001 AI without the fog

AI, only
simpler.

Click, watch and see what really happens under the hood of artificial intelligence. No jargon. No 40-slide lecture. Just the moment: “oh, I get it.”

07ideas
to start
00text
walls
clicks
to “aha”
watch the system
not magic / mechanics
EXPLAIN / NEURAL-NETWORK_01 live
INPUT OUTPUT / 0.87
layerdata
layerspatterns
answercat?
data patterns decision system responding
01small networkmany connections

002 The first seven building blocks

Start with the dots.
Then get the aha.

Pick a concept. Open the experiment. Stay as long as you need.

07lessons
01 / interactive lesson

Neural
network.

A pile of simple filters that learns to spot patterns.

02 / interactive lesson

Gradient
descent.

A tiny compass that helps AI make the next guess better.

03 / interactive lesson

Embedd
ings.

Turn words into locations so related ideas can sit close together.

04 / interactive lesson

Atten
tion.

A weighted spotlight that helps a model focus on useful context.

05 / interactive lesson

Feed-
forward.

A private workshop that expands, activates and compresses one token signal.

06 / interactive lesson

Trans
former block.

The repeatable circuit that mixes context, preserves a route and rewrites the signal.

07 / flagship interactive lesson

How it
works.

Follow one sentence from text to the next-token distribution, with every handoff left visible.

003 How we teach

Three moves.
No stress.

Every idea gets one picture, one interaction and one sentence you can carry with you.

01see it

Picture first.

Before a definition, watch the mechanism move.

02touch it

Then interact.

Change one input and see the result move with it.

03connect it

End with aha.

One clear sentence connects the picture to real AI.

004 The big idea

AI is not magic.
It is signal.

Information comes in. Something transforms it. An answer comes out. We will unpack the details one by one.

the human version / no equations

005 What is next?

Small queue.
Big aha.

Seven field guides are live. The next mechanisms are already in the buffer.

08 queued

Tokenization

How text turns into tiny building blocks a model can address.

09 queued

Generation

How a model turns its learned patterns into the next token.

10 queued

Training

How errors travel backward and change the parameters.

This is only the beginning

See you
under the hood.

Have a concept that sounds like a spell? Put it on the list. We will turn it into the next interactive lesson.

Suggest a topic