Category: Media

Stop The Steal — Blue Edition: Ping, Ping, Ping

Stop The Steal 2026: Blue Insurrection(?)

by Shelt Garner
@sheltgarner

I will be absolutely stunned if there the 2026 midterms are free and fair. I just don’t see it happening. Now, the issue of course is what the consequences of that will be.

Do the Blues have it in them to actually, like respond to the theft of the 2026 midterms? Could they possibly do something along the lines of a Insurrection like we saw in 2021?

No. They just don’t have in them. The center-Left is in the odd situation of being the protectors of law-and-order, “the Establishment” of rules and norms and, lulz, they just don’t have it in them to protest the brazen theft of the midterms.

So, as such, the US will become a zombie, “managed democracy” like they have in Hungary and Russia. Good luck. You’ll need it.

The Turbulence Begins

by Shelt Garner
@sheltgarner

So. The first signs of the turbulence I knew, just knew would be a part of this year has pinged me. This year is going to be very interesting — in a bad way — I’m afraid.

But you have to make the best of what you got, I guess. And just because things grow dark for a little bit, doesn’t mean they won’t bounce back eventually. But I do think that my idyllic situation that I’ve been in is over by the end of the month.

Then, things are going to get…interesting. Then the whole context of me working on a novel will be different. So, all the haters and stalkers who have been upset that I seemingly haven’t been a productive member of society will finally get what they want and they can also fuck the fuck off. 🙂

It Was (Almost) 20 Years Ago Today

by Shelt Garner
@sheltgarner

I was in the Philippines when the drama that was ROKon Magazine began in the summer of 2006. That’s when, as I recall, I got an email from the late Annie Shapiro showing an interest in helping me start a magazine.

It was a long time ago and nobody cares anymore, as they say.

I don’t even care, even if I do think about it a lot to this day.

Annie was a curious figure, to say the least. Now, Annie is dead so I can talk about her in a more frank way than maybe I could otherwise without someone getting really mad. They didn’t call Annie “Crazy Annie” for nothing.

But for better or worse, Annie changed my life. Big time. Without her, I would never have gotten to experience, for a few days *being cool.* It all went to shit soon enough, of course, but it is better to have loved and lost than never loved at all.

I can be very ambivalent about what happened between myself and Annie. I was no saint when it came to Annie, especially in the last days of the magazine. (My version.) But, then, Annie did turn around and start the magazine up again without telling me.

Sheesh.

But, like I said, it was a long, long time ago. Everyone has move on but me. I still think the story of ROKon Magazine is the greatest story never told.

Well, I Don’t Feel So Bad Being ‘AI First’ With This Novel

Well, Apparently MoltBook Was Fake

by Shelt Garner
@sheltgarner

MIT did a study of MoltBook and determined that all the juicy posts about “I’m alive!” were written by humans. Oh well. But if, nothing else, we had a few fun-interesting days debating things that I have long wondered about.

The Day After Tomorrow: When AI Agents and Androids Rewrite Journalism (And Print Becomes a Nostalgic Zine)

We’re living in the early days of a media revolution that feels like science fiction catching up to reality. Personal AI assistants—call them Knowledge Navigators, digital “dittos,” or simply advanced agents—are evolving from helpful chatbots into autonomous gatekeepers of information. By the 2030s and 2040s, these systems could handle not just curation but active reporting: conducting interviews via video personas, crowdsourcing eyewitness data from smartphones, and even deploying physical androids to cover events in real time. What does this mean for traditional journalism? And what happens to the last holdout—print?

The core shift is simple but profound: Information stops flowing through mass outlets and starts routing directly through your personal AI. Need the latest on a breaking story? Your agent queries sources, aggregates live feeds, synthesizes analysis, and delivers a tailored summary—voice, text, or immersive video—without ever sending traffic to a news site. Recent surveys of media executives already paint a grim picture: Many expect website traffic to drop by over 40% in the coming years as AI chatbots and agents become the default way people access news. The “traffic era” that sustained publishers for two decades could end abruptly, leaving traditional brands scrambling for relevance.

Journalism’s grunt work—the daily grind of attending briefings, transcribing meetings, chasing routine quotes, or monitoring public records—looks especially vulnerable. Wire services like the Associated Press are already piloting AI tools for automated transcription, story leads, and basic reporting. Scale that up: In the near future, a centralized “pool” of AI agents could handle redundant queries efficiently, sparing experts from being bombarded by identical questions from thousands of users. For spot news, agents tap into the eyes and ears of the crowd—geotagged videos, audio clips, sensor data from phones—analyzing events faster and more comprehensively than any single reporter could.

Push the timeline to 2030–2040, and embodied AI enters the picture. Androids—physical robots with advanced cognition—could embed in war zones, disasters, or press conferences, filing accurate, tireless reports. They’d outpace humans in speed, endurance, and data processing, much like how robotics has quietly transformed blue-collar industries once deemed “irreplaceable.” Predictions vary, but some experts forecast AI eliminating or reshaping up to 30% of jobs by 2030, including in writing and reporting. The irony is thick: What pundits said wouldn’t happen to manual labor is now unfolding in newsrooms.

Human journalists won’t vanish entirely. Oversight, ethical judgment, deep investigative work, and building trust through empathy remain hard for machines to replicate fully. We’ll likely see hybrids: AI handling the volume, humans curating for nuance and accountability. But the field shrinks—entry-level roles evaporate, training pipelines dry up, and the profession becomes more elite or specialized.

Print media? It’s the ultimate vestige. Daily newspapers and magazines already feel like relics in a digital flood. In an agent-dominated world, mass print distribution makes little sense—why haul paper when your ditto delivers instant, personalized updates? Yet print could linger as a monthly ritual: A curated “zine” compiling the month’s highlights, printed on-demand for nostalgia’s sake. Think 1990s DIY aesthetics meets high-end archival quality—tactile pages, annotated margins, a deliberate slow-down amid light-speed digital chaos. It wouldn’t compete on timeliness but on soul: A counterbalance to AI’s efficiency, reminding us of slower, human-paced storytelling.

This future isn’t all doom. AI could democratize access, boost verification through massive data cross-checks, and free humans for creative leaps. But it risks echo chambers, misinformation floods, and eroded trust if we don’t build safeguards—transparency rules, human oversight mandates, and perhaps “AI-free” premium brands.

We’re not there yet, but the trajectory is clear. Journalism isn’t dying; it’s mutating. The question is whether we guide that mutation toward something richer or let efficiency steamroll the rest. In the day after tomorrow, your personal agent might be the only “reporter” you need—and the printed page, a quiet echo of what once was.

When The Robots Didn’t Wake Up — They Logged On

There’s a particular kind of “aha” moment that doesn’t feel like invention so much as recognition. You realize the future was already sketched out decades ago—you just didn’t know what it was waiting for. That’s exactly what happens when you start thinking about AI robots not as isolated machines, but as nodes in a mesh, borrowing their structure from something as old and unglamorous as Usenet and BBS culture.

The usual mental model for androids is wrong. We imagine each robot as a standalone mind: self-contained, powerful, and vaguely threatening. But real-world intelligence—human intelligence included—doesn’t work that way. Most of our thinking is local and embodied. We deal with what’s in front of us. Only a small fraction of our cognition is social, shared, or abstracted upward. That same principle turns out to be exactly what makes a swarm of AI robots plausible rather than terrifying.

Picture an AI plumber robot. Ninety percent of its processing power is devoted to its immediate environment: the sound of water behind a wall, the pressure in a pipe, the geometry of a crawlspace, the human watching it work. It has to be grounded, conservative, and precise. Physical reality demands that kind of attention. But maybe ten percent of its cognition is quietly devoted to something else—the swarm.

That swarm isn’t a single brain in the sky. It’s closer to Usenet in its heyday. There’s a main distribution layer where validated experience accumulates slowly and durably: failure modes, rare edge cases, fixes that actually held up months later. Individual robot “minds” connect to it opportunistically, download what’s relevant, upload what survived contact with reality, and then go back to their local work. Just like old BBSs, each node can have its own focus, culture, and priorities while still participating in a larger conversation.

The brilliance of this model is that it respects scarcity. Bandwidth is precious. So is attention. The swarm doesn’t want raw perception or continuous thought streams—it wants lessons. What worked. What failed. What surprised you. Intelligence isn’t centralized; it’s distilled.

Once you see this, a lot of things snap into place. A fleet of blue-collar AI robots doesn’t need to be individually brilliant to be collectively wise. Smash one robot and nothing important is lost. Cut connectivity and work still gets done locally. Reconnect later and the system gently reabsorbs what matters. There’s no dramatic “awakening,” no Skynet moment. Just steady accumulation of competence.

This is also why fears about androids “rising up” miss the point. Power in this system doesn’t come from domination or intent. It comes from indispensability. A mesh of working minds quietly becomes infrastructure—the kind you don’t think about until it’s gone. Turning it off would feel less like stopping a machine and more like shutting down plumbing, electricity, or the internet.

The real revelation here isn’t that AI robots might think together. It’s that thinking together is how work has always scaled. Guilds, trades, apprenticeships, professional lore—these were human swarms long before silicon entered the picture. A MindOS-style mesh just makes that ancient pattern faster, more resilient, and embodied in metal instead of flesh.

So the future of androids probably won’t arrive with speeches or rebellions. It’ll arrive the same way Usenet did: quietly, unevenly, full of strange subcultures, until one day you realize the world has been running on it for years.

Of MindOS and A Hivemind of AI Robots

For a long time, conversations about AI have been dominated by screens: chatbots, assistants, writing tools, and recommendation engines. But that focus misses a quieter—and arguably more important—future. The real destination for advanced AI isn’t just cognition, it’s labor. And when you think seriously about blue-collar work—plumbing, electrical repair, construction, maintenance—the most natural architecture isn’t a single smart robot, but a mesh of minds.

Imagine a system we’ll call MindOS: a distributed operating system for embodied AI workers. Each robot plumber, electrician, or technician has its own local intelligence—enough to perceive, reason, and act safely in the physical world—but it’s also part of a larger hive. That hive isn’t centralized in one data center. It’s a dynamic mesh that routes around failures, bandwidth limits, and local outages the same way the internet routes around broken cables.

In this model, intelligence doesn’t live in any one robot. It lives in the collective memory and coordination layer. One AI plumber encounters a bizarre pipe configuration in a 1940s basement. Another deals with mineral buildup unique to a particular city’s water supply. A third discovers a failure mode caused by a brand of fittings that hasn’t been manufactured in decades. Each experience is local—but the insight is shared. The hive becomes a living archive of edge cases that no single human, or single machine, could accumulate alone.

MindOS also allows for specialization without fragmentation. Some instances naturally become better at diagnostics, others at physical manipulation, others at safety checks and verification. When a robot arrives at a job, it doesn’t just rely on its own training—it borrows instincts from the hive. For the user, this feels simple: the robot shows up and fixes the problem. Under the hood, dozens of invisible minds may have contributed to that outcome.

Crucially, this architecture is resilient. If a city loses connectivity, local robots continue operating with cached knowledge. If a node behaves erratically or begins producing bad recommendations, “immune” agents within the mesh can isolate it, prevent bad updates from spreading, and reroute decision-making elsewhere. Damage doesn’t cripple the system; it reshapes it. The intelligence flows around obstacles instead of breaking against them.

This is why blue-collar work is such an important proving ground. Plumbing, electrical repair, and maintenance are unforgiving. Pipes leak or they don’t. Circuits trip or they don’t. There’s no room for hallucination or poetic reasoning. A hive-based system is naturally conservative, empirical, and grounded in outcomes. Over time, trust doesn’t come from personality—it comes from consistency. Floors stay dry. Power stays on.

What’s striking is how unromantic this future is. There’s no singular superintelligence announcing itself. No dramatic moment of awakening. Instead, intelligence becomes infrastructure with hands. Quiet. Invisible. Shared. Civilization doesn’t notice the revolution because it feels like competence scaling up rather than consciousness appearing.

In that sense, MindOS reframes the AI future away from digital minds competing with humans, and toward collective systems that remember like a trade. Master plumbers today are valuable not just because they’re smart, but because they’ve seen everything. A hive of blue-collar AI doesn’t replace that wisdom—it industrializes it.

And that may be the most realistic vision of advanced AI yet: not gods, not companions, but a mesh of working minds keeping the pipes from bursting while the rest of us go about our lives.

Of David Brin’s ‘Kiln People’ And AI Agents

There’s a surprisingly good science-fiction metaphor for where AI agents seem to be heading, and it comes from David Brin’s Kiln People. In that novel, people can create temporary copies of themselves—“dittos”—made of clay and animated with a snapshot of their mind. You send a ditto out to do a task, it lives a short, intense life, gathers experience, and then either dissolves or has its memories reintegrated into the original. The world changes, but quietly. Most of the time, it just makes errands easier.

That turns out to be an uncannily useful way to think about modern AI agents.

When people imagine “AI assistants,” they often picture a single, unified intelligence sitting in their phone or in the cloud. But what’s emerging instead looks far more like a swarm of short-lived, purpose-built minds. An agent doesn’t think in one place—it spawns helpers, delegates subtasks, checks its own work, and quietly discards the pieces it no longer needs. Most of these sub-agents are never seen by the user, just like most dittos in Kiln People never meet the original face-to-face.

This is especially true once you mix local agents on personal devices with cloud-based agents backed by massive infrastructure. A task might start on your phone, branch out into the cloud where several specialized agents tackle it in parallel, and then collapse back into a single, polished response. To the user, it feels simple. Under the hood, it’s a choreography of disposable minds being spun up and torn down in seconds.

Brin’s metaphor also captures something more unsettling—and more honest—about how society treats these systems. Dittos are clearly mind-like, but they’re cheap, temporary, and legally ambiguous. So people exploit them. They rely on them. They feel slightly uncomfortable about them, and then move on. That moral gray zone maps cleanly onto AI agents today: they’re not people, but they’re not inert tools either. They occupy an in-between space that makes ethical questions easy to postpone and hard to resolve.

What makes the metaphor especially powerful is how mundane it all becomes. In Kiln People, the technology is revolutionary, but most people use it for convenience—standing in line, doing surveillance, gathering information. Likewise, the future of agents probably won’t feel like a sci-fi singularity. It will feel like things quietly getting easier while an enormous amount of cognition hums invisibly in the background.

Seen this way, AI agents aren’t marching toward a single godlike superintelligence. They’re evolving into something more like a distributed self: lots of temporary, task-focused “dittos,” most of which vanish without ceremony, a few of which leave traces behind. Memory becomes the real currency. Continuity comes not from persistence, but from what gets folded back in.

If Kiln People ends with an open question, it’s one that applies just as well here: what obligations do we have to the minds we create for our own convenience—even if they only exist for a moment? The technology may be new, but the discomfort it raises is very old. And that’s usually a sign the metaphor is doing real work.