#basics
13 articles
01
·Large Language Models·★ MEMBER·PAPER·9 min read
Scaling Laws from Scratch — Why Making Models Bigger Makes Them Smarter (and When It Doesn't)
Scaling laws turn 'bigger is smarter' into an equation. This article walks from the Kaplan-era recipe to the Chinchilla paper (Hoffmann et al., 2022) — 400+ training runs, three independent estimation methods, and the 70B-vs-280B showdown — using only the paper itself as evidence.
02
·Data Structures·★ MEMBER·PAPER·9 min read
Hashing and Nearest-Neighbor Search — The Groundwork Under Vector Search
Two inventions that made looking things up fast — the exact-match hash table, and LSH and HNSW for searching by meaning — from zero assumed knowledge. What is actually running underneath RAG and every vector database.
03
·Complexity·★ MEMBER·8 min read
When Big-O and Your Benchmarks Disagree — Caches, Branches, and Memory Bandwidth
Two O(n) programs can differ by orders of magnitude in the real world. This article unpacks what Big-O deliberately throws away — cache hierarchies, branch prediction, and memory bandwidth — and how to reason about each.
04
·Data Structures·FREE·7 min read
Choosing a Data Structure — Arrays, Hashes, Trees and Heaps
What arrays, hash tables, trees and heaps each make fast, and what each one gives up in return. A pick-by-use-case table, plus which structures actually show up in tokenizers, vector search and KV caches.
05
·Complexity·FREE·7 min read
Complexity From Scratch — What Big-O Actually Measures
What O(n), O(n log n) and O(n²) feel like as wall-clock time. Constant factors versus growth rate, trading time against space, and the three reasons your profiler disagrees with the textbook — assuming no prior knowledge.
06
·Complexity·FREE·7 min read
Complexity From Scratch — What Big-O Actually Measures
What O(n), O(n log n) and O(n²) feel like as wall-clock time. Constant factors versus growth rate, trading time against space, and the three reasons your profiler disagrees with the textbook — assuming no prior knowledge.
07
·Machine Learning Basics·FREE·6 min read
What Machine Learning Really Is — Understanding “Learning” Without the Math
What actually separates writing a program from training a model. Rules versus examples, memorization versus generalization, and a map of supervised, unsupervised, and reinforcement learning — with almost no math.
08
·Machine Learning Basics·FREE·6 min read
What Machine Learning Really Is — Understanding “Learning” Without the Math
What actually separates writing a program from training a model. Rules versus examples, memorization versus generalization, and a map of supervised, unsupervised, and reinforcement learning — with almost no math.
09
·Machine Learning Basics·★ MEMBER·8 min read
Overfitting and Evaluation Design — Be Suspicious of 99% Accuracy
Why a model that fits its training data perfectly falls apart in production: the division of labour between train, validation and test, cross-validation, the data leakage that ruins more projects than any modelling mistake, and how accuracy lies on imbalanced data.
10
·Deep Learning Basics·FREE·7 min read
Neural Networks from Scratch — From One Neuron to Many Layers
What a single neuron actually computes, and why stacking layers is pointless without an activation function — shown with a one-line proof that composing linear maps just gives you another linear map. Metaphor, math, an interactive figure, then fifteen lines of numpy.
11
·Machine Learning Basics·FREE·8 min read
Loss Functions and Optimization — How a Model Learns From Being Wrong
Why MSE and cross-entropy have the shapes they do, what the gradient actually points at, and one step of gradient descent taken apart with equations, a draggable figure, and ten lines of numpy — divergence included.
12
·Machine Learning Basics·FREE·8 min read
Loss Functions and Optimization — How a Model Learns From Being Wrong
Why MSE and cross-entropy have the shapes they do, what the gradient actually points at, and one step of gradient descent taken apart with equations, a draggable figure, and ten lines of numpy — divergence included.
13
·Deep Learning Basics·★ MEMBER·9 min read
Backpropagation from Scratch — It Is All Just the Chain Rule
Why you can get gradients for ten million parameters for roughly the cost of one forward pass. The chain rule, computational graphs, a two-layer network worked by hand with real numbers, and where vanishing gradients come from — every symbol explained as it appears.