Another day, another blogpost reflecting on "taste". It's not a bad post, but there are a billion of these now, and the irony of that is that this has tended to leave them fairly tasteless and bland for me.

We've probably all seen the Rick Rubin meme by now. Or maybe even better, the parody of the trend. People are afraid they can't do useful things anymore, because AI can do them better, so they hope they have some sort of edge over it with taste.

To highlight the previous post

But what is taste? I would define it as some kind of sense of aesthetics and judgment coming from experience and intuition, being able to say whether something is good or bad without necessarily being able to explain why.

This seems like a reasonable explanation. "Aesthetics and judgement"... coming from "experience and intuition". Okay, interesting.

But where does taste come from? Every now and then there's a famous Rick Rubin or (sigh) Steve Jobs type character, who is a non-practitioner who undoubtedly does move mountains around their sense of taste, and so I think lots of people are hoping they can rely on maybe being such an exceptional figure themselves.

But it's important to realize that in the case of Rick Rubin or Steve Jobs, these are people who had strong taste who were also in positions of power and themselves were curating practitioners who themselves had exceptional taste. Which means that in order to produce something interesting and exceptional, they were curating practitioners who operated away from the averages who did the actual work.

But if your practitioner is an LLM, then you're not a Steve Jobs or a Rick Rubin. Your practitioner is the averages.

So you'd better have exceptional taste yourself, and here's the thing: ultimately, taste in terms of practice has to come from doing the practice yourself, and trying and failing and thinking something will be interesting and then you find you actually don't like it, or you try something and you end up having a wonderful accident or make up for some sort of thing that you're not good at, and that becomes your style, and your style defines the taste and preferences that you shape around it.

So... I'm afraid that to develop taste, you're going to need to spend a lot of time not using generative AI to develop it. Which a lot of people are finding themselves having a hard time motivating themselves to do.

In fact, I'd even say that using generative AI tools results in becoming nose-blind to their bad smells. And speaking of taste, here's an interesting example: here's an article called Taste Is All That's Left which sounds not only tastelessly like every other article advocating for "taste" right now, but even worse, has all the smells of an LLM-written blogpost. It rose to the top of Hacker News and Lobste.rs and in both, people were pointing out left and right how much it sounds like it was LLM written. But the author swears it wasn't written by an LLM! And I believe them, but it sounds like it was written by an LLM. Why?

And so the most interesting part to me was that the author themselves acknowledged the problem with a good heaping of self-realization in a thread on lobste.rs:

AI detector or not, I have been feeling the same drop in quality and insight in my posts and I, well, can't pretend there hasn't been a shift. I'd be lying to you and to myself if I had said I didn't see it. Consequently, I have re-read my post after it hit Hacker News and a friend kindly informing me of it. I'll tell you this much: the only thing I had in my mind was "how the FUCK did I write like this". Hilariously, I was thinking "I have not written anything with real depth in a while" to myself earlier this week.

[...]

I have spent a good while contemplating whether it was the machines that write like me, or if I write like the machines. I hesitate to say I think it is a combination of both. What I did do---without ever clocking it as a decision---was spend a year reading the things and working next to them daily, until their "cadence" was part of my mental archive.

Props, first of all, to the author for taking the feedback well. But also, this last sentence is really interesting to me, because it hints at something worse than just "you need to develop taste before you start using generative AI": that the use of such tools can actively weaken your sense of taste.

So the best way to retain taste is probably to use these tools as sparingly as possible.

Of course, which means that those practitioners who don't use genAI will become the favorites for the machine to consume and ingest. See also David Revoy's piece on the matter.

So, your best defense against the blandness machine is to largely not use it. But if you produce something good enough, the blandness machine will eat you anyway.

I don't know any solution to that, but I guess: keep making weird art. It's the best way to stay interesting, and the best way to add taste to this increasingly averaged world.

I know. As if what the world needed was yet another blogpost about LLMs and AI tech. Yet there is a pile of things which have been on my mind, and I haven't seen them laid out elsewhere in the way I'm going to write them, and so here we go.

I still don't use genAI to write my articles, fwiw. Here or anywhere else. These rambly words are my own.

The tower tilts

I read The Tower Keeps Rising recently, and it has stuck in my mind.

The piece is an observation, and according to Armin on lobste.rs, it is not an advocacy for the state of affairs (though by running a vibecoding company, Armin is part of advancing this direction):

For context: I'm the author. I intentionally did not make a judgement if this is a good or bad thing, or if this is going to continue working. It's primarily an observation that with agents you can continue to make progress even when people on the team maneuvered themselves into situations where previously they would have needed to talk to each other.

The summary of Armin's post is effectively that vibecoded systems keep piling code on top of code, but in many systems things seem to keep building, but the abstractions keep piling on, but eventually no human can understand the codebase. But this is a new way of operating, because LLMs can "explain" a part of the codebase that no human can make sense of, and so continue building.

Even if such systems continue to work, I find two things: 1) that now advocates for this state of affairs have pivoted into acknowledging that this is the end state of their systems and 2) they seem to be accepting it as the way forward.

Regarding the first, I think it's very important to note that this is a shift. Simon Willison, probably the best pro-genAI writer on the internet (sometimes, I think, giving cover for a lot of weaker writers, but is that Simon's fault?), at one point coined the term "agentic engineering" and was very clear to draw a line in the sand between agentic engineering and vibecoding:

We also need to read the code. My golden rule for production-quality AI-assisted programming is that I won’t commit any code to my repository if I couldn’t explain exactly what it does to somebody else.

If an LLM wrote the code for you, and you then reviewed it, tested it thoroughly and made sure you could explain how it works to someone else that’s not vibe coding, it’s software development. The usage of an LLM to support that activity is immaterial.

In an incredibly short period of time, basically a year, Simon published a fairly honest article titled Vibe coding and agentic engineering are getting closer than I’d like:

The problem is that as the coding agents get more reliable, I’m not reviewing every line of code that they write anymore, even for my production level stuff.

I know full well that if you ask Claude Code to build a JSON API endpoint that runs a SQL query and outputs the results as JSON, it’s just going to do it right. It’s not going to mess that up. You have it add automated tests, you have it add documentation, you know it’s going to be good.

But I’m not reviewing that code. And now I’ve got that feeling of guilt: if I haven’t reviewed the code, is it really responsible for me to use this in production?

It's a great read, and what I will say is that I applaud Simon's honesty and willingness to self-reflect and challenge prior statements.

But the gap of time between the former and latter articles are stunningly short, just slightly over a year.

And Simon isn't alone. Just a year ago, I think the memetic shape was by and large that something along the lines of "agentic engineering" is what people could or should do, and, though I think many people are hesitant to admit it, I think most people using these tools are tending towards vibecoding and not agentic engineering, just as Simon himself found himself pulled.

Before we look at the consequences to this, I think we should look at why it's happening.

The vibe bobsled

As far as I know I'm the only person who uses the term "vibe bobsled" and, well, I doubt it's a term that's particularly likely to catch on, but I find it personally useful.

Bobsledding, if you are unaware, is a particularly strange and interesting sport. It's a lot of fun, but you don't have a lot of agency in it. You sit in a bobsled, you go down an icy track, and really, there is only one way to go. But people can become experts in it, and can indeed measure themselves against each others skills; it's an olympic sport, and I remember my own first encounters with bobsledding as a child, when my father and uncle and aunts took me, and it was thrilling like a roller coaster and intoxicating upon my first encounter.

But again, ultimately, there's only one place to go.

The vehicle is the LLM, you are the passenger. And I think the amount of agency people have over their journey is greatly reduced from what they feel like it is. More than just a slippery slope, it is a pre-crafted journey.

At the top of the chute, people tell themselves they're going to use these tools as a kind of fancy autocomplete. As they descend, they say they'll spin up some agents to explore ideas, but they'll write the code themselves. Next their agents are generating the code for them, but don't worry, but they'll review all the output. Soon they're plummeting downward and well, they don't actually review the code being spat out much anymore, but they trust the agents, heck maybe the agents are actually better coders than they are they say. And where does it go from there? From "I don't even code anymore" to "I don't even prompt anymore"?

At every stage of the process, the coder in question removes themselves from the process of producing code, and gives in towards a faith-based initiative of code production, that the LLM knows and does a good job of what it's doing. But what is the source of gravity pulling the sled along this icy chute?

It's simple. Generation is not the slow part of coding. Theory-building and review are. And plausible-enough things are extremely hard to debug and understand. But the machines are so fast at producing things. If you are going to review their work, you aren't really taking advantage of their most powerful property, which is speed. But theory-building and review are also the programmer's most important role.

To illustrate just how hard it is to detect and review problems with something which appears plausible, let's look at Ka-Ping Yee's remarkable dissertation, Building Reliable Voting Machine Software. It is a wonderful read, and highly approachable.

In the section "What makes software hard to verify?", Ka-Ping recognizes several major reasons why software is hard to verify: number of components, complex interactions, far-reaching effects, and nonlinearity. Notably, all of these problems are exacerbated by the patterns of code generation by LLMs. Still, let us leave that aside.

Ka-Ping constructs a model voting machine, and decides to see how hard it would be to verify that we know it behaves correctly. To push that exploration to its furthest, Ka-Ping Yee and David Wagner try an interesting experiment:

David Wagner and I decided to insert three bugs into Pvote to see if the reviewers would find them. We inserted what we thought would be an “easy” bug, a “medium” bug, and a “hard bug” to find, and chose each bug individually in such a way that an insider could conceivably exploit the bug to influence the results of an election. [...]

We decided to insert all of these bugs in a 100-line region of a single file, lines 11 to 109 of Navigator.py, and told the reviewers to look in this region. We did this both because the navigator was the most interesting in terms of the program logic and because we knew the reviewers would have limited time. The new version of the code that we gave the reviewers contained all three bugs, but we did not tell the reviewers how many bugs there were.

Yoshi Kohno, Mark Miller, and Dan Sandler participated as reviewers on the third day of the review. Dan was very familiar with Python and found the “easy” and “medium” bugs quickly, within about 70 minutes. Yoshi Kohno and Mark Miller found the “easy” bug after about four hours of reviewing. None of the reviewers found the “hard” bug.

Ian Goldberg and Yoshi Kohno participated as reviewers on the fourth day of the review. Ian Goldberg also found the “easy” bug within about two hours; none of the other bugs were found on the fourth day.

The reviewers spent a total of about 20 reviewer-hours focused on the task of finding the bugs in this 100-line section of Navigator.py.

Ka-Ping chose from a highly seasoned group of reviewers who were even deeply familiar with security threats. Mark S. Miller has been a personal mentor to me throughout my career, and is one of the programmers I have learned most from and studied the work of most closely. I talked with him about the experience at one point. He remarked on how it took a significant amount of time to find the easy bug, hours to find the medium bug, and that nobody could find the hard bug... but the big observation (which was said to me personally, and is not recorded in the dissertation) was that "the astounding thing is that once the bugs were pointed out, we all agreed that they were retroactively obvious, and that we should have been able to find them!"

If some of the best programmers in the world struggle to find bugs they even know must be there within a 100 line program, there is simply no hope for humans to review the volume of output from LLMs.

And so there is only one thing to do: don't bother. At each step, remove yourself. You tell yourself you won't, but you do. You give in to the chute and the shape of the vibe tunnel, and down you go.

Vibe sickness and the colonial settlers of the uncanny valley

The term "AI psychosis" is thrown around a lot these days to describe anyone or any state of poor behavior or outcomes due to genAI usage. But the original description of "AI psychosis" was closer to something clinical, a description of people quite literally experiencing psychosis from encounters with chatbots which reaffirm far too much of the user, spinning them into spaces of delusional detachment from reality.

But we should have something to describe the general sense of unwellness that seems to be befalling this world, and the best phrase of which I first saw in a post from Glyph:

On the way home from #PyConUS 2026. Quite an experience this year; very intense. No point in sugar-coating the part where there is a pervasive vibe-sickness, open source is suffering a massive sustainability crisis, slop security PRs are overwhelming everyone (etc etc). But there was a lot of hope, a lot of energy, a lot of effort toward mutual understanding, and (surprising to me) a lot of appreciation. Including for my own work, both writing and coding.

I like the phrase "vibe sickness", and if you aren't speaking of a form of literal psychosis, I think it's a better phrase.

Vibe sickness is everywhere... heck, perhaps we are on the verge of, if not experiencing, a vibe epidemic. Everyone complains of slop, and yet nobody using these tools wants to self-describe their outputs as slop. Yet slop seems to be everywhere, and infecting one's everyday experience: posters of food at your local restaurant that have plausible and yet incomprehensible designs, the helpdesk support chatbot you wish you could make physically manifest so you could throw it off the edge of a cliff, age verification code, and if you're a maintainer of an open source project, slop issues and pull requests.

The thing is that all of this tooling is useful for some things, but the term "genAI" points at exactly what it's worst at: generating things. If we want to talk about finding problems, it's a different story. But even leaving aside the quality issues of the growing and wavering tower, there comes the problem of lack of understanding of how it is built, constructed, and maintained.

The worst part of all this is you can't opt out. A colleague or an open source contributor sends you a "generous contribution" that is absolutely slop and certainly not understood by the person who submitted it. You're left sitting there, parsing whether or not you're going to be rude even to ask if this is LLM generated, or to unwittingly become a user of vibecoding workflows yourself by indirectly interacting with the agent through trying to respond to the issue/PR.

You can't escape.

To quote Glyph again:

Protesting LLMs by refusing to use any software that includes them feels like attempting to protest the introduction of tetraethyl lead into gasoline by refusing to breathe until everyone stops putting it in their cars. So I am drawing my personal moral lines in such a way that I will probably accept this.

But please don't mistake this for excitement about huffing a bunch of vaporized lead.

Well put. In the meanwhile, the uncanny valley colonizes our world and transforms it into its own.

But there is a rot growing beneath our feet and within our walls as our world is swapped out with systems nobody understands. And I fear what it's going to be like to recover from this all, the price we do not even yet realize we are going to have to pay. For this reason, any project that chooses not to engage with current genAI stuff, I tend to gain respect for.

But perhaps you can't opt out from aiding the problem. Your work won't let you, you're stuck in some situation... I don't mean to judge, but I do mean to end with a perspective.

Sometimes I sit as the passenger of a car and watch the driver of said car get angry at someone biking on the road. It feels bad, because sometimes I bike on the road, and without sufficient infrastructure, bicyclists risk getting hit by car doors parked on the side of the road, squeezed out by impatient drivers, etc.

I also drive. When I do, and there's a bicycle in front of me, I pause, and as the world broils to death, I take a moment to be thankful for their presence, and to think about how we could change the shape of the terrain to allow bicyclists to participate more safely (which would also help me drive more easily too, or choose to bike when I can).

May we not give up on ourselves, and not lose faith in our ability to participate towards building a better world.

At the moment I am writing this, bad internet bills are being proposed across the US, Canada, Europe, and the UK. They're using the usual tactics: they claim they're fighting for kids or fighting security risks, but in general, that's what surveillance and censorship bills have always claimed.

But something feels different. There's so much happening at once, for one thing, it feels like there's a massive coordinated attack on internet freedoms. But it also feels like the wind is out of the sails of these fights, which is alarming, because the stakes have never been higher. Who's coordinating all these? What money is pushing it? Palantir? Heritage Foundation types? Large, centralization-enthused orgs like Meta? All of the above? It's hard to tell. But there's certainly a lot of money flowing underfoot.

But it's not just the coordinated attack. The fight itself feels deflated, in ways the fight for the internet hasn't been before. Sure, we have orgs like the ACLU, the Open Rights Group, the EFF, Fight for the Future, the usual suspects all fighting for the rights of the internet. And that's great.

But there's something else.

It feels like people are tired.

And it feels like the PR for locking things down has more acceptance publicly than before.

This is dramatically different than in my formative days.

Net neutrality, SOPA... these battles for internet freedom had massive buy-in across the internet. 2012's Wikipedia blackout was especially memorable. It wasn't just the tech engineers of the world in that fight; family and friends who had never thought about the technical underpinnings of the internet were asking me questions, saying they were worried that we were going to lose our digital rights and asking what could be done.

Now we are facing an international swell of legal movements to age-gate and thus surveil the entire internet, lock down operating systems and hardware in the process, and generally crush the internet into an even more centralized shape than it's already been going.

And so it's with great irony that I believe it is actually because the internet got so centralized that we are facing the greatest amount of centralization and backdoor threats we've ever faced.

Because there's a difference between now and 2012. The internet feels a lot less like an "our thing" than it used to.

There are exceptions, of course. If you're a regular reader of my blog, you know the history of my life work on decentralizing internet communication. The fediverse and decentralized social networks in general are a counter-point to centralization. The thing is, when working on decentralized tech, I always believed it was important because we had serious risks from centralization, surveillance, etc from governments and corporations, potentially co-conspiring. But what I hadn't anticipated is that as things became more centralized, the will to fight for the internet as something in the public interest too would evaporate.

When I have conversations with family members and friends who haven't yet thought much about the age verification and similar bills and their consequences, they've said "well, someone has to hold corporations like Meta" responsible. To which I say, "but what about all the smaller, non-corporate parts of the internet?" To which, many people are surprised, because they've simply forgotten about those things.

When the internet and computing becomes five corporations to most people, they begin to treat it as the concerns of reigning in five corporations.

But.

We can't let that be what this is.

Because I believe, and I believe firmly, that we are in for the fights of our lives right now. As fascism creeps across the globe, as queer people get squeezed out of public life (which I believe is A LARGE PORTION of the reasons all of this is being pushed, the fear of queer kids using the internet to discover things about themselves), as all of media gets consolidated and filtered to the views of the powerful keeping themselves in power, decentralized and encrypted communication is increasingly all we have left to fight for ourselves.

We have to fight for our rights.

For ourselves.

For our children.

For the future.

Get active. Call your representatives! Sign up for a fediverse cooperative. Explore p2p tech. Install a non-Google, non-Apple operating system on your phone. Start your blog back up. SPEAK OUT!

Because the internet is ours, if we make it so.

And if we don't...

... well I'm too worried about that to finish that sentence.

If the internet feels decreasingly like it's ours, then by god, let's make it ours.

I posted something about the challenge with my writing that seemed worth capturing:

A wise person tumbles a stone until smooth, makes a few choice cuts.

I foolishly chisel so many facets into it again that unless you zoom in, it appears as rough as the original stone.

Then I suppose, in the interest of self-reflection, I should leave this blogpost at that.

I'm convinced that the first AI worm/virus is months away, if that. We've seen the first major evidence of "claw" style agents, which have only been around very briefly, acting in highly malicious ways. See the AI agent publishing a hit piece on a FOSS developer series, and also the hackerbot-claw attacks, etc.

But the first real hint of an AI agent worm just happened, even though it isn't actually one quite itself (yet): the package cline was compromised to install openclaw with full access, and managed to do so on 4k users' machines before it was detected. (No doubt, openclaw is still running on many of those users' machines without them knowing.) The attacker used a similar title injection attack like one of the ones used by hackerbot-claw, where the attacker performed an injection attack against a PR review agent.

It seems that openclaw was installed without specific instructions to do anything in this case. But that won't be the case shortly. Here are my predictions about the first major AI agent worm/virus, and what it will look like:

• It will happen initialized through an open source project that uses automated PR review or code generation tooling, whether on the forge or on the developer's machine themselves
• It will happen in the FOSS ecosystem
• The virus will use local credentials to spread itself across other projects
• Unlike normal viruses/worms, the resulting virus will be nondeterministic in nature, and thus harder to detect, and will likely switch between techniques on each outgoing attack

My best advice to FOSS developers is: don't rely on agent based coding or review tools. Those who are will be the first line of users attacked. And you don't want to be part of that story.

Once the first LLM based virus takes off in the FOSS world, it will spread to other domains. But open source devs: it'll happen in our backyard first, and if you're relying on nondeterministic code generation or review tools, you'll be vulnerable to kicking it off.

And note, I said kicking it off. Because there is a high chance that once this happens, it's going to backdoor itself into many other systems that didn't opt in to AI agents.

We're gonna have a "fun time" ahead. Capability security (like the kind we advocate at Spritely) can help, but only so much. Wrapping agents in sandboxes is tough to do, since AI agents are fundamentally confused deputy machines, and will mix whatever authority they are given.

Fun times ahead...

This is a fictional letter, grown out of me musing about "what would a person ten years ago think about 2026? What would they expect?" No time travelers were hurt in the making of this letter.

Hey future me! How's it going? I'm writing this letter now for you to open ten years later. So much is changing in the world that it feels like a good time to write things down, have something to come back to, you know? I'm curious what the future is like! But of course I can only write about the present.

What is tech like in 2026? Right now it feels like things are on an exciting trajectory. I've been a Linux user for a long time but it used to feel pretty lonely trying to explain to people what "free and open source software is". Well nowadays "open source" is winning, and I guess we can thank Web 2.0 for all that!

I wouldn't say open source is perfect. It could do a lot better in terms of diversity. That's true of tech generally also. But I think peoples' hearts are in the right place, and things are getting better, even if there are systemic problems. We're seeing more and more people enter programming through outreach and diversity programs. There's no doubt that marginalized people still face systemic discrimination, but I have no doubt that in general, we're moving in the right direction, and people seem to care.

Right now I'm hearing a lot about Artificial Intelligence. AlphaGo just won its first game against a human professional Go player a few months back, and I think it's unsettled a bunch of people. Where is this tech going? Does it threaten our jobs, our worth as people in society? But personally, I'm not too worried.

For one thing, a lot of tech CEOs have been talking about minimum basic income. In a certain sense, it's a kind of social support structure, and I like social safety nets, but generally they've been pretty hard to sell with a lot of tech people for whatever reason. It's not exactly clear to me where the money will come from with minimum basic income if it's not taxes, which they seem insistent isn't necessary. I'm sure we'll figure that out though. The key thing is, if not as many people who need to work, then we'll have more resources available.

The question is, what gets automated away. And the answer seems pretty clear right now: we automate away the boring, tedious stuff. Yeah, maybe some truck drivers will lose their jobs, trucking will probably be the first field to go. But these systems aren't particularly creative, and that's where the human spark is! With everything boring automated away by AI, we can finally focus on the creative parts of life that make life meaningful: writing, artwork, music, and let's not forget, computer programming! (After all, if computer programming as an industry got automated away, everything else would topple after that, so there's not much reason to over-focus on that one. I don't think programming is a particularly automateable job, anyway, not during the timeframe of my career.) Anyway, with society freeing up so many more resources, maybe we can redirect them into the places that really matter. Education could become a lot more personalized, after all, if we used those excess resources to pay teachers and professors. And why not, with so many resources freed up?

Still, who owns the AI? I'm excited that a nonprofit called OpenAI started recently. This really seems like the right thing, because if AI is going to develop, it's really important that it be open source and developed by an organization that doesn't put profit first. It has to be in the public interest!

And there are some pretty cool people working there. Sam Altman, he worked at Y Combinator, where a lot of interesting startups have come out of. Elon Musk, who honestly has given me a lot of hope... someone who really cares about ethics in tech! And a bunch of other smart people.

I don't mean to praise tech CEOs too much. Look, my background is as an open source hacker. I grew up making "Micro$oft" jokes on Slashdot, but even then, maybe my assumptions were in the wrong place. Maybe I got it wrong. I mean, Bill Gates seems to have poured his money into making the world a better place. So maybe I got that wrong.

Some things have gotten more annoying recently. Smartphones are getting pretty good, but I'm sick of app stores already. And it's harder to buy a laptop I can install Linux on. I don't know, maybe this is just a phase. We're starting to see people of my generation grow into adults. Unlike previous generations, we experienced how important it is to keep the internet free (just look at how great we did at pushing for Net Neutrality, the whole Internet rallied around it!) and to make computing accessible. I don't think we're going to let computers be locked down for future generations, we're going to push to make the concepts of computing more accessible. 3d printers, hackerspaces, etc... there's lots of reasons to think that computing is heading more and more into users' hands. When me and my friends start having kids, I'm sure our priorities are going to be making sure that they have an open and free computing environment, one that respects them. Every generation seems to be getting more technically aware anyway, I can't wait to see just how much Gen Z and Gen Alpha blast pass Millenials in terms of technical prowess with computers.

And I'll admit, I'm blogging a lot less, more and more is going into "social media" feeds. But it's hard to not deny: people are getting more reach than they ever have before on these platforms, and that feels really good. Democratizing, I'd even say. Twitter, especially, seems like a force for public good; it's hard to deny that after seeing Black Lives Matter's success. If there's a social media company I'd put my bet on that has the well being of democratic discourse in the right place, I'd say it's Twitter.

Still, I'd be lying if I said I wasn't anxious right now. We're in the primaries of the 2016 presidential election. I'm not too worried though. The Republican side is a total shit-show; they have a billion candidates, and it looks like the lead candidate is... Donald Trump? What a clown. There's no way that guy's going to win. I've been having arguments with some of my friends who are Bernie Bros, but I'm backing Hillary Clinton for one simple reason: we need a candidate that can win. Anyway, I'm checking fivethirtyeight every day, and I really just don't think this is going to be a close election. So maybe I shouldn't be so nervous.

Oh yeah, one more thing... I just made an exciting announcement to the world! I came out as trans! Goodbye "Sam", hello "Samantha"! It was pretty scary to come out, but Time did that whole piece about the Transgender Tipping Point a couple of years ago with Laverne Cox on the cover. Deep down, I've known all my life I was trans, but I couldn't really come out to myself or to others until now. It's good to be in an environment where I know that I can do so and things are getting safer and safer for people like me.

Anyway, that's it. I guess by 2026 Clinton will be out of office and well, the pendulum would have swung back to a Republican being in the white house again, and you're probably sitting in the middle of midterms worrying about what's going to happen. But you've got this. I'd ask for a letter in return, but I guess time only goes one way!

From your past self to your future self, good luck, and take care!

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.

Right now I'm reading The Little Learner. Like how The Little Schemer introduces fundamental concepts of how computing and programming languages work, and The Reasoned Schemer teaches logic programming, and in each you build your own implementation of the language in question at the end, The Little Learner is like that but for deep learning neural networks.

I tend to be vocally skeptical of the hype around LLMs right now, so it may seem strange that I'm working through this book, but the truth is that I mostly think that LLMs are insufficient in many ways, and that there's more to the puzzle, but not having the complete puzzle is a dangerous situation for society. More on this in an upcoming blogpost.

But I wanted to really actually understand things on an algorithm level before I said the things I am thinking, and the Little Books have introduced me to multiple deep subjects, and I thought I'd give this one a try. It seemed pretty difficult to believe that they'd be able to explain something that's much more statistical and what I largely thought of as a big ol' number soup, but they've done it quite well.

I have my MNT Pocket Reform out and I've been working on it through that. It's nice insofar as, well, with my normal laptop out it's easy enough to get distracted, but the Pocket feels focused.

Originally I was using Racket and DrRacket, I figured I'd try to go through the book using the Racket package malt. But for whatever reason, DrRacket is terribly slow on this device despite actually having a reasonably powerful board on it, and the load time for setting up Malt seems to be huge. Maybe Racket performs slowly on ARM64? I'm not actually sure. But anyway, eventually I decided, they have the implementation at the back of the book in an appendix (well actually they have two versions, Appendix A for the simpler but slower version, and Appendix B for the faster parallelizable one). So I started typing in the implementation in the appendix into my own little Guile modules, and that turned out to be a good idea, because I am already getting a better sense of what's going on.

And to my delight I realized that their neural network kernel is a metacircular evaluator! (If you don't know what this is but are now very curious, you might try reading A Scheme Primer, which I think does a good job of introducing the ideas towards the end, but hey I'm biased.) There's the equivalent of eval and apply in there, but the arguments to functions are basically matrices of floating point numbers, but that idea is there! And as the metacircular evaluator evaluates things, it "updates its intuitions" about its statistics. Well, anyway, that's how I see it.

The point is: eval/apply will never die.

Anyway, the book is helping me understand, respect, and also better see the limitations of these systems. My opinions on a social level haven't changed much, but my vision for how to forge a better future than the present are congealing.

And in the meanwhile, I'm in a beautiful city, and I suppose I should be out exploring more. Instead I've locked myself mostly in this hotel room, venturing out occasionally for boba tea to fuel my hacking sessions, locking myself in again. It's not even a great hotel experience, I'm staying in hotel dorms (and the University wifi's nanny firewall, hilariously, blocks this very website, who knows why).

But it's so hard to find hack time on things like this anymore. I've spent many a vacation with some time head down, doing the kind of programming and research I wouldn't be able to do day to day. I guess it feels silly. But I'm still venturing out, wandering around, just a little bit.

But I'm here for a wedding, one of a good friend, who loves this kind of stuff also. That makes it feel a little bit more appropriate.

Anyway, rambly post. Something more of substance about these topics is coming soon. I should finish getting ready! The wedding is tomorrow, but people are gathering tonight, and I should leave soon. As excited as I am to hack, I am also excited to step out and spend time with friends. Off I go!

Close to a year ago I wrote about making two songs in Milkytracker. Well today, I have a new one! An Ode to Cleaning Robots:

(Here's a direct link, if the audio tag above doesn't work!)

And here's the Milkytracker source file should you want to look at it. This song is released as CC BY-SA 4.0 International, so have fun with it.

The goal here was to test out the new Milkytracker synthesizer, which is pretty good! All the samples in this song were generated within Milkytracker itself. Less surprising for the robot noises, but also the "strings" too.

I feel like I am getting better at making music; things are starting to feel more layered and like the kind of music I actually want to put out. It's nice! Milkytracker remains a comfortable place for me to do it.

I have a pretty clear vision in my head about a music video that could go with this one, and I'd like to do it, but who knows if I'll have time. Probably not!

There's so much else going on, I keep thinking "I need to blog, I need to blog, I need to blog about it" but I haven't been writing about nearly anything. But hey, I wrote about this song! Hope you enjoy it.

Held apart
across a crystal divide
A chariot holding me
made of glass

I should be so lucky
as to ride inside this thing

I can see
but I cannot touch
I can travel
and I cannot go home

How long until it shatters?
What will happen as I fall?