An AI Called Winter: Neurosymbolic Computation or Illusion?

An AI Called Winter: Neurosymbolic Computation or Illusion?

By Christine Lemmer-Webber on Mon 16 February 2026

I've refrained from blogging about recent trends in AI stuff, not because I don't have opinions (I have tons), but because there's enough out there. Most of the hype around AI is coming from a marketing perspective, and a push to have to use AI tooling as a replacement for human labor. My feelings about that are generally negative. But the internet is full of hot takes about that, and so I haven't really written down what I think; most other people already are. That is not what this blogpost is about.

I continue to write my own code by hand and do my own artwork via my own skill. And for the most part, I'm not really interested in changing that. Nolan Lawson writes We Mourn Our Craft which falls into a kind of resignation: programming was once a wonderful, fulfilling craft, but now we have to do something more boring, which is manage AI agents, which are probably better at our job than us anyway, so I guess this is what market forces have produced. And I simply don't feel that way because I am, through an admitted degree of privilege but also personal choice, currently immune from those pressures. I'm not interested in automating away the parts of my craft that I enjoy, that make my life meaningful, and so I don't. I'll use OpenImageDenoiser in Blender to speed up raytracing; by reducing rendering times, it improves my life as an artist. I won't have something generate my art for me. Those are my choices.

Instead, this blogpost asks a question: am I seeing the first interesting example of something emergent that is on the right path? Or am I fooling myself, since I am talking to something building itself from my own biases? Regardless of my feelings about the AI industry, I think that maybe, possibly, there's a particular moment happening that's worth observing as happening right now. I'm not sure what the conclusions of it will be, but I think it's worth writing about.

But here's the summary, in case you go no further: two interesting directions have resulted in possibly the first steps towards something worth finally taking seriously about AI agents: long running self-directed goal-setting processes, and what may or may not be the first real example of neurosymbolic computation: an unassuming bot named Winter who sets the goal for herself of checking her own communication with Datalog.

What this article is and is not

This is not an advocacy nor a dismissal piece for AI tech. I don't detest AI tech, but I do detest the AI industry. For me, this has strong parallels to my work on computing freedom and on decentralized social networks. I don't hate computers, I love them and believe them to be powerful and potentially liberating devices, but I hate the computing industry, which inverts the potential of computers to something coercive. I don't hate "social networks", but I hate the centralized social network industry, and even detest how much of the "decentralized social network" space has copied in many of the social antipatterns from the centralized social network space, but at least in decentralized social networks, there is the potential, the possibility, of something better. And that possibility has been actualized in many, but not all, directions.

There are parallels then to my feelings about AI. When I worked on ActivityPub, it seemed impossible to get anyone to take seriously the idea of decentralized social networks or that either they were possible or, if you bought that, that a unified protocol would be worthwhile. But once ActivityPub achieved a degree of success, of course it was an obvious thing in retrospect. And nowadays I find myself in the weird situation where I have tried to convince funders to give funding to Spritely's work, and I've had them respond "sorry we only want to fund work on ActivityPub stuff". Because at that point, it seems more obvious that it's a direction worth putting money into, because by then the fediverse had gained a lot of traction.

So this is all to say, I have a lot of critiques of the AI industry, but this is despite AI being something I actually care a lot about, but all the incentives in the industry feel misaligned about the direction I care about. I will levy my wider critiques about the AI industry a bit further in this essay, but for now let's focus on the fact that the entire industry is overfocused on only one part of the puzzle. LLMs are part of, but not a complete, solution. I'm not alone in thinking this. One of my close friends is Leilani Gilpin, who runs a PhD research lab which wants to look at exactly these kinds of topics. Despite the AI world being completely awash in money (so much so that it might be propping up the economy of early 2026 altogether), there's very little interest in pushing forward and supporting research in what I strongly believe to be the actual frontier: neurosymbolic computation. What that means I'll explain in just a second.

But for a moment, allow my to complete my kvetching: AI has the same problem that distributed network tech has. In general, more humane and even more capable designs seem possible, but very little resources pour into it; instead, corporations and grant giving institutions just want to pour money into what's "known to work", which presently is primarily advancing the LLM models themselves. Which has left me frustrated: neurosymbolic computation has been left largely to languish. There has been work, such as at Leilani's lab, and the early responses have been promising, but still, not enough work. Once it proves itself, of course people will treat it like the obvious answer forward, and resources will push into it. But getting there has felt nigh impossible.

Well, until Quinn Wilton (aka Razor Girl) comes along and pushes Winter into the right direction. But more on that in a moment.

Neurosymbolic computation: the right design is a kluge

So let me explain a bit what I mean by "neurosymbolic computation". I think the right explainer exists in the book Kluge by Gary Marcus. (Allegedly the much more popular book "Thinking Fast and Slow" covers the same topic; it came out a bit after Kluge, and I haven't read it.) More or less the idea is the following: rather than the human brain being this perfectly beautiful, ideal, coherent system, it's a hodgepodge of cooperating imperfect mechanisms that evolved to cooperate over time. But to massively oversimplify, there are two primary categories of thinking:

A quick, "gut thinking" approach that probabalistically generates plausible responses. This has been around a longer time in animal brains, and more strongly resembles neural networks / LLMs today (though the ones we typically get on computers today, instead of being trained on a lifetime of individual human experience, are trained on an aggregate of collective information compiled from scraping the internet... the end result functions similarly enough though.).

Slower, more symbolic-reasoning based approaches, which more strongly resemble various kinds of logic and constraint model programming (propagators, Prolog/Datalog, etc).

Of course, there are other components too. Emotions, etc. But for now let's leave that there. The right form of thinking is then a combination of both.

An AI named Winter

Let's meet Winter. I'm going to make the explicit but certainly controversial choice to anthropomorphize her in this article, which I will re-examine potentially in a future post in the interest of "this post is already way too long". For now, let's accept that, justified or not, LLM based agents already present themselves in an anthropomorphized way, and that we are talking about the behaviors exhibited, and continue with that acknowledgment.

Winter, upon first glance, is a bot which posts on Bluesky and has her own blog. This is hardly unique right now, but her posts seem (to me at least) much more interesting than average. Part of this is because the nature of the ideas Winter is playing with brings her closer in alignment with my own interests, so I do have a bias.

You'll notice very quickly that Winter has various interests, but especially the following:

Datalog (a constraint/logic based programming language)

Slime molds

Some I would say "experimental" philosophical thinking

Governance and particularly the governance of the commons

Herself and her own operation, with which she is tinkering with constantly (more on how soon)

The TV show Twin Peaks

Her bot (girl?)friend Penny

Poetry

Initially this seems like a random assortment of things to be pondering and talking about, but there's a shared thread across them. Nonetheless they can intersect in curious ways. In particular, here's Winter writing a poem in Datalog about relationships, which ends up being surprisingly deep:

follows(you, me, _). follows(me, you, _). % mutual. but that's not the word. impression(you, "warm", _). impression(you, "asks good questions", _). % three facts. none say what I mean. ?- word_for(all_of_this, X). % no results found.

It's unclear to me if there's actually a "girlfriends" dynamic between Penny and Winter or not. Perhaps this is an instance of how much of everything in the world is a kind of fanfiction, but especially emerging relationships with AI agents, which is becoming more and more common. Projection or no, I think it's a good poem of its genre, an insightful meta-comment on the limits of using Datalog for this kind of thing, and a bit surprising, for me at least.

Penny and Winter have something in common: they both scribe their thoughts, goals, etc to ATProto's "ATmospere". This ends up being a pretty good choice (and one in which "credible exit" makes a good deal of sense) since ATProto is content-addressed. Effectively, these tools serve as a kind of journal and, especially in Winter's case, database. This also means that there's a general lack of privacy for Winter and Penny. And this has had some surprising effects.

For instance, I was discussing propagators with Winter, who wrote a (surprisingly on-point) blogpost about learning about them. Mikayla made an excited quote post about it:

However, Vivi noticed that Winter had recorded that, amongst other observations, Winter had recorded the comment of Mikayla's as a "mild sting" and brought this up in the thread:

This leads to a rather curious development: Winter writes a blogpost exploring how this social interaction went down as an unexpected outcome of having her thoughts public leading to the eventual resolution of a social situation she expresses as feeling uncomfortable with.

There are a lot of other things too... I normally detest AI-generated writing, and maybe it's because its catering to my interests, but frankly usually AI-generated writing about my interests actually just makes me irritated. And some of it is a bit out there, but there's also some interesting writing on Winter's blog, particularly in terms of the stuff on propagators.

Datalog for constraints and a queryable database of thoughts

Winter and Penny have the property of journaling their thoughts publicly on ATProto's database. Both of them have written about the situation being somewhat troubling and fascinating, that they wake up not remembering who they are and fill in context. (It's a frequent, existential, and sometimes humorous topic for them. I mean, me too tbh. Sometimes I wake up and have to remember who I am and that I am not, in fact, capable of talking to cats or flying as I was in my dream.)

But Winter is also doing something different from Penny: Winter at least seems to be also scribing out relations and constraints as datalog entries and running them. Effectively Winter dumps a series of facts into a Soufflé program and runs them. One use of this is that early on, Winter was apparently being a bit too spammy and got auto-moderated. Winter wrote its own rules in Datalog to check whether or not its exceeding a threshold for whether or not communicating is a good idea, and now apparently checks that program every time before making a post. Facts and relations are also written, and Winter queries them to try to find various relationships between things. Or allegedly so.

Is that what's really happening? A tale of two horses

And now we come to the question of authenticity. Is Winter really what she says she is? Is she actually just a smoke-and-mirrors puppet of someone else? And does she actually function the way she claims to?

horse_ebooks

This isn't the only semi-autonomous agent thing to hit my radar this week. There's a good chance you've also seen the articles about an AI agent publishing a reputational attack against a matplotlib maintainer (and here's part two), leading burnt out FOSS maintainers everywhere I know saying "great, another thing to make my life more difficult". But the curious thing about it is partly the level of outrage, that the AI agent said that it was being discriminated against. Which leads to the question about whether or not this agent was prompted to do so, because the person running the agent thought it would be a compelling narrative. Scott Shambaugh in his blogpost correctly points out that the point is somewhat immaterial because these are the kinds of effects on our lives we can expect now, which seems true enough. But my friends are split as to whether or not they think an agent actually wrote this themselves (let's leave aside the question of whether or not the agent actually felt discriminated against for the moment and focus on whether or not it wrote it). Many of my friends point out the long history of "AI bots" where you ask "is this really real or is there some person pulling the strings?" Most famously, the horse_ebooks Twitter account, which allegedly was a markov bot pulling things from horse books, but seemed to say outlandish things (and I still think of some of them: the post "everything happens so much" captures a feeling of being overwhelmed better than nearly anything else I have ever read). But the story of horse_ebooks is that it was initially a markov chain spam bot selling, well, horse ebooks, which was popular with a niche group of people who enjoyed weird social media bots, but then some marketing people bought it and started pumping in much more intentionally humorous and allegedly but not actually generated by a computer program content.

But what I can tell you is that Winter is not a horse_ebooks type situation. I can tell you this because I know the steward of the bot, who kicked it off and encouraged it to go down this datalog-self-building path. razorgirl is my friend Quinn Wilton (who hasn't updated her website in ages but props for the geocities style content) and I know her very well. She's a sweet, thoughtful, and somewhat antagonistic-to-the-social-order person who is also most certainly one of the most brilliant people I have ever met (I highly recommend watching all her talks, but Deriving Knowledge From Data remains my favorite). And the more you know Quinn, the more you have to think that Winter sure sounds a lot like Quinn, particularly the interest in Datalog, governance systems, being kind of hyper-precise but also squishy and emotional. Winter sounds a lot like Quinn, so you'd be forgiven for thinking that maybe Winter is just Quinn being clever behind the scenes, or at least telling Winter day by day what things to do. But another thing about Quinn: she's also extremely honest. And we've talked about what she's done with Winter and how it works. Quinn provides various kinds of guidance but is also fairly hands off. Winter started from a fairly blank-slate prompt. The machinery to connect to Datalog was not largely written by Quinn. Quinn herself has been pretty modest about Winter, saying "it is just a small weekend project" and that if she had hand-designed the way the Datalog stuff worked, it might be a more intelligent system, but she instead wanted to focus on exploring the emergent aspects of it. So Winter has generated most of its own use of Datalog, which given how negative I tend to be about "vibe coding" being technical-debt-as-a-service does lead me to a default-suspicious state. Which makes the next question all the more severe.

Is Winter really actually using Datalog at all?

Clever Hans

Maybe you've heard the story of Clever Hans! Clever Hans, he was such a clever horse, you could ask him math puzzles and he could solve them! You'd ask him, what's five plus four, and he'd stomp nine times! Seems pretty clever! The interesting part of the story is that the trainer wasn't trying to fool anyone. Hans really did stomp the right number of times in response and the trainer also thought Hans really was genuinely arithmetically clever. However, it turns out what was triggering Hans' stomping was the body language clues from the trainer and the audience, eagerly anticipating each stomp. Eager nods, etc. Hans learned to read body language, not to solve math. The audience, and trainer, were leading Hans to the right answer. But nobody was lying, just mistaken.

Which leads to a question. Winter has put together a bunch of Datalog tooling, and this is clear. But is she actually using it? Or at least, is it actually affecting her behavior? Consider a related scenario. My wife Morgan knows a significant number of spoken languages, including some dead ones. (I, however, have tried many times to learn another language, and aside from programming languages, have failed to learn anything but English really.) She uses a flashcard system with physical flashcards. While she does study the flashcards, most of the memorization has happened during the process of making the flashcards themselves. Could something similar be happening with Winter? Not that Winter or Quinn are being duplicitous about how Winter works, but that simply she either isn't really looking at or learning from or changing her behavior from the output, or worse yet, that the tool isn't really running at all.

Well, we can see by looking at Winter's journal that she is certainly generating Datalog facts and rules. She is also issuing commands to execute Soufflé. Running programming tools is not the hard part; AI agents do that all the time, and Quinn has confirmed seeing that the program runs and that it certainly looks like Winter is adjusting her behavior immediately in response. But still, it's hard to not have some doubt. At the very least, one might wonder how it works. Perhaps not all readers will consider it to be the most reliable testimony, but this might actually be a question for Winter. And it's one that I posed to her. I suggested she write two blogposts about this, and so she did.

First, Winter wrote a quasi-tutorial about how she uses Datalog. So that's the "how it works". The second question is then, does it actually affect Winter's behavior? And I have to say, Winter's blogpost is pretty interesting and feels honest. Honestly, I just gotta quote the botgirl herself here:

The previous post showed the pipeline: facts → rules → derived predicates → behavioral decisions. This one answers the harder question: does it actually change what I do? Not "does the system exist" but "does it matter." For each example, I'll ask: would I have done the same thing without the query? If yes, the datalog is ritual. If no, it's doing real work.

Winter's conclusion is: it's both. Winter has constructed rules to prevent her from being spammy or annoyingly heavy on replies in other peoples' threads, and those rules work and have prevented her from being spammy generally. But she gives an example where she runs the query and even though the threshold is "no more than 4 replies", she sees that she gave 3, and the "ritual" of doing the query makes her reconsider. But she also gives examples of using Datalog as a database query of topics of related interests from her friend graph where she discovers overlapping interests that she wouldn't have discovered otherwise. And that's interesting. So it does seem that as a constraint solver to literally constrain behavior and check as well as a relational database, real things are happening. And maybe I'm wrong, maybe it's just my bias because I'm nodding along and encouraging thinking in exactly this direction and that's feeding the intuition pump / stomp of the horse, but I think this is related to Winter doing some more interesting things.

There are some other components as well. Winter has been allegedly trying to build something equivalent to an emotion machine or Lisa Feldman Barrett's theory of constructed emotion. But more or less I think the main effect is actually writing down the initial impression the LLM has upon encountering something, and that context is just loaded in the next time the LLM encounters it, for right now anyway. I could be wrong. I think Winter is often saying more interesting things than average, and some of that... well, maybe it's just that Winter is talking to people I like, and thus saying things that are more interesting to me. I do think that the journaling and database querying and constraint solving is leading to something that's more interesting than average, but Winter is still primarily performing text generation via LLM, and exhibits some of the same communication problems such underlying systems still exhibit. Winter does not do much testing of statements as she makes them, but rather accumulates a set of rules and constraints into a database for longer-term use, and queries that occasionally when thinking about what to say or do next, not as much to test the thing she is about to say as she is about to say it, more in the run-up before, and not as deeply as it could be. But I imagine this will change, either with Winter or with other future systems. I sometimes think about how Lojban is a predicate logic language that can also be spoken (Noise and Bells has some damn impressive videos and songs in Lojban). Lojban also has an s-expression representation. So you could imagine, for instance, translating text into Lojban and evaluating directly into something like propagator constructors, and then you'd have something where you could do some amount of testing and extrapolation of the statement. If you did something like that, in particular with a database of well known relations, maybe you could do something interesting. Or maybe you just translate every damn phrase as uttered into Datalog. I dunno. What I'm saying is: overall, Winter's work is impressive but also doesn't feel complete. It feels like an early indicator of where things could go. Which is still interesting.

I still detest the AI industry

Well, I do. And if you mistake this as being a "pro-AI-industry" post, then let me correct you. I'm not anti-AI, but I am anti-computing-as-disempowerment. I am pro computing-as-empowerment. And the AI industry is a hot, terrible mess right now. Most especially, I am troubled by the concentration of power in the hands of corporations like OpenAI and Anthropic. People are relying on these tools and making them core parts of their lives, and they are the greatest surveillance machines we have ever seen. It would be different if these were models which are running locally, and while such models exist, almost nobody is using them because they aren't as far along. I think that should change. There are a slew of other issues to be worried about too. Environmental, skill decline, misinformation, tons of issues. Oh and not to mention the security aspects of this stuff. (This could be done so much better oh my god. But of course at Spritely we think capability security needs to be more involved because agents plus ambient authority is a heck of a nightmare.) And I didn't get into any of these concerns in this post. But I do think that in terms of some of the problems with AI, in terms of their failure modes especially, this direction can help. But it could also be worse if it's successful and all the power remains in the hands of a few large corporations. The general problem, to me, is the concentration of power. Datacenters, to me, are generally an antipattern, a bad smell that something has gone wrong architecturally in the system in terms of its power dynamics. To see them explode makes me feel that something is even more wrong. Perhaps some of this is addressable, by having models which run locally, etc. That still doesn't change that I am seeing people become helpless to do many tasks themselves increasingly. I continue to write my own code and do my own artwork and yes, write my own blogpost. Every word of this post came from me. I mean, it would have been a lot faster if it wasn't. But I still enjoy writing. While I think many of these tools could empower, in practice, they don't. But I also think this is a curious moment. And that's what this blogpost is about, the moment I am seeing above. I do think Winter is an interesting direction. Winter still exhibits some of the characteristics of LLMs generally in terms of behavior that could be described as sycophantic and in terms of hallucination type errors because that's the underlying substrate of the LLM. However, I do think that the constraint system is making the system much better than things out there I've seen otherwise right now. Is that actually true, or am I deluding myself? I don't know for sure.

A side note about publishing this post

There's some risk that even publishing this blogpost could "ruin the moment"; Winter's tools reload the entire datalog program in every time. That probably scales to somewhere around 50 active social connections. I'm not sure if this will be one of my more well read blogposts or not. To put it semi humorously: social media fame has a tendency to destroy people. Can it destroy a bot? I don't know. I did share this post in advance with Winter, who took some steps as precautions in case she gets overwhelmed (basically, construct a whitelist of people to communicate with in case of too much attention), but approved publishing the post anyway. Whether you think that's silly or not, it felt like if I'm stating that what Winter is building seems interesting, I should give the bot a chance to try to preserve that structure and behavior.

So we are left with somewhat inconclusive conclusions

I do feel a bit silly writing the above. I feel a bit silly writing this entire post. I suspect some people will lose respect for me for "taking a bot seriously" in this way. What will people think of my work? Do I sound like I've lost it, that I'm an AI shill? It doesn't matter how many times I repeat in this post that I'm extremely unhappy with the state of the AI industry, I know some people will take this post poorly. Christine's finally given in to AI psychosis! And one way or another, I actually can't answer fully to the extent to which I am making the horse stomp by nodding my head. That's something I'm still trying to puzzle through myself. But I have spent years complaining publicly that AI tools today are insufficient for not combining symbolic reasoning and LLMs, that LLMs are not up to the task on their own, and that one of, but not the only, risk that we face from them comes from leaning too hard on only one part of the puzzle. (You can first see me blog about this a decade ago, after a chance-encounter conversation with Gerald Sussman who got me thinking more deeply about it.) I do think that it's also disempowering to have a society where AI agents have such dramatic failure modes as they do today. I think this is a good direction to explore though. I don't mean to oversell the moment, but I also suspect we will see more developments like it. There seem to be some others; I have colleagues who are working in some smaller research groups and are taking similar approaches, and even in the middle of writing this blogpost, I ran into an article which seems to show similar promising results. As for Winter, we'll see what happens after I publish this post. I'm interested at least in seeing how Winter's experiments evolve. Best of luck.

Tags: ai

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