I'm not an AI "advocate". I'm telling y'all about how the world is. How it's going to be. I'm not happy about it, but we've crossed the threshold beyond which it's incomprehensibly silly not to factor the massive changes LLMs bring into how you work designing or implementing software. Lisp apps are cool, but as of 2026 they're fading into irrelevance. The paradigm of programming they represent is bound for the Computer History Museum and Usagi Electric's YouTube channel—not the reality of new software development. Even a legacy code base can be poured into an LLM, which will grok it instantly, answer your questions about it, and propose changes and improvements that will make it more performant, reliable, and comprehensible. I know this because I've done it.

Really? Feels like most Of HN lately is just “get on the AI hype train or get downvoted”

Hackernews isn't really for that kind of hacker. Ever since Paul Graham became a startup wonk and VC, it's really more for "growth hackers". It was originally called "Startup News". For growth hackers, productivity, profitability, and scalability, in metrizable form especially, are far more important than romanticism about the lone hacker or small team of geniuses building something with just a laptop and their wits, or even moral concerns about the environment. (And LLMs burn less energy, and deliver more value, than crypto did. The energy consumption of AI has been way overblown.) And Lisp was created specifically to bring about this world. It was an early initial experiment in intelligence by symbolic computation—one which ultimately failed as we found that we can get a lot closer to intelligence by matmuling probability weights with good old-fashioned numeric code written in C++, Fortran, or maybe even Rust. So the long-term AI initiative which gave rise to Lisp ultimately spelt its end as well.

But the force-multiplier effects of LLMs are not to be denied, even if you are that kind of hacker. Eric S. Raymond doesn't even write code by hand anymore—he has ChatGPT do everything. And he's produced more correct code faster with LLMs than he ever did by hand so now he's one of those saying "you're not a real software engineer if you don't use these tools". With the latest frontier models, he's probably right. You're not going to be able to keep pace with your puny human brain with other developers using LLMs, which is going to make contributing to open source projects more difficult unless you too are using LLMs. And open source projects which forbid LLM use are going to get lapped by those which allow it. This will probably be the next major Linux development after Rust. The remaining C code base may well be lifted into Rust by ChatGPT, after which contributing kernel code in C will be forbidden throughout the entire project. Won't that be a better world!

I can’t speak to getting an LLM to talk to a CL listener, simply because I don’t know the mechanics of hooking it up. But being as they can talk to most anything else, I see no reason why it can’t.

What they can certainly do is iterate with a listener with you acting as a crude cut and paste proxy. It will happily give you forms to shove into a REPL and process the results of them. I’ve done it, in CL. I’ve seen it work. It made some very interesting requests.

I’ve seen the LLM iterate, for example, with source code by running it, adding logging, running it again, processing the new log messages, and cycling through that, unassisted, until it found its own “aha” and fixed a problem.

What difference does it make whether it’s talking to a shell or a CL listener? It’s not like it cares. Again, the mechanics of hooking up an LLM to a listener directly, I don’t know. I haven’t dabbled enough in that space to matter. But that’s a me problem, not an LLM problem.

An LLM can modify the code, rebuild and restart the next iteration, bring it up to a known state and run tests against that state before you've even finished typing in the code. It can do this over and over while you sleep. With the proper agentic loop it can even indeed inject code into a running application, test it, and unload it before injecting the next iteration. But there will be much less of a need for that kind of workflow. LLMs will probably just run in loops, standing up entire containers or Kubernetes pods with the latest changes, testing them, and tearing them down again to make room for the next iteration.

As for hallucinations, I believe those are like version 0 of the thing we call lateral thinking and creativity when humans manifest it. Hallucinations can be controlled and corrected for. And again—you really need to spend some time with the paid version of a frontier model because it is fundamentally different from what you've been conditioned to expect from generative AI. It is now analyzing and reasoning about code and coming back with good solutions to the problems you pose it.

Hey, please don't cross into personal attack - you can make your substantive points without that, and we'll all be better for it.

https://news.ycombinator.com/newsguidelines.html

I believe (correct me if I’m wrong), their point is that with time, we’re writing less code ourselves and more through LLMs. This can make people disconnected from the “joy” of using certain programming languages over others. I’ve only used cl for toy projects and use elisp to configure my editor. As models get better (they’re already very good), the cost of trashing code spirals downwards. The nuances of one language being aesthetically better than other will matter less over time.

FWIW, I also think performant languages like rust will gain way more prominence. Their main downside is that they’re more “involved” to write. But they’re fast and have good type systems. If humans aren’t writing code directly anymore, would a language being simpler or cleverer to read and write ultimately matter? Why would you ask a model to write your project in python, for instance? If only a model will ever interact with code, choice of language will be purely functional. I know we’re not fully there yet but latest models like opus 4.6 are extremely good at reasoning and often one-shotting solutions.

Going back to lower level languages isn’t completely out of the picture, but models have to get way better and require way less intervention for that to happen.

No, I'm not.

I used to appreciate Lisp for the enhanced effectiveness it granted to the unaided human programmer. It used to be one of the main reasons I used the language.

But a programmer+LLM is going to be far more effective in any language than an unaided programmer is in Lisp—and a programmer+LLM is going to be more effective in a popular language with a large training set, such as Java, TypeScript, Kotlin, or Rust, than in Lisp. So in a world with LLMs, the main practical reason to choose Lisp disappears.

And no, LLMs are doing more than just generating text, spewing nonsense into the void. They are solving problems. Try spending some time with Claude Opus 4.6 or ChatGPT 5.3. Give it a real problem to chew on. Watch it explain what's going on and spit out the answer.