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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
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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
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…

Model Intuition

[MI 1] An Intuitive Guide to CNNs and RNNs

When your phone recognizes “Hey Siri,” a CNN is probably listening. When Google Translate converts your sentence into…

Model Intuition

[ML x] Machine Decision: From One Tree to a Forest

Every time a bank approves or denies a loan in milliseconds, every time Netflix decides what to recommend next, every…

Encryption

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

If you’ve spent any time in tech, you’ve heard of AES, RSA, and Diffie-Hellman. We treat them like digital…

Encryption

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

In Part 1, we covered the “Safe” (Symmetric) and the “Mailbox” (Asymmetric). The TL;DR: we use…

Concepts

Exploring “Linear” in Linear Regression

Linear regression is one of those things you learn early, use forever, and never quite slow down to inspect. So…

Browse Tag

Machine Learning

1 Article

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

Archit Sharma By Archit Sharma
17 Min Read

Every piece of software you’ve ever shipped works the same way. A developer thinks through the logic and writes explicit rules — if the user clicks here, do this; if the input exceeds 100, reject it; if the date passes the deadline, send an email.…

Read More
Machine Learning Basics

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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
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