Skip to content
icon icon Building AI Intuition

Connecting the dots...

icon icon Building AI Intuition

Connecting the dots...

  • Home
  • ML Basics
  • Model Intuition
  • Encryption
  • Privacy Tech
  • Concepts
  • Musings
  • About
  • Home
  • ML Basics
  • Model Intuition
  • Encryption
  • Privacy Tech
  • Concepts
  • Musings
  • About
Close

Search

Subscribe
icon icon Building AI Intuition

Connecting the dots...

icon icon Building AI Intuition

Connecting the dots...

  • Home
  • ML Basics
  • Model Intuition
  • Encryption
  • Privacy Tech
  • Concepts
  • Musings
  • About
  • Home
  • ML Basics
  • Model Intuition
  • Encryption
  • Privacy Tech
  • Concepts
  • Musings
  • About
Close

Search

Subscribe
Recent Posts
April 7, 2026
Exploring “Linear” in Linear Regression
April 7, 2026
The curious case of R-Squared: Keep Guessing
March 11, 2026
[C1] What Machines Actually Do (And What They Don’t)
March 11, 2026
[ML x] Machine Decision: From One Tree to a Forest
November 2, 2024
[ML 1] AI Paradigm Shift: From Rules to Patterns
November 5, 2025
[ML 1.a] ML Foundations – Linear Combinations to Logistic Regression
November 14, 2025
[ML 1.b] Teaching AI Models: Gradient Descent
November 19, 2025
[ML 2] Making Sense Of Embeddings
November 22, 2025
[ML 2.a] Word2Vec: Start of Dense Embeddings
November 28, 2025
[ML 2.b] Measuring Meaning: Cosine Similarity
December 3, 2025
[ML 2.c] Needle in the Haystack: Embedding Training and Context Rot
February 16, 2026
[MI 3] Seq2Seq Models: Basics behind LLMs
February 13, 2026
[MU 1] Advertising in the Age of AI
December 9, 2025
[EN 1.a] Breaking the “Unbreakable” Encryption – 1
December 13, 2025
[EN 1.b] Breaking the “Unbreakable” Encryption – 2
December 18, 2025
[PET 1] Privacy Enhancing Technologies – Introduction
December 21, 2025
[PET 1.a] Privacy Enhancing Technologies (PETs) — Part 1
December 25, 2025
[PET 1.b] Privacy Enhancing Technologies (PETs) — Part 2
December 30, 2025
[PET 1.c] Privacy Enhancing Technologies (PETs) — Part 3
February 2, 2026
[MI 1] An Intuitive Guide to CNNs and RNNs
November 9, 2025
[MI 2] How CNNs Actually Work
January 16, 2026
How Smart Vector Search Works
Model Intuition

[MI 2] How CNNs Actually Work

In the ever-evolving world, the art of forging genuine connections remains timeless. Whether it’s with colleagues,…

Privacy Tech

[PET 1] Privacy Enhancing Technologies – Introduction

Every time you browse a website, click an ad, make a purchase, or train an ML model, data flows through systems.…

Privacy Tech

[PET 1.c] Privacy Enhancing Technologies (PETs) — Part 3

Privacy-Preserving Computation and Measurement In Part 1, we covered how organizations protect data internally —…

Machine Learning Basics

[ML 1] AI Paradigm Shift: From Rules to Patterns

Every piece of software you’ve ever shipped works the same way. A developer thinks through the logic and writes…

Musings

[MU 1] Advertising in the Age of AI

When you search for a product today, ads quietly shape what you notice. When you scroll Instagram, ads compete for…

Machine Learning Basics

[ML 2.c] Needle in the Haystack: Embedding Training and Context Rot

You’ve probably experienced this: you paste a 50-page document into ChatGPT or Claude, ask a specific question…

Browse Tag

bitcoin-risk-from-quantum-computing

1 Article

[EN 1.b] Breaking the “Unbreakable” Encryption – 2

Archit Sharma By Archit Sharma
7 Min Read

In Part 1, we covered the “Safe” (Symmetric) and the “Mailbox” (Asymmetric). The TL;DR: we use high-speed symmetric safes to store our data, but we rely on mathematical “couriers” to deliver the keys. Today,…

Read More
Encryption

Categories

icons8 pencil 100
ML Basics

Back to the basics

screenshot 1
Model Intuition

Build model intuition

icons8 lock 100 (1)
Encryption

How encryption works

icons8 gears 100
Privacy Tech

What protects privacy

screenshot 4
Musings

Writing is thinking

Recent Posts

  • Exploring “Linear” in Linear Regression
  • The curious case of R-Squared: Keep Guessing
  • [C1] What Machines Actually Do (And What They Don’t)
  • [ML x] Machine Decision: From One Tree to a Forest
  • [MI 3] Seq2Seq Models: Basics behind LLMs
  • [MU 1] Advertising in the Age of AI
  • [MI 1] An Intuitive Guide to CNNs and RNNs
  • How Smart Vector Search Works
  • [PET 1.c] Privacy Enhancing Technologies (PETs) — Part 3
  • [PET 1.b] Privacy Enhancing Technologies (PETs) — Part 2
Copyright 2026 — Building AI Intuition. All rights reserved. Blogsy WordPress Theme