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- Hacker News
- > the act of familiarizing yourself with a language no longer matters
Not so fast. I've seen all sorts of LLM-generated code that was not not inline with a what a human would write if such a human were familiar with the sharp edges of the language in question.
by jgalt212 - Fast and Hard - very much like Zapp Brannigan's loveby sorokod
- I do not know where things will go but here are some things I have been feeling:
- We have tons of languages (and libraries/frameworks on top) because we, humans, have too many preferences
- Many of these preferences are also abstractions because of how quickly (and with less knowledge of the underlying systems) we wanted to build things
- LLMs can operate on most (any?) of these layers and the results we get depend on how much high level knowledge the operator has
- LLMs + agents are able to do more and more of the digging, experimenting and ranking of different paths on their own to select good paths
- As someone who is assisted by LLMs, I can focus on codifying my internal process into custom agent/harness while also building end-user software (build the shipyard while building ships)
- I can reach out to any language (I am one of those who do not know Rust deeply but use it all the time) or any part of the stack now
by brainless - what i'm seeing is agentic coding helps be(come) more ambitious
build your own framework, database, operating system, game engine etc
things that used to be infeasible (too hard, too big, …)
by tosh - Is it really building your own if it's just rephrasing training data? Or is it license laundering?by inigyou
- When I first started learning game engines there were a lot of "understend a game engine by building one" courses in various forms, and a lot of open source projects (plus the closed source/never published ones I don't know about) that where the implementation of an engine or a kernel or a database based on a known book on the field. So it's at least not entirely a result of agentic coding and AI in general, even tho it probably incentivized the phenomenonby br121
- > agentic coding
In my personal experience, agentic coding wasn’t useful, but using a chat inference, was. I still need to be the critical path, but the LLM has, indeed, become a major force multiplier.
A few minutes ago, I submitted an app for review, that I started work on, alone, in February. The Quality of the new version is astounding. I’m absolutely thrilled.
It’s a full rewrite (backend server, and frontend client) of a fairly large app that’s been shipping for a couple of years, and that took over two years, to originally write.
I wouldn’t have even tried it, without an LLM. That made all the difference. The majority of the work was done with the $20/month ChatGPT Plus subscription, but the last few days, as I developed supporting materials and Web sites, I used the $100/month Pro level. After my work, over the last few months, the upgrade was a “no brainer.”
But, at every step of the way, I needed to be there, to intimately review and manage the interaction with the LLM. There’s no way that I could trust it to “just do it.”
I’m sure that, sooner or later (likely sooner), LLMs will have progressed to the point that I can trust them to vibe-code a project like this, but I guarantee, that they aren’t quite there, yet.
To be fair, I know that I may have much higher standards than a fairly significant number of developers, but the end product of my work is about as far from “AI slop” as you can get.
- I don't even know any more. I wanted a weather bapp that shows the whole forecast on one page with no scrolling, so I was ambitious and asked anti-gravity to build one for me. Half an hour later, done. Then I asked codex to review it and half the stuff didn't even work, made up wind gust data, misaligned hourly data, polluted caches. So I asked codex to fix the issues. Did it? I don't even know, it said it did.by adammarples
- I think this is true to an extent, but it really depends on how much information the LLM has on a topic.
They’re amazing at maths because maths has been open source since day 0. They’ve great at algorithms for similar reasons. C bindings are a doddle because there’s so much prior art available. They’re terrible at using cutting edge features in languages because there’s not much data yet.
I think it’s fairly easy to predict what they’re good at on this basis. I’m not sure why they suck at UI design though.
- > I’m not sure why they suck at UI design though.
I think they do OK, if you are fine with middle-of-the-road, default stuff. Also, I have found that it's important to provide guidance, constraints, and context to the LLM.
I have been working on a native iOS Swift app, for the last few months, and our team consulted an LLM (might be ChatGPT, might be Claude -I wasn't the one that consulted it), for graphic and interaction design.
Some context: We have written a 2.0/full rewrite of an existing app, that has been shipping for a couple of years. The 1.X version was designed by a professional graphic designer, but we didn't have him available for the rewrite, so we had to make do with our own guidance (questionable), and LLMs (also questionable, but for different reasons).
Anyway, As I started on the project, I kept getting designs that were, quite frankly, awful. They looked like generic Apple SwiftUI app screens, or worse, old Facebook app screens, with absolutely no relation to the current app. I had to basically ignore most of the graphic layout, and use only the interaction design.
After hearing complaints about "not making it look like the screens I sent you," I explained that they needed to train their LLM on the current design. There was no way that I was going to implement those ghastly graphics (It is so good to be able to do that. I'm retired, and working for free. In the old days, I would have had to take out a spoon, tuck in a bib, and eat shit).
They did that, and suddenly, their submissions were great, and I could start using them.
- How do you check the code if you don't know the language?by andai
- Fine article, though it ended on a low note with the comment about cryptography engineers gatekeeping the technology.
I have worked on high stakes crypto code. Getting it right requires a level of knowledge, care and engineering conservativism that is very hard to come by. I was teaching students. I wasn't gatekeeping. But a vanishingly small percentage of them were able to make secure software, or analyze existing code to know if it was secure.
In as much as AI coding makes testing or code verification cheaper, sure let's use it. But cryptography engineering is hard for reasons that are not "write moar code" and so AI coding should not be used on production crypto work.
by jeffrallen - What I got was, be more generalist and become a polymath.
My thoughts below
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This means, we need to read more to produce more in many different fields, and domains.
The idea is also captured in today's top 10 article, "To become a better writer, read as much as you can" (https://news.ycombinator.com/item?id=49405870) where if you want to produce good writings, read more.
Reading more here means, familiarizing/learning/understanding many different fields/domains.
I have never heard of "DWARF files, eBPF," and at least reading gave me an idea on what to learn about, so that when I work on projects, I can consider them by guiding AIs to even consider them.
Until today, I'd never woulda have though to even consider "DWARF files, eBPF,".
Now I can dig deeper into them and see what they are, and at least know what they used for and how they are used in practice.
by yipinwong - > one thing is pretty clear: the act of familiarizing yourself with a language no longer matters
I completely disagree with this statement as a premise to the article.
Sure, if you have zero care about whether the agent's work is reliable or not, then there's no need to learn a language. But if you're a developer who cares about their work and isn't just outputting 100% vibe code, then at a minimum you should be familiar enough with the language that you can review the agent's output ,follow along with the code, and make an informed decision about whether to commit.
Interestingly, I've found that my approach to learning languages has fundamentally changed. Before, I'd study a book on the one or two languages which were important to me at that time. The goal was to become proficient. Now I choose to read about a variety of languages simply because they demonstrate some interesting paradigm that is new to me (e.g. Haskell -> functional, Elixir -> concurrency). I read a programming book like I'd read an engaging narrative non-fiction book: cover to cover relatively quickly, then I'm done. This gives me that "familiarity" which is useful for agentic coding, without bothering to learn every obscure bit of syntax or library call. My priority has become breadth (get an overview of many languages and paradigms) rather than depth (learn a language or two really well).
by jbstack - The author and his cited examples are folks with decades of experience shipping broadly used OSS so for them it’s probably a lot less of an issue to switch languages, even if the agent is papering over a lot of the details for them in the initial releases.by dpritchett
- I'd say it depends.
On the degree the software evolves and will be used.
The reason we've spent years discovering patterns, creating special syntaxes has been to tackle certain domain problems more efficiently and to have systems that can evolve through time.
The only constant is change.
Albeit LLMs chunk out code (and with great know how impressive output), the developer must have the know how to pass a certain threshold.
Writing in unknown languages may seem fine at first. But once you go to the edge, you'll be finding certain quirks, inefficiencies along the way that the LLM may work around it instead of removing it from root.
For example, I've been learning Effect.ts for some weeks now. I've used LLMs extensively, but before that there were a series of manual coding rounds first.
To understand composability, the nitty gritty, where things break, how, how the syntax is formed, and how could I structure some observability challenges I had around the library.
If I hadn't gone through that process, the code quality would be subpar. It wouldn't have been evident at first, but once the system would begin to evolve and adapt to feedback, things would be brittle, existing customers would be affected, and more.
I like to move fast without breaking things
by superjose - As someone not in the know about cryptography, I know just enought that the words "custom cryptography" scare the hell out of me.by noduerme
- It's time to get over the "never roll your own crypto!" bs.
AI can roll you crypto far better than what humans have built by hand. They can literally test things to an extent that no human ever would.
by deadbabe - I'm not clear why someone would roll their own crypto. There are so many good solutions out there that are free that it seems pointless.
I keep hearing about post-quantum being an issue though.
by datakan - I don't love it, but I have to acknowledge that a frontier model building a feature that uses cryptography is going to have a better track record than the median human developer. The immediate practical problem with cryptography engineering has always been that there's a huge amount of detail you need to understand in order to get something right, but only a little bit to know to get something working. LLMs know all the details. They take shortcuts, but fuckups in mainstream cryptography features aren't really about shortcuts, they're about knowledge.
The most important thing to do with cryptography features is to stress test them ("spend $10 on verification for every $1 on implementation"), and that too has become a lot easier with frontier models.
by tptacek - > All of a sudden I have seen people do some really impressive stuff with DWARF files, eBPF, custom network drivers, custom crypto and really old computing hardware. Many of these things were previously off-limits for lots of developers. In some cases (eg: crypto) you were even pushed away because those things were intentionally gatekept by the people in the know.
Before at least some of us were forced to understand stuff, because without understanding it was not possible to do stuff that we really wanted to do, and some those got passionate and kept digging, and some of us even made even better stuff based on their experience.
Now no one needs to understand anything, and now we will never have better stuff.
by qsera - Before: millions of web programmers and only an elite few that dug all the way down to metal
After: millions of vibe coders and only an elite few that dig all the way down to metal
I'm not sure the world will be all that different. The people who want to dig will still exist, they'll just be using excavators instead of shovels.
- I'm not saying you're entirely wrong, but LLMs can actually be a way to create even better things than those better things. I've seen it in action.by meowface
- >Now no one needs to understand anything, and now we will never have better stuff.
Even worsr in that those starting out during this time, wouldn't even understand enough to guide and evaluate the AI output, or even even to know what to ask for.
by coldtea - > now we will never have better stuff.
I disagree. Some of us love tinkering and learning and experimenting. AI can help with that. People like us will always do it. Hobbyist will continue to innovate even if others don’t.