A Database File

When I was 10 years old, and going through a fairly difficult time, I was lucky enough to come into the possession of a piece of software called Claris FileMaker Pro™.

FileMaker allowed its users to construct arbitrary databases, and to associate their tables with a customized visual presentation. FileMaker also had a rudimentary scripting language, which would allow users to imbue these databases with behavior.

As a mentally ill pre-teen, lacking a sense of control over anything or anyone in my own life, including myself, I began building a personalized database to catalogue the various objects and people in my immediate vicinity. If one were inclined to be generous, one might assess this behavior and say I was systematically taxonomizing the objects in my life and recording schematized information about them.

As I saw it at the time, if I collected the information, I could always use it later, to answer questions that I might have. If I didn’t collect it, then what if I needed it? Surely I would regret it! Thus I developed a categorical imperative to spend as much of my time as possible collecting and entering data about everything that I could reasonably arrange into a common schema.

Having thus summoned this specter of regret for all lost data-entry opportunities, it was hard to dismiss. We might label it “Claris’s Basilisk”, for obvious reasons.

Therefore, a less-generous (or more clinically-minded) observer might have replaced the word “systematically” with “obsessively” in the assessment above.

I also began writing what scripts were within my marginal programming abilities at the time, just because I could: things like computing the sum of every street number of every person in my address book. Why was this useful? Wrong question: the right question is “was it possible” to which my answer was “yes”.

If I was obliged to collect all the information which I could observe — in case it later became interesting — I was similarly obliged to write and run every program I could. It might, after all, emit some other interesting information.

I was an avid reader of science fiction as well.

I had this vague sense that computers could kind of think. This resulted in a chain of reasoning that went something like this:

1. human brains are kinda like computers,
2. the software running in the human brain is very complex,
3. I could only write simple computer programs, but,
4. when you really think about it, a “complex” program is just a collection of simpler programs

Therefore: if I just kept collecting data, collecting smaller programs that could solve specific problems, and connecting them all together in one big file, eventually the database as a whole would become self-aware and could solve whatever problem I wanted. I just needed to be patient; to “keep grinding” as the kids would put it today.

I still feel like this is an understandable way to think — if you are a highly depressed and anxious 10-year-old in 1990.

Anyway.

35 Years Later

OpenAI is a company that produces transformer architecture machine learning generative AI models; their current generation was trained on about 10 trillion words, obtained in a variety of different ways from a large variety of different, unrelated sources.

A few days ago, on March 26, 2025 at 8:41 AM Pacific Time, Sam Altman took to “X™, The Everything App™,” and described the trajectory of his career of the last decade at OpenAI as, and I quote, a “grind for a decade trying to help make super-intelligence to cure cancer or whatever” (emphasis mine).

I really, really don’t want to become a full-time AI skeptic, and I am not an expert here, but I feel like I can identify a logically flawed premise when I see one.

This is not a system-design strategy. It is a trauma response.

You can’t cure cancer “or whatever”. If you want to build a computer system that does some thing, you actually need to hire experts in that thing, and have them work to both design and validate that the system is fit for the purpose of that thing.

Aside: But... are they, though?

I am not an oncologist; I do not particularly want to be writing about the specifics here, but, if I am going to make a claim like “you can’t cure cancer this way” I need to back it up.

My first argument — and possibly my strongest — is that cancer is not cured.

QED.

But I guess, to Sam’s credit, there is at least one other company partnering with OpenAI to do things that are specifically related to cancer. However, that company is still in a self-described “initial phase” and it’s not entirely clear that it is going to work out very well.

Almost everything I can find about it online was from a PR push in the middle of last year, so it all reads like a press release. I can’t easily find any independently-verified information.

A lot of AI hype is like this. A promising demo is delivered; claims are made that surely if the technology can solve this small part of the problem now, within 5 years surely it will be able to solve everything else as well!

But even the light-on-content puff-pieces tend to hedge quite a lot. For example, as the Wall Street Journal quoted one of the users initially testing it (emphasis mine):

The most promising use of AI in healthcare right now is automating “mundane” tasks like paperwork and physician note-taking, he said. The tendency for AI models to “hallucinate” and contain bias presents serious risks for using AI to replace doctors. Both Color’s Laraki and OpenAI’s Lightcap are adamant that doctors be involved in any clinical decisions.

I would probably not personally characterize “‘mundane’ tasks like paperwork and … note-taking” as “curing cancer”. Maybe an oncologist could use some code I developed too; even if it helped them, I wouldn’t be stealing valor from them on the curing-cancer part of their job.

Even fully giving it the benefit of the doubt that it works great, and improves patient outcomes significantly, this is medical back-office software. It is not super-intelligence.

It would not even matter if it were “super-intelligence”, whatever that means, because “intelligence” is not how you do medical care or medical research. It’s called “lab work” not “lab think”.

To put a fine point on it: biomedical research fundamentally cannot be done entirely by reading papers or processing existing information. It cannot even be done by testing drugs in computer simulations.

Biological systems are enormously complex, and medical research on new therapies inherently requires careful, repeated empirical testing to validate the correspondence of existing research with reality. Not “an experiment”, but a series of coordinated experiments that all test the same theoretical model. The data (which, in an LLM context, is “training data”) might just be wrong; it may not reflect reality, and the only way to tell is to continuously verify it against reality.

Previous observations can be tainted by methodological errors, by data fraud, and by operational mistakes by practitioners. If there were a way to do verifiable development of new disease therapies without the extremely expensive ladder going from cell cultures to animal models to human trials, we would already be doing it, and “AI” would just be an improvement to efficiency of that process. But there is no way to do that and nothing about the technologies involved in LLMs is going to change that fact.

Knowing Things

The practice of science — indeed any practice of the collection of meaningful information — must be done by intentionally and carefully selecting inclusion criteria, methodically and repeatedly curating our data, building a model that operates according to rules we understand and can verify, and verifying the data itself with repeated tests against nature. We cannot just hoover up whatever information happens to be conveniently available with no human intervention and hope it resolves to a correct model of reality by accident. We need to look where the keys are, not where the light is.

Piling up more and more information in a haphazard and increasingly precarious pile will not allow us to climb to the top of that pile, all the way to heaven, so that we can attack and dethrone God.

Eventually, we’ll just run out of disk space, and then lose the database file when the family gets a new computer anyway.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. Special thanks also to Ben Chatterton for a brief pre-publication review; any errors remain my own. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor! Special thanks also to Itamar Turner-Trauring and Thomas Grainger for pre-publication feedback on this article; any errors of course remain my own.

Today on stream, I updated PINPal to fix the memorization algorithm.

If you haven’t heard of PINPal before, it is a vault password memorization tool. For more detail on what that means, you can check it out the README, and why not give it a ⭐ while you’re at it.

As I started writing up an update post I realized that I wanted to contextualize it a bit more, because it’s a tool I really wish were more popular. It solves one of those small security problems that you can mostly ignore, right up until the point where it’s a huge problem and it’s too late to do anything about it.

In brief, PINPal helps you memorize new secure passcodes for things you actually have to remember and can’t simply put into your password manager, like the password to your password manager, your PC user account login, your email account¹, or the PIN code to your phone or debit card.

Too often, even if you’re properly using a good password manager for your passwords, you’ll be protecting it with a password optimized for memorability, which is to say, one that isn’t random and thus isn’t secure. But I have also seen folks veer too far in the other direction, trying to make a really secure password that they then forget right after switching to a password manager. Forgetting your vault password can also be a really big deal, making you do password resets across every app you’ve loaded into it so far, so having an opportunity to practice it periodically is important.

PINPal uses spaced repetition to ensure that you remember the codes it generates.

While periodic forced password resets are a bad idea, if (and only if!) you can actually remember the new password, it is a good idea to get rid of old passwords eventually — like, let’s say, when you get a new computer or phone. Doing so reduces the risk that a password stored somewhere on a very old hard drive or darkweb data dump is still floating around out there, forever haunting your current security posture. If you do a reset every 2 years or so, you know you’ve never got more than 2 years of history to worry about.

PINPal is also particularly secure in the way it incrementally generates your password; the computer you install it on only ever stores the entire password in memory when you type it in. It stores even the partial fragments that you are in the process of memorizing using the secure keyring module, avoiding plain-text whenever possible.

I’ve been using PINPal to generate and memorize new codes for a while, just in case², and the change I made today was because encountered a recurring problem. The problem was, I’d forget a token after it had been hidden, and there was never any going back. The moment that a token was hidden from the user, it was removed from storage, so you could never get a reminder. While I’ve successfully memorized about 10 different passwords with it so far, I’ve had to delete 3 or 4.

So, in the updated algorithm, the visual presentation now hides tokens in the prompt several memorizations before they’re removed. Previously, if the password you were generating was ‘hello world’, you’d see hello world 5 times or so, times, then •••• world; if you ever got it wrong past that point, too bad, start over. Now, you’ll see hello world, then °°°° world, then after you have gotten the prompt right without seeing the token a few times, you’ll see •••• world after the backend has locked it in and it’s properly erased from your computer.

If you get the prompt wrong, breaking your streak reveals the recently-hidden token until you get it right again. I also did a new release on that same livestream, so if this update sounds like it might make the memorization process more appealing, check it out via pip install pinpal today.

Right now this tool is still only extremely for a specific type of nerd — it’s command-line only, and you probably need to hand-customize your shell prompt to invoke it periodically. But I’m working on making it more accessible to a broader audience. It’s open source, of course, so you can feel free to contribute your own code!

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more things like it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

Have you ever written some Python code that looks like this?

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from enum import Enum, auto

class SomeNumber(Enum):
    one = auto()
    two = auto()
    three = auto()

def behavior(number: SomeNumber) -> int:
    match number:
        case SomeNumber.one:
            print("one!")
            return 1
        case SomeNumber.two:
            print("two!")
            return 2
        case SomeNumber.three:
            print("three!")
            return 3

That is to say, have you written code that:

1. defined an enum with several members
2. associated custom behavior, or custom values, with each member of that enum,
3. needed one or more match / case statements (or, if you’ve been programming in Python for more than a few weeks, probably a big if/elif/elif/else tree) to do that association?

In this post, I’d like to submit that this is an antipattern; let’s call it the “passive enum” antipattern.

For those of you having a generally positive experience organizing your discrete values with enums, it may seem odd to call this an “antipattern”, so let me first make something clear: the path to a passive enum is going in the correct direction.

Typically - particularly in legacy code that predates Python 3.4 - one begins with a value that is a bare int constant, or maybe a str with some associated values sitting beside in a few global dicts.

Starting from there, collecting all of your values into an enum at all is a great first step. Having an explicit listing of all valid values and verifying against them is great.

But, it is a mistake to stop there. There are problems with passive enums, too:

1. The behavior can be defined somewhere far away from the data, making it difficult to:
   1. maintain an inventory of everywhere it’s used,
   2. update all the consumers of the data when the list of enum values changes, and
   3. learn about the different usages as a consumer of the API
2. Logic may be defined procedurally (via if/elif or match) or declaratively (via e.g. a dict whose keys are your enum and whose values are the required associated value).
   1. If it’s defined procedurally, it can be difficult to build tools to interrogate it, because you need to parse the AST of your Python program. So it can be difficult to build interactive tools that look at the associated data without just calling the relevant functions.
   2. If it’s defined declaratively, it can be difficult for existing tools that do know how to interrogate ASTs (mypy, flake8, Pyright, ruff, et. al.) to make meaningful assertions about it. Does your linter know how to check that a dict whose keys should be every value of your enum is complete?

To refactor this, I would propose a further step towards organizing one’s enum-oriented code: the active enum.

An active enum is one which contains all the logic associated with the first-party provider of the enum itself.

You may recognize this as a more generalized restatement of the object-oriented lens on the principle of “separation of concerns”. The responsibilities of a class ought to be implemented as methods on that class, so that you can send messages to that class via method calls, and it’s up to the class internally to implement things. Enums are no different.

More specifically, you might notice it as a riff on the Active Nothing pattern described in this excellent talk by Sandi Metz, and, yeah, it’s the same thing.

The first refactoring that we can make is, thus, to mechanically move the method from an external function living anywhere, to a method on SomeNumber . At least like this, we present an API to consumers externally that shows that SomeNumber has a behavior method that can be invoked.

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from enum import Enum, auto

class SomeNumber(Enum):
    one = auto()
    two = auto()
    three = auto()

    def behavior(self) -> int:
        match self:
            case SomeNumber.one:
                print("one!")
                return 1
            case SomeNumber.two:
                print("two!")
                return 2
            case SomeNumber.three:
                print("three!")
                return 3

However, this still leaves us with a match statement that repeats all the values that we just defined, with no particular guarantee of completeness. To continue the refactoring, what we can do is change the value of the enum itself into a simple dataclass to structurally, by definition, contain all the fields we need:

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from dataclasses import dataclass
from enum import Enum
from typing import Callable

@dataclass(frozen=True)
class NumberValue:
    result: int
    effect: Callable[[], None]

class SomeNumber(Enum):
    one = NumberValue(1, lambda: print("one!"))
    two = NumberValue(2, lambda: print("two!"))
    three = NumberValue(3, lambda: print("three!"))

    def behavior(self) -> int:
        self.value.effect()
        return self.value.result

Here, we give SomeNumber members a value of NumberValue, a dataclass that requires a result: int and an effect: Callable to be constructed. Mypy will properly notice that if x is a SomeNumber, that x will have the type NumberValue and we will get proper type checking on its result (a static value) and effect (some associated behaviors)¹.

Note that the implementation of behavior method - still conveniently discoverable for callers, and with its signature unchanged - is now vastly simpler.

But what about...

Lookups?

You may be noticing that I have hand-waved over something important to many enum users, which is to say, by-value lookup. enum.auto will have generated int values for one, two, and three already, and by transforming those into NumberValue instances, I can no longer do SomeNumber(1).

For the simple, string-enum case, one where you might do class MyEnum: value = “value” so that you can do name lookups via MyEnum("value"), there’s a simple solution: use square brackets instead of round ones. In this case, with no matching strings in sight, SomeNumber["one"] still works.

But, if we want to do integer lookups with our dataclass version here, there’s a simple one-liner that will get them back for you; and, moreover, will let you do lookups on whatever attribute you want:

1

by_result = {each.value.result: each for each in SomeNumber}

enum.Flag?

You can do this with Flag more or less unchanged, but in the same way that you can’t expect all your list[T] behaviors to be defined on T, the lack of a 1-to-1 correspondence between Flag instances and their values makes it more complex and out of scope for this pattern specifically.

3rd-party usage?

Sometimes an enum is defined in library L and used in application A, where L provides the data and A provides the behavior. If this is the case, then some amount of version shear is unavoidable; this is a situation where the data and behavior have different vendors, and this means that other means of abstraction are required to keep them in sync. Object-oriented modeling methods are for consolidating the responsibility for maintenance within a single vendor’s scope of responsibility. Once you’re not responsible for the entire model, you can’t do the modeling over all of it, and that is perfectly normal and to be expected.

The goal of the Active Enum pattern is to avoid creating the additional complexity of that shear when it does not serve a purpose, not to ban it entirely.

A Case Study

I was inspired to make this post by a recent refactoring I did from a more obscure and magical² version of this pattern into the version that I am presenting here, but if I am going to call passive enums an “antipattern” I feel like it behooves me to point at an example outside of my own solo work.

So, for a more realistic example, let’s consider a package that all Python developers will recognize from their day-to-day work, python-hearthstone, the Python library for parsing the data files associated with Blizzard’s popular computerized collectible card game Hearthstone.

As I’m sure you already know, there are a lot of enums in this library, but for one small case study, let’s look a few of the methods in hearthstone.enums.GameType.

GameType has already taken the “step 1” in the direction of an active enum, as I described above: as_bnet is an instancemethod on GameType itself, making it at least easy to see by looking at the class definition what operations it supports. However, in the implementation of that method (among many others) we can see the worst of both worlds:

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class GameType(IntEnum):
    def as_bnet(self, format: FormatType = FormatType.FT_STANDARD):
        if self == GameType.GT_RANKED:
            if format == FormatType.FT_WILD:
                return BnetGameType.BGT_RANKED_WILD
            elif format == FormatType.FT_STANDARD:
                return BnetGameType.BGT_RANKED_STANDARD
            # ...
            else:
                raise ValueError()
        # ...
        return {
            GameType.GT_UNKNOWN: BnetGameType.BGT_UNKNOWN,
            # ...
            GameType.GT_BATTLEGROUNDS_DUO_FRIENDLY: BnetGameType.BGT_BATTLEGROUNDS_DUO_FRIENDLY,
        }[self]

We have procedural code mixed with a data lookup table; raise ValueError mixed together with value returns. Overall, it looks like this might be hard to maintain this going forward, or to see what’s going on without a comprehensive understanding of the game being modeled. Of course for most python programmers that understanding can be assumed, but, still.

If GameType were refactored in the manner above³, you’d be able to look at the member definition for GT_RANKED and see a mapping of FormatType to BnetGameType, or GT_BATTLEGROUNDS_DUO_FRIENDLY to see an unconditional value of BGT_BATTLEGROUNDS_DUO_FRIENDLY. Given that this enum has 40 elements, with several renamed or removed, it seems reasonable to expect that more will be added and removed as the game is developed.

Conclusion

If you have large enums that change over time, consider placing the responsibility for the behavior of the values alongside the values directly, and any logic for processing the values as methods of the enum. This will allow you to quickly validate that you have full coverage of any data that is required among all the different members of the enum, and it will allow API clients a convenient surface to discover the capabilities associated with that enum.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

Over the last decade, it has become a common experience to be using a social media app, and to perceive that app as saying something specific to you. This manifests in statements like “Twitter thinks Rudy Giuliani has lost his mind”, “Facebook is up in arms about DEI”, “Instagram is going crazy for this new water bottle”, “BlueSky loves this bigoted substack”, or “Mastodon can’t stop talking about Linux”. Sometimes this will even be expressed with “the Internet” as a metonym for the speaker’s preferred social media: “the Internet thinks that Kate Middleton is missing”.

However, even the smallest of these networks comprises literal millions of human beings, speaking dozens of different languages, many of whom never interact with each other at all. The hot takes that you see from a certain excitable sub-community, on your particular timeline or “for you” page, are not necessarily representative of “the Internet” — at this point, a group that represents a significant majority of the entire human population.

If I may coin a phrase, I will refer to these as “Diffuse, Amorphous, Nebulous, Generalized Internet Takes”, or DANGITs, which handily evokes the frustrating feeling of arguing against them.

A DANGIT is not really a new “internet” phenomenon: it is a specific expression of the availability heuristic.

If we look at our device and see a bunch of comments in our inbox, particularly if those comments have high salience via being recent, emotive, and repeated, we will naturally think that this is what The Internet thinks. However, just because we will naturally think this does not mean that we will accurately think it.

It is worth keeping this concept in mind when participating in public discourse because it leads to a specific type of communication breakdown. If you are arguing with a DANGIT, you will feel like you are arguing with someone with incredibly inconsistent, hypocritical, and sometimes even totally self-contradictory views. But to be self-contradictory, one needs to have a self. And if you are arguing with 9 different people from 3 different ideological factions, all making completely different points and not even taking time to agree on the facts beforehand, of course it’s going to sound like cacophonous nonsense. You’re arguing with the cacophony, it’s just presented to you in a way that deceives you into thinking that it’s one group.

There are subtle variations on this breakdown; for example, it can also make people’s taste seem incoherent. If it seems like one week the Interior Designer internet loves stark Scandinavian minimalism, and the next week baroque Rococo styles are making a comeback, it might seem like The Internet has no coherent sense of taste, and these things don’t go together. That’s because it doesn’t! Why would you expect it to?

Most likely, you are simply seeing some posts from minimalists, and then, separately, some posts from Rococo aficionados. Any particular person’s feed may be dedicated to a specific, internally coherent viewpoint, aesthetic, or ideology, but if you dump them all into a blender to separate them from their context, of course they will look jumbled together.

This is what social media does. It is context collapse as a service. Even if you eliminate engagement-maximizing algorithms and view everything perfectly chronologically, even if you have the world’s best trust & safety team making sure that there is nothing harmful and no disinformation, social media — like email — inherently remains that context-collapsing blender. There’s no way for it not to be; if two people you follow, who do not follow and are not aware of each other, are both posting unrelated things at the same time, you’re going to see them at around the same time.

Do not argue with a DANGIT. Discussions are the internet are famously Pyrrhic battles to begin with, but if you argue with a DANGIT it’s not that you will achieve a Pyrrhic victory, you cannot possibly achieve any victory, because you are shadowboxing an imagined consensus where none exits.

You can’t win against something that isn’t there.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more things like it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

Kamala Harris lost. Here we are. So it goes.

Are you sad? Are you scared?

I am very sad. I am very scared.

But, like everyone else in this position, most of all, I want to know what to do next.

A Mission For Progress

I believe that we should set up a missionary organization for progressive and liberal values.

In 2017, Kayla Chadwick wrote the now-classic article, “I Don’t Know How To Explain To You That You Should Care About Other People”. It resonated with millions of people, myself included. It expresses an exasperation with a populace that seems ignorant of economics, history, politics, and indeed unable to read the news. It is understandable to be frustrated with people who are exercising their electoral power callously and irresponsibly.

But I think in 2024, we need to reckon with the fact that we do, in fact, need to explain to a large swathe of the population that they should care about other people.

We had better figure out how to explain it soon.

Shared Values — A Basis for Hope

The first question that arises when we start considering outreach to the conservative-leaning or undecided independent population is, “are these people available to be convinced?”.

To that, I must answer an unqualified “yes”.

I know that some of you are already objecting. For those of us with an understanding of history and the mechanics of bigotry in the United States, it might initially seem like the answer is “no”.

As the Nazis came to power in the 1920s, they were campaigning openly on a platform of antisemitic violence. Everyone knew what the debate was. It was hard to claim that you didn’t, in spite of some breathtakingly cowardly contemporaneous journalism, they weren’t fooling anyone.

It feels ridiculous to say this, but Hitler did not have support among Jews.

Yet, after campaigning on a platform of defaming immigrants, and Mexican immigrants specifically for a decade, a large part of what drove his victory is that Trump enjoyed a shockingly huge surge of support among the Hispanic population. Even some undocumented migrants — the ones most likely to be herded into concentration camps starting in January — are supporting him.

I believe that this is possible because, in order to maintain support of the multi-ethnic working-class coalition that Trump has built, the Republicans must maintain plausible deniability. They have to say “we are not racist”, “we are not xenophobic”. Incredibly, his supporters even say “I don’t hate trans people” with startling regularity.

Most voters must continue to believe that hateful policies with devastating impacts are actually race-neutral, and are simply going to get rid of “bad” people. Even the ones motivated by racial resentment are mostly motivated by factually incorrect beliefs about racialized minorities receiving special treatment and resources which they are not in fact receiving.

They are victims of a disinformation machine. One that has rendered reality incomprehensible.

If you listen to conservative messaging, you can hear them referencing this all the time. Remember when JD Vance made that comment about Democrats calling Diet Mountain Dew racist?

Many publications wrote about this joke “bombing”¹, but the kernel of truth within it is this: understanding structural bigotry in the United States is difficult. When we progressives talk about it, people who don’t understand it think that our explanations sound ridiculous and incoherent.

There’s a reason that the real version of critical race theory is a graduate-level philosophy-of-law course, and not a couple of catch phrases.

If, without context, someone says that “municipal zoning laws are racist”, this makes about as much sense as “Diet Mountain Dew is racist” to someone who doesn’t already know what “redlining” is.

Conservatives prey upon this confusion to their benefit. But they prey on this because they must do so. They must do so because, despite everything, hate is not actually popular among the American electorate. Even now, they have to be deceived into it.

The good news is that all we need to do is stop the deception.

Politics Matter

If I have sold you on the idea that a substantial plurality of voters are available to be persuaded, the next question is: can we persuade them? Do we, as progressives, have the resources and means to do so? We did lose, after all, and it might seem like nothing we did had much of an impact.

Let’s analyze that assumption.

Across the country, Trump’s margins increased. However, in the swing states, where Harris spent money on campaigning, his margins increased less than elsewhere. At time of writing, we project that the safe-state margin shift will be 3.55% towards trump, and the swing-state margin shift will be 1.69%.

This margin was, sadly, too small for a victory, but it does show that the work mattered. Perhaps given more time, or more resources, it would have mattered just a little bit more, and that would have been decisive.

This is to say, in the places where campaign dollars were spent, even against the similar spending of the Trump campaign, we pushed the margin of support 1.86% higher within 107 days. So yes: campaigning matters. Which parts and how much are not straightforward, but it definitely matters.

This is a bit of a nonsensical comparison for a whole host of reasons², but just for a ballpark figure, if we kept this pressure up continuously during the next 4 years, we could increase support for a democratic candidate by 25%.

We Can Teach, Not Sell

Political junkies tend to overestimate the knowledge of the average voter. Even when we are trying to compensate for it, we tend to vastly overestimate how much the average voter knows about politics and policy. I suspect that you, dear reader, are a political junkie even if you don’t think of yourself as one.

To give you a sense of what I mean, across the country, on Election day and the day after, there was a huge spike in interest for the Google query, “did Joe Biden drop out”.

Consistently over the last decade, democratic policies are more popular than their opponents. Even deep red states, such as Kansas, often vote for policies supported by democrats and opposed by Republicans.

This confusion about policy is not organic; it is not voters’ fault. It is because Republicans constantly lie.

All this ignorance might seem discouraging, but it presents an opportunity: people will not sign up to be persuaded, but people do like being informed. Rather than proselytizing via a hard sales pitch, it should be possible to offer to explain how policy connects to elections. And this is made so much the easier if so many of these folks already generally like our policies.

The Challenge Is Enormous

I’ve listed some reasons for optimism, but that does not mean that this will be easy.

Republicans have a tremendously powerful, decentralized media apparatus that reinforces their culture-war messaging all the time.

After some of the post-election analysis, “The Left Needs Its Own Joe Rogan” is on track to become a cliché within the week.³ While I am deeply sympathetic to that argument, the right-wing media’s success is not organic; it is funded by petrochemical billionaires.

We cannot compete via billionaire financing, and as such, we have to have a way to introduce voters to progressive and liberal media. Which means more voters need social connections to liberals and progressives.

Good Works

The democratic presidential campaign alone spent a billion and a half dollars. And, as shown above, this can be persuasive, but it’s just the persuasion itself.

Better than spending all this money on telling people what good stuff we would do for them if we were in power, we could just show them, by doing good stuff. We should live our values, not just endlessly reiterate them.

A billion dollars is a significant amount of power in its own right.

For historical precedent, consider the Black Panthers’ Free Breakfast For Children program. This program absolutely scared the shit out of the conservative power structure, to the point that Nixon’s FBI literally raided them for giving out free food to children.

Religious missionaries, who are famously annoying, often offset their annoying-ness by doing charitable work in the communities they are trying to reach. A lot of the country that we need to reach are religious people, and nominally both Christians and leftists share a concern for helping those in need, so we should find some cultural common ground there.

We can leverage that overlap in values by partnering with churches. This immediately makes such work culturally legible to many who we most need to reach.

Jobs Jobs Jobs

When I raised this idea with Philip James, he had been mulling over similar ideas for a long time, but with a slightly different tack: free career skills workshops from folks who are obviously “non-traditional” with respect to the average rural voter’s cultural expectations. Recruit trans folks, black folks, women, and non-white immigrants from our tech networks.

Run the trainings over remote video conferencing to make volunteering more accessible. Run those workshops through churches as a distribution network.

There is good evidence that this sort of prolonged contact and direct exposure to outgroups, to help people see others as human beings, very effective politically.

However, job skills training is by no means the only benefit we could bring. There are lots of other services we could offer remotely, particularly with the skills that we in the tech community could offer. I offer this as an initial suggestion; if you have more ideas I’d love to hear them. I think the best ideas are ones where folks can opt in, things that feel like bettering oneself rather than receiving charity; nobody likes getting handouts, particularly from the outgroup, but getting help to improve your own skills feels more participatory.

I do think that free breakfast for children, specifically, might be something to start with because people are far more willing to accept gifts to benefit others (particularly their children, or the elderly!) rather than themselves.

Take Credit

Doing good works in the community isn’t enough. We need to do visible good works. Attributable good works.

We don’t want to be assholes about it, but we do want to make sure that these benefits are clearly labeled. We do not want to attach an obligation to any charitable project, but we do want to attach something to indicate where it came from.

I don’t know what that “something” should be. The most important thing is that whatever “something” is appeals to set of partially-overlapping cultures that I am not really a part of — Midwestern, rural, southern, exurban, working class, “red state” — and thus, I would want to hear from people from those cultures about what works best.

But it’s got to be something.

Maybe it’s a little sticker, “brought to you by progressives and liberals. we care about you!”. Maybe it’s a subtle piece of consistent branding or graphic design, like a stylized blue stripe. Maybe we need to avoid the word “democrats”, or even “progressive” or “liberal”, and need some independent brand for such a thing, that is clearly tenuously connected but not directly; like the Coalition of Liberal and Leftist Helpful Neighbors or something.

Famously, when Trump sent everybody a check from the government, he put his name on it. Joe Biden did the same thing, and Democrats seem to think it’s a good thing that he didn’t take credit because it “wasn’t about advancing politics”, even though this obviously backfired. Republicans constantly take credit for the benefits of Democratic policies, which is one reason why voters don’t know they’re democratic policies.

Our broad left-liberal coalition is attempting to improve people’s material conditions. Part of that is, and must be, advancing a political agenda. It’s no good if we provide job trainings and free lunches to a community if that community is just going to be reduced to ruin by economically catastrophic tariffs and mass deportations.

We cannot do this work just for the credit, but getting credit is important.

Let’s You And Me — Yes YOU — Get Started

I think this is a good idea, but I am not the right person to lead it.

For one thing, building this type of organization requires a lot of organizational and leadership skills that are not really my forte. Even the idea of filing the paperwork for a new 501(c)3 right now sounds like rolling Sisyphus’s rock up the hill to me.

For another, we need folks who are connected to this culture, in ways that I am not. I would be happy to be involved — I do have some relevant technical skills to help with infrastructure, and I could always participate in some of the job-training stuff, and I can definitely donate a bit of money to a nonprofit, but I don’t think I can be in charge.

You can definitely help too, and we will need a wide variety of skills to begin with, and it will definitely need money. Maybe you can help me figure out who should be in charge.

This project will be weaker without your support. Thus: I need to hear from you.

You can email me, or, if you’d prefer a more secure channel, feel free to reach out over Signal, where my introduction code is glyph.99 . Please start the message with “good works:” so I can easily identify conversations about this.

If I receive any interest at all, I plan to organize some form of meeting within the next 30 days to figure out concrete next steps.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more things like it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor! My aspirations for this support are more in the directions of software development than activism, but needs must, when the devil drives. Thanks especially to Philip James for both refining the idea and helping to edit this post, and to Marley Myrianthopoulos for assistance with the data analysis.