I really don't want this to be AI writing because I enjoyed it, but as other commenters have pointed out, the rate of publishing (according to the linked Twitter account) is very rapid. I'm worried that I can't tell.
> it — a crisis not of computer science but of procurement
> a subtype — not in the object-oriented sense of a type that extends another, but in the mathematical sense of a constrained set
A number of em dashes and "not X, but Y" constructs unfortunately, sometimes even right next to each other like the above.
I'm not convinced this work is wholly AI but it has at least the smell of augmentation or assistance, and a sloppy mindset in terms overseeing it. That indicates a lack of investment from the author which I always think is... unfortunate as a reader, to say the least.
>the rate of publishing (according to the linked Twitter account) is very rapid.
I've written almost 50 blog posts in the last 3 years. All in draft, never published mostly because a crippling imposter syndrome and fear of criticism. But every now and then I wake up full of confidence and think "this is it. today I'll click publish I don't give a fuck. All in". Never happens. Maybe this author was in the same boat until a month ago. I know there's a high chance that's just a bot but I can understand if it's not and how devastating has to be to overcome the fear of showing your thoughts to the world and being labeled a bot. If it's not already obvious English is not my first language and I've used LLMs to check my grammar and improve the style. Maybe all my posts smell like chatpgt now and this just adds to the fear of being dismissed as slop.
LLMs do not currently improve the style of typical HN writing. Maybe someday they will; this article is less painfully bad than those of a few months ago.
The main problem with this article is that it appears to have been basically written out of whole cloth by the LLM, there’s no novel insight here about Ada beyond what you could fit in a short prompt + the Wikipedia article.
That does look a little suspicious. There do exist AI-based tools now that can take other people's blogs and rewrite them with other words. Those are all the rage over on Reddit subs on blogging for ad revenue ...
I think so. Who writes something and why are important context for what we do with the information. It's an issue with the lack of disclosure, not AI in general.
Most longform readers will assume an author has deep expertise and spent a lot of time organizing their thoughts, which lends their ideas some legitimacy and trust. For a small blog, an 8,000 word essay is a passion project.
But if AI is detected in the phrasing and not disclosed, it begs a lot of questions. Did AI write the whole thing, or just light edits? Are the facts AI generated, too, and not from personal experience? What motivated someone to produce this content if they were going to automate parts of its creation; why would they value the output more than the process?
> But if AI is detected in the phrasing and not disclosed, it begs a lot of questions.
absolutely zero questions from me.
If I see two exactly same writings: one - by human, another - by AI. For me its doesn't matter.
> Most longform readers will assume an author has deep expertise and spent a lot of time organizing their thoughts, which lends their ideas some legitimacy and trust.
It's the incorrect assumption of "most readers". Before AI there are enough methods to throw a long read. So, AI isn't really a gamechanger here
I'm surprised no one in the comments is mentioning Kotlin. Out of all the languages I've worked with it has been the most enjoyable by far. I agree with the article that there isn't much community but I feel like that's arguably the least important category there. You should definitely give it a shot if you've never tried it before.
For a while using kotlin as a simply better java "front end" syntax shim increased productivity.
These days java has been "catching up", aka stealing the good ideas from kotlin--so it's less appealing to me. I think where it still shines is in cross platform targeting like ios, android, desktop and server as few other languages really enable that. C# did, but only because of one vendor (Ximian or whatever it was called, some offshoot of Mono)
I think this sums up my thoughts on the LLM writing "style" pretty well:
> If a student submitted a piece of writing to me that sounded like this—and I was sure they wrote it themselves—I wouldn’t know where to start. I guess I would tell them to stop writing for a while and go read some old novels, or work a crummy job, or backpack around the other side of the world. But that would be bad advice, because I know people who have done all of those things in the hopes of becoming a more interesting person, and it hasn’t worked. So I might ask them instead: “Have you ever considered a career in consulting?”
Code doesn't need subjective intelligence. I think LLMs, as much as I dislike offloading thinking to them, are likely to become a large part of software engineering. I'm hoping that it'll never nail the subjective experience - I read to explore others thoughts, however ungrammatical, broken, or convoluted their prose is. Give me that over a bowl of bland and tasteless slop any day
It's not long range by any stretch. The use case is more "my group split up on a trail and I want to know which fork they took" vs "track someone across a mountain." The ghost marker system helps here too. If someone walks out of range, their last known position and direction stays on your map, so you at least know where they're heading and how long ago they traveled that direction.
I have sync to support the amazing devs, and for convenience, and an automatic git-based backup that runs in the background. It's good to double dip sometimes
When learning, motivation is first, everything else follows.
At some point I felt the drive to move on from Python as my main language. There was no question of “how”: when I needed or wanted to build anything, I would simply go with Go (later TypeScript) and plow on. It is hard to pinpoint exactly what motivated that drive, but I think it was probably curiosity after seeing examples in other languages, wanting to be more competitive, and—let’s be honest—the basic desire to feel a little cooler in the eyes of peers.
Be mindful of second-order volition here. Like when someone says “I want to quit %BAD_HABIT%”, what they really say is “I want to want to quit %BAD_HABIT”—if they really wanted to quit, they would have already done it. Similarly, if you want to learn a programming language, you are all set (unless it is so esoteric that there are no suitable resources or references, which never happens), but if you want to want to learn a programming language then what you need is some lateral move (tricking yourself, putting yourself in some situation, etc.) that makes you actually want to learn it.
These days learning a new programming language is a more sketchy question, because LLMs drain a few major sources of motivation: you can hardly feel cool for knowing how to program in a new language, because anyone would rightfully assume it was written with an LLM; you increasingly do not actually need to know a language, because a model writes everything for you; the competitive advantage is decreasing. Unlike speaking some human language, there is no society of native speakers that would accept you more or treat you better thanks to you speaking their language.
> I'm guessing the answer is making small things, but what exactly? I've made so many to do list apps I don't know what to do with them
My favorite way has always been to not just build small things, but build small useful things. There is always something that could be better, and there is always a subset of languages best for the task at hand. If it's a CLI, then a language that can compile to binary tends to be best (for me at least), so that already limits the languages somewhat. Then depending on what the task is, it might make sense to learn a new language for it.
Then naturally over the years I've picked up 10-15 languages this way, by just following what each language seems best at, and not being afraid of spending 2-3 weeks writing something basic.
Then for each language you learn, next one gets a lot easier, especially when most mainstream languages today are Algol-like languages and more similar to each other than different.
As any language, the core is "why" do you want to learn it. Is it to add it to a list and that's it? Then you might struggle by creating todo lists or play pretend on Duolingo.
On the other hand, if you do have a goal in mind try to do tiny bits of that.
My goal for natural languages is always connecting with another culture at a deeper level than just using English. If that's the case, you get someone to talk/write to and slowly do it. It won't be instantaneous or dopamine fueled but after a few years you might realise that you've been chatting with someone completely in their language without major hiccups.
For programming languages, I understand that filling a CV is tantalising and useful, so you've got to come up with projects and things you'd actually like to be doing with such a language.
You could say you want to pick up COBOL for a future job, well figure out what would make sense to use it for and go with that.
And if you really cannot think of anything, then you can fall back to make something up: make a game with such a language (even better if it is not meant for games), automate something, recreate a small tool which you find frustrating. And even if after you have read this and still cannot find a thing which gets you, maybe learning this language is not within your current interests and you might start considering to move on.
Create something you actually need, or port something you already created in another language.
I needed a tool to get the contents of a remote zip file without downloading the whole file. I wanted to learn Go, so I created the tool with Go, then I ported it to Rust when I wanted to learn Rust.
Make things that require you to use the parts of the language you don't have a strong grasp on yet, so as to get to know those better. Sorting algos, data structures, a kanren, and a library website would be a good variety pack. And aside from that, reading codebases is also important. Read the code of your CPAN equivalent, your Alexandria equivalent, your Spring equivalent, and your SDL equivalent.
Writing one from scratch gives a lot of understanding to how it works under the hood and in the process you learn right phraseology and treat all languages as computational fronteds.
I'm sick of using React for personal projects so I've been building a lightweight, functional, and minimalistic reactive web framework. Turns out there are a lot of decisions that go into something like this, it truly is an iceberg of complexity. It creates plenty of enjoyable problems to think about though
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