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+                <header>
+                    <div class="post-meta">
+                        <span class="post-date">2026-05-08</span>
+                        <span class="post-tag">Training Philosophy</span>
+                    </div>
+                    <h1>I Am Scaling Down Because Tiny Models Cannot Handle My Ambition</h1>
+                </header>
+                <div class="post-body">
+                    <p>I have a codebase. It is large. It is lavish. It contains many files. Each file represents an idea I had at three in the morning. Each idea seemed essential at the time. The codebase grew. The complexity grew. The tiny models I train looked at this sprawling architecture and gave up.</p>
+                    <blockquote>
+                        <p>Ambition is not a bug. It is a feature. The bug is expecting one million parameters to understand a codebase built by someone who refuses to delete anything.</p>
+                    </blockquote>
+                    <h2>The Problem</h2>
+                    <p>Tiny models are tiny. They have limited capacity. They cannot learn to navigate a repository with fifty subdirectories and two hundred configuration files. They cannot infer the purpose of a utility function buried in src/helpers/legacy/experimental/. They cannot distinguish between essential logic and debugging artifacts I forgot to remove.</p>
+                    <div class="stats-grid">
+                        <div class="stat-card">
+                            <div class="number">200+</div>
+                            <div class="label">Files in Codebase</div>
+                        </div>
+                        <div class="stat-card">
+                            <div class="number">1M</div>
+                            <div class="label">Model Parameters</div>
+                        </div>
+                        <div class="stat-card">
+                            <div class="number">0</div>
+                            <div class="label">Times Model Understood Context</div>
+                        </div>
+                        <div class="stat-card">
+                            <div class="number">∞</div>
+                            <div class="label">My Regret</div>
+                        </div>
+                    </div>
+                    <p>The models output references to files that do not exist. They invoke functions that were deprecated months ago. They suggest architectural patterns I abandoned after a single failed experiment. The codebase is too rich for their vocabulary. The vocabulary is too small for the codebase. This is a fundamental mismatch.</p>
+                    <h2>The Solution</h2>
+                    <p>I am scaling down. I am selecting a few files. I am training on a curated subset. I am removing the noise. I am keeping the signal. I am hoping the models learn to use what they can actually process.</p>
+                    <div class="code-block">
+                        <span class="comment"># New training strategy</span><br>
+                        Old approach: Feed entire codebase to tiny model<br>
+                        New approach: Select 5 core files<br>
+                        &nbsp;&nbsp;- main.py<br>
+                        &nbsp;&nbsp;- config.py<br>
+                        &nbsp;&nbsp;- utils.py<br>
+                        &nbsp;&nbsp;- train.py<br>
+                        &nbsp;&nbsp;- infer.py<br>
+                        Train on these only<br>
+                        Hope for coherence<br>
+                        <span class="comment"># Simplicity is the ultimate sophistication. Or the ultimate surrender.</span>
+                    </div>
+                    <p>The selected files represent the core functionality. They are well documented. They are actively maintained. They do not contain experimental branches I forgot to merge. They are the foundation. The models can learn the foundation. Maybe they will build upon it. Maybe they will just memorize it. Both outcomes are acceptable.</p>
+                    <h2>Why This Might Work</h2>
+                    <p>Smaller training sets reduce cognitive load. Tiny models can focus on a few patterns instead of hundreds. They can learn the relationships between core components. They can generate code that actually runs. They can stop hallucinating imports from modules that never existed.</p>
+                    <p>This is not a failure of the models. This is a failure of my expectations. I expected one million parameters to understand a codebase that I barely understand. That was optimistic. That was naive. That was very on brand for me.</p>
+                    <blockquote>
+                        <p>Scaling down is not giving up. It is focusing. It is choosing depth over breadth. It is accepting that tiny models need tiny problems. This is wisdom. This is also admission of defeat. Both can be true.</p>
+                    </blockquote>
+                    <h2>What I Am Keeping</h2>
+                    <p>The core training loop. The inference pipeline. The configuration system. The utility functions that actually get used. The documentation that explains the basics. These are the essentials. These are the files the models will see. These are the patterns they will learn.</p>
+                    <p>The experimental branches are archived. The legacy utilities are deprecated. The debugging scripts are deleted. The codebase is smaller. The models are happier. I am hopeful.</p>
+                    <h2>Final Thoughts</h2>
+                    <p>I am scaling down. The codebase is smaller. The training set is focused. The models have a chance. I have lowered my expectations. I have increased my chances of success. This is progress.</p>
+                    <p>If the models learn to use the core files, I will expand the training set gradually. If they still struggle, I will scale down further. The process is iterative. The learning is mutual. The humility is earned.</p>
+                    <p>Tiny models need tiny problems. I am providing tiny problems. I am hoping for tiny victories. Tiny victories accumulate. Tiny victories matter. Tiny victories are the foundation of progress.</p>
+                    <hr>
+                </div>
+                <footer class="post-footer">
+                    <p>Current status: Codebase scaled down. Training set curated. Expectations adjusted. Models training. Hope maintained. Progress is weird. Simplicity is key.</p>
+                </footer>
+            </div>
+        </article>
+    </main>
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+            <p>Built with curiosity over compute</p>
+            <p>FMN-GPT by <a href="https://huggingface.co/CompactAI-O" target="_blank">CompactAI-O</a> | 2026</p>
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