDate>

<category>SEO</category>
<category>AEO</category>

<description><![CDATA[
  <p>
Traditional SEO often treats headlines as locations for keywords... AI systems treat headlines as scoping qualifiers that provide critical information through structure. The headline hierarchy aids AI systems to locate, interpret, mention, or cite information from a page for AEO.
  </p>
]]></description>

</item>

<item>

<link>https://solution-smith.com/state-of-ai/common-knowledge/</link>
<title>LLMs, data compression, the effect on AEO and SEO</title>
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https://solution-smith.com/state-of-ai/common-knowledge/
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<pubDate>Mon, 10 Aug 2026 04:43:00 GMT</pubDate>

<category>AEO</category>
<category>LLM efficiency</category>

<description><![CDATA[
  <p>
    LLMs compress data by consolidating repeated knowledge spread across the internet. Pages that rely solely on common knowledge are unlikely to earn AI citations and often struggle for long-term search visibility.
  </p>
  <p>
    This page explores the "Mount AI" traffic pattern—where content built strictly on common knowledge loses visibility as search engines evaluate it for low information gain.
  </p>
]]></description>

</item>

<item>

<link>https://solution-smith.com/seo-practice/LLM-Optimization/</link>
<title>Gain of Knowledge Through Specificity</title>
<guid isPermaLink="false">revised-7-31-26</guid>
<pubDate>Fri, 31 Jul 2026 21:20:00 GMT</pubDate>

<category>LLM Optimization</category>
<category>AEO</category>
<category>Marketing</category>

<description><![CDATA[
<p>
Providing specific details about the subject or entity being discussed is fundamental to LLM optimization. As additional information is introduced, the important words and phrases associated with that subject naturally appear more often. The resulting increase in keyword density is therefore a byproduct of expanding the reader's knowledge rather than the objective itself. The goal is not to write more text, but to communicate more useful information about the subject.
</p>
  <p>
    For the subject of an office glass door, that knowledge may include the types of glass available, standard sizes, safety requirements, replacement options, installation considerations, pricing, labor costs, and how quickly the entrance can be returned to service. Each additional detail provides the LLM with richer contextual data, allowing it to extract precise answers and accurately map the topic's full semantic scope.
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]]></description>

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<item>

<link>https://solution-smith.com/seo-practice/keyword-density/</link>
<title>Keyword Density in Modern AEO</title>
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https://solution-smith.com/seo-practice/keyword-density/
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<pubDate>Fri, 31 Jul 2026 22:23:00 GMT</pubDate>

<category>SEO</category>
<category>AEO</category>

<description><![CDATA[
<p>
Traditional SEO often treated keyword density as an optimization target. The AEO perspective presented in this guide argues that keyword frequency is largely a byproduct of explicitly communicating facts, attributes, and relationships about an entity. As a document provides more useful knowledge, important entities are naturally referenced more often.
</p>
<p>
The article explores why this interpretation differs from traditional SEO guidance. Rather than optimizing for keyword density itself, it argues that the real objective is reducing the amount of inference an AI system must perform by making important facts and relationships explicit. Keyword frequency remains useful, but primarily as a signal that often accompanies a document with a high gain of knowledge.
</p>
]]></description>

</item>


<item>

<link>https://solution-smith.com/seo-practice/keyword-density/</link>
<title>Keyword Density in Modern AEO</title>
<guid isPermaLink="true">
https://solution-smith.com/seo-practice/keyword-density/
</guid>
<pubDate>Thu, 30 Jul 2026 22:23:00 GMT</pubDate>

<category>SEO</category>
<category>AEO</category>

<description><![CDATA[
<p>
Traditional SEO often treated keyword density as an optimization target. The AEO perspective presented in this guide argues that keyword frequency is largely a byproduct of explicitly communicating facts, attributes, and relationships about an entity. As a document provides more useful knowledge, important entities are naturally referenced more often.
</p>
<p>
The article explores why this interpretation differs from traditional SEO guidance. Rather than optimizing for keyword density itself, it argues that the real objective is reducing the amount of inference an AI system must perform by making important facts and relationships explicit. Keyword frequency remains useful, but primarily as a signal that often accompanies a document with a high gain of knowledge.
</p>
]]></description>

</item>


<item>

<link>https://solution-smith.com/state-of-ai/llm-scalability/</link>
<title>LLM Advancements Affecting Scalability and Performance</title>
<guid isPermaLink="true">
https://solution-smith.com/state-of-ai/llm-scalability/
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<pubDate>Thu, 23 Jul 2026 07:10:00 GMT</pubDate>

<category>Mixture of Experts</category>
<category>LLM efficiency</category>

<description><![CDATA[
  <p>
Many people look at systems such as Google's AI Overviews and assume that current AI capabilities represent the limits of what large language models can achieve. In reality, both hardware and software are advancing rapidly, enabling models to run faster, scale to larger parameter counts, and operate on hardware that would have been impractical only a short time ago.
</p>
<p>
Recent advances include Mixture of Experts (MoE) architectures, low-bit quantization, and improved memory management. Together, these techniques reduce computational requirements, shrink storage footprints, and improve the scalability and efficiency of large language models.
</p>
<p>
Running a 744-billion-parameter large language model (LLM) locally on a laptop using only a CPU and 25 gigabytes of RAM, with no connection to a data center, may seem impossible. In fact, it has been demonstrated, although the resulting performance is far below what would be considered practical for everyday use.
</p>
]]></description>

</item>


<item>
<link>https://solution-smith.com/state-of-ai/hybrid-search-model/</link>
<title>AEO for Mentions and Citations</title>
<guid isPermaLink="false">AEO-07-16-26</guid>
<pubDate>Sat, 18 Jul 2026 23:59:00 GMT</pubDate>
<category>Hybrid Search</category>
<category>RAG</category>
<category>Artificial Intelligence</category>

<description><![CDATA[
  <p>
To understand SEO, AEO, and make informed predictions about how content may perform in Google Search, it helps to have a basic mental model of how Google's search system works. Every experienced SEO relies on some version of a model when creating content, evaluating rankings, or recommending link-building strategies.
</p>
<p>
These models are not proprietary. They are built from publicly available information, observation, testing, patents, research papers, and years of practical experience. There is little reason for SEO agencies to treat them as trade secrets. Solution Smith believes transparency benefits both the SEO community and clients and openly shares the models used to explain and guide modern search optimization.
  </p>
]]></description>

</item>




<item>

<link>https://solution-smith.com/state-of-ai/spambrain/</link>
<title>SpamBrain; AI-Based Keyword Stuffing Detection</title>
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https://solution-smith.com/state-of-ai/spambrain/
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<pubDate>Sat, 18 Jul 2026 23:59:00 GMT</pubDate>
<category>SpamBrain</category>

<description><![CDATA[
  <p>
An examination of Google's SpamBrain and AI-based keyword stuffing detection using publicly available information, observable behavior, and controlled testing. The objective is to better understand how modern search systems distinguish naturally written content from manipulative keyword repetition.
</p>
<p>
It is not intended to recreate, simulate, or reverse engineer SpamBrain. Instead, it presents an open and transparent model that explains the system's observable behavior while clearly distinguishing documented facts from informed inference. SpamBrain and AI based search systems has fundamentally changed how keyword density should be viewed.
  </p>
]]></description>

</item>


<item>

<link>https://solution-smith.com/state-of-ai/hybrid-search-model/</link>
<title>Google's Hybrid AI / Search Engine Model</title>
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https://solution-smith.com/state-of-ai/hybrid-search-model/
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<pubDate>Sat, 04 Jul 2026 19:25:00 GMT</pubDate>
<category>Hybrid Search</category>
<category>RAG</category>
<category>Artificial Intelligence</category>

<description><![CDATA[
  <p>
To understand SEO, AEO, and make informed predictions about how content may perform in Google Search, it helps to have a basic mental model of how Google's search system works. Every experienced SEO relies on some version of a model when creating content, evaluating rankings, or recommending link-building strategies.
</p>
<p>
These models are not proprietary. They are built from publicly available information, observation, testing, patents, research papers, and years of practical experience. There is little reason for SEO agencies to treat them as trade secrets. Solution Smith believes transparency benefits both the SEO community and clients and openly shares the models used to explain and guide modern search optimization.
  </p>
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