I really enjoyed this Learning DNS in 10 years talk by Julia Evans. Not only does it present a great, lightweight walkthrough on DNS, but it espouses a maxim for learning I really appreciate: keep digging, and when you get stuck, keep digging.
A while ago I mentioned that I thought that Rust was a fiendishly complex language at first glance. Well, that hasn’t changed. Don’t get me wrong, I love Rust, and I wish I had the time to dive deeper into it, but it has a steep learning curve.
That said, I recently came across a fantastic video, “Rust for Everyone”, presented by Will Crichton at Jane Street that explores the various tools he’s built to make Rust more accessible to newcomers. It’s not in the video description, but here’s an outline of the tools Will discusses:
Like many people nowadays, I find LLMs an invaluable tool for learning new concepts or vibecoding in unfamiliar stacks and langauages. It’s undeniably a massive accelerator when it comes to quickly iterating and learning.
But it has its downsides. There’s a strong argument to be said that by offloading the challenge of learning something new, your core critical thinking atrophies and your ability to focus slides down the slippery slope of instant gratification.
One of the most frustrating problems in ad-hoc data science projects is broken file paths.
You write a script that loads a model, grabs data, or instantiates a config from your local disk. It works perfectly on your machine, then someone else runs it and… catastrophe: FileNotFoundError: No such file or directory. Uh oh, looks like someone just got bit by hard-coded paths or assumptions about where the script is being run from.
I’ve been working on a small side project that requires embedding an interactive map. I spent some time evaluating different mapping providers - initially OpenStreetMap with leaflet for annotations. OpenStreetMap is great for rapid prototyping since it’s free and doesn’t require any API key, but it doesn’t look great (by default, at least) and it doesn’t have the same rich POI data integration.
I dabbled with Mapbox next and was really impressed. It has end-to-end API coverage for heaps of mapping use cases, and provides lots of examples of how to integrate Mapbox into your choice of language/platform/framework. However, the thing that really stood out to was their onboarding design. Once you create an account and login, Mapbox injects your API keys directly into the example code on the webpage. They literally inject your personal API key into the standard examples for logged in users so that you can copy/paste and immediately start working.
I came across a UI glitch today in my Uber app. At first glance it appears to be a preposterous oversight, the s in Favourites has been orphaned!
I live in Australia, where we closely follow British English spelling - meaning it’s “Favourites” and not “Favorites”. In the world of UI/UX, it’s common to use localisation dictionaries to map strings to locale-appropriate versions. I suspect some UI designer carefully crafted this screen for US English and mapped over to AU English, accidentally committing a tiny typographic crime.
GitHub has a fascinating interview with Linus Torvalds, the inventor of both git and Linux for the 20th anniversary of git.
I published a blog post last night but it never appeared on the site. My GitHub Actions workflow kicked in, my commit hit the server, my Cloudflare build completed with no warnings or errors - everything looked good.
The culprit? Timezone mismatch. I’m writing from AEST (+10, I’m in Melbourne), but Cloudflare Pages Workers builds in UTC (“server time”). Hugo saw my future timestamp and politely ignored the post.
The fix: Use hugo --buildFuture as the build command in Cloudflare Pages settings to include posts “in the future”. I’ll consider this a cautionary tale … it’s not the first time timezones have caused me havoc in production.
Apache Spark 4.0 has been released. It’s the first major version update since Spark 3.0 in 2020.
Here’s some of the highlights I’m excited about:
grepping tortuously long stack traces.DESCRIBE TABLE AS JSON option. I really dislike unstructured command line outputs that you have to parse with awkward bashisms. JSON input/outputs and manipulation with jq is a far more expressive consumption pattern that I feel captures the spirit of command line processing.pip install Spark on small clients without having to ship massive jars around.Databricks has some excellent coverage on the main release and the new pipe syntax specifically.
I’ve been a long time user of Cultured Code’s Things to-do app. It’s slick, has well designed ergonomics, and is perfectly minimalistic. Things’ Markdown support is tasteful and its approach to task management structured but pared back.
They’ve just announced a rewrite of their existing server-side infrastructure stack in Swift, the linked post and blog post are worth a read.
From a technical perspective, I’ve always appreciated its rock-solid proprietary Things Cloud syncing service. In particular, I find it interesting the app asks for Local Network access to enable faster syncing:
uv is a blazing fast Python package manager that aims to displace pip. It’s a really slick tool that lets you go from git clone to executing code with all the dependencies seamlessly. All the standard accolades apply: written in Rust, beautiful terminal UI, well thought-out user ergonomics … all written by Astral, the same company that gave the Python community ruff.
But what makes it so damn fast? Under the rusty hood, the magic is even more impressive. Charlie Marsh, the project lead, presented at Jane Street and revealed some of the inner workings. The whole talk is super interesting, but some standout highlights are:
Today I read through the jq manual cover-to-cover. For those unaware, jq is a popular CLI tool to query and manipulate JSON. It’s also a Turing-complete mini-language with nice functional semantics that fits well into the ethos of composable CLI tools.
It was an exemplar of well-written technical documentation. Concise, well-written, littered with examples, and linking to an interactive playground to test-and-learn.
Some learnings:
jq:$ jq '[.,1]|until(.[0] < 1; [.[0] - 1, .[1] * .[0]])|.[1]'
$ echo '{"search":"hello; world"}' | jq -r '@uri "https://www.google.com/search?q=\(.search)"'
# https://www.google.com/search?q=hello%3B%20world
$ echo '[[1,2],[10,20]]' | jq -r 'def addvalue(f): . + [f]; map(addvalue(.[0]))'
#[[1,2,1], [10,20,10]]
jq.jq; and you can build a jq interpreter in jq - how’s that for bootstrapping!Side note: This is one of my goals for 2025 - read through documentation end-to-end to develop mastery over tools. I’m trying to prioritise selectively depth over breadth.
I just discovered that to capture multiple lines of stdout from a shell script and redirect them to a file, you can simply wrap them in braces!
For example, my “Create a blog post via a GitHub Action triggered on an Issue creation” workflow uses this snippet:
{
echo "---"
jq 'del(.content)' "parsed_issue.json" | yq -P
echo "---"
echo ''
# Inline "content" key for the body
jq -r '.content' "parsed_issue.json"
} > content/micro-blog/"$FILENAME"
“I wonder how much it is insightful to watch someone doing a workflow and to note when discomfort kicks in. That’s a really insightful thing to realize what matters from bitter experience, right? … Experience tells you when to worry about something and when not to worry about it” - Ben Sparks
That is - the rising discomfort of a programmer when employing a new tool, framework, or library is a good window into the ergonomics of how one uses your tool, framework, or library. Source: How I animate 3Blue1Brown | A Manim demo with Ben Sparks. The whole video is worth checking out! It’s a masterclass on how to construct a programatic-animation library and demonstrate how to work within it.