Homoiconicity, as I understand, is that the code is structured data that is easy to programmatically modify, hence allowing Lisp macros. While some might disagree, I see Rust macros as the closest thing that demonstrates homoiconicity in mainstream Algol-based languages, as Rust macros modify the loosely structured token stream to produce new Rust code.
Eval, on the other hand, that’s more of a capability that comes from Lisp’s runtime, which used to be unique when Lisp was thriving, but not anymore — JS, Python, Ruby, all of the runtime-based languages have an eval function. The fact that they are not used as much is more of a security issue, not a capability issue, and I am not sure how having eval can be argued as Lisp being the language of agents.
I don't know if you consider Elixir mainstream, but IMO their macro system is much closer to lisp's ideal.
Elixir is basically Lisp, but with better syntax, a modern ecosystem, and running on the Beam. Unlike languages like Rust, Elixir's conditionals and function definitions are just calls in the AST, even though the syntax looks mainstream and not like paren soup.
Which so helpfully removes many of the benefits of lisps :P I don't understand this argument at all, if it's not s-expressions nor quacks like a lisp, in what way is it lisp at all? Making it algol/C-like already makes it like the rest of the 99% of the languages, without any of the easy benefits of the neat and simple syntax of lisps.
Because Lisp syntax is so much simpler than that of JavaScript etc. it is much easier to avoid errors when generating code. In JavaScript you can use JSON to generate data, but JSON can not carry functions around.
I think this idea makes a lot sense. Instead of making the LLM generate JSON or XML, why not make it generate Lisp, which can carry both programs and data?
The same goes for "just a list of nested lists", sure it is easy to produce it, and for trivial examples it may actually be easy, but for more complex and realistic problems lack of "higher order" structure is a negative!
For something like JavaScript, you can just have a language-native AST object with a few helper functions and then can just call "addFunction" on it with proper arguments so that the API shape validates plenty of properties of your output.
Or maybe I've just been sleeping on the power of Javascript AST.
And that's more of a library ergonomics question how nice it is. There are plenty of other languages with nice ASTs and `eval`, and that's the only ingredient you need.
But maybe I’m interpreting you too narrowly?
> Because Lisp syntax is so much simpler than that of JavaScript etc. it is much easier to avoid errors when generating code. In JavaScript you can use JSON to generate data, but JSON can not carry functions around.
First of all, the LLM does not produce structured tokens, it produces (tokens of) text. It does not have a concept of nested or structured tokens. Which means that producing a Lisp program and a JavaScript program is basically the same difficulty, i.e. LLMs producing function foo () {} is about the same task as producing (defun foo () ()).
In fact, because most Lisps uses the same token ( and ) for almost all delimiters, the LLM in fact gets confused a lot more than Algol-family languages that uses various different tokens for different purposes. It generates thinking traces that are a few screens long while trying to count the closing parenthesis and the depth, something that I have not found to be the case for other languages, even with languages much more obscure than Lisp. (And no, it is not a training data issue, because the Lisp family as a whole is pretty well represented in the data set, due to Emacs Lisp.)
> I think this idea makes a lot sense. Instead of making the LLM generate JSON or XML, why not make it generate Lisp, which can carry both programs and data?
You do realize that all programming languages contains both programs and data, right? i.e. JSON is literally a subset of the JavaScript language, all JSON documents are valid JavaScript code, can be embedded in JavaScript programs, and so on. This isn't even a JavaScript-specific thing, almost all recent programming languages have data structure literals.
The thing that makes Lisp unique is that it can modify programs as data in the language easily, and why would that be a unique capability that would be beneficial for LLMs, when it can just sed/awk or tree-sitter-parse programs with more conventional languages and modify it as easily?
Yet I still had the idea that LLMs should be better at lisp than other languages.
A fascinating contradiction, thanks for pointing this out.
With modern tool calling I wonder if a better way to go about it is for the LLM to express the program changes as a function or otherwise use an editor that auto-balances parens. There's a lot of relatively simple tooling that makes it easier to write in a Lisp. The languages tend to lend themselves to being straightforward to check like that.
What's special about Lisp's repl is that it's perfectly possible to construct your entire program in the repl, testing each addition live as you write it. (Many Lisp-focused editors assume you'll want to do this, such as Emacs making it easy to run the interpreter on a single function in a file.) That tooling is lost if you just try to one-shot the file, and before 2026 the majority of LLMs originally just tried to one-shot every file.
But, just like a lot of early LLMs had huge problems with whitespace and numbers because the tokenization was taking efficiency shortcuts that made sense for text but absolutely wrecked code syntaxe, I wonder if the current optimizations are badly formatted for Lisp.
At the very least, using a varient like Clojure that also uses [] and {} in addition to () might help.
Neural Networks and the Chomsky Hierarchy
No it doesn't
And it's good we have those for troubleshooting but those eval still offer nowhere near the power of a Lisp REPL.
> and I am not sure how having eval can be argued as Lisp being the language of agents
I've seen several Lisp programmers saying that it's really the REPL (and the 'E' in REPL is for "Eval") that is the godsend when working with LLMs.
With LLMs we've seen terminals/ssh/tmux and CLI tools calling making a huge comeback (not that they ever went very far).
Now I wouldn't be surprised if we were soon to see a Lisp AI harness also using extensively the REPL becoming succesful.
It's too early to tell it's not a powerful combo (LLMs + Lisp REPL).
What helps is how "small" the hot reloaded parts can get - lisps are good here due to a lot of functions and not much shared state. But that is again, not something specific to lisps, neither are they the best at it - there are far purer FP languages for example
I think a more accurate description is that lisp code is just cons cells and cons cells is both how we write the code and how the runtime itself implements lists. So there’s basically no distinction between a lisp list and the text representation of the source. Rust and its macros is a different situation because the text representation of the code and the syntax tree have completely different shapes
Heresy I suppose, but doesn't feel (to me) like it "specifically" has to be cons cells, as long as it's a "list" of some sort, regardless of the specific type of list, while I'd say "cons cells" is a specific implementation of a list, famously implemented by Common Lisp and similar lisps.
Besides that I agree with you, the code you write and the code the compiler uses is the same, that's why it gets easy to edit with code itself. Rust and similar don't have this feature at all, it's very different experience writing macros with lots of special syntax, compared to just writing "normal" code doing the same sort of operations. "Doing a loop of X" isn't the same inside of a normal runtime function body, as when you want to do a loop of arguments for the function signature in a macro, as just one simple example.
Not to mention in Rust you have two different types of macros, declarative vs procedural macros, already speaking to that the entire concept of homoiconicity isn't there. You want it to be easy to implement a read, evaluate, print and loop back workflow by passing native data through the entire chain, except parsing the initial user-input string into your AST (whatever shape that ends up being), which pretty-printed (basically) looks the same as the user-input.
Yeah, "cons" is definitely a implementation detail. Maybe the central ideia is just "parenthesized s-expr".
So like, what's the material difference between the two? You have a parse and an eval in both cases
There is a case to be made for a dynamically evolving "tool server", but it should be a separate process. That would be more flexible for other use cases too. For example, multiple independent agent processes could talk to one shared tool server. Like a blackboard system, more classic AI!
And if you really do want to evolve the agent itself: As the article observes, its entire state can be serialized. Nothing is gained from hanging on to one particular agent process. Serialize its state, ask the tool server to kill it, rewrite its code, then start the new version and replay the state.
So cool to watch the AI get into a tight learning loop when it has access to all the internal data structures.
Not sure what you mean. The system I outlined is one where some "state" resides outside the process in a separate server. You don't need to serialize that, you just need to serialize the information to need to reconnect.
And my first point is even more relevant the more complex/distributed/brittle you make the whole thing: The more important it is for some specific process to stay alive no matter what, the less you want to live-slop code into it.
(Edit: Yes I'm aware of the live-patched space probe story. Human live-patching is not the same as letting an LLM try to one-shot the correct patch.)
Symbolic AI lost? At what, surely not chess?
A symbolic AI solution to a problem requires vastly less energy. And is deterministic; you can cover it with expected input/output pair regression test cases.
But I have found that sometimes the best use of an LLM is to write code for symbolic AI.
It is always nice to appreciate how much power you get out of (Model + the absolute bare minimum of control flow). There is just so much baked into the models now that given an inch they will take a mile.
In contrast, `eval` runs the code in the same execution context as the agent loop. When `eval` finishes, that execution context still exists. For example, any functions defined during an `eval` call remain available for later use.
Being able to run code in the same unix process or a new one doesn’t really matter all that much in the context of self modifying code. But even if we cared about that, this isn’t a LISP specific feature. All dynamic languages support eval.
And having the agent cache the tool for reuse is a really trivial problem to solve. Though I do agree that LISP makes this much easier than in many other languages.
This is certainly a cool tech demo. But the claims of its novelty are overstated
Yeah, it's a small example (it's in the title, "100 lines") so obviously doesn't highlight the best benefits once you reach larger codebase size.
Still think ~8 lines for the core loop is probably more elegant, readable and concise than you can achieve in other algol/C-like languages, but happy to be shown that I'm wrong :)
And once you start including the boilerplate code you end up with something that’s a lot more equivalent to the other languages you tried.
I love functional languages, and LISP specifically too. But the point of that article wasn’t even to say “LISP is better at code golfing than other languages”. so this doubling down on the SLOC that you’re doing isn’t even a relevant tangent.
The part that killed it for me was losing everything if the lisp crashed (sonnet 3.5 was prone to doing that) and solving persistence had too many edge cases and confused the model.
Later realized that writing the agent as 20 lines of bash was equivalently powerful to the lisp agent, but made persistence trivial from the easy file system interop.
you get a snippet from LLM, compile it to module, and hot-load it into the running node. the module lives in the node's code table, so it persists and every other agent can call it. not just the one that wrote it.
the agents themselves are seaprate supervised processes, so if one crashes - e.g. because the snippet was crap, it doesn't take down whole system.
of course you can do that in just elixir too, the lisp is just cosmetics really.
It's pretty amazing to write your own agent BTW. I've got a zero-dependency all-in-one-file agent harness I wrote myself. I use it all the time now because I can get it from anywhere and I can know EXACTLY what it'll do (as much as you can with any model), what it's been told vs not. Using it as a harness for models I'm hosting myself makes me feel like some kind of LLM homesteader: it's a set of tools I'll always have that will only change as much as I want it to change.
import json,sys,uuid;from subprocess import getoutput as sh;from urllib.request import Request as R,urlopen
b={"model":"gpt-5.6","prompt_cache_key":uuid.uuid4().hex,"input":[],"tools":[{"type":"custom","name":"shell"}]}
while prompt:=input("> "):
b["input"]+=[{"role":"user","content":prompt}]
while True:
o=(r:=json.load(urlopen(R(sys.argv[1],json.dumps(b).encode(),{"Content-Type":"application/json"}))))["output"]
b["input"]+=o;calls=[i for i in o if i["type"]=="custom_tool_call"];used=r["usage"]["total_tokens"]/10500
if not calls: print(o[-1]["content"][0]["text"],f'\n[{used:06.3f}%]'); break
b["input"]+=[{"type":"custom_tool_call_output","call_id":i["call_id"],"output":sh(i["input"])} for i in calls]- stdlib only, 0 external dependencies
- works with openai compatible api (including local models)
- shows context usage in % when turn goes back to user
- cache friendly (keeps stable prefix and provides uuid v4 as session cache key)
- uses 'shell' as open ended tool
'shell' as tool name is sufficient context for gpt 5.6 sol
Most of those "permission"-systems are built on the idea that an LLM can decide what to ask for approval to run or not anyways, which obviously don't work out great in practice. Might as well give them blanket permission to do whatever, then put them in an isolated environment.
What does it contribute? I can read and discern this for myself, I can then stop reading or decide I don’t care.
Seriously, at some point all you “ai writing sleuths” should just get your own discussion thread together. It’s been months of this , we get it already.
(Not directly just at you, but anyone who feels the need to drop these comments in every thread)
To help other people who don't care about it so they can skip it. A subject tag would be nicer.
> It’s been months of this , we get it already.
And I am tired of the constant barrage of AI proselytizing.
Indeed. The new language, llmish, has definitely been used.
Still a good TFA IMO.
P.S: this particular construct "No X. No Y. ... just ..." is half my LinkedIn feed.