So yea hopefully a nice new addition but only my prompts now matter. So need to make sure they pickup on the new API.
I am using local LLMs and Go has a bunch of strengths where smaller contexts are concerned; less complicated flow of control being one of them.
And the things that we do to manage this, that are symbolic or based on visceral real-world analogy, are good for us and bad for them.
Go is kind of in the middle, in the way that many vaguely Wirth-like and CSP-like languages possibly are, I guess.
I hope you're not still doing this, it's nothing to brag about.
Unfortunately, the world makes it hard to keep that cycle, and so instead I mostly do 17 hours awake / 7 asleep, which makes me feel constantly tired (though less tired than 16/8 at least).
If you code in Go, the code is more high level, prettier, simpler.
If you code in Rust, the code is more robust, efficient, faster.
Both are important for LLM, why can't we have a high level, simple language that is also robust, efficient and fast? =)
Expressive and type safety of a functional language, without the odd semantics of Haskell, where laziness can make performance difficult to predict and purity can make simple things like IO difficult to understand.
Does it still lack easy to use concurrency primitives?
I' just starting OCaml, so can't really compare, but it seems a good progress there.
Go is about as far as you can "go" to the efficiency end without becoming Rust. It's a low-overhead language (like C) but with a GC that ensures memory safety. If you want to manage memory yourself and still prove safety it gets very complicated, as we see in Rust.
I think many variants from Rust can be stripped to make it simple, especially in type system, which is way more complicated than go.
It's really great fun to build stuff in Crystal, I don't have anything prod-grade yet to say how it scores there but the community seems to be happy about that too.
The few times I've needed a language without a GC and used Rust and C as target languages, I've reviewed the code less and enjoyed it less.
JIT and AOT compilers, REPL development, bleeding edge GC implementations, Java, Scala, Kotlin, Clojure, C#, F# to chose from, and modern IDEs inspired by Smalltalk/Lisp Machines experience, batteries included backed and frontend development frameworks.
In what sense? Go has a GC and binary sizes are relatively pretty huge. Its closer to Java and C# than C.
First, I like programming and don't want to stop doing it just because a machine can do it too. Second, I'm pretty sure atrophied coding skills will make me worse at directing the machines, thus worse at what seems to be "the job" these days.
In theory wouldn’t atrophied coding skills not matter if models are sufficiently good at coding? I mean isn’t whats left just design and architecture and testing?
Sure, if LLMs are producing shit code you need to have an understanding of what it should do instead. But I think that day is already gone.
Its hard to think of a skill that you’d need to build with an LLM that requires writing meaningful amounts of code by oneself. From my end all the complexity is business logic and system design.
No, because good understanding of design comes from programming experience. Atrophied skills mean you lose touch with the actual nuts and bolts of what you are building.
This is also the worst kind of lock-in you can imagine, by outsourcing all of your understanding of a codebase and your skills you are now completely at the mercy of LLM providers. Even if you switch to open weight models you're at the mercy of graphics cards producers and scalpers.
> Sure, if LLMs are producing shit code you need to have an understanding of what it should do instead. But I think that day is already gone.
Maybe your standards are too low. By my standard, they still produce shit code and require lots of iterations to get reasonable designs (I often end up just rewriting the bits that are obviously wrong rather than trying to figure out how to "prompt engineer" the right output).
They do often produce passable code but my experience is the longer you spend reading the code the more things you find wrong with it, almost like an uncanny valley.
At least that's my experience with Opus and Fable. Maybe it's too old-fashioned at this point to complain about code quality, but I find it very strange how many people have started proclaiming that their code quality is good.
“Programming” is a bit loaded. Do you mean thinking through and typing everything out yourself?
What design skills specifically can you only develop from continually programming in this way? I fully grant that you need to know how to code yourself to build things with models but what specific ability to use models atrophies alongside your no AI coding abilities?
Personally I’ve found the opposite to be true. As writing out code myself has shrank to nearly 0, I now do way more designing. There is a much shorter feedback loop on design now. You get a lot better at design when thats basically all you are doing, and seeing feedback on your design in minutes rather than weeks or even months.
Weeks and month for to wait for a feedback in developing software? Are you the sloth in zootopia?
The longest cycle for feedback for me has only been hours. And that was about redesigning the core architecture of an app. Otherwise, it’s like 10 minutes cycle if it’s edit-compile-run, 5 minutes if it’s REPL (lisp, bash, python,…).
As I write code myself I continously take into consideration stuff like following the consistency of the entire design, whether what I'm doing is efficient, etc. LLMs are terrible at this and I don't see how you could develop such skills by prompting only.
For example, I would never produce designs that require degenerate db access patterns (linear scans, etc.). Everything I write myself always takes into account using db & network efficiently from the ground-up. Every query is an index lookup, every api request is rate limited, everything is batched and cached, so on and so forth.
LLMs meanwhile don't care about any of this. I just had Claude happily try to fetch youtube page html because the api didn't expose something. Great idea, I'd love to be banned by google for scraping.
Designing with performance in mind is black magic too, I need to notice myself that stuff that I think has no reason to be slow (pretty much nothing does) is slow and hammering the llm to finally come up with a non-stupid design that's not ridiculously slow and wasteful. Yeah, why don't we pull 2.3mb of html to update a list, sounds good.
The best part is, LLMs are very obedient. If you tell them to make you a design that's inherently wasteful of resources and slow they will happily oblige, and since you have no programming experience to know better, you won't even know how bad what you're asking for is.
You don't expect everyone to stop wanting to do those just because they can be automated more or less effectively, right?
Please reread my comment and the one im replying to. That’s not what I’m addressing.
I am begging HN users to understand that some people enjoy writing software themselves
Agents make it too easy to gloss over too much. They're great at work where i care about productivity but I've zero interest at home where the point is learning and having fun
Why? Because I don’t give time to wait for code to compile on my time and I don’t have time for people to learn a new language for review at work.
Life is good! Would recommend that split.
However it kind of makes me feel like my professional life was wasted and I don't really know what kind of future I have. One of Go's main points was you can pick it up in a weekend. What career moat do people like us have?
Letting it generate machine code directly would be the opposite of that.
As for proven wrong,
"Frame was created over the past month largely via Claude Code. The Assembly code in all its glory can be found on GitHub."
Considering it just pulls and remixes blocks, this is a very tall order for today's LLMs, and maybe a couple of generations as well.
Calling GCC/clang at the back and emitting ASM is both cheating and has no advantages whatsoever as well.
This isn't futurology, besides a few projects that already came up on Phoronix and around here, with Assembly generation, there are enough SIGPLAN papers on ML driven compilers already.
However, starting with LLMs directly generating machine code and moving to ML directed optimization passes, you're moving the whole stadium with a crawler-transporter, not only the goalposts.
Helping compiler in a well defined way, and working on IR or driving it repeatedly to optimize the output is one thing, generating machine code from a training set is other. Let's not mix them.
"Compiler 2.0: Building the Next Generation of Compilers with Machine Learning"
https://eecs.berkeley.edu/research/colloquium/compiler-2-0-b...
There are other researchers if you interest yourself on the matter.
Then the whole iPaaS agentic coding environments like Workato and Boomi, that even if they might generate 3GL as intermediate step, you don't get to see it.
https://github.com/zwang4/awesome-machine-learning-in-compil...
Using small, purpose built ML "things" to make things better makes sense. We use them everywhere. I never argued against it, as well.
Enterprise applications are already being vibe coded in iPaaS platforms, and as mentioned in other comments with LLMs in the picture, that is what compiler research is switching to.
In case you answer there don't fit the same purpose, there are tiny baggers that replace shovels altogether.
I think those arguing for structured language generation, never had that much experience with 4GLs or declarative programming on the previous AI Summer.
So like the Assembly developers required those -S compiler switches, or nowadays Compiler Explorer snippets, there is a tendency to argue for 3GL code generation, when low code/no code tools with AI runtimes are already being adopted at enterprise level.
What in the past took a bunch of developers using structured programming languages to write their frontend, and the myriad of microservices, is slowly being taken over by MCP tools, SaaS AI orchestration prompts and UI generation tooling, most of which doesn't even expose whatever code gets generated.