11 Dec 2025
This post is part of the F# Advent 2025 series, which already has
bangers! Check out the whole series, and a big shout out to Sergey Tihon
for organizing this once again!
It is that merry time of the year again! The holidays are approaching, and in
houses everywhere, people are happily sipping eggnogg and hanging decorations.
But in one house, the mood is not festive. Every year on December 1st, the
first day of Advent, Santa Claus begins wrapping gifts for 2 billion children
worldwide, from his workshop in the North Pole. But this year, Krampus
unexpectedly decided to impose tariffs on Greenland, throwing the supply chain
of gifts into chaos. It is now December 11th, and Santa just received the
goods. Santa is now 11 days behind schedule, and needs to hire many, many more
Elves than usual to catch up. But… how many Elves does he need to hire?
Santa runs a tight ship at Santa, Inc., and he knows that adding new Elves to
the team won’t be seamless. Bigger teams require more coordination and
additional equipment.
Based on available data, Santa knows that:
- He needs to hire Elves to wrap
2,000,000,000 gifts,
- A single Elf can wrap at most
100,000 gifts a day,
- Instead of the normal
24 Advent days, Santa has only 13 days left,
- A team of
n Elves will only be able to produce n ^ 0.8 as much as a
single elf, that is, there are diminishing returns to scale.
Note: the function f(elves) = elves ^ 0.8 is largely arbitrary. It has the
shape we want for our problem: it is always increasing (more elves can wrap
more gifts), but the increase slows down gradually. For instance f(1)=1.00,
whereas f(2)=1.74, meaning that 2 elves will only be able to wrap 1.74
as many gifts as 1 elf, instead of twice as many.
Can we help Santa decide how many Elves to hire? And can we figure out how much
the Krampus shenanigans are costing Santa, Inc.? We certainly can, and today we
will do so using the goalSeek feature which we just added to Quipu.
More...
22 Oct 2025
I came across this post on the fediverse the other day, pointing to an
interesting article explaining the CVM algorithm. I found the
algorithm very intriguing, and thought I would go over it in this post, and try
to understand how it works by implementing it myself.
The CVM algorithm, named after its creators, is a
procedure to count the number of distinct elements in a collection. In most
situations, this is not a hard problem. For example, in F#, one could write
something like this:
open System
let rng = Random 42
let data = Array.init 1_000_000 (fun _ -> rng.Next(0, 1000))
data
|> Array.distinct
|> Array.length
val it: int = 1000
We create an array filled with 1 million random numbers between 0 and 999,
and directly extract the distinct values, which we then count. Easy peasy.
However, imagine that perhaps your data is so large that you can’t just open it
in memory, and perhaps even the distinct items you are trying to
count are too large to fit in memory. How would you go about counting the
distinct items in your data then?
The CVM algorithm solves that problem. In this post, we will first write a
direct, naive implementation of the algorithm as presented in the paper, and
try to discuss why it works. Then we’ll test it out on the same example used in
the article, counting the words used in Hamlet.
More...
08 Oct 2025
I was thinking recently about ways to combine prediction models, which lead
me to the Softmax function. This wasn’t my first encounter with it (it
appears regularly in machine learning, neural networks in particular), but I
never took the time to properly understand how it works. So… let’s take a
look!
What is the Softmax function
The Softmax function normalizes a set of N arbitrary real numbers, and
converts them into a “probability distribution” over these N values. Stated
differently, given N numbers, Softmax will return N numbers, with the
following properties:
- Every output value is between
0.0 and 1.0 (a “probability”),
- The sum of the output values equals
1.0,
- The output values ranking is the same as the input values.
In F#, the standard Softmax function could be implemented like so:
let softmax (values: float []) =
let exponentials = values |> Array.map exp
let total = exponentials |> Array.sum
exponentials |> Array.map (fun x -> x / total)
More...
26 Sep 2025
In my previous post, I took a look at handling the selected item in an
Avalonia ListBox with FuncUI, so the ListBox properly reflects what item
is currently selected, based on the current State. In this post, I will go
into another aspect of the ListBox that gave me some trouble, handling dynamic
updates to the list of items. Once again, this post is nothing particularly
fancy, and is mainly intended as notes to myself so I can remember later some of
the steps I took.
First, what do I mean by dynamic updates? The examples in the FuncUI docs
go over displaying a list of items that do not change. However, in many real
world applications, you would want to be able to change that list, in a couple
of different ways:
- adding or removing an item,
- editing the selected item,
- filtering the contents of the list.
While editing an item is not particularly complicated in general, and follows
the standard Elmish / MVU pattern, one case that tripped me up was editing an
item in a fashion that impacts how it is rendered in the list, such as changing
the display name of the item. I will go over the solution I landed on, but I am
not sure this is the best way to do it, so if anybody can suggest a better
approach, I would be very interested in hearing about it!
Anyways, let’s dig into it, and build a simple example illustrating all of
these features. The final result will look something like this, and, in case
you are impatient, you can find the full code example here.

We’ll start from where we left off last time,
with a State that contains a collection of Items, and the currently
selected item:
type Item = {
Id: Guid
Name: string
}
type State = {
Items: Item []
SelectedItemId: Option<Guid>
}
More...
20 Aug 2025
After a brief summer hiatus, I am back! I wish this pause was due to
exciting vacation plans, but unfortunately, the main reason was
that I had a gas leak in my apartment, which ended up disrupting my routine
quite a bit. Anyways, I am looking forward to enjoying simple pleasures of
life like warm showers or home cooking again hopefully soon.
Today’s post is not anything fancy. I have been working on deskop applications
in F# recently, using Avalonia FuncUI, and getting the ListBox to do
what I wanted it to do was a bit more involved than I expected. This post is
intended mainly as notes to myself, documenting some of the details that
tripped me up.
Today’s post will focus on handling selection. I intend to have a follow-up
post soon, covering dynamic updates. Until that is published, you can take
a look at the full code example on GitHub.
The ListBox in Avalonia FuncUI
The ListBox in Avalonia is a control intended to display a collection of
items, and track which item is selected. The documentation gives a pretty
good description of its basic usage in FuncUI:
ListBox.create [
ListBox.dataItems [ "Linux"; "Mac"; "Windows" ]
ListBox.selectedItem state.os
ListBox.onSelectedItemChanged (fun os -> dispatch ChangeOs)
]
ListBox.dataItems expects a collection of Items to display, which would
typically coming from the State,
ListBox.onSelectedItemChanged tracks changes of selection,
ListBox.selectedItem drives which Item should visually appear as selected
in the list.
I will focus only on single-item selection in this post. Multi-selection is
also supported, but I haven’t dug into that very much, because this wasn’t
something I needed. The use case I am after is very basic:
- Present a list of items to the user in a
ListBox,
- Allow the user to edit the item currently selected,
- Highlight the item currently selected in the
ListBox.
As it turns out, this was less straightforward than I expected. Let’s dig into
it!
More...