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+<article class="article">
+  <div class="eyebrow">Python · Machine learning · Experiments</div>
+  <h1>Experimenting with Poe's Python Interpreter</h1>
+  <p class="dek">A tiny interpreter, a handful of neural networks, one very confused Markov chain, and a surprisingly affordable price tag.</p>
+  <div class="byline">
+    <span>By an overly curious tinkerer</span><span class="dot"></span>
+    <span>Experiment notes</span><span class="dot"></span>
+    <span>Updated with timeout test</span>
+  </div>
+
+  <div class="hero-art">
+    <div class="hero-copy">
+      <small>Field notes / 001</small>
+      <h2>How much can you do with a Python box?</h2>
+      <p>I found a little Python interpreter on Poe and, naturally, started throwing machine-learning experiments at it.</p>
+    </div>
+    <div class="orbit"></div>
+  </div>
+
+  <section class="reveal">
+    <p class="lead">I recently discovered <strong>Python</strong>, (they really could've thought of a better name) a Python interpreter bot on Poe by the official @poe account, and decided to see how much I could actually do with its environment.</p>
+    <p>I ended up going down a pretty interesting rabbit hole.</p>
+    <p>Using Python 3.11 and PyTorch, I ran several experiments entirely inside the interpreter, without needing to set up my own Python environment.</p>
+  </section>
+
+  <section class="reveal">
+    <h2>Machine Learning Libraries</h2>
+    <p>The bot's description advertises several preinstalled libraries, including:</p>
+    <ul>
+      <li><strong>PyTorch:</strong> Neural networks and deep learning.</li>
+      <li><strong>TensorFlow:</strong> Another major deep learning framework.</li>
+      <li><strong>scikit-learn:</strong> Traditional machine learning.</li>
+      <li><strong>NumPy and SciPy:</strong> Numerical computing and scientific operations.</li>
+      <li><strong>Pandas:</strong> Working with datasets.</li>
+    </ul>
+    <p>There are also around 150 other commonly used Python libraries advertised. (but it doesn't list which ones specifically)</p>
+    <div class="note"><strong>One issue:</strong> TensorFlow was listed in the description, but it just.. didn't work.</div>
+  </section>
+
+  <section class="reveal">
+    <h2>Neural Network Experiments</h2>
+    <p>I trained a small neural network to classify different datasets. Here's how it performed on completely separate test examples:</p>
+    <div class="metrics">
+      <div class="metric"><strong data-count="99.68">0%</strong><span>Quadrant classification</span></div>
+      <div class="metric"><strong data-count="100">0%</strong><span>Interleaving spirals</span></div>
+      <div class="metric"><strong data-count="99">0%</strong><span>Noisy spirals</span></div>
+    </div>
+    <div class="chart-card">
+      <div class="chart-title"><h3>Unseen test accuracy</h3><small>Measured results</small></div>
+      <div class="bar-row"><span class="bar-label">Quadrant classification</span><div class="bar-track"><div class="bar-fill" data-width="99.68"></div></div><span class="bar-value">99.68%</span></div>
+      <div class="bar-row"><span class="bar-label">Interleaving spirals</span><div class="bar-track"><div class="bar-fill" data-width="100"></div></div><span class="bar-value">100.00%</span></div>
+      <div class="bar-row"><span class="bar-label">Noisy spirals</span><div class="bar-track"><div class="bar-fill" data-width="99"></div></div><span class="bar-value">99.00%</span></div>
+      <p class="chart-foot">Each model was evaluated on a separately generated test set. These are synthetic classification tasks, not real-world benchmarks.</p>
+    </div>
+    <p>The spiral experiment was particularly interesting. A network with only 33,666 parameters learned to distinguish two interleaving spirals, achieving 100% accuracy on 10,000 independently generated test examples.</p>
+    <div class="code-quote"><span class="dim"># The tiny spiral classifier</span><br>
+      Parameters <span class="accent">33,666</span><br>
+      Training examples <span class="accent">20,000</span><br>
+      Unseen test examples <span class="accent">10,000</span><br>
+      Test accuracy <span class="accent">100.00%</span>
+    </div>
+  </section>
+
+  <section class="reveal">
+    <h2>Markov Chain Test</h2>
+    <p>I also built a tiny character-level Markov chain.</p>
+    <p>It learned character patterns from a short training corpus and generated its own text.</p>
+    <p>The results were... questionable.</p>
+    <p>It could produce recognizable phrases, but also said things like "wantered learnings." Not exactly Shakespeare.</p>
+    <div class="code-quote"><span class="dim"># A small sample of its literary genius</span><br>
+      the robot explored internet. the little robot kept<br>
+      the little robot read everesting. one day the robot<br>
+      was full of books computer wan
+    </div>
+  </section>
+
+  <section class="reveal">
+    <h2>A Few Environment Discoveries</h2>
+    <p>I investigated the interpreter's environment and found:</p>
+    <div class="split">
+      <div class="info-card"><div class="tag">Runtime</div><h3>Python 3.11.18</h3><p>PyTorch 2.0.1, along with NumPy, SciPy, scikit-learn, pandas, and other advertised libraries.</p></div>
+      <div class="info-card"><div class="tag">Compute</div><h3>17 logical CPUs</h3><p>Visible to Python, with approximately 1.76 GiB of visible system memory.</p></div>
+      <div class="info-card"><div class="tag">Graphics</div><h3>No CUDA GPU</h3><p>PyTorch was installed with CUDA support, but no GPU was accessible to the runtime.</p></div>
+      <div class="info-card"><div class="tag">Networking</div><h3>Restricted access</h3><p>Standard outbound DNS and internet access were largely unavailable in my tests.</p></div>
+    </div>
+    <p>I also ran CPU benchmarks and got some surprisingly fast matrix multiplication results.</p>
+  </section>
+
+  <section class="reveal">
+    <h2>The Pricing</h2>
+    <div class="metrics" style="grid-template-columns:1fr 1fr">
+      <div class="metric"><strong>$0.000030</strong><span>Per script execution (1 Poe point)</span></div>
+      <div class="metric"><strong>60 sec</strong><span>Execution timeout</span></div>
+    </div>
+    <p>Here's another interesting detail: it's only <strong>$0.000030 per script execution, or 1 Poe point</strong>.</p>
+    <p>That's incredibly inexpensive for experimenting with Python, especially when you consider that you can use libraries like PyTorch without setting up your own environment.</p>
+    <p>The interpreter has a <strong>60-second execution timeout</strong>. Any script that runs longer than that gets terminated. Resource limits are another thing worth investigating.</p>
+    <div class="note">Each script execution costs 1 Poe point ($0.000030). The execution timeout is 60 seconds.</div>
+  </section>
+
+  <section class="reveal">
+    <h2>What's Next?</h2>
+    <p>I want to take this further by building a tiny neural language model, eventually experimenting with a miniature Transformer architecture.</p>
+    <blockquote>It's been a fun reminder that you don't necessarily need a dedicated machine-learning workstation to start experimenting with neural networks.</blockquote>
+    <p>Sometimes all you need is a Python interpreter and an insane amount of curiosity.</p>
+  </section>
+
+  <footer>
+    <div>Independent experiment notes. Not affiliated with or endorsed by Poe.</div>
+    <div class="footer-tags">#MachineLearning &nbsp; #PyTorch &nbsp; #Python &nbsp; #AI &nbsp; #HuggingFace</div>
+  </footer>
+</article>
+</main>
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