It’s easy to fool ourselves into believing the world is normal, sensible, and without rare-but-undeniably-valid edge and corner cases. Maybe you’ve done this to yourself with email. I’m guilty.

One example, we do email-based authentication (no passwords!) because I believe(d) this particular falsehood:

Emails will be received soon after they’re sent

I wish it were always true! Thankfully, there’s a lot of things on the linked list that I don’t believe. Do you?

Every so often, engineers need to pick up a new language. After ~6 years of professional development using Java, with a bit of Ruby sprinkled in, coming to Deliveroo meant that I’d be starting to work on some Go codebases.

So when it came to accepting the offer, I ended up thinking about getting started with learning Go, so I could hit the ground running. In this post I share different ways to get practical experience of the language, as well as thoughts on what Go is like to a newbie

A 12-week, 24-course curriculum covering:

• Different approaches to Artificial Intelligence, including the “good old” symbolic approach with Knowledge Representation and reasoning (GOFAI).
• Neural Networks and Deep Learning, which are at the core of modern AI. We will illustrate the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch.
• Neural Architectures for working with images and text. We will cover recent models but may lack a little bit on the state-of-the-art.
• Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems.

Not every hash algorithm is appropriate in all scenarios, and in fact, very few algorithms are usable in more than a couple of situations. Even worse, using the wrong algorithm will lead in the best case scenario to performance problems, but in the worst case scenario to security issues and even financial loss. Thus, knowing which algorithm to pick for which application is crucial.

Therefore I’ll try to summarize how I approach the topic of hashing, including use-cases, recommended algorithms, and links to other articles.

Chip Huyen:

As machine learning moves towards real-time, streaming technology is becoming increasingly important for data scientists. Like many people coming from a machine learning background, I used to dread streaming. In our recent survey, almost half of the data scientists we asked said they would like to move from batch prediction to online prediction but can’t because streaming is hard, both technically and operationally…

Over the last year, working with a co-founder who’s super deep into streaming, I’ve learned that streaming can be quite intuitive. This post is an attempt to rephrase what I’ve learned.

Mahdi Yusuf is back with another set of Architecture Notes, this time covering everyone’s favorite “data structure server”, Redis:

Rather than iterating over, sorting, and ordering rows, what if the data was in data structures you wanted from the ground up? Early on, it was used much like Memcached, but as Redis improved, it became viable for many other use cases, including publish-subscribe mechanisms, streaming, and queues.

This week we’re joined by On Freund, former VP of Engineering at WeWork and now co-founder & CEO of Wilco. WeWork you may have heard of, but Wilco maybe not (yet).

We get into the details behind the tech and scaling of WeWork, comparisons of the fictional series on Apple TV+ called WeCrashed and how much of that is true. Then we move on to Wilco which is what has On’s full attention right now. Wilco has the potential to be the next big thing for developers to acquire new skills. Wilco aims to be the ultimate simulator to gain new skills on a real-life tech stack. If you want to skip ahead, you can request access at trywilco.com/changelog — they are moving our listeners to the top of the waiting list.

User Uptrenda asked HN about their favorite data structures, sharing their own to get it started:

I’ll start: bloom filters. Lets you test if a value is definitely NOT in a list of pre-stored values (or POSSIBLY in a list - with adjustable probability that influences storage of the values.)

Good use-case: routing. Say you have a list of 1 million IPs that are black listed. A trivial algorithm would be to compare every element of the set with a given IP. The time complexity grows with the number of elements. Not so with a bloom filter! A bloom filter is one of the few data structures whose time complexity does not grow with the number of elements due to the ‘keys’ not needing to be stored (‘search’ and ‘insert’ is based on the number of hash functions.)

The responses are numerous with a lot of signal, which is increasingly rare on the orange website these days. Worth a read!

How do beginners learn Go? This episode is meant to engage both non-Go users that listen to sister podcasts here on Changelog, or any Go-curious programmers out there, as well as encourage those that have started to learn Go and want to level up beyond the basics. On this episode we’re aiming to answer questions about how to learn Go, identify resources that are available, and where you can go to continue your learning journey.

Matt Holt (creator of Caddy) gives a deep dive into how the web server works and some of the design decisions and patterns that make it reliable, extensible, and delightful.

One of the interesting things about the design of Caddy is that its core is just configuration management. It pushes out all other functionality into modules so that basically the only thing left in core is accepting and reacting to configuration changes.

We’ve linked this up before, but I’m re-upping it because Tauri co-creator Daniel Thompson raved yesterday during our (shipping soon) conversation for The Changelog. He thinks it’s the best way to learn Rust.

Functional programming is powerful in many ways, but it can be difficult for devs to learn because the jargon stops them in their tracks. If that’s been your experience, this glossary of terms and examples might help. Pair that with listening to Eric Normand talk about FP in simple terms with us on The Changelog and JS Party and you’ll be well on your way.

A collection of ASCII data structures, state machines, logical structures, and more.

People tend to be visual: we use pictures to understand problems. Mainstream programming languages, on the other hand, operate in an almost completely different kind of abstract space, leaving a big gap between programs and pictures. This piece is about pictures drawn using a text character set and then embedded in source code.

Excellent prose and super-useful diagrams make this post from architecture notes a 10/10 🏆

I almost brought up the Law of Demeter on Go Time a couple weeks back:

That being said, sometimes you just have to follow the other person’s path until you realize when it doesn’t actually work for you. I’m totally fine with cargo-culting some sort of rule… I was gonna say the Law of Demeter, but that one’s too hard to explain. What’s a very simple – DRY, right?

See how quickly I abandoned ship there? Ok, it’s not that hard to explain. I just didn’t feel like taking the time to explain it when it wasn’t even my point. Good thing Double Agent Caleb Hearth did a nice write-up on Test Double’s blog about it!

If you’ve ever wondered how the Linux kernel does its thing:

The goal is simple - to share my modest knowledge about the insides of the linux kernel and help people who are interested in linux kernel insides, and other low-level subject matter. Feel free to go through the book Start here

Bonnie I-Man Ng:

I was working on bioinformatics a few years ago and was amazed by those single-word bash commands which are much faster than my dull scripts, time saved through learning command-line shortcuts and scripting.

Recent years I am working on cloud computing and I keep recording those useful commands here. Not all of them is oneliner, but I put effort on making them brief and swift. I am mainly using Ubuntu, Amazon Linux, RedHat, Linux Mint, Mac and CentOS, sorry if the commands don’t work on your system.

This guide by Josh Comeau is Josh doing what Josh does so well: taking complex/arcane/technical topics and making them approachable & fun for his audience.

It takes years of practice to become a terminal guru, but here’s the good news: we can take a shortcut. We don’t really need to know 98% of the stuff you can do with a terminal. If we focus on the most-important critical fundamentals, we should be able to become comfortable with the command line in a remarkably short amount of time. ✨

That’s what this blog post is all about.

I thought Ali and Jen had some excellent advice in this segment of our JS Party episode on web dev for beginners, so I’m re-sharing it here in hopes that you might share it with people you know who may be stuck in the early phases of their web development path.

I also like what I had to say (imagine that!)

One thing that is true about software development in general… is that it’s actually really hard. And we need to just say that. Because you’re gonna hit bumps. You’re gonna hit walls. And some people will power through those walls and other people won’t, and that’s just the way life is…

But I think having something real that you want to exist… having a real project that you want to see into the world helps you get through those moments.

Adam Chalmers:

Over the last few weeks I built my own DNS client. Mostly because I thought dig was kinda clunky. Partly because I wanted to learn more about DNS. So here’s how I built it, and how you can build your own too. It’s a great weekend project, and I learned a lot from finishing it.

DNS is simple enough that you can grok it quickly, but still complex enough that Adam learned a ton along the way: reading RFCs, network sockets, Bitvec, and more.

Has Go caught your interest, but you just haven’t had the time/opportunity to really dig into it? Are you relatively productive in your current language/ecosystem but wonder if the grass truly is greener on Go’s side of the fence? If so, this episode’s for you!

Jen Looper from Web Dev for Beginners and Front-end Foxes joins Jerod and Ali to discuss the exciting (but also intimidating) prospect of getting in to web development in 2022! Where should you start? What technologies should you focus on? Is it better to go all-in on a framework or stick with the fundamentals? Stuff like that!

A falsehood is an idea that you initially believe was true, but in-reality it is proven to be false.

E.g. of an idea: valid email address exactly has one @ character. So, you will use this rule to implement your email-field validation logic. Right? Wrong! The reality is: emails can have multiple @ chars. Therefore your implementation should allow this. The initial idea is a falsehood you believed in.

I love this list with my whole heart. We need to do a show on this…

What does it take to master a programming language like Go? Joining us is the author of Mastering Go to help us answer that very question and to discuss the third edition of the book.

George Mathew:

For a software engineer, the hardest thing about developing Machine Learning functionality should be finding clean and representative ground-truth data, but it often isn’t. If you already have a source of good quality data (perhaps because it is already gathered by your application), here are some obstacles that still lay ahead of you

From the difficult to grasp concepts to the varied (and variable quality) software you’ll need to explore and manage the data, there’s still a lot of obstacles in our path before we can put a model to good use.