Making Coding Fun Again

Us engineers have always looked down on UI/UX designers - but those guys were doing it right all along, and it’s our jobs that are at risk now.

We are going to see a new generation of thinkers and designers come into their own with AI. These people have always been blocked off from coding because of the learning curve involved, but now they can think in whatever way feels right, and let the AI take care of the implementation.

I am not saying that this is about whiteboarding or UI mockups. I am saying that for far too long, we have been trying to force human thinking into something a computer can understand – for the first time, a human can think in their own way and the computer gets what they mean.

I think I was asking the wrong questions at the beginning of this article. It’s not that good stuff used to come out of the 80s - it’s that computers and technology were restricted enough, and people had so little time, that every ounce of human effort spent on a problem meant something.

Most of the examples I mentioned started out as a tiny set of useful abstractions that one person started building to solve a personal problem. Then others found them useful, and they endured. We have modern examples of excellent software too, like VSCode, but I think they succeeded in spite of what modern software engineering has become, not because of it.

Humans have to be efficient about what we do because our cognition is the most precious resource in this optimization problem. That might simply mean writing “BROKEN” in the middle of your code so you can forget about it for a bit and go to lunch.

The thing is, there is not much difference between human slop and AI slop, and there’s nothing bad about slop by itself. Slop is just complexity without thinking put into it. The problem with AI slop is that it can create a convincing-looking system, and a massive one at that, but the whole thing is imagined - it has never once met implementation with reality.

Hopefully most of the people reading this remember the Bun rewrite from earlier this year. A refresher: Bun rewrote their entire codebase from Zig into Rust using AI in about four months, with Jarred Sumner claiming the first experiments took about nine days.

I remember a lot of people hemming and hawing and dooming about the AI rewrite. I remember even personally thinking, “wow, bun was interesting, but i may never use it again, it’s slop now”, because of my own experiences with slop. Then a couple of weeks ago, Deno, my preferred runtime, announced that they were joining Cloudflare, and Deno itself would be sunset.

So I had to try Bun. I wasn’t going back to Node.js. Guess what? It’s just fucking fine. They had a concrete goal – Rewrite it in Rust. They had a stellar test suite. The “well-formed questions” were already there. From there, the translation was mechanical. Rust is an especially great target for LLMs, because it’s so well-documented and the compiler errors point us to relevant info and fixes.

Bun works great. I use it for everything now. I wrote my reproducible desktop configuration in Bun, with AI. Most of the tooling and projects in Thursday are written in Bun, too.

It’s basically replaced whatever I was previously using Deno+zx for, which themselves replaced bash+python - now I can write everything in TypeScript. This site you’re reading now is compiled/served by Bun, too.

I vibe coded for a month straight after I lost my job. I wanted to build the perfect coding agent around DeepSeek Harness. I built a ticket kernel+board, a gate system that removed and added tools from the model as needed, tons of look-and-feel improvements to DSH. I thought I was building stuff for myself, it would be fun, but I was completely miserable.

It didn’t use to be this way. Why was it that I was able to complete a 17,000 line refactor of our core libraries at my old team in just one week using GPT-4.1? The refactor survived through the untimely demise of the org.

I remember the response to GPT-4.1. People said it was too careful, too slow, etc. I was delighted with GPT-4.1 though. I think OpenAI had something fucking great at the time, but “conventional wisdom” (which is usually neither conventional nor wise) and how we were already being conditioned to think about AI, prevented it from becoming something great.

The thing is, early coding agents were good at one thing and one thing only - take this text, make it into some other text. That’s incredibly powerful already! Suddenly you could think in your best language, still being a careful coder, but you could automate the mechanical parts of coding away and keep the fun parts for yourself.

This led to a feedback loop. We became more and more willing to trust our agents, and more and more lazy about putting in the high-quality thinking that goes behind a stellar product.

LLMs are still “just” fancy autocomplete. And that’s fine.

I learned something important after writing thousands of lines of AI slop. The slop was never the problem. I’ve had sloppy code all my life. The difference was that the slop was well-understood, and able to be reasoned about, and if I ever needed to excise it, I knew where to start. But these were scripts and systems and services that were running my life and they were slop, and I was fine with that.

Another question I kept hitting: Why were some people able to create cool shit that felt good with AI? Why was my vibe-coded software always falling short? I knew I could do it properly and get the perfect result, but that would be too slow. Somehow, these guys were able to do stuff fast and good.

Someone vibe coded a port of GTA V into the browser. Someone else clean-room reverse engineered the entire Adobe suite and released it for free, open source. Clearly AI is letting some people get stuff done! It’s enabling some kind of thinking!

I suspect that these people understand AI is just another tool in their belt, and they know when to reach for it vs when to sit down and do some thinking/ whiteboarding/what-have-you.

“What do you get if you multiply six by nine?"

“Six by nine. Forty two.”

“That’s it. That’s all there is.”

"I always thought something was fundamentally wrong with the universe.”

– Douglas Adams, The Hitchhiker’s Guide to the Galaxy

Generative AI is great for coming up with plausible answers to questions. However, the question itself must be well-formed - then we can narrow down the search space by building prototypes to find out what feels right.

The people who are excelling with AI understand that there is more than one way to approach any problem. You cannot be absolutist and you have to be willing to step back and throw stuff away.

Hence, guide coding. The developer is back in the hot seat. They are not giving a prompt to the LLM to build something - they are using the LLM as a tool to figure out what they actually want. Then they apply the relevant amount of autonomy to solve that concrete problem. Rinse and repeat.

It is important to realise and then remember, that frontier AI labs are starting to be experiments in making AI that can take over an entire human life and do every single thing a human is able to do.

For most of your day-to-day work, you do not need these massive, ugly, expensive, proprietary behemoths. It’s a work of art, sure, but I’d rather have an office calendar than the Mona Lisa when I’m working.

For most of your day-to-day work you do not need that stuff. This entire blog site, and all Thursday experiments, were guide coded using DeepSeek V4.1 Flash. I spent less than $15 this month on AI, and I’m coming up on 3 billion tokens.

Vote with your wallet. Support open models and open source.

The thing is, frontier labs are actively discouraging people from trying AI at home by pretending that they are the only ones who can do it. Really we should put cheap AI into every laptop and phone, and make sure as many people as possible can use it with no strings attached. The frontier is almost home - I suspect something like Qwen3.8 Flash Next is already enough for a lot of agentic coding work, because the agent should be a multiplier of your own force, not something that steals your job from under you.

An AI service I recently started using is Instinct - a personal assistant that lives in your WhatsApp. This is the first time that I’ve found a generative AI assistant to be useful, simply because it’s there when you need it, then it gets out of the way. This is what I wanted all along with ChatGPT or Gemini. The feature I use it for the most is simply setting reminders. The fact that it’s able to ping me on WhatsApp, a place where I already spend most of my day talking to friends and family, is a game changer for stuff like taking my medication, following up on clients, getting errands done. I also tried it on an online shopping research expedition trying to find a toy from my childhood. This one was less successful, but it at least got me some good leads. This is what AI should feel like!

We all know FAANG is producing slop right now. Or, more defensibly, the default output mode is slop. FAANG is a system composed of several thousands or dozens of thousands of people. It would be patently untrue and incredibly unfair to say they are all slop coders.

There are still people who are powering through, cutting through the slop, and continuing to build for people, even at Facebook or Google.

Facebook is the only American lab that makes an open frontier-level model.

Google is still making delightful UX. I daily drive a Pixel 9a on the stock Google ROM. Disregarding the privacy concerns for a second and the adware doomerism, this is the most delightful phone experience I have had. I bought a Chromebook as a second laptop to take on vacation, watch movies in bed with, draw on. It’s the most delightful computer I’ve owned.

Every time I have a cool idea I find something similar someone at Google is researching (their widget vibe-coding system in Android 17, and more recently the Elephant-Goldfish model).

We cannot reject all of FAANG as bad, it’s just that most FAANG engineers are highly intelligent people forced to produce slop as a result of misaligned incentives in the systems they operate within.

Why do I keep coming back to aviation in this article? I think it’s the safest industry in the world for a reason, and ours could learn some stuff from it.

They understand that while being extra safe has a cost, it’s well worth not being wracked by guilt and fear. It’s also the framework that helped me understand all of this. Let’s look at the situation through some aviation lenses.

The AI is one of the many resources in your crew, just like cabin crew or your first officer in an aircraft. Your family, friends, coworkers, are all resources too – you can learn from them and their experiences to build a better life for yourself and them.

Everyone on a crew does what they do best, and in exchange, we get thousands of safe flights every day. The AI is good at transforming text, writing efficient bash-isms, and writing exploratory code in Python/TypeScript. You are good at thinking. How can you use the AI as a tool to enable your own thinking?

Aviation tries to mitigate the human factors in a problem. Human cognition is a resource in the optimization problem. Tedious tasks that computers do better anyway, like maintaining the aircraft’s heading, pitch and roll, are automated so the humans can focus on what matters – a safe flight from takeoff through landing. The human’s “workload” – the amount of things they have to keep track of – needs to be minimized so they can keep focusing on the things that matter.

Anything safety-critical goes in a standard operating procedure or checklist. Enable the human to forget, so they can focus when it matters. Everything is designed to minimize human friction.

It’s not that pilots don’t make mistakes. It’s that when they do, there are systems to ensure their and the passengers’ contin