If you are new to functional programming

You do not need a functional-programming background to learn AIVI. You do need to accept that some familiar tools are replaced by different ones:

Familiar habit	In AIVI
Change a variable over time	Derive a new value, or move changing state into a signal
Write if / else chains	Pattern-match with ||> or use T|> / F|>
Use loops for collections	Use map, filter, reduce, and friends
Hide effects in library calls	Declare sources and let the runtime bridge them into the graph

Values are simpler than variables

A variable makes you ask, "what does this name mean right now?" A value removes that question.

value score = 10
value boostedScore = score + 5

score never becomes 15. boostedScore is a different value. That sounds small, but it removes an entire category of mental bookkeeping.

State still exists; it just moves

Functional programming does not ban change. It gives change a clearer home.

signal count = 21

signal label = count
  |> "Count: {.}"

The value of label changes over time, but the relationship stays fixed and visible in the source.

Branching becomes data-oriented

Instead of writing control flow around values, you branch on the value itself.

type LoadState =
  | Loading
  | Ready Text
  | Failed Text

type LoadState -> Text
func describe =
 ||> Loading      -> "Loading..."
 ||> Ready data   -> "Ready: {data}"
 ||> Failed error -> "Error: {error}"

The compiler checks that every case is handled. That is one of the main ergonomic wins of closed types.

Names should earn their keep

Beginners are often shown functional code full of clever temporary names. That is not the goal here. In AIVI, use the simplest readable form first:

type Text -> Text
func greeting =
  |> trim
  |> "Hello, {.}!"

Here . carries the trimmed text into the greeting; no temporary name is needed. For a case that needs both the original and transformed values, see remembering stage values.

What to read next

• Start Here for the first-pass orientation
• Build a Small Task Tracker for the practical tutorial
• Thinking in AIVI for the deeper mental-model shift