Advocacy = empathy + speaking to someone else’s conceptual framework. When I’m trying to convey an idea from my head to someone else’s head, the biggest challenge is converting from my conceptual framework and values to theirs. Hence, The words that work:

For example, one partner in a conversation might use concepts like power and tradition and authority to make a case, while the other might rely on science, statistics or fairness. One person might argue with tons of emotional insight, while someone else might bring up studies and peer reviews.

Rare is the success of advocacy that doesn’t involve a lot of empathy, understanding your conversation partner, and letting go of the little details to reach a new local maximum of mutual understanding.

It’s dangerous to go alone, take dotfiles

Yesterday I was handed a fresh, nifty new laptop. This is, for me, mildly terrifying. Last time I did a clean operating system install was seven years ago. I’ve carried an idiomatic mixtape of dotfiles, macOS preferences, files, and cruft with me on my personal laptop ever since.

A brand-new, stock laptop is a shock to my highly acclimated and particular system.

I started contemplating how exactly I could get setup relatively quickly. At the same time, I want to pay down a little bit of automation debt. By the next time I’m faced with this situation (when I buy my own computer, if a disk is struck by lightning, etc.) I shouldn’t feel so much like a deer in headlights.

At first I thought I’d attempt to transmogrify my current lightsaber into something like Gina Tripani’s dotfiles. I like how this is structured, and that the initial setup of apps and Unix-y things is bootstrapped by Homebrew. But, then I remembered Thoughtbot’s laptop and dotfiles and convinced myself this was the way to go.

Indeed, laptop helped me cut the Gordian knot of setting up my new machine so I can write code and feel at home on it. I highly recommend it if you have the means.

New dotfile repo forthcoming!

I’m starting a new job tomorrow. I decided to take a week off in-between jobs, mostly to make a quick trip to Disney Land.

I hid most social media apps away, stopped paying attention to news, and caught up on reading. I gave myself a 3-day weekend before our trip to decompress, we went to Disney Land for 3 days, and had a 3-day weekend to relax before I start the next thing. I’ve done a fair bit of writing, watching movies, tinkering with Ableton, and playing games too. A great vacation sandwiched between two stay-cations, in the lexicon of our times.

My mind feels like it’s had a chance to reset and get back to a neutral state. I’m hoping this will help me keep my frame-of-mind looking forward as I start the next job. This was a great decision and I highly recommend you do something like this (granted, Disney Land isn’t everyone’s thing) yourself, if you have the means.

Scribbling through TensorFlow.js

I’ve been trying to wrap my head around machine learning lately. Today I worked through the TensorFlow.js tutorial on recognizing handwritten numbers with a neural network. Herein, my notes and scribbles.

Hand-written notes on machine learning
TensorFlow: it’s about turning linear algebra into models built of layers built of math

My previous forays into machine learning left me a little frustrated: I could tell there was language, pattern, and notations to this, but I couldn’t see them from the novelty of new-to-me words like sigmoids, convolution, and hidden layers. Turns out those are part of the language.

But the really handy idioms are encoded in TensorFlow’s high-level model-and-layer API. A model encapsulates a chunk of machine learning that can be trained to classify inputs (images, texts, etc.) based on a mess of training data (pre-classified stuff). Every model is built from a network of layers; layers use linear algebra to transform numbers into classifications.

Once you’ve built a model, you feed it a bunch of training data so that it can learn the coefficients and other number-stuff that goes inside the math-y network. You also provide it with an optimizer and loss function so that as the model is trained, it can know whether its getting better or worse at classifying data.

A really cool thing is you run this training process on your computer’s GPU. GPUs, like machine learning models, are big networks of fast math-y stuff. Beautiful symmetry! On the other hand, you usually can’t fit your training data set into GPU memory, so you end up batching your test data and submitting it to the GPU in loops.

Once all this runs, you’ve got a trained model that can take image inputs (in this case, hand-written digits) and classify them to decimal numbers (0-9). Magic!

Code minutiae, October 23, 2017

For some reason, identifier schemes that are global unique, coordination-free, somewhat humanely-representable, and efficiently indexed by databases are a thing I really like. Universally Unique Lexicographically Sortable Identifier (ulid, for humans) is one of those things. Implementations available for dozens of languages! They look like this: 01ARZ3NDEKTSV4RRFFQ69G5FAV.

Paul Ford’s website is twenty years old. For maybe half that time I’ve been extremely jealous of how well he writes about technology without being dry and technical. When I grow up, I’ll write like that!

How Awesome Engineers Ask For Help. So much good stuff there, I can’t quote it. There’s something in there for new and experienced engineers alike. In particular: don’t give up, actively participate in the process of getting unstuck, take and share notes, give thanks afterwards.

The best time to work on your dotfiles is on weekends between high-intensity project pushes at work. No better time to do some lateral thinking and improving of your workflow. Feels good, man.

You must be this tall to ride the services

If I were trying to convince myself to extract a (micro)service, today, I’d do it like this. First I’d have a conversation with myself:

  • you are making tactical changes slightly easier at the expense of making strategic changes quite hard; is that really the trade-off you’re after?
  • you must have the operational acumen to provision and deploy new services in less than a week
  • you must have the operational acumen to instrument, monitor, and debug how your applications interact with each other over unreliable datacenter networks
  • you must have the design and refactoring acumen to patiently encapsulate the service you want to build inside your current application until you get the boundaries just right and only then does it make sense to start thinking about pulling a service out

I would reflect upon how most of the required acumen is operational and wonder if I’m trying to solve a design problem with operational complexity. If I still thought that operational complexity was worthwhile, I’d then reflect upon how close the code in question was to the necessary design. If it wasn’t, I would again kick the can down the road; if I can’t refactor the code when it’s objects and methods, there’s little hope I can refactor it once its spread across two codebases and interacting via network calls as API endpoints, clients, data formats, etc.

If, upon all that reflection, I was sure in my heart that I was ready to extract a service, it’d go something like this:

  • try to encapsulate the service in question inside the current app
  • spike out an internal API just for that service; this API will become the client contract
  • wrap an HTTP API around the encapsulation
  • make sure I have an ops buddy who can help me at every provisioning and deployment step, especially if this sort of thing is new and a monolith is the status quo
  • test the monolith calling itself with the new API
  • trial deploy the service and make some cross-cutting changes (client and server) to make sure I know the change process
  • start transferring traffic from the monolith to the service

In short, I still don’t think service extraction is as awesome as it sounds on paper. But, if you can get to the point of making a Modular Monolith, and if you can level up your operations to deal with the demands of multiple services, you might successfully pull off (micro)services.

How methodical and quality might keep up with fast and loose

I’ve previously thought that a developer moving fast and coding loose will always outpace a developer moving methodically and intentionally. Cynically stated, someone making a mess will always make more mess than someone else can clean up or produce offsetting code of The Quality.

I’ve recently had luck changing my mindset to “make The Quality by making the quantity”. That is, I’m trying to make more stuff that express some aspect of The Quality I’m going for. Notably, I’m not worrying too much if I have An Eternal Quality or A Complete Expression of the Quality. I’m a lot less perfectionist and doing more experiments with my own style to match the code around me.

I now suspect that given the first two developers, its possible to make noticeably more Quality by putting little bits of thoughtfulness throughout the code. Unless the person moving fast and loose is actively undermining the quality of the system, they will notice the Quality practices or idioms and adopt them. Code review is the first line of defense to pump the brakes and inform someone moving a little too fast/loose that there’s a Quality way to do what they’re after without slowing down too much.

Sometimes, I’m an optimist.

A strange world of mathematical and computational complexity

Over the past few weekends, I’ve been reading on two topics which are way out of my technical confidence. I’ve spent the majority of my software development career building web applications and neither of these are very coincident with web apps right now:

  • blockchains, cryptocurrencies, and autonomous contracts
  • machine learning, neural networks, general purpose GPUs, deep learning

With blockchain stuff, there are very interesting fundamentals underlying a sprawling system of hype and information asymmetry. Every time I go in, it’s “shields up!”, time to defend myself from people trading reputation for short-term speculation or actively spreading inaccurate information. In other words, here comes the snake oil salesmen!

That said, there are cool ideas in there. Solidity is a language built into Ethereum for writing programs that run alongside the blockchain. You wouldn’t want to build a normal application this way, but if you want some degree of confidence in a system, like voting or accounting, a system inside Ethereum and Solidity might make sense. Even more strange, to a web developer, you have to pay for the compute time that program requires in Ethereum itself. Strange and intriguing!

By comparison, machine learning is equally hyped but has little speculation. They both involve about the same level of mathematical and computational complexity. Which is probably how I’ve managed to avoid both so far: I’m far better at social reasoning, which is a big deal in web applications, than I am at math. But I’m trying to change that!

I found deeplearning.js and it seems like a nice gateway into the domain of building neural networks for machine learning, computer vision, etc. And it utilizes your GPUs, if present, which is pretty neat because GPUs are strange little computers we seem to have increasingly more of as the days go on.

No idea where this line of thinking is going. All I know is it’s more fun than reading about yet another client or server side framework. ;)

Just keep writing, October 16, 2017

I watched pal Drew Yeaton work in Ableton briefly and it was pretty incredible. He laid down a keyboard and drums beat, fixed up all the off-beat stuff, and proceeded to tinker with his myriad of synthesizers and effects rack with speed. I had no idea what his hands were doing as he moved from MIDI keyboards, mouse, and computer keyboard like a blur. Seems pretty cool!

I talked myself into and out of porting this website to Jekyll three times over the past week. Hence, the writing dropped off, which is silly because I just blogged about not tinkering with blog tools in the last month. WordPress.com doesn’t quite do the things I want it to and its syntax highlighting is keeping the dream of the nineties alive. I’m writing these short form bits in lieu of a sidebar thing for now. No idea how I’ll make do with the code highlighting.

The Good Place is an amazing show. Ted Danson, Kristen Bell, and the rest of the cast are fantastic. There is an amazing-for-a-comedy twist. Do not read the internet until you watch the first season of this show. It’s just started season two, get on board now!

One step closer to a good pipeline operator for Ruby

I’ve previously yearned for something like Elm and Elixir’s |> operator in Ruby. Turns out, this clever bit of concision is in Ruby 2.5:

object.yield_self {|x| block } → an_object
# Yields self to the block and returns the result of the block.

class Object
  def yield_self
    yield(self)
  end
end

I would prefer then or even | to the verbosely literal yield_self, but I’ll take anything. Surprisingly, both of my options are legal method names!

class Object

  def then
    yield self
  end

  def |
    yield self
  end

end

require "pathname"

__FILE__.
 then { |s| Pathname.new(s) }.
 yield_self { |p| p.read }.
 | { |source| source.each_line }.
 select { |line| line.match /^\W*def ([\S]*)/ }.
 map { |defn| p defn }

However, | already has 20+ implementations, either of the mathematical logical-OR variety or of the shell piping variety. Given the latter, maybe there’s a chance!

Next, all we need is:

  • a syntax to curry a method by name (which is in the works!)
  • a syntax to partially apply said curry

If those two things make their way into Ruby, I can move on to my next pet feature request: a module/non-global namespace scheme ala Python, ES6, Elixir, etc. A guy can dream!