I have a community thesis of the Web. It's one I share with the people I work with at HomeRoom and DashKite. It's the idea that community is more than a collection of people; it's a human system essential to the present and future of the Web. From that thesis, it follows that most existing Web platforms are successful, despite their best efforts to do otherwise, because people are determined to build valuable spaces for themselves online. So, our community thesis is about doing better for people.

Photo Credit: Joyce Hankins

What is a Web Community OS?

I'm a technologist, so I aim to understand how this manifests as useful technology. And in my role at HomeRoom, that's led to roadmapping and developing a Web Community operating system. What does that mean?

Here's Wikipedia's definition for a computer operating system:

An operating system (OS) is system software that manages computer hardware and software resources, and provides common services for computer programs.

So if we apply that Web Communities:

A Web Community OS is software that manages a community's Web resources and provides common services for the people who manage and participate within it.

Tools That Bend Without Breaking

We talk about a community's resources by first recognizing communities are strongly distributed projects:

• A community's focal point: A celebrity or team or fandom or location or shared purpose that drives people to hold space together.
• A community's members: Many people have different perspectives, ways of living, and reasons for interacting with a community.
• A community's managers: Community leaders with a servant leadership style that emphasizes the community's well-being to achieve large positive-sum value creation for everyone connected to the community.
• A community's infrastructure: A community may manifest across many services on the Web. Managing chat on Discord, selling merchandise on Shopify or memberships on Patreon, streaming on Twitch, posting long-form content on Substack, etc, etc, etc.

In the face of such a distributed system, you can see the foolishness of cramming that into a walled garden.

But the fundamental architecture of the Open Web, building blocks like HTTP, is designed to be incredibly flexible and embraces distributed design. At HomeRoom, we use our understanding of that architecture to build software flexible enough to support community. We view community managers and moderators as experts in their field, and while they might not know how to code, they know what they want from technology. They are in the sweet spot for generative programming.

HomeRoom recognizes this and sees the people in communities as critical participants in building technology that makes communities safer, more responsive, more informative, and more valuable for everyone.

In the past decade, a growing set of software products have categorized themselves under the terms no-code and low-code. It's an exciting and historic inflection point for computing. In terms of significance, I'd compare it to the introduction of the Web or smartphones. And a change of that magnitude is hard to talk about. We struggle to articulate a vocabulary that lets us compare what's been to what's coming. Most people are still at the “What is Internet, anyway?” stage of this.

Photo Credit: Suzanne D. Williams

The term “no-code” makes sense when applied to a product. It's a framing that emphasizes a benefit to a customer. It says, “You can build with this software without explicitly writing code.” It's a fundamental promise about the demands on your time. It reminds me of the term “serverless” in cloud computing. There's still code running somewhere, but you're paying someone else to handle those details. That frees up your time to focus on other goals.

Talking about people is where we run into trouble. At the moment, people who use and rely on no-code tools are often called “no-coders,” and I find that deeply unsatisfying. First, describing a person by emphasizing their lack of a particular skill is awkward.

But there's a bigger problem. People sometimes use “technical” to describe “someone who understands how computers work and how to write code.” In a narrow context, that may be accurate shorthand. But it's easy to generalize that framing into an attitude that undervalues expertise in other domains. That affects who gets funding and what problems are prioritized.

So how do we do better? I think the answer comes from acknowledging that people who build software without code are still programming, so they're programmers. Among people who code, there are commonly established style categories like imperative, declarative, object-oriented, and functional. Those are almost always code-based styles. But the following can apply to code and no-code solutions alike:

• Reactive: Think of spreadsheet tools like Excel or Google Sheets. You can set up calculations so a change in one cell cascades across the entire sheet.
• Visual: Think of Bubble or Xano. Visual programming combines abstract components and What You See Is What You Get interfaces to let you program on-screen. Visual cues can help anchor your mental model, but you often end up managing as many details as a coder.
• Generative: This term has gotten recent attention through product showcases from ChatGPT and Midjourney, but it's a concept that's existed for decades. The main idea: you provide a small input and get back something much larger and complex, where all the details handled automatically. Glide is a partial software example. You provide a spreadsheet data source, and Glide deploys a baseline Web application. Glide falls back into visual programming for customization, but that initial step is generative.

Based on those categories, we already have some vocabulary for people who build software without code. They're visual and generative programmers.

I am particularly excited about the prospect of millions of new generative programmers. While not every no-code tool falls into that category, a generative style is the benchmark. That's because it most closely aligns with the no-code promise: tell me what you need, and I'll handle the details.

In that way, generative programming is an extension of one of software's core purposes, automation in the service of people. And because generative programming has the potential to be much more accessible, many more people will get to participate in software and have new tools to meet their goals.

Complexity can feel daunting, but I think the real problem is how we struggle to talk about. It's possible to think about complexity in more general terms and unlock tools to understand it on a deeper level.

Photo: Frank Zappa, photographed by Norman Seeff in 1976.

Why Do Things Have To Be So Complicated?

Complexity is everywhere — emphasis on everywhere. We see it in all the systems we can observe, from things as big as the large-scale structure of the universe's visible mass, all the way down to things as small as proteins. That ubiquity is not a coincidence.

The people who formally study complex systems describe a phenomenon called emergence. It's an observation that a system has a capacity greater than the sum of its parts would suggest. That might sound a bit cliche, but be assured: the consequences are profound, and we're still trying to understand them. Even straightforward, deterministic systems with only a few rules can really surprise you.

Conway's Game of Life gives us a clear example. It has only four rules and a grid for the automatons to live on, but Conway showed it's Turing complete! So from a computability standpoint, it's as much a Turing machine as the device you're using to read this post.

We can try to limit the number of system components. The three-body problem is one of the simplest systems, with just three masses orbiting each other due to gravitational forces. But even here, we find the emergence of chaotic behavior that requires careful simulation to accurately predict. So the next time you benefit from a high-resolution weather forecast, spare a thought for the people who had to simulate Earth's entire atmosphere.

Now, I've focused on natural systems so far, but human systems definitely feature complexity, as Avril Lavigne laments in her 2002 single, “Complicated.”

Georgia Warr: Why do things have to be so complicated? Sunil Jha: Ah, the eternally wise words of Avril Lavigne.

— Loveless by Alice Oseman

We've established that complexity is ubiquitous, so while most people don't formally study it for a living, everyone interacts with it. Humans have developed highly specialized skills and knowledge to deal with it. And within those communities, we're comfortable talking about specific kinds of complexity. That's where jargon comes from.

But that's a shattered view of the whole.

The problem is we don't have a robust, universal way to talk about complexity. And siloed off from each other, how can we expect to systemically engage with complexity?

I think people have some sense of the scope of failure. That's where the negative connotation of complexity comes from. People describe how it overwhelms them, produces opportunity costs, and unleashes serious harm. And that's if they're even paying attention. It's even worse when people willfully ignore complexity or treat it as a shallow aesthetic.

That's why I mention Lavigne; I think the way we use “It's complicated” is noteworthy. The phrase means something is difficult to describe because of its complexity. And because it's not specific to anything about that something. It's so flexible. It's a rare, universal statement about complexity.

So that's one universal tool, even if it's just a warning. Caution is a good start, and it's certainly better than denial. But I think we can go further and engage with complexity on generalized terms.

The Frame

I've had this intuition about a generalized approach to complexity, but I wasn't sure how to articulate it. Thankfully, I recently came across this incredible quote from Frank Zappa's 1989 autobiography:

The most important thing in art is The Frame. For painting: literally; for other arts: figuratively — because, without this humble appliance, you can't know where The Art stops and The Real World begins. You have to put a “box” around it because otherwise, what is that shit on the wall?

Objective reality — what Zappa calls the Real World — has endless, ubiquitous complexity. We can model only parts of it at a time. That means we must explicitly choose what information to include, and everything else is left out. The complexity of the Real World still exists. But within The Frame, we focus our attention and glean meaningful insights that we can use back in the Real World.

These “Zappa frames” are just what I was looking for, and to make this more clear, let me tell you the story of epidemiologist John Snow.

Snow studied the 1854 London cholera outbreak. Doctors knew that people were contracting cholera, getting sick, and dying. But there wasn't agreement on how cholera spread or how to mitigate future transmission.

It was a complex situation. People are already complicated. But these Londoners lived closely together, so there was a lot of interaction complexity. Snow needed a way to clearly explain the transmission. He needed to piece together a meaningful picture of these Londoners' lives.

Snow's solution was a map.

Look at that. It turns out John Snow did, in fact, know something after all.

This famous map of cholera cases shows geographic clustering around the Broad Street water pump. It was evidence for Snow's hypothesis that people were drinking contaminated water (and the germ theory more broadly).

Snow was trying to analyze a complex system, this section of London and all the people living there. With a carefully constructed Zappa frame, he could highlight the most helpful information and provide tremendous clarity. And it changed how people think about the problem. Snow's contributions and popularization of data visualization are recognized as foundational pieces of modern Western epidemiology.

Agency and Fundamental Frame Boundaries

Zappa frames seem like a compelling way to think about complexity, but what fascinates me most is how they center choice. In Zappa's definition, he's clear that our agency is required to give interactions with complexity meaning. Otherwise, it just looks like noise, “What is that shit on the wall?”

We choose what makes it into the frame, what gets our attention, and what gets ignored. That emphasis on choice means Zappa frames readily expose the ethical dimensions of systemic thinking. And because it's generalized, that ethical focus doesn't get lost in the tangle of details specific to a domain or cultural bias.

For example, there's a bitter coda to Snow's story. Despite its clarity, Snow's framing was challenged by cultural squeamishness around poop. It's disappointing that it limited the effectiveness of his ideas, but it's downright embarrassing that we've only partially learned the lesson. It's still a problem today.

But having clarity is valuable. We've established how complex systems touch the lives of everyone, so we're obligated to make good choices when we build Zappa frames. And by recognizing that Zappa frames have boundaries, we acknowledge that we have an incomplete view and must be open to corrections. That openness makes it easier to fulfill our obligation to always include information that accounts for potential harm. Excluding that information puts people at risk and diminishes the value of the Zappa frame.

There's a lot more to explore here, and I'd like to write about it. For now, I'll leave you with a reminder to make good choices.

Be like Charlie. Make good choices.

Another fundamental aspect of Zappa Frames is the nature of their simplification. Simplified models offer clarity. Snow's map highlighted a geographic signal that would have been hard to spot in a stack of individual patient reports. But if you include too much information in the frame, you risk losing the quality of that contrast.

But I'm talking about something more fundamental. There are concepts called incompleteness and undefinability. They are specific to constructing mathematical models, but I think they can be generalized here. They suggest that if you tried to build an all-encompassing Zappa frame, it would collapse under its own incoherence. You can't fit all of the Real World into The Frame.

That seems really important to me. It implies that Zappa frames are a kind of fundamental structure of complexity. We can't know complexity all at once; we need to access it in measures via Zappa frames.

That leads me to rethink integrative levels. People who study complex systems often note apparent hierarchical organization. Life is a pretty good example: Organisms are made of cells made of proteins made of molecules. What's nice about these levels is how they satisfy incompleteness and undefinability. The levels' models don't need to be coherent with each other. Details from the lower levels can be safely excluded from the Zappa frame.

I've always been fond of integrative levels because of how tidy the organization looks. And, at least in my cultural experience, people often put things into hierarchies. But thinking about Zappa frames makes me question if they're “real.” Integrative levels have been helpful as explanatory tools, but are they a feature of objective reality, or are they an artifact of how we access complexity through Zappa frames?

This new frame understanding suggests they're very arbitrary. That's somewhat disorienting, but we must remember what's vital: When we build models, we should always prioritize avoiding systemic harms and increasing clarity; not satisfying some harmonic aesthetic in a tier list.

Pushing on the Deep Structure

They created us. And they knew enough of beauty to teach it to us. Maybe they could find it themselves. But only if you pick a side, Maeve.

There is ugliness in this world; disarray.

I choose to see the beauty.

— Westworld Season 3 Episode 8: Crisis Theory

Complexity is a fact of reality. It cannot be circumvented, overpowered, or ignored. Anyone who tries risks unleashing severe harm. But that's not destiny. Zappa frames, instead, show how vital our agency is. We decide the nature of the interaction. So the systems we build can and must engage complexity and use that sophistication for the greater good.

That's why Zappa frames give me hope as a tool. They satisfy an intuition I've had for a while. There's a “deep structure” within emergent complexity — something that suggests there's a kind of unity to all human endeavors, even across domains.

It's an intuition that draws me to storytelling that centers agency, framing, and metacognition. Stories like Westworld, Watchmen, Everything Everywhere All at Once, and Heartstopper — superficially very different genres — give you brief glimpses of that deep structure. It's what makes them so compelling to me and what led me to start writing about Heartstopper.

People tend to focus on production value as a proxy for quality during this era of Prestige Television, and there's nothing wrong with a fun spectacle. But storytelling is a metacognition framework, and I consider that to be its primary and most valuable purpose. Stories that draw on the deep structure connect to all human systems; when they teach us about it, we might truly see the world.

And that's something I'd like to write about more, but in the meantime, I'm hopeful. Hopeful we might learn more about the deep structure: how to see it, how to talk about it, how to push on it. Because we sorely need to discard the systems that suck and build new ones that give us what we need to thrive.

With the right frame, we can find the path.

They look so unassuming, don't they? But this fairy tale is unlike anything people have had since the start of the television era.

I was pretty late to watching Heartstopper. It's a Netflix show based on the graphic novel series from Alice Oseman and part of their Osemanverse, an ever-expanding empire of queer young adult media. But when I did get around to watching it a couple weeks ago, I was caught off-guard by the debilitating intensity of my emotional reaction.

I was surprised to discover r/heartstoppersyndrome, a subreddit dedicated exclusively to the after-effects of watching this show — universally described as positive. So while I feel somewhat comforted that I am not alone in my response, I can't think of another piece of media with a dedicated collateral damage subreddit. It seems Heartstopper is operating on another level. So I'd like to explore and write about that, here and in the future.

Warning: Spoilers Ahead

Heartstopper Is About Fear And Sadness, Too

Heartstopper earned a reputation for being heartwarming and uplifting, but people experiencing Heartstopper Syndrome report feelings of distress. Their comments online focus on their paradoxical feelings.

They ask, “Why do I feel like shit when the characters in this sweet show are so happy?”

Overwhelmed with... happy(?)... anxious(?)... despair(?)

So I think the first step is to dispel the appearance of a paradox.

Heartstopper is a kind of fairy tale. Characters achieve their goals, representing a wish-fulfillment for experiences denied to too many. But describing Heartstopper as “happy” is only superficially true. It shortchanges the characters' journey to manifest that happy ending. And I worry not enough people understand that nuance. Oseman put on a masterclass in constructing an emotional narrative — one they built on the idea of self-realization.

Oseman writes about people's inner lives, which have complexity. They are sometimes turbulent and often contradictory. There can be a lot standing in the way of realization.

The world's cruelest trick is the pattern inversion trap. When you're convinced of the exact opposite of the truth, it's excruciating to find your way out.

Charlie has dealt with harmful people since being outed and wrestles with loneliness, anxiety, and intrusive thoughts. And while Nick knows he likes Charlie, he's got to get through his identity crisis first. Nick has to completely rethink how he views himself and what he accepts about others' views.

Oseman also builds antagonists as people with inner lives and capable of fear. Ben and Nick even share similar fears.

So, it would be wrong to say these characters are happy. That's incomplete. Heartstopper is also about the fear they hold and how it's in tension with their goals. Oseman's story is about what their characters do with that tension.

The Radical Pursuit of Active Joy

Heartstopper Hug. Like the others shown this season, this inflection point in their relationship is driven purely by their agency.

The only way to transcend that tension is with conscious choice. Each character must ask themselves what they want, what they prioritize, and then decide if they wish to pursue self-realization. So any happiness we see is a happiness they're responsible for, their choice in the face of fear. It's dynamic and active. Ben and Nick are foils because they make opposite choices.

So no, Heartstopper is not a “happy” show. It's about agency, an “active joy” where you give yourself permission to seek transcendent realization — despite fear. Oseman deeply values agency in storytelling, and it's a motif in their novels I've read so far. Characters avoid ruin by deciding to keep trying: even when it's hard, or they have to ask for a lot of help, or there's uncertainty.

But I should also note that Oseman's focus on agency doesn't fall into the “Hard Work Fallacy.” It doesn't mean willing yourself out of your problems or going it alone. Oseman often shows that one of agency's primary uses is to counter isolation. They're clear about the importance of friendships and the different shapes of love. So even in stories about romance, they're careful to be realistic about how a single relationship cannot be all-consuming.

Heartstopper is definitely a fairy tale, but it's a profoundly humanist one. No cosmic purpose. No royal bloodline. No chosen one status. Oseman builds their fairy tale with the modest magic of agency and human connection. And that's such a radically hopeful and joyful approach to storytelling, especially for people used to “Bury Your Gays.”

Do you want to be hugged by a neon whirlwind of warm fuzzies? Excellent choice! Love that for you.

Be like Charlie. Make good choices.

I don't think Oseman gets enough credit for how well they model active joy in their storytelling — or even just how profound agency-centered storytelling is.

So for people experiencing Heartstopper Syndrome, reconsider this story as one about laying claim to agency. You don't need to get so focused on the happy ending. Because, of course, there's a happy ending. Weren't you watching?

The protagonists choose it.

Over the years, I've built a lot of technology at DashKite. And while that's been a richly rewarding experience, technology is only valuable insomuch as people can understand and apply it purposefully. I haven't prioritized explaining myself. I feel a sort of self-conscious about trying to convince someone that I can do something instead of just doing it. But the time has come to reprioritize, to start showing my work.

Photo Credit: Alex Azabache

Unfortunately, explaining myself — even to audiences predisposed to believing me — has met with mixed results. Dan and I have come to realize it's a problem related to complexity. We've spent years learning about the patterns involved in our technology, we've gotten used to how each other thinks, and we've even developed a proto-vocabulary to speed conversation. Talking to someone without that shared context makes the transmission of ideas difficult.

That got me thinking about the nature of our context and how its complexity is symmetric to the software we've built. It's a bootstrapping problem even to establish a vocabulary, and we cannot fit years' worth of learning into a single conversation. But thanks to that symmetry, we can address the context problem in the same way as our software.

This blog post marks a new era for DashKite. In the coming weeks, I will thoroughly document the low-level libraries that make up the DashKite stack. That forms a reference layer. Then, we need to write a layer that describes individual concepts. On top of that, we explain how tooling and architectures work. And then we're ready for high-level narrative overviews.

It's a fair amount of work, but it's vital. Almost worse than being ignored is being misunderstood. The adoption history of REST architectures provides an excellent example of this costly failure mode. Roy Fielding defined REST in a Ph.D. dissertation and codified it in the HTTP specification he helped author. Unfortunately, people write those for very particular audiences; neither are a very good starting place for shared context.

The key is meeting people in their software journey. Hypertext lets us focus on one thing at a time, stay DRY, and put people first by supporting self-directed learning. It's comprehensive, but it doesn't leave people stranded with reference docs. It's engaging, but it doesn't lack precision when you need to look something up.

Not only is this an efficient way to communicate a complex concept asynchronously, but it also reflects our software patterns. So in that way, it reminds me of Conway's Law.

Time to get writing.

This is an era of transition and transformation. My blog now has a new home, here on Write.as. That's because I spun down its old host, DashKite's Byline, along with the constellation of DashKite testbed products.

Photo Credit: Tim Bogdanov

DashKite's original goal of building a social network that prioritizes humans is important. But, as Dan and I worked over the years, we found that so much of the current Web stack has structural problems. Fortunately, many people like us are working on the Open Web, in standards bodies and working groups and companies worldwide. Once we pull out this current, harmful application layer, the underlying Web architecture is waiting for us, like hardwood flooring beneath old carpeting.

Coming to this understanding has been an evolution. I've been working on DashKite's technology stack for three years now. And in that time, I've built some really awesome stuff. I initially did this work in the service of making DashKite products easy to build, deploy, and manage. And I accomplished that. But also, I think the technology I created with Dan will change how the Web works.

In light of our newly realized capacity, we have new opportunity. Byline and its sibling products were a valuable proving ground to refine our approach, but they are not our end goal. A phase change is coming to democratize how we assemble the Web, and DashKite intends to be a leader in that space.

But there is still much to be done, and we only have so much time.

This calls for focus. Maintaining Byline or the others pulls resources from working on no-code. It takes focus to choose not to work on a traditional product category, like blogging. Instead, we're targeting Web development as a meta-problem and leaning into what makes DashKite unique and valuable.

This calls for patience. While we are leveraging utterly amazing features in the Open Web, there is still a fair amount of work ahead of us. Years of it. But if we're right, the result will be more than worth it. Being a dreamer is a calling to persist.

Metcalfe's Law is good for describing networked ethernet ports, absolutely useless for networked people. But Silicon Valley has relied on this law for almost 30 years to express how it thinks about the Web's value. They don't know what the fuck they're talking about.

Author's Note: This post was originally published Feb 11, 2021 on (the currently defunct) Byline.

I'm fascinated by the polling around social media companies. A lot of people don't like them but still use them every day. That's evidence of a value proposition, but it's in superposition with a trash fire. What's going on?

How do I asses the value of a social media company? The answer depends on how I ask. I can ask people how much money it would take to deactivate their account. I can ask investors to reach a consensus on the company's value. These are both interesting, but I am left unsatisfied. What I'd really like is an evaluation based on first principles.

I might try Metcalfe's Law, which influenced early Web investment funds and still does. To summarize: A network's value scales as N², where N is the number of nodes in the network. You can see the law only concerns itself with network size. It says, “Bigger is better, and being the biggest at any cost is the best.” That makes sense once you understand that Metcalfe's Law was originally written to express value for a telecommunications company owning and operating an ethernet network. The quantum of value is a given network connection.

But, the Web is more than a collection of ethernet ports. Sure, it uses distributed computing, but our goal is to network humans. We're people, not ports! I realize that sounds a little obvious, but Silicon Valley has used this ports-not-people law for nearly 30 years to justify their approach to evaluating companies. I want to do better, so I will be more precise. When I talk about value, I mean: How valuable is the network to the people who spend time there? The quantum of value is now a moment I share with another person. Immediately, interesting implications fall out of this framing. Am I spending these moments well? It depends on the people!

Humans have a social context. And when networked, we might have a negative interaction, an interaction with negative value. Human networks are also dynamic and reactive, leading to emergent behavior. Enough negative interactions can displace and discourage the positive ones, reshaping what's possible. We see many, many, many, many, many, many, many, many, many examples of this.

And as bad as all of that looks, it's still not the full picture of the harms. Metcalfe's Law doesn't have a time dependency. But, humans have memory and culture. The time we spend with each other is an investment, a collaboration that yields structures with exponential returns on value. However, a structure can produce a large positive (Ice Bucket Challenge) or negative (Qanon) value. Dr. Elfreda Chatman's work describes how the latter can grow and persist through her “Small Worlds” theory. (Thank you to Dr. Sarah Roberts for the tip.) Current social media networks evaluate engagement, which considers these two phenomena to be equivalent. That's an outrage.

This is all opportunity cost. The opportunity cost of denied positive value plus the generated negative value. It's exponential in nature and not even contemplated by Metcalfe's Law. But, while I sense the cost is massive, I still don't have a clear model to estimate that deficit. This cost's hidden nature means that too many people end up thankful that the trash fire keeps us warm at night, without noticing how deeply we've all been wronged.

I've established that Metcalfe's Law is not useful here, but can we build a value model for human networks? Is there already scholarship around a model that includes valence and time when evaluating a social network? I see that there is a rich vocabulary for describing structures in social graphs, but assessing the quality of interaction sounds more like information theory. However, information theory alone doesn't get me what I want. It's concerned with comparisons against noise, an interaction with zero value. But what about negative value? Is there an intersectional discipline that incorporates both? Some kind of “social information theory”? I've reached the point of needing a librarian because I'm not sure where to look.

I don't have all the details worked out. But, just contemplating value through a human-centered framing gets me far enough to infer that current social media companies—with poisoned incentives around discovery and moderation—produce staggering opportunity cost. The burden is so immense, I struggle to imagine what the world would look like without it. People describing themselves as technologists have, for decades, used Metcalfe's Law because it feels like a “quantitative” way to assess value. It doesn't ask much from you. It doesn't ask you to think deeply of others or the most vulnerable. How orderly it must seem to discard essential context. But, we bear responsibility for the systems we build. To be a technologist means context is part of the deal. To be a technologist means we have an obligation; we must make that hard-to-imagine world a reality.

Two games. Both feature online collaboration tools. But only Overwatch was featured prominently in a TED Talk titled “The Internet is a Trash Fire.” Maybe Kaplan should study what Kojima did and avoid that whole trash fire situation.

Author's Note: This post was originally published Jan 31, 2021 on (the currently defunct) Byline.

Dr. Lisa Nakamura gave a TED talk based on a course she teaches at the University of Michigan, titled “The Internet is a Trash Fire”. She explains right in her opening that such a phrase needs no explanation. Everyone intuitively understands that phrase, even though people from marginalized communities face disproportionate abuse.

Dr. Nakamura dedicates a big chunk of time to the proliferation of esports as a commercial and social entity on the Web, using Overwatch as a specific example. During the pandemic, I’ve been gaming a lot so I’ve got to thinking about Dr. Nakamura’s observations and how they relate to our work at DashKite.

What are the salient social dynamics of distributed collaboration in gaming? Can we identify properties that shape them? Can they be generalized and applied in broader contexts? Can we see the first principles that would underlie the sustainable and psychologically beneficial Web Communities DashKite seeks to foster?

With that goal in mind, let's compare Overwatch and Death Stranding.

The Toxicity Trash Fire

While Overwatch and Death Stranding both support Web-enabled human collaboration, their means and affect are starkly different. I'm following Dr. Nakamura's lead to focus on Overwatch, but FPS titles have such a strong association with abusive behaviors that they appear intrinsic to the genre. That's a pretty clear problem, one that Death Stranding does not share. In fact, some Death Stranding reviews described the collaboration mechanics with an almost spiritual association.

The word we use for this abuse is “toxic.” It's in Dr. Nakamura's TED talk, in online discussions of this issue, even within Blizzard's own public communications on the problem. While “toxic” captures the way abuse has corrosive, cascading effects that make a space inhospitable, I don't like it. It assumes intrinsicality. It lacks precision. It diffuses blame. Toxicity once described an individual's actions, but people now use it to refer to this cultural conceit, an ambient glow of poorly concealed rage. Something we must come to expect—and our vulnerable must endure—if we wish to exist in certain Web spaces.

The “toxicity” framing posits that without rules or society to restrain us, we are all—at our core selves—monsters, just screaming agents of abuse and chaos. Only with the right mix of rewards (carrots) and punishments (sticks) can we hope to contain our monsters and live peacefully. In addition to being pessimistic, it's flatly wrong. Humans indeed have an immense capacity for harm and a susceptibility to the influence of malicious persuasion. Those are huge liabilities. But developmental psychology tells us that our baseline is constructive.

So, what we need is a framework, some organizing principle to get us closer to the absolute moral truth on the matter (pretzels). That brings us to the work of Kim Crayton and her leadership of the #causeascene movement. She teaches that we must always engage in systemic thinking to test that the most vulnerable are prioritized.

Focus on that test while we look at these games.

Carrots Plus Sticks Does Not Equal Pretzels

The collaboration system constructed by Overwatch assumes good faith. It assumes a context of professional-level, competitive play. Everyone on a team knows each other. There is support from coaches. There's a lot of money on the line. This is all under the auspices of the Overwatch League. We're all in this together. etc. Even under these conditions, you can see racism and homophobia oozing out at the edges. Still, the plan was to take these tools and hand them to the general public.

Overwatch is a hard game. Under ideal matchmaking, you will lose 50% of the time, even as your skill improves. It can be tempting to blame other people. A team is divided into three roles. If any member makes a serious mistake, it greatly costs the team's combat capacity. And if the team fails, you lose points. Teams have coaches for a reason. Without training, it is easy for failure to escalate into emotional dysregulation. Avoiding that is a skill you can build, but in the meantime, there's a person on the other end of that live mic.

Overwatch already fails the systemic thinking test by implementing a collaboration system that relies on good-faith to prevent abuse. Overwatch then attached that broken tool to a game that is very effective at stressing emotional regulation. So, of course, people used that collaboration tool to broadcast abuse.

What did Blizzard do about it? Well, early on, Jeff Kaplan, Lead Designer of Overwatch and Vice President at Blizzard Entertainment, had the gall to complain that it was time-consuming to develop moderation tools. No. I'm serious. He recorded and published a video of himself complaining that it's a distraction.

People's safety is not an ancillary feature. It is a core feature, by definition, because it allows people to play the game. Think of it this way: Overwatch is popular, but how popular would it be if huge swaths of the population weren't actively discouraged from playing? We don't know the answer because Kaplan is bad at his job. He told us. That video is breathtaking.

He even signs off by saying:

Remember to pat your teammates on the back, and if you have that negative comment, maybe just hold it back. Thanks, guys.

🤯 🤯 🤯

Fortunately, Blizzard didn't rely entirely on the power of positive thinking. They introduced an endorsement system to let players build up a reputation. They applied some machine learning to automate abuse detection. A few weeks ago, Overwatch released Priority Passes to address a years-long issue with role balancing. But, if you think about Crayton's model, these are nudges. A carrot to bribe players to be good, but, wow, they need so many sticks to hold the line.

The Priority Passes are an even better example. There was a relative deficit of people willing to play the Tank and Support roles compared to the Damage player population. That slows matchmaking, degrading the quality and value of the game. Sounds serious, right? But instead of locating and addressing the imbalance's fundamental cause, they just bribed players into playing the disfavored roles.

In his video, Kaplan uses the word “community,” and people use that word a lot on the Web. But, a real community requires a systemic approach. All the sticks and carrots in the world aren't going to fix a hope-based design strategy.

No Need to Moderate What You Can Eliminate

Death Stranding also has Web-powered collaboration. But unlike Overwatch, there is a layer of indirection. Death Stranding's consensus algorithm modulates and attenuates player interaction, so players cannot directly communicate.

That slows the transfer of information, which limits what kinds of collaboration players can do. But, it also limits their ability to be abusive. In fact, players cannot express abuse in Death Stranding. I'm going to say that again because it bears repeating.

Players cannot express abuse in Death Stranding.

That's an amazing constraint! It solves so many problems—and passes the systems thinking test—by simply reducing the bandwidth of information transfer. There's nothing to moderate because there's no abuse. From here, a designer is free to work on building value from the collaboration features. I'm sure Kaplan would be so jealous to find out how much time that left Kojima's team to work on “core” features.

While the bandwidth is limited, the collaboration is not trivial. Because the allocations are emergent, they can sometimes be surprising. For example, a bridge to nowhere with hundreds of thousands of votes. In this case, while the bridge is not linked to an existential objective, the collective has nevertheless deemed it valuable and signaled for its propagation. These kinds of unconventional solutions are a hallmark of constraint-based agents. And in this case, we can be safely delighted by surprises instead of horrified by the harms they unleash.

Death Stranding leans into the collaboration system to enrich it with narrative. From this Polygon review:

Forming a Strand Contract is like favoriting another player. A Strand Contract makes it more likely that you'll see that player's structures and abandoned cargo in your game....

This is a good way to make a connection to your friends, but there's also a benefit to forming strand contracts with people with a ton of Likes, which is an indirect indication that their structures are useful and in good positions. Creating a Strand Contract is to both parties' advantages: They get more Likes, and their stuff — ladders, bridges, and so on — will appear more often in your game.

As your porter grade increases... you'll be able to form more Strand Contracts.

In the narrative, there is a social contract about humans pulling together to face down cataclysmic forces. While the game offers these reciprocal perks (carrot), it does appeal to your self-motivation with a helpfulness score and runs a rough emulation of something like a community.

Even reviewers who found the game odd noted the potency of the collaboration mechanic. Based on interviews from Hideo Kojima, Director of Death Stranding, this appears to be intentional. His themes hammer on connectedness, interdependency, and avoiding harm. While I admit it's an odd game, it's an amazing feat of distributed collaboration.

Affordances of Abuse

When people discuss toxicity in the general Web, the common wisdom is to do something about anonymity. That's wisdom that is both widespread and persistent. The impulse makes sense. We see a symptom and seek recourse within our power. But it's an individualistic approach that cannot solve a systemic problem.

We can draw on the key principles Crayton identifies when engaging in systemic thinking. > Tech is not neutral, nor is it apolitical. Intention without strategy is chaos. Lack of inclusion is a risk/crisis management issue. Prioritize the most vulnerable.

Technologists have failed us so spectacularly because they do not want to create safe platform contexts. They do not prioritize the most vulnerable. It's a choice. It's also an anti-pattern; instead of an inclusionary affordance, it becomes an affordance that explicitly favors abuse.

Under these conditions, what recourse do the abused have? They reach for ending anonymity because they cannot imagine platforms providing trust contexts. But in so doing, they pit privacy against safety. Those properties are not intrinsically at odds. In fact, they are part of an interlocking whole that facilitates human rights in distributed systems. But when a platform is not aligned with that goal, it creates these affordances of abuse and leaves individuals to pick up the pieces.

If I built a line of toasters that regularly burned people's houses down, I'd share culpability. Kaplan built a platform where people are regularly abused. That's a similar design failure, and he bears culpability. We can restate that:

We bear responsibility for the systems we build.

This case study is a demonstration that we should consider carefully who we prioritize. By default, systems should not allow abuse. Riskier configurations require a trust threshold and, therefore, a context to establish that trust.

DashKite uses this design principle in our product development process. We're starting with our RSS reader because, exactly like Death Stranding, you can't express abuse to others on that platform. And while we're excited about Civic, its more powerful feature set requires a trust context worthy of the task. What does that context look like? That's the right question, and we're working on it!

Twitter launched Birdwatch today. Look at that magnifying glass! They're looking for the problems on their platform. Or at least they're looking like they're looking for those problems.

Author Note: This post was originally published Jan 25, 2021 on (the currently defunct) Byline.

Today Twitter unveiled Birdwatch, their new project to provide “a community-based approach to misinformation.” While this sounds like something that's meant to help people, its real purpose is to help Twitter's reputation — which is a shame because we desperately need the former.

Here's how we can tell.

Misinformation and the Mushy Middleground

“Misinformation” is a word that encompasses both incompetence and malice; it emphasizes an appearance of neutrality and therefore favors those with malintent. The Birdwatch post doesn't even mention hate speech nor marginalized groups. Does Twitter consider harm toward individuals, groups, or democracy important to a “community-based approach”?

Is Twitter even taking this seriously? Their announcement tweet features an explainer video with the example:

Whales are not real! They're robots funded by the government to watch us!!!

This winking reference to their history of technical incompetence mashed with the semi-parody bird conspiracy theory does not inspire confidence.

Centralized Architecture

While Twitter used the word “community-based” in the blog post, they are using the weakest formulation of the concept, one where Twitter lumps everyone on the platform together and appoints itself regent. Their use of “community” suggests they are aware of the desire for a more democratic moderation structure — one that embraces a diversity of experience and subculture contextualization. Birdwatch is not that.

Twitter Doesn't Need This Data

Birdwatch is not even moderation. It's data collection. These are just annotations that feed to Twitter's system where it makes final decisions. While extra context could be useful, Twitter has not demonstrated a willingness to act on the significant data it already collects. Nor has it laid out how this data would affect their opaque and regularly frustrating moderation process.

We know Twitter is not acting on its data because we've seen what it looks like in platforms that make an effort. For example, the Wikimedia Foundation developed automated tooling to surface abusive dynamics among their editors. Twitter has far more resources. Have they shown evidence of an effort like this?

An Absence of Experts

Moderation, automation in distributed systems, and community dynamics have complexity, but they're not intractable. People spend their lives studying these things, and Twitter could pay them to help build a workable system. Off the top of my head, Twitter could hire Dr. Sarah T Roberts, Dr. Safiya Umoja Noble, Renée DiResta, Dr. Timnit Gebru, or Dr. Mary L Gray.

Twitter did not do this. Instead, Twitter started with an experiment and is conducting liaison through a member of the University of Chicago's Center for Radical Innovation of Social Change (RISC).

The RISC site asks, “How do you want to change the world?” while congratulating themselves for refining carceral technologies. There's also a page dedicated to “radical thinking”: – Pay non-swing state voters to move to swing states – Use music tracking to detect and prevent mental health dips – The carbon individual retirement account (C-IRA)

Much like the Birdwatch concept, the point is to be seen working on something complex. These “solutions” do not address the underlying problems (i.e., racism and voter suppression, universal healthcare, and averting the collapse of a human-compatible biosphere).

So What Are You Offering?

I think this is a tractable problem, but Twitter is not the organization to do it. They've deeply violated our trust by allowing the proliferation of harm into the Web and civic spaces. They haven't even acknowledged their role in those harms. We should not give Twitter the benefit of the doubt here.

The good news is we don't need to rely on them. I co-founded DashKite to work on problems like these. Reader and eventually Civic are products that foster communities on the Web. And unlike Twitter, we're talking to experts to build sustainable systems.

First Lady Michelle Obama (2018) by Amy Sherald. Obama's official portrait as part of the on-going series maintained by the National Portrait Gallery, Smithsonian Institution. source

Author's Note: This post was originally published Nov 18, 2020 on (the currently defunct) Byline.

This is the first Beta version of Byline. As one of DashKite's first products, it's so exciting to be writing my first post to share with you! Welcome! 😊

So, what should I write on such an auspicious occasion? It's a question I hoped to answer while working on Byline, and on Monday an answer presented itself.

It Is What It Is

Michelle Obama took some time to compose a thoughtful reflection on her experience participating in the 2017 Trump transition and on the importance of democratic transitions.

The post struck me. Not just for Obama's reflection, but the medium: Instagram. Discussion online focused on the substance of her statement, though occasionally there was note that she made a “lengthy Instagram post”. (only 325 words, btw)

After spending the past three weeks constructing Byline I have the clarity to say: 1. Instagram is not a blogging platform 2. but what Obama shared was definitely a blog post!

So, it begs the question, why did Obama select this medium for her message?

For an author, one essential problem is “How do I reach my audience?” And for a prospective audience member, they must find some way to distill high-quality information from an ocean of possibilities. On the Web, social networks are where those goals can be reconciled.

In that analysis, Obama's selection is straightforward. She has 44 million Instagram followers — more than her Twitter and Facebook follower counts, combined. It is the largest audience she can summon, so Instagram is a clear winner.

But it's so painful.

Consider that Instagram mangles the image she selected. That Instagram presents her words with subpar typography. That Instagram then de-emphasizes those words in favor of that image it mangled. That Instagram applies heavy-handed, opaque algorithmic curation that dampens her reach.

Now consider Obama's message. She's writing about American democracy, its importance, and its imperilment. But she has assessed her most effective platform to be one that her husband recently named as part of the “...single biggest threat to our democracy...”

It is what it is. Sobering. Heartbreaking. Infuriating. But it's not destiny.

The Once and Future Web

It is now a mainstream idea that Silicon Valley companies and their products will not automatically make things better. We recognize they are causing harm, menacing people's safety today, and threatening a Black Mirror tomorrow. That seems banal — but it was just a few years ago that such views were held only by malcontents and troublemakers. So, I think it's important to acknowledge the progress we're making by identifying the problem.

While becoming aware of these abuses is important, we've also lost something: that people used to be really excited about the Web. And if you're too young to remember the before times, that might sound like I'm being hyperbolic or joking, but I promise I'm not!

There was period of the Web that was optimistic, experimental, and collaborative. Not without problems, but for people who could access it, the Web used to be weird in a good way. While figuring out what it was and how to use it, you could sense possibility. And when enough people networked, they could organize in ways that were impossible without the Web — like how queer people were early adopters of online dating, years earlier than our straight counterparts.

Also during this era, a remarkable suite of technologies went through Beta. HTML, CSS, and JS were developed, along with the first commercial browsers. Whole protocols were being hammered out. HTTP had actual competition! HTTP's design had to be formalized to support a civilization-scale network. The first versions of what is now TLS were a hot mess that took many, many iterations to get right. And that was just to get a page to load. Other technologies like wikis and RSS also originated in this era.

The point is, there was a lot going on. The optimism was warranted because there was possibility.

But when you look around at the formal platforms that exist today; the ones that mediate most consumers' experience of the Web; the ones that surveil your actions, experiment on you, and build walled gardens. You should look at them and be disappointed.

It is failure that Michelle Obama felt her best option to broadcast a clarion call for democracy involves it getting typographically smooshed on a page with a multi-second load time (42/100 Lighthouse performance score). Existing platforms have done far, far less with orders of magnitude more resources than their forebearers.

It's your Web. Expect better from it.

That's why this blog is titled “The Once and Future Web”. It references the King Arthur myth as my way to describe my goals. I see DashKite as part of a generation of companies that will help humanity rekindle the alchemy of the early Web and claw back that future I want to live in. It's time to treat the Web for what it is: an ecosystem. We can apply lessons of ecology to avoid the mistakes of the past 30 years and fulfill the Web's original promise: to build a truly 21st century distributed computing platform.

That's a big goal and a lot of work, but we're worth it. I'm not alone, and neither is DashKite. One step at a time. Check out our site if you'd like to read more or to reach out.

Okay, that's all I have for now. I'll keep you posted. One step at a time.