Update post: Neural Networks From Zero: From a Single Number to a Billion Parameters

src/content/posts/neural-networks-from-zero/.write-source.json

@@ -3,7 +3,7 @@
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     "title": "Neural Networks From Zero: From a Single Number to a Billion Parameters",
-    "summary": "A neural network never sees a word, an image, or a sound — only a list of numbers. Starting from that one fact and a single neuron, this day-zero guide builds the whole machine: how any input becomes numbers, why weights, biases, and activations each exist, how neurons stack into layers and layers into a model, and how to compute a model's size and running cost by hand.",
+    "summary": "A neural network never sees a word, an image, or a sound — only a list of numbers. Starting from that one fact and a single neuron, this guide builds the whole machine: how any input becomes numbers, why weights, biases, and activations each exist, and how neurons stack into layers and layers into a model.",
     "authors": [
       "dinesh"
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src/content/posts/neural-networks-from-zero/index.mdx

@@ -1,6 +1,6 @@
 ---
 title: 'Neural Networks From Zero: From a Single Number to a Billion Parameters'
-summary: 'A neural network never sees a word, an image, or a sound — only a list of numbers. Starting from that one fact and a single neuron, this day-zero guide builds the whole machine: how any input becomes numbers, why weights, biases, and activations each exist, how neurons stack into layers and layers into a model, and how to compute a model''s size and running cost by hand.'
+summary: 'A neural network never sees a word, an image, or a sound — only a list of numbers. Starting from that one fact and a single neuron, this guide builds the whole machine: how any input becomes numbers, why weights, biases, and activations each exist, and how neurons stack into layers and layers into a model.'
 authors: ['dinesh']
 date: '2026-07-12'
 updated: '2026-07-18'
@@ -410,6 +410,6 @@ Start to finish, with no hand-waving, you can now explain:
 
 That is the atom, the molecule, the organism, its size, and its running cost — the day-zero ground truth. Every advanced topic — attention, transformers, KV caches, quantization, distributed training — is built on exactly these pieces. When they show up, they won’t be magic. They’ll be assembly.
 
----
+<hr className="article-hr article-hr--line" />
 
 _Next: why generating text one token at a time makes a GPU spend 99% of its time waiting on memory instead of computing — the compute-bound vs memory-bound story that shapes every serving system._