From 4f0f7b35d9e9eab66d97f0f126eec20d5c64bcd7 Mon Sep 17 00:00:00 2001
From: "ml-systems-publisher[bot]"
 <304658946+ml-systems-publisher[bot]@users.noreply.github.com>
Date: Sat, 18 Jul 2026 11:19:47 +0000
Subject: [PATCH] Update post: Neural Networks From Zero: From a Single Number
 to a Billion Parameters

---
 .../.write-source.json                        | 28 ++-----------------
 .../posts/neural-networks-from-zero/index.mdx |  4 +--
 2 files changed, 5 insertions(+), 27 deletions(-)

diff --git a/src/content/posts/neural-networks-from-zero/.write-source.json b/src/content/posts/neural-networks-from-zero/.write-source.json
index 2086417..4a0ac4c 100644
--- a/src/content/posts/neural-networks-from-zero/.write-source.json
+++ b/src/content/posts/neural-networks-from-zero/.write-source.json
@@ -3,7 +3,7 @@
   "version": 1,
   "meta": {
     "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"
     ],
@@ -3595,21 +3595,10 @@
       ],
       "children": []
     },
-    {
-      "id": "paragraph-102",
-      "type": "separator",
-      "props": {},
-      "children": []
-    },
     {
       "id": "44bdadb3-da54-4a7b-9d64-0799eadb9d0e",
-      "type": "paragraph",
-      "props": {
-        "backgroundColor": "default",
-        "textColor": "default",
-        "textAlignment": "left"
-      },
-      "content": [],
+      "type": "divider",
+      "props": {},
       "children": []
     },
     {
@@ -3630,17 +3619,6 @@
         }
       ],
       "children": []
-    },
-    {
-      "id": "9e403107-b71e-4bb2-86b2-573648d35a88",
-      "type": "paragraph",
-      "props": {
-        "backgroundColor": "default",
-        "textColor": "default",
-        "textAlignment": "left"
-      },
-      "content": [],
-      "children": []
     }
   ],
   "tableVariants": {}
diff --git a/src/content/posts/neural-networks-from-zero/index.mdx b/src/content/posts/neural-networks-from-zero/index.mdx
index b62d9c6..76d4f58 100644
--- a/src/content/posts/neural-networks-from-zero/index.mdx
+++ b/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._