As of now, unlike Python or NodeJS, Go doesn’t allow you to specify your development dependencies separately from those of the application. However, I like to specify the dev dependencies explicitly for better reproducibility. While working on a new CLI tool1 for checking dead URLs in Markdown files, I came across this neat convention: you can specify dev dependencies in a tools.go file and then exclude them while building the binary using a build tag. ...

I love dynamically typed languages as much as the next person. They let us make ergonomic API calls like this: import httpx # Sync call for simplicity r = httpx.get("https://dummyjson.com/products/1").json() print(r["id"], r["title"], r["description"]) or this: fetch("https://dummyjson.com/products/1") .then((res) => res.json()) .then((json) => console.log(json.id, json.type, json.description)); In both cases, running the snippets will return: 1 'iPhone 9' 'An apple mobile which is nothing like apple' Unless you’ve worked with a statically typed language that enforces more constraints, it’s hard to appreciate how incredibly convenient it is to be able to call and use an API endpoint without having to deal with types or knowing anything about its payload structure. You can treat the API response as a black box and deal with everything in runtime. ...

While I tend to avoid *args and **kwargs in my function signatures, it’s not always possible to do so without hurting API ergonomics. Especially when you need to write functions that call other helper functions with the same signature. Typing *args and **kwargs has always been a pain since you couldn’t annotate them precisely before. For example, if all the positional and keyword arguments of a function had the same type, you could do this: ...

I needed to integrate rate limiting into a relatively small service that complements a monolith I was working on. My initial thought was to apply it at the application layer, as it seemed to be the simplest route. Plus, I didn’t want to muck around with load balancer configurations, and there’s no shortage of libraries that allow me to do this quickly in the app. However, this turned out to be a bad idea. In the event of a DDoS1 or thundering herd2 incident, even if the app rejects the influx of inbound requests, the app server workers still have to do a minimal amount of work. ...

You can use @dataclass(frozen=True) to make instances of a data class immutable during runtime. However, there’s a small caveat—instantiating a frozen data class is slightly slower than a non-frozen one. This is because, when you enable frozen=True, Python has to generate __setattr__ and __delattr__ methods during class definition time and invoke them for each instantiation. Below is a quick benchmark comparing the instantiation times of a mutable dataclass and a frozen one (in Python 3.12): ...

When I started my career in a tightly-knit team of six engineers at a small e-commerce startup, I was struck by the remarkable efficiency of having a centralized hub for all the documents used for planning. We used a single Trello board with four columns—To-do, Doing, Q/A, Done—where the tickets were grouped by feature tags. We’d leverage a dummy ticket as an epic to sketch out the full plan for a feature and link related tickets to it. The rest of the discussions took place in Slack or GitHub Issues. ...

I’ve always had a thing for old-school web tech. By the time I joined the digital fray, CGI scripts were pretty much relics, but the term kept popping up in tech forums and discussions like ghosts from the past. So, I got curious, started reading about them, and wanted to see if I could reason about them from the first principles. Writing one from the ground up with nothing but Go’s standard library seemed like a good idea. ...

Despite using VSCode as my primary editor, I never really bothered to set up the native debugger to step through application code running inside Docker containers. Configuring the debugger to work with individual files, libraries, or natively running servers is trivial1. So, I use it in those cases and just resort back to my terminal for debugging containerized apps running locally. However, after seeing a colleague’s workflow in a pair-programming session, I wanted to configure the debugger to cover this scenario too. ...

Data classes are containers for your data—not behavior. The delineation is right there in the name. Yet, I see state-mutating methods getting crammed into data classes and polluting their semantics all the time. While this text will primarily talk about data classes in Python, the message remains valid for any language that supports data classes and allows you to add state-mutating methods to them, e.g., Kotlin, Swift, etc. By state-mutating method, I mean methods that change attribute values during runtime. For instance: ...

Despite being an IC for the bulk of my career, finding my groove amidst the daily torrent of meetings from the early hours has always felt like balancing on a seesaw during a never-ending earthquake. Now, pair that with the onslaught of Slack inquiries and the incessant chiming of email notifications, and you have a front-row ticket to the anxiety circus. There are days when carving out a single hour of focus time is a wild goose chase, pushing me to work after hours to get stuff done, followed by a guilt trip about screen-gazing my life away. ...

Ever been in a situation where you landed a software engineering job with a particular tech stack, mastered it, switched to another company with a different stack, nailed that too, and then found yourself in a third company that used the original stack? Now, you suddenly sense that your hard-earned acumen in that initial stack has not only atrophied over the years but also a portion, or all of it, has become irrelevant, making it a bit of a struggle to catch up with the latest changes. ...

Adopting existing tools that work, applying them to the business problems at hand, and quickly iterating in the business domain rather than endlessly swirling in the vortex of technobabble is woefully underrated. I’ve worked at two kinds of companies before: One that only cares about measurable business outcomes, accruing technical debt and blaming engineers when no one wants to work with their codebase, ultimately hurting the product. Another that has staff engineers spending all day on linter configurations and writing seldom-read RFCs while juniors want to ditch Celery for Kafka because the latter is hip. ...

I like writing custom scripts to automate stuff or fix repetitive headaches. Most of them are shell scripts, and a few of them are written in Python. Over the years, I’ve accumulated quite a few of them. I use Git and GNU stow1 to manage them across different machines, and the workflow2 is quite effective. However, as the list of scripts grows larger, invoking them becomes a pain because the tab completion results get cluttered with other system commands. Plus, often I even forget the initials of a script’s name and stare at my terminal while the blinking cursor facepalms at my stupidity. ...

There are a few ways you can add URLs to your Markdown documents: Inline links [inline link](https://example.com) This will render as inline link. Reference links [reference link] Define the link destination elsewhere in the document like this: [reference link]: https://example.com This will render the same way as before, reference link. Footnote style reference links footnote style reference link[^1] Define the link destination using a footnote reference: [^1]: https://example.com This will render a bit differently with a clickable number beside the origin text that refers to the backref at the bottom of the document. Like this: footnote style reference link1. ...

I’m one of those people who will sit in front of a computer for hours, fiddling with algorithms or debugging performance issues, yet won’t spend 10 minutes to improve their workflows. While I usually get away with this, every now and then, my inertia slithers back to bite me. The latest episode was me realizing how tedious it is to move config files across multiple devices when I was configuring a new MacBook Air and Mac Mini at the same time. ...

Every once in a while, I love browsing the Wayback Machine1 to catch a glimpse of the early internet. I enjoy the waves of nostalgic indie hacker vibes that wash over me as I type a URL into the search box and click to see an old snapshot of the site frozen in time. Being a kid of the early ’00s, I missed the spectacular cosmic genesis of the ’90s internet in its entire nascent glory. However, I did briefly get a coup d’œil of the raw, untainted web 1.0 right before SEO firms, ads, pop-ups, modals, autoplays, and heavy frontend frameworks started prowling and gentrifying the whole space into an anxiety-inducing corporate circus. ...

This site1 is built with Hugo2 and served via GitHub pages3. Recently, I decided to change the font here to make things more consistent across different devices. However, I didn’t want to go with Google Fonts for a few reasons: CDN is another dependency. Hosting static assets on GitHub Pages has served me well. Google Fonts tracks users and violates4 GDPR in Germany. Google Analytics does that too. But since I’m using the latter anyway, this might come off a bit apocryphal. I wanted to get a few extra Lighthouse points. Turns out, it’s pretty easy to host the fonts yourself. ...

Suppose, you have a function that takes an option struct and a message as input. Then it stylizes the message according to the option fields and prints it. What’s the most sensible API you can offer for users to configure your function? Observe: // app/src package src // Option struct type Style struct { Fg string // ANSI escape codes for foreground color Bg string // Background color } // Display the message according to Style func Display(s *Style, msg string) {} In the src package, the function Display takes a pointer to a Style instance and a msg string as parameters. Then it decorates the msg and prints it according to the style specified in the option struct. In the wild, I’ve seen 3 main ways to write APIs that let users configure options: ...

I was curious to see if I could prototype a simple load balancer in a single Go script. Go’s standard library and goroutines make this trivial. Here’s what the script needs to do: Spin up two backend servers that’ll handle the incoming requests. Run a reverse proxy load balancer in the foreground. The load balancer will accept client connections and round-robin them to one of the backend servers; balancing the inbound load. Once a backend responds, the load balancer will relay the response back to the client. For simplicity, we’ll only handle client’s GET requests. Obviously, this won’t have SSL termination, advanced balancing algorithms, or session persistence like you’d get with Nginx1 or Caddy2. The point is to understand the basic workflow and show how Go makes it easy to write this sort of stuff. ...

I was cobbling together a long-running Go script to send webhook messages to a system when some events occur. The initial script would continuously poll a Kafka topic for events and spawn new goroutines to make HTTP requests to the destination. This had two problems: It could create unlimited goroutines if many events arrived quickly It might overload the destination system by making many concurrent requests In Python, I’d use just asyncio.Semaphore to limit concurrency. I’ve previously written about this here1. Turns out, in Go, you could do the same with a buffered channel. Here’s how the naive version looks: ...