We also have an opportunity here to stop and ask ourselves what it truly means to be human, and what really matters to us in our own lives and work. Do we want to sit around being fed by robots or do we want to experience life and contribute to society in ways that are uniquely human, meaningful and rewarding?

I think we all know the answer to that question and so we need to explore how we can build lives that are rooted in the essence of what it means to be human and that people wouldn't want to replace with AI, even if it was technically possible.

When I look at the things I’ve used ChatGPT for in the past year, it tends to be one of these two categories:

1. A reference for something I’d like to know (e.g. the etymology of a phrase, learning a new skill, generate ideas for a project, etc.)
2. Doing stuff I don’t want to do myself (e.g. summarize meeting notes, write boilerplate code, debug tech problems, draw an icon)

I think most of us knowledge workers have stuff at our work that we don’t like to do, but it’s often that stuff which actually provides the value for the business.

What happens to an economy when businesses can use AI to derive that value that, to this date, only humans could provide?

And what happens to humans when we don’t have to perform meanial tasks anymore? How do we find meaning? How do we care for ourselves and each other?

Think of the people you actually give a shit about in real life. They would like your website, and you would like their websites. Fuck the person you spoke to one time at a party 4 years ago, they don’t need to get play by play updates about the concert you’re at. Your life is not better because 15 people saw your Snapchat story instead of 14.

I think people online should slow down and scale back. Personal websites are so much more, uh, personal. I think of it like sending a text message to someone versus sending them a letter. Seeing an update of any kind to a friend’s website would be so much more interesting than a Facebook status or a profile picture change. Again, maybe I’m the weird one.

Nearly posted this whole article verbatim because it’s exactly how I feel about this site. I know I’m supposed to make this site be a direct reflection of my braaaand (apologies to my friend who is helping me with my braaaand at the moment), but I just want this to be a cool place that people who are interested in me can see things that I’m interested in.

Coming soon: probably more shares from this collection of posts about how the internet used to be fun and how we can make it fun again.

”With the elite titles," he says, "if you make a mistake reporting something that is important to the community, it could have repercussions down the line. You don't want to treat it with an asterisk if it's tainted and just doesn't sit well." Mruczek says he's worried that the handling of the Wiebe record has set a dangerous precedent that could set back the community to the '80s, when people would claim records that were impossible to achieve. Twin Galaxies has long since abandoned its original verification process, which required a photo of the screen showing the high score and a signed affidavit from the player. Now, a player must videotape his or her game according to strict guidelines or perform the game live in front a Twin Galaxies judge.

I’ve been working on unpacking justice, one of my core values, in an attempt to understand why that value means so much to me.

I find rules to be so helpful in making sense of the world. If you know what the expectations are, then you should be able to understand what it takes to excel.

As I get older, I'm realizing that (a) that’s not always a true principle in practice, and (b) not everyone needs those rules to make sense of the world and get ahead in life.

Articles like this (legendary) profile on a Tetris world record holder make me really question my insistence on clearly defined rules.

Because when you think about it, what possible serious repercussions could happen if you botch the title of ‘Tetris world record holder’?

1. The world isn’t as bad as you think, because news reporting aggregates the worst events in the world, giving you a skewed perspective.

2. To truly understand the world, we don’t need more happy-go-lucky stories to make us feel good, but we do need more reporting of bigger trends and what’s driving them. Many of those big picture trends are invisible, but positive.

I found myself smack dab in the middle of a Reddit doomscrolling session this morning when I decided I was sick of the feeling of impending doom. I launched Instapaper instead and found this article.

It serves as a good reminder that we get dopamine hits from reading terrible news, and maybe we should all take some time to break that addiction.

(I’m saying “we” here, but honestly, who reads this stuff? It’s all reminders for me haha)

I saw this post on Matt Birchler's awesome blog and thought I'd join in the fun.

It's interesting to look back on these a few years down the road and see how much/little has changed.

📨 Mail Client: Apple Mail
📮 Mail Server: Fastmail
📝 Notes: Apple Notes
✅ To-Do: Apple Reminders
📷 Photo Shooting: Apple Camera
🌅 Photo Management: Apple Photos
📆 Calendar: Fantastical
📁 Cloud File Storage: Dropbox
📖 RSS: Reeder
🙍🏻‍♂️ Contacts: Apple Contacts
🌐 Browser: Safari
💬 Chat: Slack
🔖 Bookmarks: Pinboard
📑 Read It Later: Instapaper
📜 Word Processing: Google Docs
📈 Spreadsheets: Google Sheets
📊 Presentations: Google Slides
📰 News: AP News (once a week or so)
🎵 Music: Plexamp
🎤 Podcasts: Pocketcasts
🔐 Password Management: 1Password
🎮 First game I play each morning: Retro Bowl
🔈 Podcast editing: Logic Pro X
🎞️ Video editing: Final Cut Pro X
🧮 Code Editor: Visual Studio Code
🚀 Application launcher: Alfred

I first learned Kubernetes ("k8s" for short) in 2018, when my manager sat me down and said "Cloudflare is migrating to Kubernetes, and you're handling our team's migration." This was slightly terrifying to me, because I was a good programmer and a mediocre engineer. I knew how to write code, but I didn't know how to deploy it, or monitor it in production. My computer science degree had taught me all about algorithms, data structures, type systems and operating systems. It had not taught me about containers, or ElasticSearch, or Kubernetes. I don't think I even wrote a single YAML file in my entire degree. I was scared of ops. I was terrified of Kubernetes.

Eventually I made it through and migrated all the Cloudflare Tunnel infrastructure from Marathon to Kubernetes. I didn't enjoy it, and I was way over my deadline, but I did learn a lot. Now it's 2022, and I'm leading a small team of engineers, some of whom have never used Kubernetes before. So I've found myself explaining Kubernetes to them. They seemed to find it helpful, so I thought I'd write it down and share it with the rest of you.

I immediately identified with this post after the author mentioned he was terrified of Kubernetes, because insert Chris Pratt meme here.

This post not only made the point of Kubernetes abundantly clear, it also was unveiled in such a clever and delightful way whereby each problem unveils a slightly more complex way to utilize Kubernetes.

I saved my company half a million dollars in about five minutes. This is more money than I've made for my employers over the course of my entire career because this industry is a sham. I clicked about five buttons.

Oof, this is a very good read that hits pretty close to home. I’ve seen stuff like this in several organizations I’ve worked with.

I wonder why it’s so prevalant?

Embeddings are a really neat trick that often come wrapped in a pile of intimidating jargon.

If you can make it through that jargon, they unlock powerful and exciting techniques that can be applied to all sorts of interesting problems.

I gave a talk about embeddings at PyBay 2023. This article represents an improved version of that talk, which should stand alone even without watching the video.

If you’re not yet familiar with embeddings I hope to give you everything you need to get started applying them to real-world problems.

The YouTube video near the beginning of the article is a great way to consume this content.

The basics of it is this: let’s assume you have a blog with thousands of posts.

If you were to take a blog post and run it through an embedding model, the model would turn that blog post into a list of gibberish floating point numbers. (Seriously, it’s gibberish… nobody knows what these numbers actually mean.)

As you run additional posts through the model, you’ll get additional numbers, and these numbers will all mean something. (Again, we don’t know what.)

The thing is, if you were to take these gibberish values and plot them on a graph with X, Y, and Z coordinates, you’d start to see clumps of values next to each other.

These clumps would represent blog posts that are somehow related to each other.

Again, nobody knows why this works… it just does.

This principle is the underpinnings of virtually all LLM development that’s taken place over the past ten years.

What’s mind blowing is depending on the embedding model you use, you aren’t limited to a graph with 3 dimensions. Some of them use tens of thousands of dimensions.

If you are at all interested in working with large language models, you should take 38 minutes and read this post (or watch the video). Not only did it help me understand the concept better, it also is filled with real-world use cases where this can be applied.

Grandpa loved craft. He didn't put it that way but other people did. My dad said grandpa was a craftsman. I didn’t understand what this meant until one day when we were visiting. We sat down at a restaurant table and it wobbled. We ignored it. But Grandpa dropped as quickly and efficiently as a seal, vanishing underneath the table to fix the cheap, badly-screwed leg with a coin for a screwdriver.

He fixed things often and silently. Grandpa just cared about things working. He had an instinct for not just broken things but soon to be broken things. He would point out risky work, bad decision making in the form of shoddy materials or shifting angles. He was offended by the trace measures left in the world that signified short-term planning. So I learned that this too had something to do with craft. He had a visual vocabulary that amazed me. I think about how he could see these details. He saw choices and constraints and tensions and frictions where I just saw chairs. He saw effort where most people just saw end products.

I’ve got a few posts in mind that I’ll get cracking on soon about my recent malaice and regression towards a mid-life crisis, but this post helped me keep one thing in perspective.

As someone commented on Lobsters, caring is at the root of a craft. How else could you possibly spend your life?

So, if there’s one thing we can praise Spirit Halloween for, it’s maybe not their great urban design elements or place making chops, but just for making it as transparent as possible that this kind of built environment simply isn’t built for long term success.

And instead of trying to build more of it, maybe we should be moving a lot more aggressively to retro fit what we already have.