Libraries can start processes too!

It’s the kind of thing that, if you are used to other languages, would make you very suspicious. Library code starting its own processes — you can almost feel the mess and sense the bugs just musing over it. Yet in Elixir (and, of course, Erlang too), this is a totally normal thing to do.

The Elixir approach to shared mutable state is wrapping it in a process. In this case, I needed a counter and the easiest way to implement it is to use an Agent which is a kind of process designed to handle simple state. In this case, the get_and_update function allows me to return the counter and increment it as an atomic operation.

To start an Agent you use the Agent.start_link function. But where to call it? Do I have to add some equivalent initializer function to my library? While not exactly onerous it felt awkward somehow like my implementation was leaking into the caller. Then again did I have to stop the agent process somewhere? Where would I do that?

Now I figured out how to manage the life-cycle of the agent process myself within the library. But it turns out to be unnecessary. All I had to was make one change to my mix.exs file and one addition to a module in my library.

def application do
    extra_applications: [:logger]


def application do
    extra_applications: [:logger],
    mod: {Ergo, []}

along with changing the referenced library module, Ergo to look like:

defmodule Ergo
  use Application

  def start(_type, _args) do
    Supervisor.start_link([Ergo.AgentModule], strategy: :one_for_one)


This is enough that any application using my library (called Ergo, btw) knows will automatically start the Agent and manage its life-cycle. Without me, or the calling application, needing to know anything about it at all.

This is a pretty neat trick.

Elixir Protocols vs Clojure Multimethods

I am coming to appreciate José Valim’s creation, Elixir, very much. It is fair to say that Rich Hickey set a very high bar with Clojure and Elixir for the most part saunters over it. I think that is a reflection of two very thoughtful language designers at work. But there is some chafing when moving from Clojure to Elixir.

I wrote previously about how Clojure’s metadata feature lets one subtly piggyback data in a way that doesn’t require intermediate code to deal with it, or even know it exists. It creates a ‘functional’ backchannel. Elixir has no equivalent feature.

If your data is a map you can use “special” keys for your metadata. Elixir does this itself with the __struct__ key that it injects into the maps it uses as the implementation of structs. You mostly don’t know it’s there but would have to implement a special case if you ever treated the struct as a map.

However, if the value you want to attach metadata to is, say, an anonymous function then you’re out of luck. In that case, you have to convert your function to a map containing the function and metadata and then change your entire implementation. That could be a chore, or worse.

Today I hit the problem of wanting to define a function with multiple implementations depending on its arguments. Within a module, this is not hard to do as Elixir functions allow for multiple heads using pattern matching. It’s one of the beautiful things about writing Elixir functions. So:

defmodule NextWords do
  def next-word("hello"), do: "world"
  def next-word("foo"), do: "bar"

Works exactly as you would expect. Of course, the patterns can be considerably more complex than this and allow you to, for example, match values inside maps. So you could write:

def doodah(%{key: "foo"}), do: "dah"
def doodah(%{key: "dah"}), do: "doo"

And that would work just as you expect too! Fantastic!

But what about if you want the definitions of the function spread across different modules?

defmodule NextWords1 do
  def next-word("hello"), do: "world"

defmodule NextWords2 do
  def next-word("foo"), do: "bar"

This does not work because while and share the same name they are in all other respects unrelated functions. In Elixir a function is specified as a tuple of {module, name, arity} so functions in different modules are totally separate regardless of name & arity.

In Clojure, when I need to do something like this I would reach for a multi. A Clojure multi uses a function defined over its arguments to determine which implementation of the multi to use.

(defmulti foo [x y z] (fn [x y z] … turn x, y, z into a despatch value, e.g. :bar-1, :bar-2 or what have you))

(ns 'bar-1)
(defmethod foo :bar-1 [x y z] … implementation)

(ns 'bar-2)
(defmethod foo :bar-2 [x y z] … another implementation)

The dispatch function returns a dispatch value and the methods are parameterised on dispatch value. Different method implementations can live in different namespaces and a call with the right arguments will always resolve to the right implementation, regardless of where it was defined.

Now Elixir has an equivalent to multi, the Protocol. We can define the foo protocol:

defprotocol Fooish do
  def foo(x)

Now in any module, we can define an implementation for our Fooish protocol. But, and here’s the rub, an implementation looks like:

defmodule Foo-1 do
  defimpl Fooish, for: String
    def foo(s), do: …impl…

So an Elixir protocol can only be parameterised on the type of its first argument! This means that there’s no way to dispatch protocol implementations based on lovely pattern matching. Disappointing.

It may even be that a smarter Elixir programmer than I could implement Clojure style multi-methods in Elixir. For now, I can find a work-around and I’m still digging Elixir a lot.

Value metadata is a subtle but useful language feature

Since I am using it to create AgendaScope, i’ve written quite a lot of Elixir code to get myself up to speed. Most recenty a parser combinator library, Ergo. And it’s in building Ergo that I’ve realised I really miss a feature from Clojure, metadata.

In short Clojure allows you to attach arbitrary key-value metadata to any value. While there is syntax sugar it boils down to:

meta(obj) to get the map of metadata key-value pairs for the value obj.

vary-meta(obj, f, args) to modify the meta-data associated with the value obj.

If you make a copy of obj the metadata is carried with it. In all other respects obj is unaffected. Why is this so useful?

Well I first used it to manage whether a map, representing a SQL row, comes from the database or not as well as associated data about the record. I could have put that in the map using “special” keys but this pollutes the map with stuff not strictly related to its purpose and more complex code with special cases to handle those keys. But what if obj wasn’t a map to which I could add special keys?

Clojure makes heavy use of it for self-documenting, tracking attributes, and tagging of all different kinds of structures.

What about Ergo? Well being a parser combinator library Ergo is about building functions, usually returning them as anonymous functions. When debugging a parser you’d really like to know more about it & how it was constructed but what you have is a function f.

In Clojure I’d just put all that in metadata of f and it would be no problem later to get at that metadata for debugging purposes by calling meta(f). The code of the parser itslef needing to know nothing about the metadata at all. But in Elixir… I am not sure what to do.

Elixir functions can have multiple heads so it occurs to me that I could return my parsers like:

  %{Context} = ctx -> do parsing stuff here
  :info -> return some metadata instead

And this could work but also feels brittle to me. If a user defines a parser and doesn’t add an :info head then my debugging code that invokes f.(:info) is going to generate an error. It doesn’t smell right. Is this the best I can do?

I being to wish Elixir had an equivalent meta functionality to Clojure.

Connecting Elixir GenServer and Phoenix LiveView

My new company AgendaScope is building it’s product using the Elixir/Phoenix/LiveView stack and while I have spent some time in recent months learning Elixir and Phoenix, LiveView was entirely new to me. This weekend I decided it was time to crack it.

I had a little project in mind which is creating a dashboard of thermal info from my Mac. There’s a reason for that which is another post but suffice to say I needed information displayed on a second computer, preferably my iPad.

The way I chose to tackle this problem was to create a GenServer instance that would monitor the output of a few commands on a periodic basis (at the moment once every 5 seconds). The commands are:

thermal levels

powermetrics --samplers smc | grep -i "die temperature"


The Elixir Port module makes this a pretty trivial thing to do with the GenServer handle_info callback receiving the command output that I parse and store in the GenServer state.

On the other end it turns out LiveView is pretty simple. A LiveView template is an alternative to a regular static template (this part had not been clear to me before and I thought they sat, side-by-side).

The LiveView module in my case ThermalsLive stores things like CPU pressure, GPU pressure, & CPU die temp, in the Socket assigns. And a template is simple to create. LiveView takes care of all the magic of making things work over a web socket.

For example, in a LiveView template the markup <a href='#' phx-click='guess' phx-value-number='1'>1</a> generates a link that results in the running LiveView module receiving the handle_event callback with the value 1. Like this:

def handle_event("guess", %{"number" => guess} = data, %{assigns: %{score: score, answer: answer}} = socket) do

Something I learned about LiveView is that each browser session gets its own stateful server side LiveView module that lasts throughout the session. When a LiveView event handler changes the assigns in the socket structure LiveView responds by re-rendering the parts of the template that depended on those particular assigns. This is good magic!

Now the question was: How does my GenServer (ThermalsService) notify the LiveView (ThermalsLive) when one of the variables it’s monitoring (cpu_pressure) has changed? This event is not coming from the browser so the regular LiveView magic isn’t enough.

I couldn’t find a good simple example and so what I’ve learned was gleaned from some books and Google searching. There short answer is Phoenix.PubSub, here’s the longer answer.

First we need a topic. When three friends are at a table for lunch their topic might be “politics” and one of them might tune out and not be listening. But when the topic changes to “cheese” they tune back in and hear those messages.

For our GenServer & LiveView the topic (defined as a module attribute @thermals_topic) is going to be thermals_update because I want them to talk about these and I don’t plan to use individual message types for different variables like cpu_pressure and gpu_die_temp.

On the LiveView end we need to subscribe to the same topic. The right place to do this appears to be in the mount callback where the socket assigns are first set.

def mount(_params, _session, socket) do
  if connected?(socket) do

We test if the socket is connected because it turns out a LiveView module calls mount twice. Once in response to the initial browser request where it returns the HTML page that starts a web socket connection back to the server. The second call to mount is in response to setting up the web socket and long-term communication between client and server. This seems to be the best point to subscribe to the topic.

It wasn’t obvious that the DashletWeb.EndPoint module had a subscribe method ready made for this purpose and I was mucking about the the Phoenix.PubSub module for a bit before I hit on this.

My GenServer process ThermalsService receives handle_info callbacks from the Port that contain the terminal output of the running command as a string. With a little bit of parsing & conversion we end up with something like cpu_pressure: 56 and as well as storing this in the GenServer state we need the LiveView module to know about it. We need to broadcast a message to the thermals_update topic.

This turned out to be pretty simple:

alias Phoenix.PubSub
@thermals_topic "thermals_update"

def handle_info({_port, {:data, text_line}}, state) do
  PubSub.broadcast(Dashlet.PubSub, @thermals_topic, {"cpu_pressure", cpu_pressure)

I confess I am still not sure what Dashlet.Pubsub is. By my Elixir knowledge it should be a module but I can’t find it defined anywhere. Anyway, it works.

The last piece of the puzzle that I couldn’t find stated but inferred from examples is that, for each broadcast, there is a call to the handle_info callback on all topic subscribers (in this case our LiveView module).

So, in my live view ThermalsLive I add:

def handle_info({"cpu_pressure", pressure}, socket) do
  {:noreply, assign(socket, cpu_pressure: pressure)}

This is super simple. It recevies the cpu_pressure message from the GenServer along with the pressure value and stores it in the LiveView socket assigns (the same way the handle_event callback would do in respond to clicking a browser link). This is enough for LiveView to take the hint and trigger a client update.

What foxed me was that regular LiveView events arrive via the handle_event callback that receives the LiveView socket instance. You need the socket to change its assigns to trigger an update. It didn’t occur to me that PubSub might ensure that the handle_info path would be equivalent.

And, there you have it, sending data from a GenServer via Phoenix PubSub to a LiveView module. I learned quite a bit via this exercise and I hope this might help anyone following the same path.

Codesign is not a heap of fun

My Apple Developer certificate had expired so I had to get a new one. Having forked out the Apple-Tax I duly had a new certificate but…

codesign went into a loop prompting me for my password.

mdwrite popped up asking for permission on my metadata keychain which I didn’t know I had (seems is a file in ~/Library/Keychains).

And after all that nonsense the app wouldn’t run.

Message from debugger: Error 1

Not very helpful. I went off to find the crash report file (in ~/Library/DiagnosticReports) and that was more useful:

EXC_CRASH (Code Signature Invalid)


Digging deeper with codesign ---verify --verbose ~/…/MacOS/Mentat got me back:


Well that’s fine but what does it mean?

Checking my KeyChain my Apple Developer certificate was listed as “Not trusted” but why?

Eventually, someone in the Core Intuition Slack gave me that answer. My Apple WWDR certificate, although not expired, was out of date. So I downloaded and installed the latest certificate.

I nuked the DerivedData folder and built from scratch and I am back in business.

I’m glossing over the time I spent trying to figure this out, ask the wrong (and later righter) questions, and stare in despair at this problem not of my own making.

I’m grateful for the help or this would be baffling me yet.

Stop aliasing String.t and integer

I’m learning Elixir and, a little unwillingly, learning about it’s system of type specifications. I will concede that being able to specify the types of return values is helpful but I am not sure about the rest. Nevertheless I am told the Dialyzer system (that analyses type information and looks for errors) can be very helpful so for now I am playing along.

While reading the documentation something caught my eye:

Defining custom types can help communicate the intention of your code and increase its readability.

defmodule Person do
   @typedoc """
   A 4 digit year, e.g. 1984
   @type year :: integer

   @spec current_age(year) :: integer
   def current_age(year_of_birth), do: # implementation

I found myself puzzled by this statement. The argument in question year_of_birth is already clearly indicating it’s a year. And the type cannot enforce the rule “A 4 digit year, e.g. 1984”.

So what is the type spec adding? It seems to me that what it’s adding is a step of cognitive overhead in understanding that what is being passed is an integer.

I’ve seen other examples of creating types for username and password that alias the String.t type and again I find this unconvincing since the function being spec’d almost certainly calls it’s arguments username and password so what is being added?

Where a type adds useful information I buy it. A struct for example is already a kind of alias since it defines compound structure. But for primitive types like String.t and integer it seems like adding aliases is hiding information not adding to it.

Inserting associated models with Ecto

I’m building a “learning app” using the PETAL stack and it’s taxing some of the grey cells that haven’t worked since I was working with Ruby on Rails many years ago.

It’s hit a point where I need to insert two associated records at the same time. I am sure this involves Ecto.Multi but I’m also trying to understand how to build the form since the form_for expects an Ecto.Changeset and so far as I can see these are schema specific. Or, at least, in all the examples I’ve seen so far they have been.

I make a lot of introductions by email so I decided to build a little helper application to make it easier for me. My schema at the moment is quite simple:

Contact <-> Role <-> Company

At the moment I have a very simple CRUD approach where you create Contacts and Companies separately. But, of course, in practice when I create a Contact I want to create the Company and specify the Role at the same time. And that’s where I run into a problem. The common pattern is something like:

def new(conn, _params) do
  changeset = Introductions.change_contact(%Contact{})
  render(conn, "new.html", changeset: changeset)

In this case we are creating an Ecto.Changeset corresponding to a new Contact. Later when we want to build a form to edit the details we have:

<%= form_for @changeset, @action, fn f -> %>

Where the form relates the fields of the schema to the fields in the form.

So the question is how you create a “blended” or “nested” Changeset that can contain the details of each of the 3 schemas at work.

I’ve not seen any examples covering this case. I’m muddling my way through it but it would be great to have something to work from.

Always Future Agents

I’ve been interested in software agents since I came across Graham Glass’ software ‘ObjectSpace Voyager’ in 1998. The idea behind agents is software that can act on its own on behalf of it’s “owner”, much like a human agent in the sports or entertainment field.

If you’ve ever used something like Spotlight, you’ve used a local agent. Spotlight works away in the background indexing the files on your computer so that it can answer questions like “Where did I put that presentation where I mentioned ‘Bitcoin futures’?”

There are quite a few “local agents” that are useful. But what if it’s someone else’s presentation that you are looking for? What if it’s on their laptop? To be truly useful to their owners, agents must be capable of being distributed.

In 1998 Object-Oriented was all the rage, but distributed software was still a mess. If there was a lot of money riding on it, you could use CORBA. I was significantly techy back then, and even I had trouble with CORBA. Java had Remote Procedure Calls (RPC) calls by which objects could message other objects, but the whole edifice of distributed computing was fragile. There was no platform on which you could write distributed communicating agents.

Then along came Voyager. At a stroke, Voyager let you turn a Java object into an agent that could communicate with other agents wherever they were. More amazing still was that an agent running on Voyager on your machine could “hop” to another device and execute there. It took my breath away.

Sadly, it was also useless. Almost nobody else had heard of Voyager or seemed to see its potential. There was nowhere for your agents to go and nothing much for them to do if they got there. I could never see how to make real use of it. I think this reality started to bite because Voyager pivoted and became a good, boring web application server.

But for a brief moment, I saw a beautiful future of agents communicating with each other to help their users solve problems (yes, Tron made a big impression on me as a child)!

Though it faded over the years, I’ve never entirely lost that vision. It sits as an, as yet, unexplored part of Mentat. In Mentat, scripts are a first-class citizen, and I want to make it easy to create agent scripts that perform functions on my behalf. The distribution will be achieved using TupleSpaces (an overlooked concept in distributed computing).

A simple but powerful use-case could be finding answers to questions. Imagine something like this:

  • You pose a question and post it.
  • One of your agents sends the question metadata to one or more shared tuple spaces.
  • My agents are waiting for tuples matching things I am interested in.
  • One of my agents spots your question and, realising it (a) matches my interests and (b) meets my priority requirements, ‘takes’ it.
  • It presents your question to me along with the related resources that I have on hand.
  • I select from among those resources to compose my answer.
  • My agent posts my answer back into the tuple space.
  • Your agent spots an answer and collects it to present it, and potentially others, to you at an appropriate moment.

Sounds a bit like posting a question to a web forum, right? Yes, but the differences have the potential to be transformative.

  • You don’t have to decide where to put your question; your agent can do that. Depending on your preferences, it might put it in many spaces and with different metadata depending on the space.
  • I don’t have to look for your question; my agent decides if it’s something I will want to respond to. Or ignore. Or maybe just file it away for some other purpose.
  • My agent can assemble resources on my behalf to make it easier to answer that question.
  • You don’t have to look for replies; your agent will assemble them. Potentially using a quality filter (oh, Matt replied, you’ll want/not want to see that) and potentially digesting answers. Your agent might just as well say, “You’re busy right now, but I suspect you’ll want to see Matt’s question; I will present it at another time”.

Since our agents are software under our control, we can determine how they work and improve them over time to better use our knowledge and attention.

For example, your agent might not be tasked with trying to answer my questions but simply to reflect, “Matt seems to be interested in topic X right now”. Indeed your agent might notify you not about my questions but with analysis of my questions. This could go in all kinds of directions.

I have many of the pieces of this infrastructure in place but can’t make progress right now. I really wish I could find someone to collaborate with on this platform. Hopefully, I still have a few years to get back to it and turn it into something real that people can use to solve problems.

Sharing our work

When I started working on Mentat back in 2018 I had in mind a kind of “total information store” that I could use for all sorts of things but often about outputs either to questions or in terms of content.

This was a reflection of the blurring of my work & life and the way that information tends to disappear into other peoples silos over time. I am interested in what I am interested in, no matter the context, and I would like to know what I know or at least what I thought at some point.

Meanwhile, Roam Research has come along and hoovered up a lot of use cases. I use Roam as a habitual note-taking environment. A light-weight TODO system, calendar, and personal CRM. I use it for drafting LinkedIn posts, blogs, and newsletters. And I’m a relatively unsophisticated Roam user (for example I’ve never written a query in anger, don’t use Roam/js plugins, and still use the default theme) and yet it has certainly come to dominate my digital life.

At the same time, I can reflect that one of Roam’s great strengths: its focus on blocks of written text (with tags and backlinks) is also its Achilles heel. You can put anything in Roam but structure appears only sporadically and with effort. How can you act upon what Roam knows?

In the context of, say, writing an article it works well. But what if I wanted to see if I could answer a specific question relating things that I know. That could be a lot more tricky.

Mentat comes from the opposite perspective. It deals with structured ‘things’ (indeed the root of the Mentat mental taxonomy is something called Thing). We can have a Thing called Note that represents free text. This is never going to be as powerful as Roam but, at the same time, we know what is a note and what is a instead a Person. Roam can approach this through the use of tagging. I routinely add Tags: #Person to people I add to Roam as part of my CRM but it’s not the same thing.

As yet, Roam provided few tools to act on this and of course it relies upon my consistently tagging people — which often I forget — and applying the same schema over time (mine has changed 3 times as the advice has changed). Again there are solutions to these problems but they are always a compromise of being based upon free text. Mentat has it’s own compromises but, similarly, strengths.

Three things I see as being very important to using a future version of Mentat for work are:

Being able to structure questions with appropriate metadata that allows them to be shared and acted upon by others.

A “shared space” in which questions can be placed and taken.

The ability to create agents that can act on things in the shared space. Taking them, acting upon them, changing things locally and potentially placing things back into the shared space.

Roam is going to have to tackle the problem of people sharing their graphs. That is going to be a hard problem. Mentat will allow people to create shared spaces and exchange information without needing to create a total mapping.

It will be interesting to see if (a) I can build this, (b) if it might work as well as or better (for some problems) as what Roam will come up with.

The curse of craftsmanship

I’m not a computer scientist; I’m not a software engineer; I am a programmer. I see the difference as being between a theoretical view, an engineering view, and a “craftsman’s” view of creating software to run machines. That is to say; I don’t build software, I craft it. Everything is unique, hand-made. Tools matter to me in so far as they let me express my craft. I want the tools that best help me do the work.

Here is a list of languages that I have used to solve real problems. It does not include those I learned purely for the fun of it.

  • 80×86 assembly language
  • C
  • C++ (although the C++ of the ’90s, exceptions were new back then)
  • Java
  • Perl
  • Javascript
  • Usertalk
  • Ruby
  • Objective-C
  • Clojure
  • Elixir

The choices in bold are those languages I’d go back to reasonably happily. Here are languages I actively avoid:

  • Python
  • Javascript
  • PHP
  • Go
  • Swift
  • Scala

My reasons here, as much as anything else, are reasons of taste. Python I dislike because I don’t think significant white space is a good idea (as opposed to programming in an outliner which works well and I’ve no idea why the Python community never cottoned on to this) and I never liked its feel.

Go, Swift, and Scala, are on the list because they make choices I find distasteful. For example, Swift feels like the authors have to include every possible good idea from the programming field. I’m reminded of this by Antoine de Saint-Exupery “Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away.”

More language authors should take a stand like Rich Hickey and José Valim by having a language philosophy dictating that they reject good ideas because they don’t fit that philosophy. Their languages feel coherent, and designed toward a purpose. They choose not to include the kitchen sink.

The reason I am thinking about all of this is Javascript, a language I have used to solve real problems yet, given a choice, actively avoid.

I’m sure there are good things about Javascript if you look hard enough. It’s near-ubiquity makes it a winner, and I wouldn’t criticise another’s choice to use it (esp. if they are a competitor). But, improve as it may, Javascript has cruft encoded into its DNA, and that cruft leaks out in every direction.

For a new project, I am learning Elixir and its web framework Phoenix. And do you know what has been the most challenging thing? Webpack. Worse yet, Webpack is a tool written in Javascript (using nodejs) for managing project Javascript!

Whenever nodejs hoves into view, I tend to find myself waist-deep in a mess of dependencies and version incompatibilities. Perhaps I have been lucky, but I don’t experience this level of pain elsewhere. So it makes me think it’s a function of the choice of language and the kinds of people who are aligned with those choices.

I guess it is the craftsman’s curse: You come to care about the way the tools allow you to express the work and, therefore, feel pained when forced to confront tools that feel wrong.