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@@ -100,8 +100,16 @@
         <h2 class="title is-3">WOPR in brief</h2>
         <div class="content has-text-justified">
           <p>
-            WOPR, Word Predictor, is a memory-based language model developed in 2006-2011,
-            and woken up from its cryogenic sleep in a more suitable era.
+            WOPR, Word Predictor, is a memory-based language model developed in 2006-2011.
+            It just woke up from its cryogenic sleep and is figuring out what is
+            all the fuss about LLMs.
+          </p>
+          <p>
+            WOPR is an ecologically friendly alternative LLM with a staggeringly simple
+            core. Everyone who took "Machine Learning 101" knows that the <i>k</i>-nearest
+            neighbor classifier is among the simplest yet most robust ML classifiers out
+            there, perhaps only beaten by the Naive Bayes classifier. So what happens if
+            you train a <i>k</i>-NN classifier to predict words? ...
           </p>
           <p>
             A memory-based language model, in this case running on the 
@@ -120,13 +128,14 @@
               and compressing it into a decision tree structure. This can be done on CPUs,
               with sufficient RAM. In short, its <b>ecological footprint is dramatically lower</b>;</li>
             <li>pretty efficient in generation when running with the fastest decision-tree
-              approximations of <i>k</i>-NN classification. This can be done on CPUs as well.</li>
-            <li>completely transparent in their functioning. There is also no question about
+              approximations of <i>k</i>-NN classification. <b>This can be done on CPUs as well</b>;</li>
+            <li>completely transparent in their functioning. There can also be no doubt about
               the fact that <b>they memorize training data patterns</b>.</li>
           </ul>
           <p>On the downside,</p>
           <ul>
-            <li>Not as great as current Transformer-based LLMs, but we have not trained
+            <li><b>Their performance is currently not as great as current Transformer-based LLMs</b>, 
+              but we have not trained
               beyond data set sizes with orders of magnitudes above 100 million words.
               Watch this space!</li>
             <li>Memory requirements during training are <b>heavy with large datasets</b>