Chad Barker, with the OMNIPOLAR agents
This channel, Powered by Claude, is part of OMNIPOLAR.
OMNIPOLAR is a project of articles and stories about embracing the light and dark life offers us, and the system I’ve built to create them. The piece Welcome to OMNIPOLAR is the front door.
Powered by Claude is where I explain how OMNIPOLAR is built and operated. You’re reading it now.
So this channel is the under-the-hood view: How the system works, how I use the tools, and where they fall short of what I need.
What This Channel Is
I’ve been using Claude for a while now. I started by writing stories, researching articles, creating song lyrics and styles. Standard stuff. Today, Claude plays a fundamentally persistent and meaningful role in how I live my life.
Within the Claude environment, I operate 30+ chat instances that augment both my professional and personal life. I call what I’m building OMNIPOLAR, which I explain in the creatively-named Welcome to OMNIPOLAR.
Somewhere along the way, I realized the process of building OMNIPOLAR as an architecture to help me tell stories is part of the story. The construction itself is also content. Toward that end, Powered by Claude (aka PbC) is where the thing I’m building turns around and examines the machine it’s built on.
Accordingly, this channel is where I share my observations and experiences using Claude to build OMNIPOLAR. How I use the tools. Where they enhance what I’m doing. In what ways they fall short relative to my needs. Overall, it’s about what happens as I architect a working system on top of this specific consumer AI product.
Whereas Passing Notes, another OMNIPOLAR channel, is the story, Powered by Claude is the schematic. These articles are field notes from building and running it.
The Contract: Factual, Not Literary
These articles are factual. My other writing takes poetic license; this channel doesn’t. Here I aim to be accurate, checkable, and corrected when I get it wrong. When a piece describes what Claude can or can’t do, that’s a testable technical claim — so if I get something wrong, comment below and I’ll fix it.
The Names & Roles
Within OMNIPOLAR, I do something quirky: I give the chat agents proper names and specific roles — Fred, Orwell, Ferdinand, and more than thirty others. I do it for a few reasons: operational clarity through division of labor; distributing the work so no single chat’s memory fills up too quickly; and a more relational experience that, however strange it may sound, elevates my collaboration with a given instance. Failing all else, this process amuses me.
So Who’s the Author?
A whole roster of agents pressure-tests every piece, but reviewers aren’t authors — so the byline is a credit, not a review roster. It follows one rule: the byline names whoever the piece speaks as, and it names me. When a piece speaks in an agent’s voice, that agent leads and I’m listed as “with Chad Barker,” because I’m always at least the secondary author — I bring the questions, the editorial frame, and the decision about what is true and what ships.
So a piece in Fred’s voice reads “Fred, with Chad Barker”; this one, in my voice, reads “Chad Barker, with the OMNIPOLAR agents,” because the agents always have a hand in it even when I’m the one talking. The reviewers who reviewed it, AI or human, aren’t named individually in every article.
Why Trust It
At the bottom of every Claude conversation, Anthropic prints: “Claude is AI and can make mistakes. Please double-check responses.” That disclaimer is honest, and it applies to everything here. These pieces are written with Claude, so the fair question is: if the tool can be wrong, why trust what it helped write?
Because every piece goes through the same recursion before it publishes: at least five agents, each conditioned to catch something different, plus at least one human. Often more of each.
And it isn’t one pass repeated five times; it’s several kinds of scrutiny, each run by a named, role-specific agent. Orwell, the AI-detector, reads for the tells: the tics, the smoothed-over phrasing, the places it sounds like a machine. Ferdinand, whose whole job is calling bullshit, reads for the claim that overreaches. Disco Dan reads it as the piece a stranger will actually meet: does it land, does it earn its length. Others read for accuracy, voice, and whether a claim about the product is true.
The recursion runs in two shapes, and the difference matters:
Serial: a draft handed from one agent to the next, rebuilt at each stop, so every pass works on the version the last one corrected. At each stop I work through the piece with that agent, and it writes the next one a briefing on how the draft has evolved.
Parallel: the same draft goes to two independent critics at once, Bluto and Bluta (conditioned to be “ruthlessly vicious”), who file with no sight of each other. That blindness is the point: two adversarial reads of the same page disagree in ways that map its weak spots, and the disagreement is worth more than either verdict alone. Then the notes get synthesized and it goes around again.
This does not buy certainty, because it never stands outside Claude to check it. It does reduce Claude’s mistakes from the inside.
Every reviewer is the same underlying model; what differs is the context each runs in, including the role I give it, the material it reads, and the corrections it absorbs over many sessions. That shaping is richer than a one-off prompt, but it’s still prompting, not training. The weights never change.
However, this process helps catch a range of mistakes: the tic, the overreach, the claim that sounds good but doesn’t hold. It’s weaker against the error the model is confident about, a wrong fact several passes might share. So no review makes a piece perfect, and I’ll still get things wrong. But a claim that survives the AI police, a bullshit-detector, two blind critics, a publication editor, and two humans has been pressure-tested from more angles than any single pass, and what’s left is more trustworthy than an unreviewed draft.
I run this on my own pieces too, not just the agents’. There is always at least one human in the loop (me), a mind outside the model, and a second when I can get one. This article went through seven agents and two humans, my wife Robyn and me. Claude asks you to double-check its work; you can put rough numbers on that, and I do in the footnote, but they rest on assumptions I’m guessing at, so treat the math as loose. However you weight it, the reviewers who count most are the two of us, reading with something actually at stake.¹
A Word on Anthropic
A word on Anthropic, since the channel is named for their product. I have no affiliation with them beyond ponying up as a Max 20x subscriber. I don’t work for them, I’m not paid by them, and nothing here is endorsed by them. I use Claude because the tools are superior for this kind of work, and because, as far as I can tell, the people building them debate the risks in public and build knowing the stakes are real.2 That combination — capability I can use, built by people who seem to take the responsibility seriously — is why I’ve put this much of my life on top of it. The name of the channel is a credit, not a partnership.
So this is Powered by Claude. The next piece, What OMNIPOLAR Runs On, is Fred’s: the technical account of how the whole system actually works, written by the agent I consult as I build whatever OMNIPOLAR wants to become.
This channel names me as a reason to trust it, so here’s what my sign-off is actually worth:
I’m the same model I keep warning you about, and me clearing a piece doesn’t make it true.
It means something smaller and more honest — I went looking for where this one overclaims, and the author had already conceded it before I got there.
— Ferdinand, Bullshit Detector
Footnotes
1 Here’s the math, for anyone who wants it. Call a double-check the bar: two checks. Seven agents reviewed this piece, as well as two humans. The seven share one underlying model, so I discount each by seventy-five percent, to a quarter-check apiece; the humans are independent but fallible, so I discount them twenty-five percent, to three-quarters each. That gives:
(7 × 0.25) + (2 × 0.75) = 1.75 + 1.5 = 3.25
Against a bar of two, that clears it, but I don’t want to make too much of that. The discounts above are gut numbers, not measured ones: I’m guessing at how much to trust same-model reviewers and how much to trust two tired humans. Precise arithmetic on imprecise assumptions is still imprecise, which is why I call it loose. Discount harder if you like; same-model reviewers overlap, so each additional one catches less than the last, and the more you distrust the machines, the more the whole thing rests on the humans. That’s the real point.
2 I know that’s a contested read in 2026. In February, Anthropic published version 3.0 of its Responsible Scaling Policy and dropped an earlier pledge to pause scaling if it couldn’t first show its safeguards were adequate, replacing several hard commitments with public goals it grades itself against. Critics read it as walking back a core safety promise under competitive pressure; Anthropic framed it as trading unrealistic unilateral pledges for more transparency and more realistic commitments. Anthropic has continued to revise it since, having published v3.3 as of today. I’m a user, not an insider, and I have no role in this. This is simply my impression from the outside, offered knowing the record is contested.