Accelerated LearningLearn AI Math

Where your expertise
gets fueled fire by AI

Learn the mechanics behind AI, understand the work you actually do, and figure out where AI belongs in your workflow.

1. Why AI Math

Everyone has something new to tell you about AI.

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  • A new model. A new Claude skill. A GitHub repo that shoots up the rankings in four days. Someone says this tool will change everything. Someone else says you're already behind.
  • So you save the post for later. Then another one. And another. Your bookmarks grow, but your understanding doesn't.
  • The problem isn't that you're learning too little. It's that you're missing a way to judge what matters.

2. The mechanics of AI

Let's make the complicated stuff make sense.

We turn technical concepts into plain language and visual explanations, so you can understand what they mean and why they matter.

SELECT A CONCEPT

Quantization

A way to make an AI model smaller and often faster by using less precise numbers.

Why it matters

Smaller models can fit on more hardware and use less memory. The trade-off: some quality may be lost.

Get clear explanations in your inbox ↗
ONE CONCEPT AT A TIMEChoose a label to unpack it ↗

3. The Approach

Knowing AI isn't the same as knowing where to use it.

A model can be impressive and still be the wrong choice for the job. Good decisions come from understanding both the technology and the work you're trying to improve.

Understand the technology.

Know what AI can do, where it struggles, and what the trade-offs look like in practice.

Capability→Constraint→Trade-off

Understand the work.

Break down the real workflow: the tasks, decisions, dependencies, and outcomes that make it work.

Task→Decision→Outcome
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AI knowledge + Workflow knowledge = Better decisions

Try it: break down a hiring workflow

SELECT A STEP TO EXPLORE THE TRADE-OFFS

Illustration of a friendly green robot carrying a block

Where AI can help

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What needs attention

4. What you'll get

Less noise. More useful understanding.

A newsletter to help you build a mental model of AI, evaluate the claims, and make more grounded decisions about how it fits into your work.

1. UNDERSTAND

Learn the mechanics.

Understand what's happening behind the interface, without needing to become an AI engineer.

2. EVALUATE

Question the claims.

Build enough context to compare options, understand limitations, and spot when the hype outruns the evidence.

3. APPLY

Connect AI to real work.

Recognize where AI helps, where automation fits better, and where human judgment remains essential.

Stop collecting AI content. Start understanding it.

A newsletter to help you build a mental model of AI, evaluate the claims, and make more grounded decisions about how it fits into your work.