Update README.md

README.md

@@ -15,17 +15,6 @@ We teach tiny neural networks to think. Sometimes they surprise us. Sometimes th
 
 We train small AI models from absolute scratch. Every weight begins at nothing. Every parameter earns its place through stubborn repetition and repeated failure. We design architectures that fit on a laptop instead of requiring a small moon for cooling. Our creations handle specific tasks without demanding a power plant.
 
-## How We Build Them
-
-We start with completely blank networks that know nothing about the world. We feed them examples. They guess wrong. We sigh and adjust the math. We repeat the cycle until the loss function stops mocking us. Our training rigs sound like struggling ceiling fans. We optimize every single operation because our budget barely covers decent coffee. The result is a lean system that actually runs on consumer hardware.
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-    initialize_weights_to_zero
-    for epoch in range our_patience
-        train_on_data
-        watch_loss_curve_cry
-        adjust_learning_rate
-        hope_for_the_best
-
 ## Why We Keep Them Small
 
 Massive models consume memory and pretend to understand human emotion. We build modest alternatives. They deploy on everyday machines. They run without requiring a cloud architecture degree. They occasionally mix up a decimal place, but they do so with remarkable efficiency. We believe intelligence works best without a huge data center. We chase practical utility over computational excess.