Working on your bike is fun. Working on the bike you need to ride to work tomorrow is stressful.
I miss writing toy software. It’s fun. But inevitably I want to use said software and that’s where the trouble starts. You find all the bugs and none of the time to fix them.
With that said it's a challenge and a chore on its own to curate what "AI" writes. So it's definitely not all roses.
Disclaimer: I'm still early in the experimentation phase. We'll see if i hold these opinions in a few months.
I needed to get an invoice out in a timely fashion. As much as I wanted to use my app, I found certain kinks I needed to work out (with styling, adding addresses, etc.) -- This was where I realized what you have articulated.
At some point, it becomes better to prioritize the "fun" in working on my bike, and the "usefulness" of the daily driver bike.
In doing so, perhaps the fun/usefulness of each converges over time.
Actually, I really like this analogy because
1) We can’t really work on non-trivial fixes for our cars because they are too complex
2) We’ve totally structured our living situations around these over-complex beasts
3) Sometimes they do just break down at random, and it becomes a mini-crisis for everyone involved, which can’t really be resolved without missing a day of work to go to a professional
4) The bike is in exactly the same situation, except you can actually fix most bike problems yourself with a little kit
And yet, it is seen as a serious adult thing to have a car, instead of a bicycle.
Some thoughts:
Outside of, like, customer service, most places are throughput-oriented and it doesn’t even really matter all that much if somebody misses a day.
If we really cared about “uptime” of employees, we’d all be living in bicycle range of our jobs, and able to fix our bikes. Maybe there’d be a bus route as well.
If my bike flats and I'm out of tubes and I have a busy day planned, I have to take it to a local bike shop but those generally close pretty early. If I miss the window to take it to an LBS then I'm SOL. With a car there's generally a developed enough ecosystem that I can get a roadside assistance type service to put a spare on the car until I can actually get my car into a shop.
This is a good analogy for SaaS vs using a tinkerer tool. A tinkerer tool often can be customized exactly to your needs. I can work on my bike at 10 PM in my garage after dinner as long as I have the parts. But sometimes your tinkerer tool is going to leave you working all night or you'll be SOL. But SaaS is generally a full-time job for other folks. Sure you pay money, but there's fewer situations you're SOL.
(Personally when my bike is inop and I need to head to work I use transit.)
And then reality hits. You're married and your partner's job is on the other side of the city. You just bought and house and now you want to switch jobs but you'd be limited to only jobs in biking distance. eTc....
I bike to work (was 4 miles, now 2), but it's unrealistic to expect people to be in bicycle range of their jobs.
How about working on your bike that other people also work on daily? That's work.
Seems like the link changed, and thus not caught by the dupe detector.
Sad but true. I especially feel that comment about losing the most “joyful” part.
I don’t like this either but every time I use LLMs it feels like we’re talking about completely different things. It moves waaay to fast and makes bad decisions at every turn, if I accepted them all it would be complexity deadlock within a week tops. Pooping out boilerplate sure but then you’re generally holding it wrong anyway (or there’s an opportunity to automate things). Plus even if you don’t have the time to automate it, sure, but then are you enjoying the act of shitting out your own boilerplate?
Out of the things I consider fun the LLM is at best a good rubber duck. It needs constant hand-holding, sometimes polluting the context window (and physical space) with a barrage of poorly written code. Code is bad, we’re trying to minimize it. No? At least that’s how I think: what’s the minimum* amount of code that can solve this problem?
*: minimum in a brain complexity sense, not char count. They correlate strongly though
GameBoy Advance game (difficulty = 3/10, time = 2 weeks)
Physics engine (difficulty = 5/10, time = 1 week)
Threaded Virtual Machine (difficulty = 6/10, time = 1 week)
POSIX shell (difficulty = 4/10, time = 3-5 days)
But regardless of the time estimates, I appreciate the idea!
That said, I feel the conclusion on learning with LLMs deserves some more nuance. It really depends on how you use them.
This is a terrible prompt for learning:
> Implement this solution for me
This is an AMAZING prompt for learning:
> Please give me an overview of ELF. Focus on the highest level abstractions, more of the 'why' than the 'how'
Certainly one can argue that it takes something away, in that you did not have to conduct the research yourself whenever a question occurred. However, if you are engaging in honest intellectual work (i.e. really thinking), then having a Socratic teacher always ready to discuss your questions is a profound accelerant to the learning process.
I decided to constrain myself to one week per project - whatever I could get done in a week was what I’d do for this project. The experience of going from zero to something useable in either a new language or a new framework or a new field in a week was enormously confidence-building - learning that I was actually pretty good at this whole programming thing and breaking through a bunch of walls I’d run into previously in my professional career gave me an incredible amount of trust in myself to be able to take on new challenges that really helped me when I was looking for a new job. At the same time, it reminded me what I loved about programming - in a week, I was able to build something that solved a problem I had or that someone I knew had, and I got to do so in a creative and intellectually challenging fashion.
If ever you find yourself in a position to take a couple months off between jobs, skip the leetcoding or whatever else and just build some toy projects - you’ll be amazed how much you already know.
I am primarily a backend engineer, though I am capable on the front end, just not particularly fast. I am especially slow at the voodoo that is CSS. In the past, I would have been turned off from doing some end-to-end personal projects because I know I would have been bogged down just battling CSS for hours and hours (it's particularly problematic that I'm slow at CSS but also a stickler for how I want things to look). With AI tools I can literally just say "make it pretty". It gets me about 85% of the way there, but importantly it gets me to the point where it puts in all the styling framework and then I just have to fix bugs/edit/tweak it, which goes very quickly.
So now I've found myself much more likely to build personal projects because I know I can get past things that in the past felt a bit like quicksand to me.
On the one hand, the machines can spin up these bridges over unfamiliar code chasms.
But on the other hand, you (at least I) have no investment in the code. No familiarity with it. No "skin in the game" when I can tell the AI to just redo it.
One way around that is by using the "Chat bots as search engine", and then, rather than cut and pasting stuff into your project, type it in by hand. Even if you're just straight up copying it, by typing it in you have a bit more connection to it. When I do that I can better see the unfamiliar patterns rise out of the code. It's much better for me than just reading it.
Also gives me opportunity to tweak it as I like, which can break things, which can teach me things.
Mind, I've been advocating this "type it, don't paste it" philosophy for years and years, for any code snippet found in a book or tutorial or wherever. It's just now with the bots, the code is more tuned to our exact use case, and the incentive for moving it over is less.
But I find grabbing a blob of code, pasting it into my project, and getting errors so that I can tell the bot whats wrong is not particularly satisfying.
Watching Claude figure out its own errors even less so. (It's very cool to watch, just not very joyful to me outside of "wow, that's cool".)
On the other hand, I have no problem pasting a snippet back with a "what exactly is this doing here" to get better explanations of what I'm seeing.
That said, my goal for this was to wear the hair shirt (and also this was before GenAI was really a Thing) - if you just want to make something neat, absolutely vibe your way there.
If you can't, it means there's something there you don't understand, and you lost an opportunity to learn. Of course, this is also true for reusing libraries and stuff.
Within a job, what matters is the _team learning_, and it's roughly the same idea. At least one person in the team should be able to explain anything the team does. Trusting it to an AI puts the whole team in a precarious situation, even if it is something as simple as a CSS layout.
I'm the opposite of you (my strength is in voodoo) but my experience is the same. Whereas before I'd spend 2-3 weekends getting bogged down on elements that aren't my strengths and never complete, now I can easily whip up a mostly functioning web app (just for me) in just a few hours.
Deployment was... run update scripts, push ASP files via FTP.
Now I know there are newer ways to do things, and I'd love to set up CI/CD in my personal life, but every time I want to do a "toy project" I get hung up researching and thinking about my own personal software development life cycle.
What you choose for hosting and deployment for your toy projects?
I found the trick with the personal projects was to pick the thing I was trying to learn with that project and focus on that - if I was trying to learn React, don't also try to learn kubernetes or lambdas or anything else. You really have to be comfortable with your project kind of sucking for all this to work.
So I'd say if you want to learn proper modern deploy + CI/CD + etc, take a project where you already know the rest of the stack (hell, make it Hello World if you have to) and use that. Keep things simple and in scope, get something stupid working, and then iterate to make it better.
(I also code like a psychopath - I'm constantly rewriting what I'm writing while I'm writing it, so a) slowing down and b) telling interviewers what I'm doing and why made that look less chaotic.)
I'm also not the person you want to hire if you're looking for deep algorithmic knowledge, though - I've got a long career of delivering high impact systems and solutions, but if you want someone to rewrite the index parsing algorithm in your mature database software to minimize lookup speed, you've got the wrong guy.
I would love the opportunity to take ~6 months off, but I feel scared that if I just quit, it might take me a longer time to find a job again.
The fear was real, for sure, but it honestly made a huge, huge difference for both my mental health and my employability afterwards. I also got to spend some time figuring out what I actually wanted to be doing, so I had a much clearer picture of where my skills and interests aligned after, which helped the search as well.
http://mikehadlow.blogspot.com/2012/05/configuration-complex...
It took me a solid decade to accept these lessons. I cannot build a product that even remotely competes with things like Visual Studio and .NET tooling. How many hours would it take a solo developer to recreate a debugger experience that anyone else could be bothered to look at for more than 5 seconds?
The hardest part of building something configurable is the UI/UX. The moment you step outside of the host language, your tooling support goes to zero. Sure, you can pick other popular languages as the language of configuration (SQL, python, Lua, etc.) and piggyback off those ecosystems. But, you still have to build a way for a "non programmer" to visit all of the patchwork sites of "configuration" in a coherent way. Telling the business people to go edit a folder full of .py and .sql files will probably not be received well. Wrapping all of this with a fancy web interface doesn't make the problem less severe either (it makes it substantially worse).
If you have a way to build/deploy your product in <60 seconds from source, worrying about elaborate configuration and customization architecture seems extremely idiotic to me now. The best path I've found is something approximating a common library and a per-customer executable that consumes it and implements the expected contracts. Each customer's project lives in a subfolder on the same monorepo. The power of the hierarchical filesystem is tragically understated throughout our industry.
I don't need to let a computer write my software. I already know how, and I enjoy it. I need the computer to do the hard part (gathering requirements, speaking with stakeholders, etc).
Software is a joy.
I had a shift of perspective at some point where I realized that software wasn't just fun to create, but you can solve problems with it, including your own, which is incredibly rewarding and kinda feels like wizardry.
Ultimately I find both to be enjoyable, though in terms of career and life outcomes, focusing on solving real problems and producing tangible outcomes has had more of an impact than noodling with neat implementations that nobody but me cared about.
Turns out if you solve your own problems (or just publicly work toward solving them), there are often other people who also struggle with the sam problems, who may throw money your way ;-)
Learning exercise lol.
Wanna do some creative thinking? Read some books, learn how normal people think. Explore latest datasets. That's where the meat is. 98% of what you read online has been written by super uncreative(boring) people. They have no imagination whatsoever, IQ is well statistically speaking not too flattering.
Internet is ran by the bottom. It's slop by the bottom, for the bottom. So you need to hang out with creative people.
Tech people write the most boring blog posts because it's aimed at getting hired. Nothing of substance is shared. That's why gwern stands out like a sore thumb. The majority of human beings are incapable of creative thought.
For example, I wanted to know if I can make 1 Kubernetes cluster span multi region, multi cloud. So I slapped TailScale for networking, replaced etcd with multi-region PostgreSQL Aurora, and span the Kubelets between my Raspberry Pi, Digital Ocean VMs, and AWS EC2. And then as the "customer app", I run CockroachDB, rqlite, and tiDB on it (one at a time, I don't want to burn money for this).
It was janky, zero SLA, I likely mis-tuned all the databases, and cost a bit of money :(. But it totally worked, all the db nodes can discover each other. I was satisfied and that's enough.
Another example would be writing my own Raft-backed database, similar to the ToyDB Rust project posted here. Is the DB useful? No. But it is so much fun and I learned a lot.
My AWS S3 bucket and private Git repo are a graveyard full of toy projects. My own Dropbox clone, Pinterest clone, Delicious clone, subset of Google Maps clone, etc. etc. are all RIP in there.
Can I ask what you had running on that cluster?
Not exactly fun :)
What's interesting is that the TailScale network allows the local daemon be exposed on the internet with small settings change. Pretty revolutionary for personal/home IoT projects.
If you're interested in writing Raft-backed databases you might be interested in my talk at GopherCon 2023. It walks through doing exactly that, step by step: https://www.youtube.com/watch?v=8XbxQ1Epi5w
I get where this is coming from, and I even agree with it today, but I also want to tag it as "don't cache this opinion too hard". It's interesting to notice when and how our advice for getting help from AI is different from our advice for getting help from other humans. It would be kind of odd, wouldn't it, to put a paragraph at the bottom of a blog post that said "by the way if you have friends who are expert programmers, I don't recommend asking them for help." I think there are two clear reasons that feels odd: 1) expert friends can actually answer your questions and get you personally unstuck, which is huge, and 2) expert friends usually understand why you're doing what you're doing and that they're supposed to help do it yourself instead of just doing it for you.
One thing I bet few people have tried (because I haven't tried it myself) is actually asking an LLM to guide you like an expert friend would, instead of just spitting out code to solve your problem. Maybe they're bad at that, I wouldn't be surprised. But if so, I bet in a year or two they'll be amazing at it. It might be good to build the habit of clarifying what sort of help you need, instead of assuming an LLM will give you the wrong kind of help?
AI is not an expert programmer [today]. and it doesnt take an expert programmer to arrive at that conclusion.
I don't care if it's a human or a robot telling me, I just want to get unstuck and I've been getting unstuck more since the advent of LLMs than in the rest of my programming career. Digging through bad documentation and Googling for old form posts of similar-but-not-quite-the-same-issues was always the worst part of programming to me, and I'm glad there's a tool that finally solved that problem.
It feels like Claude and ChatGPT have both become more sycophantic over the past months.
Got a few PRs for you when you get to this, friend.
The tip I can share is: publish them on GitHub.
You'll find out some of them are useful to a lot of people.
I have multiple toy repos with 20+ stars and one with 200+ stars.
Just put one screenshot and simple instructions to run on README.md, no need to write a manual.
As simple as `npm install && npm run start` is enough instruction for most.
The screenshot is important tho.
The number one question I have as a hiring manager is always "can this person actually write code". A GitHub profile with 2-3 projects with multiple commits to each one answers that question really well. I won't filter out people who don't have that - lots of great engineers have never published code before - but the people who do have that are much more likely to get picked for the phone screen.
If you don't have an impressive GitHub profile, that's fine. To your point - the absence of it is not a penalty, and the presence of a good one is a very positive signal. But the presence of one that turns out to be a dud is a negative.
Examples: https://github.com/bbkane
The whole thing with getting people to like your work, using it for clout or padding your resume has an appeal to it that I won't dismiss, but all that takes the fun out of it.
I just don't have much else I actually enjoy in life, so ruining it with peer-pressure, chasing clout, impressing people, worrying about career prospects is a buzz-kill. I used to think that if I am passionate about something like this and I get really good at it, maybe it will help my career and prospects but the real world doesn't work that way. it sucks the joy, fun and passion out of you if you let it.
All that to say it is perfectly fine to like coding for the sake of enjoying it and nothing more. Others play a sport (or watch it), work on arts & crafts, or raise chickens or whatever, I code. I am not particularly skilled or great at coding either, I just enjoy it, and that's it.
If you have fun coding, be protective of your joy! even if you don't take it to the extreme like I do. that's my only point.
It takes regex(s), searches attribute values and/or inner text, and applies a given CSS rule to elements that match. The UI is awful (regex escaped to be inside a json string escaped to be inside a textarea all parsed by javascript) but I know how to use it.
It can probably be used for many things, but I just use it to set visibility: none on elements that link to sites I don't ever want to see (facebook, twitter, linkedin, popular pay/register-walled sites, etc). For example, my HN and reddit views just have blank lines where links any of those sites would normally be. For the most part, I add a site once, and forget they ever existed (except when their CEOs do awful things).
1. Because half her friends and family are on iOS, and that means fighting the App Store. (This is a social problem essentially, in fighting Apple)
2. Because networking is hard. How would you have shared recipes with a computer in the C64 days? Email? BBS? (There are partial technical solutions to this, but they would require people to run something like friend-to-friend overlay networks)
3. Because most stuff happens in web browsers and that means pay-to-play, or vendor lock-in, or using AWS free tier and being a programmer. (Ass, grass, or cash, nobody hosts for free. Friend-to-friend networks may also help with this)
4. Because a recipe manager with sharing is best implemented as just emailing your recipes to your friends and storing them as txt files locally. Anything more complicated is beyond the scope of a Visual Basic-style drag-and-drop WYSIWYG anyway
5. When was drag-and-drop enough? The widgets need code behind them to save and open files, right?
6. You might be kinda onto something, and the longer I write async code I more I think the programming world is ready for another big pruning. Like when structured programming said "No goto, no long jumps, if-else and exceptions is all you need", we might be ready for "A single control thread which never blocks and never awaits, and delegates to worker tasks, is all you need until you are building some C100k shit"
Because there's no money in it.
> Repl.it
Sorry, Grandma has never heard of those fancy-pansy platforms.
In the age of powerful computers, you can use Hypercard on an emulated Mac, you can use any number of hypercard-clones out there. She can just use Google slides. etc.
Am I the only one using LLMs as if they were a search engine? So before LLMs I was searching on Google things like "pros cons mysql mongodb". I would read the official documentation of each db, forums, blog posts, stackoverflow entries, etc. It was time consuming on the searching side. The time it took to read all the sources was fine for me (it's learning time, so that's always welcomed). Now with LLMs, I simply prompt the same with a little bit more of context "pros and cons of using mysql vs mongodb when storing photos. Link references". So, I get a quick overview of what to keep an eye on, and the references are there to avoid relying on hallucination.
It's true that sometimes I go ahead and say "give me a data schema for storing photos metadata in postgres. I wanna keep X in a different table, though" (or something like that). But I do that because I know very well what the output should look like (I just don't wanna spend time typing it, and sometimes I forget the actual type that I should use (int vs integer?)).
In near future, companies will probably be able to pay lots of money to have their products come up better in the comparison. LLMs are smart enough to make the result seem "organic" -- all verifiable information will be true and supported by references, it will only be about proper framing and emphasis, etc.
It pulls in info from the docs, forums, code samples, shows me how things are connected, and may even suggest why something is good or bad.
Plenty of opportunity to look up docs I didn’t even know about yet.
"give me the terraform configuration for an AWS ECS cluster with 2 services that can talk to each other, with one of them available publicly and the other one private"
Occasionally to give some small self-contained algorithms, for example:
Give me a way to format the difference between dates as human-readable text. For example: 1 day, 16 hours.
I quite like the search-first LLMs like Gemini and Copilot for this reason. They give you links which you can use to verify the output and seem to be less prone to directing you to SEO spam than Google Search/Bing.
Although I do partly think that search engines today are delivering poor performance compared to what they delivered historically so LLMs are benefiting from that.
A recent example is when I implemented a (Kubernetes) CSI driver that makes /nix available in a container so you can run an empty image and skip a lot of infra to manage.
I talked to it a bit and eventually it wrote a Nix derivation that runs the CSI codegen for Python and packages it so I could import it. Then I asked it to implement the gRPC interface it had generated and managed to get a "Hello World' when mounting this volume (just an empty dir). I also asked it to generate the YAML for the StorageClass, CSIDriver, Deployment and DaemonSet.
So LLM left me with a CSI driver that does nothing in Python (rather than Go which is what everything Kubernetes is implemented in) that I could then rewrite to run a Nix build and copy storepaths into a folder that's mounted into the container.
Sure implementing a gRPC interface might not be the hardest thing in hindsight, but I've never done it before and it's now a fully functional(ish) implementation of what i described.
It even managed to switch gRPC implementations because the Python one was funky with protoc versions in Nix(Python bundles the grpc codegen it's so stupid) so i asked it to do the same thing for grpclib instead which worked.
Go to gym.
I work on Dart professionally. I've also been tinkering on a toy programming language that may never see the light of day. My toy language has gone through several incarnations but a while back I was working on adding algebraic datatypes, pattern matching, and exhaustiveness checking to it.
The canonical algorithm for exhaustiveness checking is this paper by Luc Maranget: http://moscova.inria.fr/~maranget/papers/warn/warn.pdf
When I first started dabbling in programming languages over a decade ago, I tried to understand that paper for weeks and just could not wrap my head around it. I don't have much of a formal CS background and that paper was impenetrable to me.
So here I am tinkering on my toy programming language and I run into again. I give it another try and laboriously implement it in my interpreter, basically doing a straight translation. I don't understand the code, but it seems to sort of work. So then I start refactoring it a tiny bit at a time into a style that fits the way I think about code. Eventually the algorithm sort of folds into itself and before I know it, I finally understand how it works. The end result was only a page or so of code, but my toy language had real exhaustiveness checking work.
Meanwhile, at work, I am working on adding pattern matching and exhaustiveness checking to Dart [1]. Exhaustiveness checking here is a much harder proposition because Dart has subtyping, unlike my toy language and the ML languages that Maranget's paper works on.
I'd been hacking away at an exhaustiveness algorithm for Dart based on a couple of papers about Scala's approach but they were sort of approximative and inelegant (in my opinion, maybe they are a great fit for Scala).
But once I understood Maranget's algorithm from implementing it in my toy project, it finally clicked for me how it could be adapted to work with subtyping in a sound, coherent way. I wrote it up as quickly as I could and (with a lot of additional help from a teammate to handle generics), that became the algorithm we shipped:
https://github.com/dart-lang/language/blob/main/accepted/3.0...
It wouldn't have happened if I hadn't coincidentally been working on a toy pattern matching implementation at home.
I agree. However: Your best bet is likely to build it by hand and then see if you can get an LLM to build it.
It is looking like we are living in a world where an important tool to have in your toolbox is going to be the skills and experience of working with the LLM/AI tooling. There are skills, knowledge, tools, tips and techniques that you need to develop to effectively use these tools, just like the honing you get from writing the code.
Over just the last half hour I did a brain dump of "hot tips" to a friend of mine who is just starting to use Claude Code to kick-start him.
The LLMs are REALLY good at writing small toy programs, and at the end of the day it's great to have little tools that you'll use every day.
-Family calendar/weather dashboard
-Bluesky reader that allows you to hide posts you've "seen"
-Work PM dashboard to make time tracking more gamified and dare I say... fun?
-Chrome Reddit extension that hides posts after viewing for X seconds
-A WordPress plugin to replace a plugin no longer maintained
What I love and had to get used to was looking/using these apps and being happy with them being only 90%. Initially I had Claude make many UI enhancements but I learned to just let most stuff go and focus on the function and building new stuff, not refinement.
I can't afford much n the way of software, so I've been trying to learn to make do with FOSS.
I'm sure I'll keep buying commercial games from companies with reasonable terms (GOG, maybe?). But writing my own is great fun. It'd be nice if someone else enjoys them, but they're mainly for my own enjoyment.
Oh, and I "vibe code" the old fashioned way: with a REPL and some chill tunes.
However, am I the only one finding those time estimates way too short?
I'm not the fastest programmer on Earth, but I'm not the slowest either, and I think most of those projects would take me a lot more time than those estimates, specially if I'd be working only 2-3 hours per day.
I feel most of those projects would take significant time researching and learning about the subjects, before even starting to code.
Example: recently I replaced my Pelican blog by my own hacky static site generator written in Odin, working 2-3h per day, it took me 2 weeks -- and this is a simpler project than many on that list.
I bet you could write a word processor in an hour. It might only add or delete one character at a time (no selections). It might persist only to a hard-coded "output.txt" filename. You might have to kill the process because there's no quit operation. But that would suffice for a toy word processor.
I still think those estimates are off, because I think many of those projects would need significant research and learning time, possibly more than actually coding -- not to mention time spent troubleshooting when something goes wrong.
> What I cannot create, I do not understand
I suspect Mr Feynman would have phrased this a bit differently if he had first-hand experience with the modern front-end technology treadmill.
LLMs spoon-feed you with information about how things are implemented. You are not supposed to know how everything works when you start these projects. You're supposed to try your best, inevitably fail, then research the topic and understand where you went wrong, then adjust your approach. If you know how everything works and just follow the tutorial, you won't know what makes other methods fail, and by proxy what makes the one you chose work.
Write a language parser with a regex. Find out that it can't parse recursive statements. You've now learnt that regex can only parse a specific subset of syntaxes. Try to workaround this by pattern-matching the most nested statement first. Find out that it blows up performance. You now know more about time complexity and know what to watch out for when you write a real parser.
Write a non-optimizing compiler from scratch. Find out that you can't make do with unsound optimizations because you can't keep track of what optimizations are applied where. Find out that implementing sound optimizations is hard because you need to track use-def chains. Then you'll understand why SSA is used. Find out that code motion is a mess. Learn about sea of nodes. Merge every optimization pass into one because you're unable to order passes right. Learn how e-graphs solve this.
Write a layout engine. Get stuck on being unable to define what a "width" is. Workaround this with min/max/natural widths, introduce binary search, etc. Learn how this stuff works in practice (this is something I haven't personally done yet).
They say we learn from mistakes. Please don't let the smart (or "smart", depending on how you look at it) machine stop you from making them. It's not a teacher and it doesn't know how to educate.
also lol toy projects expose your defaults hard. how you name vars when there's no PR. how you layout dirs when no one's looking. do you even test stuff or just printf until it works. do you log or do you trace or just guess.
The things I find fun writing and learning about and the tools I use to do it are so disconnected from anything i do professionally.
akkartik•3h ago