Learn computing by translating it into real-life systems.

Instead of abstract formulas, we use factories, traffic, mafia deals, and everyday decisions to explain programming, AI, and systems. If you can imagine it mechanically, you can understand it.

Start with an analogy → Browse topics
⚙️ Built for people who think in pictures, stories, and mechanics.

Topics you can learn through analogies

Each topic will have multiple short analogies, diagrams, and small exercises.

Programming Basics

Variables as labeled boxes, loops as conveyor belts, functions as factory machines.

Python • Logic • Mental models

Data Structures

Stacks as plate piles, queues as ticket lines, graphs as cities and roads.

Arrays • Trees • Graphs

Algorithms

Sorting as organizing shelves, searching as detective work with constraints.

Thinking in steps

AI & Machine Learning

Loss as cost of mistakes, gradient descent as walking downhill blindfolded.

Neural nets • Probability

Systems & Networks

Operating systems as city managers, packets as letters routed through post offices.

OS • Networks

Rajdeep-Style Stories

Use a fictional strategist under pressure to explain optimization, thresholds, and decisions.

Mechanical psychology

Analogy library (preview)

A few example entries. Later you can turn each into its own page or post.

1. Functions = Reusable Machines

A function is like a machine on a factory floor: you send in raw material (input), it always performs the same steps, and gives you an output.

2. Backprop = Blame Assignment

After a wrong decision, you trace back through each advisor in the chain and adjust how much you trust them next time.

3. Attention = Smart Spotlight

Instead of reading every word equally, the model shines a spotlight on the few words that matter most for the current decision.

About this project

This site is for people who feel, “I’m not bad at logic, I just hate vague explanations.”

LearnComputingThroughAnalogy starts from how your brain already thinks: stories, physical systems, and mechanical cause-effect chains.

The goal is not entertainment. The goal is a cold, clear understanding of computing that stays with you even when you are stressed, tired, or under pressure.

Future plans:

  • Step-by-step analogy courses for ML and data science
  • Printable PDFs and cheat-sheets
  • “Analogy challenges” to test if you really understood a concept