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  <title>Kevin Blissett</title>
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  <updated>2026-08-24T00:00:00+00:00</updated>
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  <entry xml:lang="en">
    <title>A Very Good AI Dog</title>
    <published>2026-08-24T00:00:00+00:00</published>
    <updated>2026-08-24T00:00:00+00:00</updated>

    
    <author><name>Kevin Blissett</name></author>
    

    
    <link rel="alternate" type="text/html" href="https://pantel.is/projects/ai-gaming-companion/"/>
    <link rel="related" type="text/html" href="https://blog.kblissett.dev/ai-companion-dog/" title="Commentary by Kevin Blissett"/>
      
    <link rel="via" type="text/html" href="https://news.ycombinator.com/item?id=49413561" title="Via Hacker News"/>
      
    

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        &lt;p&gt;pantel.is · via Hacker News&lt;/p&gt;
      
      &lt;blockquote&gt;
&lt;p&gt;A real-time intelligent gaming companion that actually plays alongside you. World agency, local inference, persistent plans, and an AI character designed to create a striking emotional experience.&lt;&#x2F;p&gt;
&lt;&#x2F;blockquote&gt;
&lt;p&gt;This post was excellent. It&#x27;s the best plug-LLMs-into-a-game demo I think I&#x27;ve ever seen. What I found most interesting about it, though, was the combination of &quot;traditional&quot; ML with LLMs all wrapped up in a low-latency, (mostly?) locally served package.&lt;&#x2F;p&gt;
&lt;blockquote&gt;
&lt;p&gt;Through this whole AI-craze people have forgotten that we had intelligent systems that could process speech since the 1970s, and somewhat LLM-like behavior with chatbots like SmarterChild in the early 2000s. There&#x27;s a lost art that is being overlooked today in things like traditional NLP and behavioral graphs.&lt;&#x2F;p&gt;
&lt;&#x2F;blockquote&gt;
&lt;p&gt;I totally agree! The world of ML outside of LLMs has definitely drifted into underrated territory. People seem to have forgotten exactly how fast, cheap, and accurate a simple scikit-learn model can be.&lt;&#x2F;p&gt;
&lt;p&gt;Actual details on the heart of the system are frustratingly scarce.&lt;&#x2F;p&gt;
&lt;blockquote&gt;
&lt;p&gt;This is the secret sauce and biggest differentiator. I call this system (ALE - Action Latent Encoder because it&#x27;s an action encoder in need of a fun acronym). Under the hood, ALE is a hybrid of embeddings, small classifiers, explicit rules and traditional ML. ALE detects structure, identifies negation, commands, continuation, pronouns, and sequences […]&lt;&#x2F;p&gt;
&lt;&#x2F;blockquote&gt;
&lt;blockquote&gt;
&lt;p&gt;It creates embeddings from the full text as well as its extracted structure, then semantically combines and compares it with action prototypes. A separate classifier estimates whether the turn is a command, question, chat, clarification or complex request. Everything gets merged together.&lt;&#x2F;p&gt;
&lt;&#x2F;blockquote&gt;
&lt;p&gt;From the Hacker News comments:&lt;&#x2F;p&gt;
&lt;blockquote&gt;
&lt;p&gt;&lt;strong&gt;&lt;a rel=&quot;external&quot; href=&quot;https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=49415051&quot;&gt;rao-v&lt;&#x2F;a&gt;&lt;&#x2F;strong&gt;&lt;&#x2F;p&gt;
&lt;p&gt;I like the approach here. It’s cleverer than just “dump context to LLM”. I couldn’t quite figure out if it genuinely runs purely locally though […]&lt;&#x2F;p&gt;
&lt;blockquote&gt;
&lt;p&gt;&lt;strong&gt;&lt;a rel=&quot;external&quot; href=&quot;https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=49415214&quot;&gt;pantelisk&lt;&#x2F;a&gt;&lt;&#x2F;strong&gt;&lt;&#x2F;p&gt;
&lt;p&gt;Hello, author here. I was intentionally a little vague about this because this is kind of the thing that makes the whole approach work.&lt;&#x2F;p&gt;
&lt;&#x2F;blockquote&gt;
&lt;&#x2F;blockquote&gt;
&lt;p&gt;I wish the creator had been a little more open here.&lt;&#x2F;p&gt;

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