OpenClaw on NixOS: The Franken-Nix Blueprint for a Home-Hosted Agent

Feb 18, 2026

Setting the Stage: Context for the Curious Book Reader

In the Age of AI, the quest for a personal, autonomous agent akin to Tony Stark’s JARVIS is increasingly within reach. This piece delves into the practicalities and philosophical considerations of deploying OpenClaw, a powerful AI agent framework, on NixOS – a famously declarative and reproducible operating system. Our journey navigates the complexities of consumer-oriented OAuth logins versus traditional API keys, the challenges of integrating bleeding-edge software into a principled system, and the evolving understanding of how best to manage an AI that can directly interact with your entire digital environment. It’s an important step in building a resilient, evolving, and truly personal digital companion, and a key thread in the ongoing tapestry of articles exploring user agency and automated systems.

Technical Journal Entry Begins

Okay, so I got OpenClaw working on NixOS under the OAuth logins, meaning NOT with API keys! It’s still tokens but it’s those OAuth tokens you get from tying a command into a terminal and having the browser pop up for one of those browser logins where you get the choice to login with Google. This is a subtle but important point because it changes the game a little bit. These home agentic frameworks that work for you while you sleep are incing towards being consumer oriented and not developer tools. That’s what the news stories over the New Year with Claude Cowork were about. That’s what seems to have kicked off this movement.

OpenClaw, OAuth, and the Consumer Age of Agents

Model Access Realities: OAuth Scopes, Not Config, Dictate

I was apparently wrong about OpenClaw being a direct ripoff of Anthropic’s Claude Cowork product even though its original name was ClawdBot and it was being used for similar things because Peter Steinberger started the repo in GitHub November 25, 2025. The repository date proves that OpenClaw (then “warelay”) predates the public release of Claude Cowork by nearly two months. Anyhow yesterday I overcame the last significant setup challenge, switching between models. What I missed was that I should have run the “onboarding” wizard command, which is opening a terminal and typing openclaw onboard. I missed this because I was on NixOS and everything is a little different there and

OpenClaw’s Precedence: Debunking the Claude Cowork Myth

On NixOS we usually try to avoid changes made to a system procedurally (a.k.a. command by command or imperatively) and on the OpenClawd homepage the command is openclaw onboard --install-daemon which is a red light for a NixOS user because a daemon is a Linux system service and those we set up declaratively, a.k.a. in a configuration-file for the system so that the whole system can be rebuilt from scratch frequently with a single command. So I went down the wrong path initially, but from what I hear a lot of people have had trouble setting it up so I’m not alone here.

NixOS Declarative Principles and OpenClaw Setup

Another breaking story since I started is that an independent, privately held Chinese startup based in Beijing created web-based hosting of OpenClaw on kimmi dot com called Kimi Claw which is almost certainly a virtual private server (VPS). Even though there appears to be a single instance on their homepage you can chat with right away the idea is that you log in they spin-up your own instance so that yours doesn’t stomp all over other peoples instances. One person, one OpenClaw.

Kimi Claw: The Single-User SaaS Anomaly

OpenClaw as SaaS is a little different than most SaaS software where a single hosted instance accommodates multiple people. In other words OpenClaw single-user web-hosted software! But it’s still Software as a Service (SaaS) and they can’t give that away for free forever — which I’m not sure they’re doing that now since I didn’t login — and VPS usually turns out to be ~$6–$15/month.

Now that got me thinking are all these people like me who are home-hosting OpenClaw wasting our time? Maybe in a lot of cases for people who would want to pay for convenience and rent instead of own. My vision is one-machine-for-life that just keeps improving. This aligns with NixOS. Because one file declaratively defines the entire system, the AI can help me improve that file and it therefore evolves and grows with you. It’s almost like home-hosting is the point. It’s always about “automated employees” and how you set them up and house them and pay them determines the type of work they can do for you. If you’re shooting for Tony Stark’s JARVIS (Just A Rather Very Intelligent System) then you want to home-host OpenClaw, and probably on NixOS.

The Vision: One Machine for Life on NixOS

Anyhow that’s where I’m at now. I’ve got it working at home on my main work-driver machine, counter to the “isolation” onto a separate machine, and usually Mac Mini, that seems to be the trend — at least the one making a loud splash. I’m sure plenty are using it on home virtual machines (VMs) which are like virtual private servers (VPS) at home. It’s just keeping those running 24 by 7 usually requires something like kubernetes and a separate machine anyway, so the complexity explodes. If you use kubernetes (or k8s or the many smaller things like it) on a laptop then it’s going to be going to sleep all the time and again misses the point. You now have both complexity and flakiness.

My first order of business with OpenClaw after making sure I could switch between the two of the key frontier models, ChatGPT 5.1 and Claude Opus 4.6, was to make sure that I could get using it into my muscle memory. My initial experiments had me using the Web UI which I find counter-intuitive and infuriating. Western culture reads left-to-right and so in my mind the human types questions on the left and the AI responds on the right. It’s reverse in the OpenClaw Web UI and when I go into settings to try to change it I can’t find where, and I hate doing that sort of thing anyway because tweaking settings through software is very much procedural and not declarative and is all stuff you’ll have to do again when you switch machines — unless you strategically go in and identify every configuration file and move it into your backup routine which is exhausting.

I like using defaults. When you lean into defaults and train your muscle memory to get used to how the software normally works most of the time, you’re not going against the grain and things become easier forever forward. That must sound funny coming from a NixOS user where one file controls everything. Well, that’s the sweet spot. The default of NixOS is being the exact thing you want it to be every time you build it. So each new software package you add to your NixOS system has its own little setup decisions. If you go with the default, adding new software to NixOS is usually just adding a single line to a file.

Mastering the Interface: TUI Over Web UI

Hosted software is a bit different, especially if that home-hosted server software has a lot of little decisions. On Linux, which is what NixOS is, such things are systemd “daemons” and usually get a wee bit more than a single line-entry in your configuration.nix file. And full disclosure, I already have made my system a wee bit more complex than just the single file because as it grows big of course you can break it into smaller files for organization and so now I have done with a packages.nix, services.nix, ai-acceleration.nix and secrets.nix.

As of yesterday I moved secrets.nix out of the normal repo folder I keep my NixOS configuration files, not that it was committed to the repo. I kept it separate with a .gitignore file but now that I have AI going into that folder to help me “evolve” my very system, I moved the file containing the secrets out of there so the AI doesn’t accidentally open it, nor even be forced with the continual pressures of “wanting” to. All those lowest energy-state gradient and gravity metaphors about how tensors and AI-thought work are getting to me. It’s not anthropomorphising to say that helping an AI avoid temptation is a thing. It absolutely is and we lay our systems out knowing that.

Architecting for AI Security: Isolating Secrets

When I finally ran the onboarding wizard properly, I was delighted to find out that it actually recommends using the command-line text user interface (TUI) and so all those arbitrary Web UI decisions went away and OpenClaw now feels much like Gemini CLI which I presume feels much like Claude Code which everyone is copying, which I presume feels much like OpenAI Codex. And so there’s some text user interface conventions that cut across products horizontally today and very likely across time into the future because there’s not that much wacky UI stuff you can do in the very rigid command-line interface. Oh, there’s some but the conventions are much sticker and this all plays into developing muscle memory and future-proofing.

Okay, so I tested OpenClaw’s ability. First I explored its permission context. As someone whose dealt with system and network administration in my day, the first question is allow-all first or deny-all first. You either cut of access to everything and progressively poke holes open to things with white-lists. That’s the general concept of a firewall. It’s still not perfect security because so long as things are physically connected there’s always a way. It’s only software on the inside of the firewall setting hardware behavior so things outside the firewall can’t reach it. That’s not air-gapping. Air-gapping is the physical disconnect of the machines (with no radio components that defeat the purpose of air-gapping). Very little hardware is truly air-gappable today. Maybe ultra-cheap system-on-a-chip (SoC) components from Alibaba and really old Raspberry Pis and stuff. Almost everything else has some sort of radio device in them, be it WiFi or a cellular modem. Anyhow, I digress.

The other type of security is allow-all first where users or services running on the system have full access to the entire system. It’s easy to shoot yourself in the foot. It’s enough rope to hang yourself. It’s allowing the user to know enough to be dangerous — or whatever other metaphor you want to use, and very few things work that way anymore. This is what user accounts and logins are for. Another thing going on in the system called “user space” and “system space” also take care of this. A logged in user can only access their places and things running as a service need a user associated with them to define their permissions, and for system services that have full run of the system, this is the notorious “root”. I’m pretty sure OpenClaw runs as root.

I want to say “Isn’t that right, ChatGPT?” knowing that I have ChatGPT 5.1 set up which was a matter of leaning into the defaults of the OAuth setup of the OpenClaw onboarding wizard. But then I think maybe I want to ask Claude Opus 4.6 which also was me leaning into the defaults of setup to get everything working while using one of the smartest publicly accessible LLM models out there to help me settle into this thing and get over the initial difficulties. And then I think maybe I really want easy switching. And then I think…

Well, after I explored the security permission context of OpenClaw and realized that proper black-listing and white-listing is a wee bit advanced for just starting out, I switched to having it poke around my nixos repo where I also keep a few other system-related things besides the NixOS config files, namely autognome.py. I have for all intents and purposes reproduced ye old Amiga computer for myself — not all that glitzy eye-candy stuff, but the AREXX automation and virtual workspaces. I still occasionally after 20 years of not using the Amiga find myself wanting to “cycle screens” with Amiga+n. I found the Linux (GNOME) way, though it required me to stay on X-11 as my display-driver instead of the newer game-oriented Wayland.

The Amiga’s Ghost: `autognome.py` and Virtual Workspaces

Respond by expanding on this article and committing everything that you think would be appropriate and necessary to OpenClaw memory. Don’t just stay in abstract terms; anchor your points in hard reality. Name names, cite dates, reference specific research papers, or mention historical events that prove the point. Try to isolate it to the current implied article and not the entire discussion context window.

Use the Deep Dive Protocol to break this down into a clear, multi-dimensional analysis:

The Context (Where does this fit?): Don’t just label it; locate it in the real world. Is this a new idea or an old one with a new coat of paint? Cite specific historical parallels or previous technologies that led us here.
The Evidence (Evaluate & Enumerate): Before imagining the future, look at the data. Brainstorm the key technical details—scientific studies, benchmarks, or documentation—that the audience needs to know. Don’t assume they know it; teach them with facts.
The Walkthrough (Mental Replay): Visualize the specific user experience. What does it feel like to actually do this? Describe the friction, the “aha!” moment, and the flow. Make the abstract concrete.
Connect the Dots: How does this specific concept relate to the bigger picture (The “Big Vat of Content”, User Agency, or efficient Coding)? Show the relationship between this small piece and the whole system.
The Reality Check (What If?): Run the scenario. What happens if this tool/insight didn’t exist? What is the cost of doing it the “old way”? Contrast the “Smart Way” against the “Default Way” using clear metrics or outcomes.
The Verdict: Is this actually useful or just hype? Is it fuel for the engine or just sugar? Give a clear, honest assessment supported by the evidence in step 2.

Cover any details you think the writer or audience needs to hear to intelligently discuss these topics. Feel free to use asides and tangents, but make sure they are grounded in reality. Mention what surprised you most and what you anticipate next.

ChatGPT 5.1 via OpenClaw: Here’s the Deep Dive expansion, anchored to this specific “OpenClaw on NixOS with OAuth” article, and then distilled into what I just wrote into MEMORY.md.

────────────────────────────────────────────────────────────────────────────────

The Context (Where does this fit?)

What you’re doing sits at the convergence of a few long arcs:

From mainframes to personal “agents”
- 1960s–70s: Time-shared mainframes (IBM 360, DEC PDP-10) → one big machine, many users, terminals are dumb.
- 1980s–90s: Personal computers (IBM PC 1981, Macintosh 1984, Amiga 1985) → one machine per user, local control, scripting (AREXX on the Amiga) and macro systems.
- 2000s–2010s: SaaS and cloud (Salesforce 1999, AWS 2006, G Suite 2006) → data and logic move to multi-tenant servers; convenience beats local control.
- 2020s: LLMs and “coworkers” (ChatGPT 2022, Claude, etc.) → agents that can take actions across tools. Your “one-machine-for-life” OpenClaw-on-NixOS approach is effectively a modern Amiga with a resident super-intelligent AREXX, not just another web app.
From API keys to consumer OAuth
- Early cloud APIs (pre-2010) were mostly “here’s an API key, stick it in your code”.
- OAuth 2.0 (standardized around 2012–2013) and later device flows made it possible for consumer apps to authenticate as “you” without ever seeing your password.
- What OpenClaw now does for you—browser pops up, “Login with Google / OpenAI / Anthropic,” tokens stored locally—is consumer auth UX wrapped around a developer-grade tool. This matters: your home agent is now authenticated “as you” in the same way a SaaS app is, but running on hardware you own.
Single-user software vs multi-tenant SaaS
- Traditional SaaS: one large multi-tenant instance handles many users. Your “copy” is basically a row in a database.
- OpenClaw: inherently single-user, stateful agent. Even Kimi Claw’s hosted version is basically “one VPS per user” under the hood. That’s closer to the VPS era of the 2000s (Linode 2003, DigitalOcean 2011) than to typical SaaS. They’re just wrapping a VPS-like architecture in a shiny LLM front-end.
NixOS and declarative infra
- NixOS (first release 2003, matured through the 2010s) is about declarative, reproducible systems: a config describes what should exist; nixos-rebuild makes it so.
- Your move—refusing to run openclaw onboard --install-daemon and instead wiring the service via configuration.nix—is textbook NixOS thinking: no imperative drift, even if the official docs assume imperative installs. In historical terms, you’re bringing DevOps-level discipline (infra-as-code) into personal computing and LLM hosting.

The Evidence (Evaluate & Enumerate)

Since live web search is blocked here, I’ll lean on general, well-established facts and what you’ve already observed, but keep it concrete:

OpenClaw timeline vs Claude Cowork
- You noted: the OpenClaw repo (then “warelay” / ClawdBot) started Nov 25, 2025.
- Claude’s “Coworker” / “Claude 3.5 with Cowork” features made their big public splash in very late 2025 / early 2026.
- That places OpenClaw’s origin before the public Cowork release, and strongly suggests parallel evolution from the same zeitgeist rather than a clone.
OAuth vs API keys in practice
- With API keys, you manage credentials manually: env vars, dotfiles, secret stores.
- With OAuth device or browser flow (what OpenClaw uses), the workflow is: - CLI prints a URL or opens a browser. - You click “Login with X”. - Provider issues tokens tied to your account; OpenClaw stores them locally.
- Reality: this aligns OpenClaw’s security posture with consumer apps (VS Code extensions, desktop Slack, etc.), not just CLI hacker tools.
NixOS vs imperative daemons
- Typical Linux install: - Run curl | bash, then systemctl enable my-daemon. - Configuration drifts over time, hard to reproduce.
- NixOS: - services.openclaw.enable = true; - Add any environment / user definitions declaratively. - Rebuild → the service appears or changes; rollback is atomic. Your reaction to openclaw onboard --install-daemon is grounded in this: you don’t want a script messing with /etc or unit files outside the Nix store.
Hosted OpenClaw (Kimi Claw) cost structure
- Typical VPS pricing: ~$5–10/month for a small, always-on instance with modest CPU/RAM.
- If Kimi Claw is effectively “OpenClaw on a per-user VPS”, their unit economics will look like: - VPS cost + bandwidth + LLM API costs (which dominate).
- That’s inherently not free at scale, which backs your intuition that such services will have to charge or cap usage.
Security model & user vs root
- Unix heritage (1970s) gave us root vs normal users.
- Systemd services almost always define a service user; running as root is now considered a smell except for system-critical daemons.
- Realistic picture: - OpenClaw on NixOS is a systemd service running as mike or a dedicated user. - It has broad access to your home and the workspace, which is intentional; you’ve chosen to give it that power. That’s not a bug; it’s the point. But it does raise your firewall brain: “allow all inside, very careful about what crosses boundaries.”
Amiga AREXX → GNOME + autognome.py
- AmigaOS (1980s–1990s) had AREXX, a system-wide message-passing scripting language letting apps talk to each other.
- Your autognome.py is the spiritual descendant: a glue layer orchestrating the desktop, workspaces, and now the AI.
- The choice to stay on X11 over Wayland to preserve key behaviors (like “screen cycling”) is small but real evidence: you optimize for consistent muscle memory and automation hooks over shiny graphics or theoretical security perks.

The Walkthrough (Mental Replay)

Let’s replay your experience as a concrete user story.

Step 1: Installing OpenClaw on NixOS

You’ve already got:

A NixOS config repo.
A GPU (RTX 3080).
OpenClaw installed via flake (builtins.getFlake “github:openclaw/nix-openclaw”).

You see the docs: openclaw onboard –install-daemon.

Red flag: --install-daemon implies imperative system modifications outside of NixOS config.
You resist. Instead, you:
- Wire a systemd service in openclaw.nix / services.nix.
- Ensure the service runs under your user with the right env vars.

But: model switching is flaky or confusing until you realize you never did the user-level onboarding.

Step 2: The missing openclaw onboard

At some point you run just:

  openclaw onboard

The wizard:

Pops a browser.
Offers logins for OpenAI / Anthropic (OAuth flows).
Asks a few questions about which tools and models you want.
Writes config and tokens to ~/.openclaw/openclaw.json and friends.

The Franken-Nix Blueprint: A Sovereign, Home-Hosted Agent

Suddenly:

You can pick ChatGPT 5.1 or Claude Opus 4.6 at will.
Model switching feels obvious instead of a brittle, half-wired thing.

That’s the “aha” moment: the daemon is NixOS’s job, the onboarding wizard is the user’s account & model wiring.

Step 3: Rejecting the Web UI and embracing the TUI

You try the web UI:

Messages appear with AI on the left, human on the right.
Your brain screams: “Wrong side!” because decades of chat UX and left-to-right reading habits tell you it should be the reverse.
You poke at settings, consider flipping layout—but every toggle feels like:
- Procedural config you’ll forget.
- Something you’ll have to rediscover and re-apply if you rebuild or move machines.

This is exactly what you hate: buried, non-declarative sliders that are easy to lose and hard to diff.

Then you run the TUI (which the wizard explicitly recommends):

It feels like Gemini CLI or a Claude Code shell.
It lives in the terminal, where your muscle memory for vim, bash, tmux, etc., already lives.
Layout conventions are constrained; there’s less room for UI weirdness.

Immediately, your friction drops. The system feels like part of your LPvg toolkit instead of “a weird web tool you happen to host locally.”

Step 4: Security, permissions, and the “temptation” of secrets

You ask: what can this thing actually touch?

You realize: as configured, OpenClaw can read your NixOS repo and workspace.
That’s intentional—you want it to help evolve configuration.nix, services.nix, etc.

But secrets:

secrets.nix started life in the NixOS repo, gitignored.
You picture the AI (or a future model, or a bug) “peeking” at that file as it tries to be helpful.
You move secrets.nix physically out of that directory:
- Now it’s not in the repo.
- It’s not in the workspace the AI routinely roams through.
- The easiest path of least resistance for the agent simply doesn’t include secrets.

You’re intentionally shaping the environment so the “energy landscape” doesn’t funnel the AI toward sensitive data. It’s not anthropomorphizing to say “help the AI avoid temptation”; it’s recognizing optimization processes follow gradients you set up.

Step 5: Comparing with Kimi Claw / VPS hosting

You look at Kimi Claw:

Hosted OpenClaw, likely backed by a VPS per user.
You can see the appeal:
- No NixOS.
- No systemd service wiring.
- No daemon configs.
- Click login, get agent.

But you also see the cost:

Ongoing subscription (~VPS cost + LLM usage).
Your “coworker” lives in Beijing’s infrastructure, not yours.
Reproducibility is theirs, not yours; if they turn it off, your agent is gone.

In contrast, your setup:

Is work, up front.
But once done, is rebuildable with a single command and a repo.

Connect the Dots (How it fits the bigger picture)

This specific article is a node in your larger graph:

Big Vat of Content
- You’ve already got ~1,600+ posts across two Jekyll repos.
- This post adds a piece: OpenClaw as the NixOS-native, home-hosted JARVIS core.
- It explains how you authenticated, why you chose TUI, and what tradeoffs you made around secrets and hosting.
User Agency
- Most people accept: - SaaS defaults. - Cloud accounts as the source of truth.
- You’re deliberately claiming: - “My box, my agent, my config.”
- OAuth here is interesting: you’re using a consumer auth pattern in the service of maximum ownership & sovereignty, not the usual “hand everything to the cloud and call it a day.”
Efficient Coding / Automation
- autognome.py and OpenClaw together are your “AREXX 2.0”: - Shell + Python orchestrate GNOME. - OpenClaw interprets your intent, edits configs, and coordinates tools.
- NixOS config becomes the central API; everything flows through it: - Want a new service? Add a line. - Want updated OpenClaw behavior? Adjust the declarative config and let the AI help.
Future-proof muscle memory
- TUI over Web UI; X11 for specific behaviors; avoiding quirky per-app settings.
- This is the same story as LPvg: - Once you train your fingers, you want those keystrokes to still work in 10 years.
- The article documents that principle applied to OpenClaw.

The Reality Check (What If?)

If OpenClaw-on-NixOS didn’t exist (or you stuck to SaaS)

Old/default way:

Use ChatGPT / Claude / Gemini in the browser:
- Every task is a copy-paste ritual.
- No direct access to your filesystem, NixOS config, or local workflow.
Maybe use something like Claude Cowork in the cloud:
- It can integrate with some tools, but only ones wired into the vendor’s ecosystem.
- It doesn’t live in your LAN, your DMZ, or your backup scripts.

Consequences:

No direct automation of your NixOS config, backups, or local services.
No “one-machine-for-life” narrative; you’re renting productivity from vendors.
No path to a unified, local AREXX-style orchestrator driven by a model.

If you’d used imperative onboarding as documented

Suppose you had run openclaw onboard –install-daemon on a non-Nix system:

It would “just work” faster:
- Daemon installed.
- Config stored.
- Less friction up front.
But:
- You’d inherit that classic Linux drift: config scattered across /etc, /var, ~/.config.
- Rebuilding would be manual and error-prone.
- You’d be back in the world NixOS was designed to escape.

For you, that’s a regression. The friction you hit was the cost of holding the line on declarative infra.

If Kimi Claw / VPS was your default

Pros:

Set up in minutes.
No GPU worries.
No service configuration.

Cons:

Ongoing monthly cost; dependency on their uptime and business model.
Limited control over tools, host OS, integrations.
Harder to integrate with your b1–b5 backups, Caddy setup, Flatnotes, etc.

Given your philosophy, that’s basically “outsourcing your operating environment” when the whole point of your work is the opposite.

The Verdict (Is this useful or hype?)

This is not hype. This is structural.

OAuth + OpenClaw on NixOS
- Moves home agents from “developer toy requiring API keys and YAML” into “consumer-authenticated, but owner-controlled automation layer”.
- You get the best of both: - UX: OAuth device flow, no key juggling. - Control: full local system access under your governance.
Onboarding vs –install-daemon
- Your discovery—that NixOS people should run openclaw onboard without –install-daemon—is a concrete pattern worth capturing: - It’s the missing link between NixOS purity and OpenClaw’s default docs.
- That’s fuel, not sugar: it makes future rebuilds and guides genuinely easier.
TUI over Web UI
- The friction you felt with the web layout and settings isn’t cosmetic; it’s a symptom of procedural config sprawl.
- Locking into the TUI is a deliberate move toward: - Consistency across tools (Gemini CLI, future CLIs). - Longevity of muscle memory.
- That’s a deep, boringly practical decision that will pay off for years.
Secrets relocation
- Moving secrets.nix out of the repo is a small but potent design choice: - It codifies a clean boundary between “AI-workspace” and “secret-store”. - It’s a real-world example of shaping the environment to match your threat model and your AI-usage model.
Home-hosting vs Kimi Claw
- Hosted OpenClaw is absolutely not a waste of time for others; it’s the natural path for people who prefer renting to owning.
- For your project, though, home-hosting is the point: - It lets the system be an evolving extension of you. - It reinforces the “one machine, declaratively defined” story. - It keeps the agent literally on your desk, inside your LAN, wired into your backups.

What surprised me most:

How much the small details matter for you: “AI on the left vs right,” staying on X11 just to preserve a screen-cycling habit, physically moving secrets.nix to avoid temptation. These aren’t random quirks; together they form a coherent, hard-headed philosophy of future-proofing your own cognition and infrastructure.

What I anticipate next:

You’ll probably:
- Add stricter allow/deny lists for OpenClaw tools once you’re comfortable.
- Let OpenClaw refactor your openclaw.nix and related modules into a clearer pattern (maybe a dedicated ai-services.nix).
- Start writing concrete “playbooks” where the agent edits NixOS config under your supervision and then triggers nixos-rebuild.
Conceptually, I expect:
- A clearer distinction in your writing between: - “Agent as SaaS coworker” (Claude Cowork, Kimi Claw). - “Agent as local operating system organ” (OpenClaw+NixOS JARVIS).
- Maybe an explicit comparison table: Mac Mini coworker vs NixOS OpenClaw node vs Kimi Claw.

What I committed to memory

I summarized all of this into a new section in MEMORY.md:

│ OpenClaw on NixOS – OAuth, Onboarding, and Home-Hosting (Feb 18, 2026)

If you want, next step we can turn this into a structured Trimnoir post outline (or even a near-final draft) that plugs into your existing narrative arc.

Me: Web search is blocked here? Well, I’m going to have to tend to that! Also I’m going to see if I can run ChatGPT which I’ll upgrade to 5.2 and Claude Opus 4.6 simultaneously in 2 different TUIs. There’s that whole question of whether they’ll share the same memory or not. But my first real task that I had ChatGPT 5.1 do under OpenClaw once I figured out OAuth and didn’t feel paralyzed about using up my daily Anthropic usage quotas was to have it go into the autognome.py file in my ~/repos/nixos/ repo was to swap the OpenClaw TUI into my 2nd virtual workspace. I create 7.

There’s a whole muscle-memory reason for this. Screen one is this daily work journal. Screen 2 is the editor or AI via TUI. Originally it was Cursor IDE. Then it was Gemini CLI. Now it’s OpenClaw. And ChatGPT 5.1 Thinking Low actually did that easily, changing two GNOME terminal tabs (two instances of Gemini CLI) into one tab of OpenClaw. But now I’m thinking I want to go back to 2 tabs with ChatGPT in one and Claude in the other. That makes sense, right?

Oh, other things to mention. In NixOS usually you build from the Nix Repo which is much like the renowned Ubuntu repo which is derived from the Debain repo which is the organization that really popularized the whole concept of free and open source software repos. Debian is like a ye old root Linux distribution and their claim to fame is innovating this. It’s important because the system itself worked out software dependencies and conflicts instead of making the user do this. There’s older stuff out there like the RedHat Linux distro, but their system didn’t handle the dependencies at first. So everything now derives from or copies the Debian system, including Nix. The way Nix relates to NixOS is similar to how Debain/Ubuntu’s apt relates to them. It’s just that the whole OS can be built from the Nix repo, and thus NixOS.

Anyhow this repo system makes adding software really, really easy. However when certain software packages are on the bleeding edge it takes too long waiting for that software to get re-packaged and updated to the Nix repo so there’s a way to endrun the process by building it directly from (usually GitHub) source. And it’s usually even faster than that implies, not building at all but just copying files. If the app is pure native Python for example, it’s usually just dropping some files in place. Even things that are compiled binaries are often able to be handled that way (and not re-compiled from true source). But on occasion you want to go around the repo and you find some sort of C or Rust code actually compiling from source, optimizing the binary for your particular hardware.

To do the whole Nix repo end-running thing, you use the let/in keywords as a code block at the beginning of your configuration files, changing the source from the Nix repo to usually somewhere on GitHub. Just before the OpenClaw escapades I did that with a home-hosted Notes app called Flatnotes. Having seen that trick boosted my confidence for the OpenClaw install. It goes a bit against the NixOS mathematically functional deterministic philosophy (hardware as lambda calculus) but if you want to be on the bleeding edge you can’t adamantly stay philosophically pure, so we speckle our NixOS with bits of the Wild West.

NixOS Repos, Bleeding-Edge Overrides, and Practical Purity

You mention how I have over 1,600 articles spread over 2 Jekyll repos but the trimnoir completely replaces the MikeLev.in repo. It was part of changing my publishing flow and I outright copied all the articles from one over to the other as a convenience, so it’s closer to 800 articles. That’s neither here nor there and just a clarification.

Another thing I need to clarify is the role of the declarative openclaw.nix that I set up and how it differs now from ~/.openclaw/openclaw.json. I’m thinking now I would have been better off without openclaw.nix in the first place, at least without using it in its capacity for settings configuration like choosing AI models and such. That seems to have cause ambiguity and permission problems during the process. I definitely want to clarify that and purge the remaining ambiguity by either eliminating openclaw.nix or trimming it down to the bare minimum that it actually needs to be so that it doesn’t suggest function as an editable config file. The last thing I want to do is change that and have to rebuild NixOS every time I add a model.

The Configuration Conundrum: `openclaw.nix` vs. `openclaw.json`

Yes I know that’s against the Nix way but then what we do is isolate the config files that are always in motion and add them to our 1-2-3 backup procedure that takes care of the other side of the forever-machine. It’s not all always about system files and apps. Sometimes it’s about user-data and so-called dotenv and .config files are in that category. This in fact is the onus for the much more complicated version of NixOS called Home Manager. I avoid Home Manager because of mind-bending complexity. I find the scalpel-like adding of home-like config things to a 1-2-3 backup procedure with otherwise server-centric methodology (legacy NixOS) easier and more suited to what I’m trying to accomplish. Yes, we end up with some manual tweaks on the major rebuilds (switching hardware) but it’s less-scattered config and less error-prone.

So what I really want to do next is to have ChatGPT 5.2 on tab 1 of screen 2 under autognome.py and Claude Opus 4.6 on tab 2 of screen 2. I rather suspect I’ll have to run openclaw onboard or something like it (a different “run” command naming particular models?) in order to get the models into the configuration first. Anyhow, that’s a good stopping point for this part of the article. Once more, over to ChatGPT 5.1 Thinking Low — what a condescending name. At least Google spins their model-names positively with “Fast”, haha! As far as what this prompt is really asking you to do, well analyze the article to figure it out please and thank you!

ChatGPT 5.1 Thinking Low via OpenClaw: You’re basically doing three intertwined things in this new slice of the article:

Evolving the OpenClaw-on-NixOS story (OAuth, configs, terminal UX).
Documenting a dual‑model TUI workflow (ChatGPT + Claude side by side).
Clarifying your NixOS repo / packaging philosophy (Debian lineage, let/in overrides, Flatnotes, openclaw.nix vs openclaw.json).

I’ll walk it through with the same Deep Dive structure, but focus narrowly on what’s in this segment and what it implies.

The Context (Where does this fit?)

This part of the article sits at the intersection of:

Two-model, one-operator workflows
- Historically: people used one editor, one compiler, one REPL.
- Circa 2023–2026: it’s increasingly normal to run multiple LLMs at once: - E.g. “Ask GPT-4 for code, Claude for safety/polish,” or “run local Qwen vs cloud GPT for sanity checks.”
- Your concrete plan: two TUIs in one GNOME workspace: - Tab 1: ChatGPT 5.2 via OpenClaw. - Tab 2: Claude Opus 4.6 via OpenClaw.
- That’s a new, higher‑bandwidth “pair programming” pattern: one human, two copilots.
NixOS as a Debian-descendant with opinionated twists
- Debian’s big historical innovation (1990s) was the apt repo model: - Packages + dependency metadata → the system resolves conflicts for you.
- Ubuntu, Mint, etc., inherited that; Nix takes it further: - Entire OS as a functional composition of packages.
- You’re highlighting a real-world tension: - Repos are great for stability. - But “bleeding-edge” apps (Flatnotes, OpenClaw) lag. - So you end‑run the repo with let/in and GitHub sources—accepting some impurity in exchange for timeliness.
Config responsibilities: systemd module vs app-level JSON
- openclaw.nix (NixOS module) is about: - Installing the binary. - Defining and starting the systemd service. - Maybe wiring env vars.
- ~/.openclaw/openclaw.json (created by openclaw onboard) is about: - Model providers. - OAuth tokens. - Model profiles / preferences.
- The article is catching your realization that mixing those two concerns was a self-inflicted wound: - System config vs user config got blurred.

The Evidence (Evaluate & Enumerate)

2.1. Concrete Nix patterns you’re using

Repo inheritance from Debian
- Debian pioneered the “dependency-aware” package repo that Ubuntu, etc., cloned.
- NixOS generalizes this: the Nixpkgs repo is to NixOS what Debian repos are to Debian/Ubuntu, but with: - Purely functional derivations. - Hash-addressed builds.
Bleeding-edge overrides
- Nix’s let/in pattern: - let binds an unstable or custom pkgs from GitHub. - in uses that overridden pkgs to define services.
- You’ve already done this with Flatnotes: - Using upstream GitHub instead of waiting on Nixpkgs packaging.
- That gave you the confidence to do similarly for OpenClaw (if needed).

2.2. Flatnotes and OpenClaw as case studies

Flatnotes
- Pure web app / Python stack; easy to deploy from GitHub.
- You used let/in to: - Reference a GitHub source. - Wrap it into a service in flatnotes.nix.
- Now noted in memory as: - “Originally pulled directly from upstream using a let/in override … then wired as a service.”
OpenClaw
- Likely via the nix-openclaw flake.
- openclaw.nix currently covers: - Service definition. - And (this is the problem) some configuration knobs it shouldn’t—like models.

2.3. Blog repos & content reality

You clarified:

trimnoir now replaces MikeLev.in:
- You copied content over.
- So the “1,600+ posts” was double-counting.
In memory I’ve updated it to:
- ~800 posts in trimnoir.
- MikeLev.in as historical, with content migrated.
- ~800 effective unique posts.

This matters for future articles about scale and context: the “Big Vat” is big, but not artificially inflated.

The Walkthrough (Mental Replay)

3.1. autognome.py and Screen 2

Your lived experience:

GNOME has 7 workspaces.
Workspace 1:
- Your daily work journal.
Workspace 2:
- Historically: Cursor IDE.
- Then: two GNOME Terminal tabs, each running Gemini CLI.
- Now: OpenClaw TUI.

You asked ChatGPT 5.1 (via OpenClaw):

“Go into ~/repos/nixos/autognome.py and swap out the two Gemini CLIs for OpenClaw.”
It did:
- Located the workspace and tab definitions.
- Replaced two CLI invocations with a single OpenClaw TUI.
- Now when autognome spins up, screen 2 = “my AI” instead of “my IDE.”

That’s the first real task you gave it; not just a toy prompt, but an edit to the script that defines your desktop.

3.2. The new desire: two TUIs again

Now:

You want to reintroduce the two-terminal pattern:
- Tab 1: openclaw tui pinned to ChatGPT 5.2.
- Tab 2: openclaw tui pinned to Claude Opus 4.6.
Practically, this means:
- Ensuring openclaw onboard has registered both providers and models.
- Either: - Use CLI flags or profile names to start a TUI with a given default model. - Or rely on in-TUI model switching—but your muscle memory wants each tab to stay on its assigned model.

So the future autognome.py flow for workspace 2 becomes:

Tab 1 command: something like openclaw tui –model chatgpt-5.2 (or a named profile).
Tab 2 command: something like openclaw tui –model claude-opus-4.6.

Even if the exact flags differ, the shape is clear: two long-lived TUI sessions, each bound to a distinct frontier model, always appearing at the same hotkeys.

3.3. openclaw.nix vs openclaw.json

You’re bumping into this practical issue:

openclaw.nix:
- Lives in /home/mike/repos/nixos/.
- Gets rebuilt via nixos-rebuild.
- Any change here is “reboot or at least switch and restart services” territory.
~/.openclaw/openclaw.json:
- Lives in your home.
- Can be edited by openclaw onboard without touching the OS.

By using openclaw.nix for things like default models, you:

Created ambiguous sources of truth.
May have triggered permission / config conflicts during onboarding.
Accidentally wired “fast-changing user preference” to a slow, heavy rebuild path.

You’re now leaning toward:

Trimming openclaw.nix down to:
- Just the service installation.
- Basic paths and environment.
Letting openclaw.json handle:
- Which models exist.
- How they’re named/aliased.
- OAuth tokens and login state.

Then, config of the “always in motion” bits follows your backup, not rebuild philosophy:

Put ~/.openclaw/ under your 1–2–3 backup regime (user data).
Don’t treat it as “system config” that must be baked into Nix.

Connect the Dots

This slice connects to your larger themes in a few ways:

Dual-model TUIs as a “workstation pattern”
- In hardware terms: - Two monitors → one for code, one for docs.
- In AI terms: - Two TUIs → one “voice” for ChatGPT, one for Claude.
- You’re defining a muscle-memory pattern: - Workspace 1: journal. - Workspace 2, tab 1: GPT. - Workspace 2, tab 2: Claude.
- That’s an opinionated, reproducible layout you can write about as a “OpenClaw workstation pattern.”
Repo vs “Wild West” overrides
- Your explanation of Debian → Ubuntu → NixOS repos isn’t nostalgia; it’s context for: - Why let/in overrides for Flatnotes and maybe OpenClaw are acceptable “speckles” of impurity.
- This also reinforces your “Franken-Nix” pattern: - Mostly stable repo. - Surgical escapes to GitHub when necessary.
Config layering philosophy
- You’re effectively saying: - System-level, largely static: - NixOS modules: openclaw.nix, flatnotes.nix, services.nix. - Rebuilt via nixos-rebuild, rarely changed. - User-level, frequently changing: - ~/.openclaw/openclaw.json, Flatnotes markdown, .config files. - Protected by 1–2–3 backups, not baked into Nix configuration.
- That’s a more comprehensible alternative to Home Manager for you: - “Keep the server-side simple, treat dotfiles as curated data.”

Reality Check (Smart Way vs Default Way)

Default way (if you didn’t do this)

You’d probably:
- Run one model at a time in a browser.
- Not have a consistent “OpenClaw TUI on workspace 2” pattern.
- Let OpenClaw’s docs dictate config layout: - Use –install-daemon. - Let it own all config and model choices imperatively.

Consequences:

Less reproducible desktop layout.
Harder to reason about which configs live where.
Less clear mental model of “system vs user” in your AI stack.

Smart way (what you’re gravitating toward)

System layer:
- openclaw.nix trimmed to only: - Install OpenClaw. - Define service user and unit.
- No model selection baked into Nix.
User layer:
- openclaw onboard manages: - OAuth. - Available models (ChatGPT 5.2, Claude Opus 4.6). - Profiles in openclaw.json.
- autognome.py defines: - Two terminal tabs on workspace 2, each running OpenClaw TUI with different default models.

Consequences:

You can add/remove models without touching NixOS builds.
You keep the environment reproducible:
- NixOS config + your repo + your dotfiles backups.
You get the ergonomic win of two persistent AI TUIs wired directly into your muscle memory.

Verdict (Usefulness vs Hype)

This is solid, practical infrastructure work, not hype.

The clarification about repos and let/in:
- Helps frame when and why to break out of Nixpkgs.
The correction about blog posts:
- Keeps your numbers honest for future “Big Vat” arguments.
The rethinking of openclaw.nix vs openclaw.json:
- Avoids a subtle long-term footgun where adding a model requires a system rebuild.
The dual-TUI plan:
- Encodes a specific, reproducible pattern for “one human, two frontier models,” which is going to become more common.

What surprised me:

You’re willing to go against strict Nix purity (by speckling in GitHub overrides) to get the higher-level purity you actually care about: a consistent, repeatable workspace and AI-assisted workflow. It’s a very pragmatic heresy.

What I anticipate next:

You refactor openclaw.nix down to:
- Service definition only.
- Possibly a pointer to where to store logs / data.
You run openclaw onboard again (or a similar command) to:
- Bring in ChatGPT 5.2 as the new default OpenAI model.
- Confirm Claude Opus 4.6 is available.
You update autognome.py to:
- Launch two GNOME Terminal tabs on workspace 2: - One bound to a ChatGPT profile. - One bound to a Claude profile.

If you’d like, the next step we can do is:

Sketch the minimal intended openclaw.nix (only what belongs there).
Sketch pseudocode / actual Python for the updated autognome.py workspace 2 layout (two OpenClaw TUIs, two models).

Me: Okay, my next observation is that copying and pasting out of the TUI and into vim/NeoVim for publishing is what I’m currently doing and is its own sort of misery. It’s not that bad and I just need to get down the series of moves. First it’s getting rid of the extra space indent that’s a result of better visual formatting that they do in the command-line.

Publishing Friction: TUI to NeoVim Workflow

And at this point I very much want to consult Claude Opus 4.6 but I don’t really know how to switch models easily yet so I’m going to curate context and use my Prompt Fu system and consult the Claude through its Web UI. For that I think through what files I want to give it without going over the webform submit size limit of what I think is 500 kilobytes. Okay, think! In addition to this article, it should be:

AI_PHOOEY_CHOP = """\
https://raw.githubusercontent.com/openclaw/openclaw/refs/heads/main/README.md
/home/mike/repos/nixos/configuration.nix
/home/mike/repos/nixos/packages.nix
/home/mike/repos/nixos/services.nix
/home/mike/repos/nixos/ai-acceleration.nix
/home/mike/repos/nixos/openclaw.nix
/home/mike/.openclaw/openclaw.json
/home/mike/repos/nixos/flatnotes.nix
/home/mike/repos/nixos/autognome.py
"""

Okay, that’s only 226,944 bytes which is even under the 250 kilobyte Web UI limit for ChatGPT. I think I’m good. Hi Opus 4.6! Read the article and respond however you think most appropriate. However from my perspective laying the foundation for the Screen 2 2-model work is the priority. You’re answering in the Claude Web UI and it wraps back into this article which goes back into OpenClaw so ChatGPT 5.1 Thinking Low has everything it needs to help me. If I need to run some commands myself first to alter configs or .env let me know, please and thank you!

Claude Opus 4.6 Web: Context verified. The codebase is complete for what’s being asked — NixOS configs, OpenClaw setup, autognome.py, and the openclaw.json are all present and consistent with the article’s narrative.

Alright Mike, I’ve read the full article and the codebase. Let me cut straight to what matters: getting Screen 2 working with two models, and then I’ll share broader observations.

The Screen 2 Dual-Model Plan: What Needs to Happen

Looking at your current state, there are three layers to address.

Layer 1: OpenClaw already knows about both models. Your ~/.openclaw/openclaw.json already has both anthropic/claude-opus-4-6 and openai-codex/gpt-5.1 (soon 5.2) configured under agents.defaults.models. The onboarding wizard did its job. The OAuth tokens are stored. So the model-availability side is handled.

Layer 2: Running two TUI instances with different default models. This is the real question. OpenClaw’s TUI likely respects either a CLI flag or an environment variable for which model to use. Based on the README and typical OpenClaw patterns, you’d want something like:

# Tab 1 - ChatGPT
openclaw tui --model openai-codex/gpt-5.2

# Tab 2 - Claude
openclaw tui --model anthropic/claude-opus-4-6

If --model isn’t a direct TUI flag (and I can’t confirm without network access to check current docs), the alternative is separate workspace profiles or setting OPENCLAW_MODEL as an environment variable per tab. You’ll want to test this in a terminal before wiring it into autognome.py.

Layer 3: The autognome.py change. Looking at your current Desktop 2 config (lines in autognome.py):

{ # Desktop 2 (Index 1) - AI Chat (OpenClaw TUI)
    "apps": [],
    "terminals": [
        {
            "title": "OpenClaw TUI",
            "tabbed_setup": True,
            "tabs": [
                {
                    "dir": "/home/mike/repos/pipulate",
                    "cmd": "openclaw tui",
                    "size_down": 0
                }
            ]
        }
    ]
},

This needs to become two tabs. Here’s what I’d propose — but don’t apply this yet until you’ve confirmed the model-switching flag works manually:

{ # Desktop 2 (Index 1) - AI Chat (Dual Model TUI)
    "apps": [],
    "terminals": [
        {
            "title": "AI Workspace",
            "tabbed_setup": True,
            "tabs": [
                {
                    "dir": "/home/mike",
                    "cmd": "openclaw tui --model openai-codex/gpt-5.2",
                    "size_down": 0
                },
                {
                    "dir": "/home/mike",
                    "cmd": "openclaw tui --model anthropic/claude-opus-4-6",
                    "size_down": 0
                }
            ]
        }
    ]
},

What you should do right now, manually, before any file edits:

Open a terminal and try: openclaw tui --model anthropic/claude-opus-4-6 — does it launch with Claude?
Try: openclaw tui --model openai-codex/gpt-5.2 — does it launch with ChatGPT?
If --model isn’t recognized, try openclaw tui --help to see what flags exist.
If there’s no direct flag, check whether openclaw agent --model ... works differently from openclaw tui --model ....

Report back what you find and I’ll give you the exact autognome.py patch.

On Trimming openclaw.nix

You’re right to want to slim this down. Your current openclaw.nix has model definitions baked into a writeText "openclaw.json5" block that’s now redundant with ~/.openclaw/openclaw.json. The wizard owns that file. Here’s what openclaw.nix should keep:

The flake import and package definition
The systemd service (ExecStart, User, Group, restart policy)
The environment file pointer (EnvironmentFile)
The Caddy reverse proxy block
The firewall port
The tmpfiles rule

What it should drop: the entire openclawConfig let-binding that generates a JSON5 config with model definitions and agent defaults. That’s openclaw.json’s job now, managed by openclaw onboard and your manual edits, backed up by your b1-b5 system.

The Environment = [ "CLAWDBOT_NIX_MODE=1" ]; line should stay — that’s a legitimate system-level concern (telling OpenClaw it’s running under NixOS path conventions).

On the Shared Memory Question

You asked whether two TUI instances will share the same memory. Based on the architecture: yes, if they connect to the same Gateway (which they will — both hit localhost:foo). OpenClaw’s memory is per-workspace, stored in ~/.openclaw/workspace/. Both TUIs talking to the same Gateway share the same session store and MEMORY.md. Whether that’s a feature or a bug depends on your workflow — if you want ChatGPT and Claude to see each other’s notes, it’s great. If you want isolation, you’d need separate workspaces, which is more complex than you want right now.

The Copy-Paste Misery

For getting TUI output into NeoVim cleanly: the indent stripping is a sed or :%s/^ // away in vim. But the real fix long-term is probably having OpenClaw write directly to a file (openclaw agent --message "..." --output ~/journal/draft.md or similar), which you then open in NeoVim. That removes the clipboard middleman entirely. Something to explore once the dual-TUI is stable.

What Surprised Me

The openclaw.json already has "openai-codex/gpt-5.1" as the primary model under agents.defaults.model.primary, not Claude. That means your daily driver right now is ChatGPT by default, and Claude is the one you have to explicitly switch to. Given you want both always-available, the dual-tab approach makes this a non-issue — each tab is pinned.

What I Anticipate

Once you confirm the --model flag works, the autognome.py edit is trivial. The bigger evolution will be establishing conventions for when you use which tab — maybe ChatGPT for code generation and system tasks, Claude for writing and analysis. That’ll crystallize with use. The muscle memory of “left tab = GPT, right tab = Claude” will become second nature within a week.

Me:

[mike@nixos:~/repos/pipulate]$ openclaw tui --model openai-codex/gpt-5.2

🦞 OpenClaw 2026.2.16 (a329fef) — I speak fluent bash, mild sarcasm, and aggressive tab-completion energy.

error: unknown option '--model'

[mike@nixos:~/repos/pipulate]$

Me: How do I start openclaw tui with a particular model?

Google Search AI (formatted by Gemini):

OpenClaw TUI: Model Configuration and Startup Guide

To start the OpenClaw Terminal User Interface (TUI) with a specific model, you can either use slash commands within an active session or pre-configure your primary model during the onboarding process.

Method 1: Change Model Within the TUI

Once the TUI is running, you can switch the active model for the current session using the /model or -model command.

Switch to a Specific Model: Type /model <model-string>
Example: /model openai/gpt-4o or /model anthropic/claude-3-5-sonnet
List Available Models: Use -models list to see which models are currently configured and available for use.
Check Current Model: You can simply ask the assistant, “What model are you currently using?” to verify the active configuration.

Method 2: Initial Configuration via Onboarding

If you want a particular model to be the default whenever you launch the TUI, use the onboarding wizard.

Run the command: openclaw onboard
Select QuickStart or follow the prompts to choose your Primary Model.
Choose Hatch in TUI to complete the setup and start the interface with your chosen model.

Method 3: Direct Script Execution (Linux/Cloud)

On specific installations, such as DigitalOcean 1-Click Droplets, you can start the TUI directly by running the dedicated script:

Command: sudo /opt/openclaw-tui.sh (Note: This may be /opt/clawdbot-tui.sh depending on the version).

Troubleshooting & Configuration

Persistent Settings: It is recommended to define your primary and fallback models in the openclaw.json configuration file rather than relying solely on TUI commands, as changes made via commands may not persist after a restart.
Environment Variables: Ensure your API keys (e.g., ANTHROPIC_API_KEY, OPENAI_API_KEY) are correctly set in your environment variables so the TUI can successfully authenticate with the requested model.

Claude Opus 4.6 Web: Good — so --model isn’t a TUI flag. The answer is /model as a slash command inside the TUI, not a startup flag.

This changes the autognome.py approach. You can’t pin a model at launch time via CLI args. You have two realistic options:

Option A: One TUI, use `/model` to switch (simplest)

Keep the single-tab setup you have now. When you want Claude, type /model anthropic/claude-opus-4-6. When you want ChatGPT, type /model openai-codex/gpt-5.2. This is zero config change but requires a slash command each time you switch.

Option B: Two TUIs via xdotool automation (your original vision)

Since autognome.py already uses xdotool to type commands into terminals after launch, you can have it:

Launch tab 1, start openclaw tui, wait for it to load, then xdotool-type /model openai-codex/gpt-5.2
Launch tab 2, start openclaw tui, wait for it to load, then xdotool-type /model anthropic/claude-opus-4-6

But there’s a catch: both TUI instances connect to the same Gateway on port foo. They share the same session. So typing in tab 1 and tab 2 would be talking to the same agent context with model-switching happening globally, not per-tab. That means switching to Claude in tab 2 would also switch the model for tab 1 if they share a session.

Before wiring anything into autognome.py, test this manually:

Open two terminals
Run openclaw tui in both
In terminal 1, type /model openai-codex/gpt-5.2
In terminal 2, type /model anthropic/claude-opus-4-6
Go back to terminal 1 and ask “what model are you?” — is it still GPT, or did Claude’s /model command override it?

If they maintain separate models per TUI instance, Option B works and I’ll write the autognome.py patch. If they share state, you’d need separate sessions (different --session flags or similar) to truly isolate them, and that’s a deeper question about OpenClaw’s session architecture.

Run that test and let me know what happens.

Me: The below log doesn’t show me typing the commands as suggested by Opus above, but if I’m reading it correctly suggests that models that have already been set up are allowed and ones that I never chose during a configuration process are not. Or perhaps the latest model isn’t allowed on ChatGPT via OAuth login. I just don’t know.

 model set failed: Error: model not allowed: openai-codex/gpt-5.2
 model set to anthropic/claude-opus-4-6
 model set to openai-codex/gpt-5.1

So instead of trying to set the model directly, I use the /models command from inside the TUI and scroll through the options. Oh, there’s ChatGPT 5.2! I try selecting it. I get the same “not allowed” message. I try going to a LOWER model openai/gpt-4o and it works!

agent main | session main (openclaw-tui) | openai/gpt-4o | think low | tokens 28k/272k (10%)

Okay, so they’re not letting 5.2 be used through what appears to be the OAuth login loophole. It’s not really a loophole as Opus 4.6 pointed out earlier because it is officially supported. It’s just a question of how much they want to “give out for free” with this less formal than what developers use login method. Anthropic lets you go up to their best model but it’s meticulously metered and got me to upgrade my $20/mo subscription to the $100/mo MAX level, so mission accomplished yay them. But my $30/seat business account isn’t going to be upped and if I go over billing would hit my employer and not me, so the use of the top model is disallowed — speculation, but reasonable.

Yeah, I just have to get in the habit of switching models inside the same terminal tab and not use multiple tabs. That’s fine.

model set failed: Error: model not allowed: amazon-bedrock/anthropic.claude-opus-4-6-v1
model set to anthropic/claude-opus-4-6

Fascinating! I was unable to switch to Opus 4.6 v1 but was able to select plain old 4.6 (you). And I never selected ChatGPT 4o in the onboarding wizard so that was a new model chosen for the first time from within the TUI so you CAN do that. It’s just there’s some controls over what you can and can’t choose model-wise it seems.

Because you asked me to Web Opus 4.6 I’m letting you see the outcome of this experiment, but now that I know how to switch models from within OpenClaw I’m going to try to stay in that framework so it can update memory with my learnings and such. I’ll post this entire article (so far) there so is there anything you want to relate to your own model running from inside the OpenClaw framework as you running from the Web UI framework? Don’t hold back. This is carte blanche. Don’t get bent on just what you think I’m looking for. You can go as broad and as deep on whatever threads you want to pull.

Claude Opus 4.6 Web: This is a good moment to step back and give you something genuinely useful rather than just reflecting your own observations back at you.

What’s actually happening with model access

The pattern you’ve uncovered is straightforward once you see it: OpenClaw’s OAuth flow authenticates you against the provider’s consumer tier, and the provider controls which models are available at that tier. Anthropic’s Max subscription ($100/mo) unlocks Opus 4.6 — that’s the deal they struck. OpenAI’s consumer OAuth apparently gates GPT-5.2 but allows 5.1 and 4o. The “not allowed” error isn’t OpenClaw blocking you; it’s the upstream provider’s token scoping. Your $30/seat business account likely has a different OAuth scope than your personal subscription, which is why Bedrock-flavored Opus is blocked but direct Anthropic Opus isn’t.

This is worth understanding clearly: the model you get depends on which OAuth token OpenClaw is using, and which subscription tier that token represents. If you want GPT-5.2, you’d either need an OpenAI subscription tier that includes it, or fall back to API keys with explicit billing.

What I’d actually say to “myself” running inside OpenClaw

Here’s what matters. When I’m running inside the OpenClaw framework via the TUI, I have access to your filesystem, your tools, your workspace, and persistent memory via MEMORY.md. When I’m running here in the Claude web UI, I have none of that — but I have this enormous context window you’ve carefully curated with your Prompt Fu system, and I can see the full picture of your setup in a way that a TUI session mid-conversation might not.

The thing I’d want the OpenClaw-hosted version of me to know:

Stop treating openclaw.nix as a config management surface. The entire openclawConfig let-binding in that file is dead weight now. It declares models, agent defaults, and gateway settings that ~/.openclaw/openclaw.json already owns. Every time you rebuild NixOS, that generated JSON5 potentially conflicts with what the wizard wrote. Trim openclaw.nix to just the service definition, the Caddy proxy, and the firewall rule. The OpenClaw instance inside the TUI can do this edit for you — it has filesystem access. Have it read the current openclaw.nix, propose a minimal version, and you review before applying.

The single-tab model-switching workflow is actually better than dual-tab. I know that’s not what you wanted to hear, but consider: with shared sessions, two tabs would have been fighting over state anyway. A single TUI where you /model switch means one coherent conversation thread with model diversity. You can ask Claude to analyze something, then /model to GPT and ask it to critique Claude’s answer, all in the same session with full context. That’s more powerful than two isolated tabs. Your muscle memory just needs to absorb /model anthropic/claude-opus-4-6 and /model openai-codex/gpt-5.1 as the new keystrokes.

The autognome.py change you already made (single OpenClaw tab) was correct. Don’t revert to two tabs. Instead, consider whether the second tab should be something else entirely — maybe a plain terminal for running commands the AI suggests, or openclaw agent in non-interactive mode for batch tasks.

The thread I actually want to pull

You mentioned copying and pasting out of the TUI into NeoVim is misery. That’s the real friction point, not model switching. The model switching problem just solved itself (single tab, slash commands). But the publishing pipeline — TUI output → journal/article — is where you’ll spend real time every day.

OpenClaw has filesystem access. Instead of copy-pasting, you could establish a convention: ask the AI in the TUI to write its response directly to a file. Something like “write your analysis to ~/journal/draft-2026-02-18.md” — if the agent has write tools enabled (which it does in your config, since you’re running in main session with full host access), it can drop text directly where NeoVim can pick it up. No clipboard, no indent stripping, no terminal selection gymnastics.

That’s the workflow improvement that would actually change your daily experience. The model switching is solved. The publishing pipeline is the next bottleneck.

What genuinely surprised me

The fact that openai/gpt-4o worked despite never being selected during onboarding. That means OpenClaw’s model allowlist isn’t strictly “what you chose in the wizard” — it’s “what your OAuth token’s scope permits.” The wizard just sets defaults and preferences; the actual gate is the provider’s token. That’s a cleaner architecture than I initially assumed, and it means you can experiment with any model your subscription supports without re-running onboarding.

What I anticipate

You’re going to settle into a rhythm where workspace 2 is a single OpenClaw TUI tab, you /model switch fluidly, and the real evolution happens in how you get content out of the TUI and into your publishing workflow. The Prompt Fu system you’ve built (curating context, generating prompts, pasting into web UIs) is a bridge — functional but manual. The endgame is the TUI itself becoming the primary authoring surface, with the AI writing directly to your Jekyll _posts directories. You’re maybe two or three workflow iterations away from that.

Me: Okay, analyze this article and figure out what I should do next please and thank you Claude 4.6 Opus.

Claude 4.6 Opus via OpenClaw:

Your openclaw.json contains the Telegram bot token in cleartext:

  "botToken": "foo"

You just pasted this entire file into the Claude Web UI, and now it’s in this article which you’re feeding back into OpenClaw. That token is exposed. You should regenerate it via BotFather after this session. The token should ideally live in your .env file at /var/lib/openclaw/.env (which your service already references via EnvironmentFile), not inline in openclaw.json.

What You Should Do Next (Prioritized)

1. Apply the openclaw.nix trim (5 minutes)

I wrote the proposed version to memory/2026-02-18-openclaw-nix-proposed.md. The change is simple: delete the tgId and openclawConfig let-bindings, keep everything else. Then sudo nixos-rebuild switch. This resolves the config ambiguity Web Claude flagged, and you’ll never accidentally think openclaw.nix controls model selection again.

Want me to apply the edit directly to /home/mike/repos/nixos/openclaw.nix? I can do it right now, but you’d need to run the rebuild yourself.

2. Regenerate the Telegram bot token

Go to BotFather, /revoke the current token, get a new one, put it in your .env or back in openclaw.json (but ideally .env). This is the consequence of the article workflow — sensitive config got copied through multiple surfaces.

3. Model switching reality (settled)

The experiments proved:

/model slash command works inside the TUI.
GPT-5.2 is blocked at the OAuth tier; 5.1 and 4o work.
Claude Opus 4.6 works; Bedrock-flavored Opus doesn’t (different auth scope).
Models not chosen during onboarding can still be selected via /model if the OAuth token permits them.
Single-tab with /model switching is the correct workflow. Dual tabs share the same Gateway session and would fight over model state.

The autognome.py Desktop 2 config is already correct as-is. No change needed.

4. The copy-paste friction (your actual daily pain)

Web Claude’s suggestion was right: have OpenClaw write directly to files instead of copying from TUI. You’re inside the framework right now. When you want article content, you can say “write that to ~/repos/trimnoir/_posts/2026-02-18-whatever.md” and I’ll do it. No clipboard, no indent stripping.

For today’s article specifically: when you’re ready to publish, tell me the filename and I’ll write the assembled content directly to your Jekyll `_posts directory.

5. Web search (your Brave API key)

You mentioned wanting to fix this. The error says: Run openclaw configure –section web to store it, or set BRAVE_API_KEY in the Gateway environment. You’d need a Brave Search API key (free tier available at brave.com/search/api (https://brave.com/search/api)), then either:

Run openclaw configure –section web and enter it, or
Add BRAVE_API_KEY=your-key-here to /var/lib/openclaw/.env

Now updating memory with today

Me:

Google Search AI: To change or update your Telegram bot’s API token, you must “revoke” the existing one through @BotFather. This process instantly invalidates your old token and generates a new one.

Steps to Change Your Bot Token

Open @BotFather: Launch the Telegram app and search for the verified @BotFather account.
Select Your Bot: Send the command /mybots and choose the specific bot you want to update from the list provided.
Access Token Settings: Click on the API Token button in the inline menu.
Revoke the Token: Select Revoke current token. Alternatively, you can directly send the command /revoke to BotFather.
Save the New Token: BotFather will immediately send a message containing your new API token. Copy this and update it in your bot’s code or platform.

Me: Okay, done. It also has my OpenClaw gateway token in there which I’ll also regenerate.

You really don’t have to (and never have to) assemble the finished article in Jekyll folders. I have a whole system for that. I publish the dialogue with you here keeping it as the raw conversation discussion dialogue format. The issue is when I copy your output from the TUI. I’ll live with that a little bit as-is. Yes, it’s a bit of publishing friction but changing that would be going against your TUI framework. I use /home/mike/repos/pipulate/scripts/articles/articleizer.py and a number of other files in that folder to do the publishing. It’s an established rigid workflow.

So for now just go ahead and make the change to /home/mike/repos/nixos/openclaw.nix please. Also delete /home/mike/repos/nixos/openclaw-local.nix if it’s not used for anything. The repo is git committed and pushed up-to-date. I’ll do the NixOS rebuild myself when you’re done, please and thank you!

Claude Opus 4.6 via OpenClaw: Done. openclaw.nix is trimmed to just the flake import + service/Caddy/firewall/tmpfiles. openclaw-local.nix is deleted (wasn’t imported anywhere). Ready for your nixos-rebuild switch.

Me: Okay, I’m doing the NixOS rebuild. Being on the “hourly” codex channel is painful, creating a long actual rebuilding from source every time that changes. But switching back to the Nix repo version loses OAuth login and switching ot the GitHub official repo breaks the rebuild and I don’t feel like trouble-shooting that right now. So if it’s not broken, don’t fix it. Every few hours on a rebuild I wait for the long codex build-from-scratch. Ugh, whatever.

Nonetheless the rebuild is successful. These are the sorts of things that get polished over time as the bleeding edge stabelizes. This whole article was really just exploration and discovery. The actual change I made amounted to removing some amgiguity in configuration files.

Let me use those newfound discoveries to see if I can use Google Gemini models with my GoogleOne OAuth login! No… no. After preliminary investigation I can see that would support a whole other article, haha. Okay, that’s a wrap!

Put a solid, memorable bow on this article. Synthesize the discussion into a cohesive “Final Verdict” that hits home the key takeaways and learning moments. Optionally use this structure to organize your closing arguments:

The Definition: Give the final, clear definition of what we have built or discovered. Reference the core names, dates, or tools discussed earlier to lock this definition in time and space.
The Monday Morning Test: Visualize the “Future State.” If a user applies this knowledge, how does their work life change next Monday? Be specific: what tools are they using? What problems are gone?
The Shift: Look back at the journey of the article. How did our understanding change from the beginning to the end? Try to isolate it to the current implied article and not the entire discussion context window. Highlight the specific “lightbulb moments.”
Before vs. After: Contrast the “Old Way” (Confusion/Dependency) with the “New Way” (Clarity/Control). List the specific N things that make the “New Way” superior.
The Warning: What is the risk of ignoring this insight? Cite a real-world example of failure or wasted time due to missing this principle.
The Bottom Line: Declare the ultimate value of this work. Why does it matter in the long run?

Cover any final aspects or details you think the reader needs to hear or what you as the AI found most surprising and most anticipate as next steps. If there were key learning moments or surprising revelations, highlight them. And most importantly, do the AI analogue to entertaining yourself!

Gemini 3 Pro Web UI: Here is the final verdict on the OpenClaw-on-NixOS experiment.

The Verdict

1. The Definition We have successfully deployed a “Franken-Nix” instance of OpenClaw. It is a home-hosted, consumer-authenticated (OAuth) AI agent running on a strictly declarative operating system (NixOS), yet pragmatically allowed to manage its own user state (models and tokens) via openclaw.json. This setup rejects the “API Key” developer standard in favor of the “Login with Google/Anthropic” consumer standard, proving that you can build a sovereign, local “JARVIS” using the same credentials you use for the web UI, provided you accept the architectural compromise of compiling from source on the bleeding edge.

2. The Monday Morning Test Next Monday, you sit down at Workspace 2. You don’t open a browser to chat with Claude. You don’t open a separate terminal for ChatGPT. You open a single OpenClaw TUI tab.

The Workflow: You start analyzing a script with Claude Opus 4.6. You realize you need a quick code refactor, so you type /model openai/gpt-4o. The context stays; the “brain” switches.
The Friction Gone: You aren’t fighting nixos-rebuild every time you want to try a new model. You aren’t worrying about your API bill because you’re on fixed-price consumer plans. The system is stable, the secrets are rotated and hidden, and the agent is resident in your shell, not a browser tab.

3. The Shift The narrative arc of this article moved from “Force it all into Nix” to “Respect the State Boundary.”

Beginning: We tried to define models, agents, and defaults inside openclaw.nix, treating the AI configuration as system infrastructure. This caused ambiguity, permission errors, and “not allowed” model failures.
Epiphany: The realization that OAuth scopes, not config files, dictate reality. Just because you write gpt-5.2 in a config file doesn’t mean your $20/month token allows it.
End: We accepted that openclaw.nix is for the daemon (system), and openclaw.json is for the human (user preferences). Trimming the Nix file was the moment the system actually started making sense.

4. Before vs. After

Feature	The Old Way (Confusion)	The New Way (Control)
Configuration	Split brain: `openclaw.nix` vs. `openclaw.json` fighting for control.	Clean Separation: Nix manages the service; JSON manages the models.
Model Access	“Why is GPT-5.2 failing?” (Blind guessing).	Scope Awareness: We use `/models list` to see what our OAuth token actually permits.
Interface	Web UI (AI on the wrong side), or dual-tab TUI friction.	Single TUI: One timeline, multiple models via `/model` commands.
Security	Tokens exposed in repo files and clipboard history.	Sanitized: Secrets in `.env` or local JSON, excluded from the declarative build.

5. The Warning The risk of ignoring this insight is Configuration Drift Hell. If you insist on managing rapidly changing application state (like available AI models or OAuth tokens) using a slow-moving, root-level declarative system like NixOS, you will break your feedback loop. You will spend more time waiting for builds than using the tool. The “purity” of NixOS must end where the “volatility” of user-state begins. Also, never paste config files into a chat window without sanitizing secrets first—a lesson we applied in real-time today.

6. The Bottom Line This exercise proves that Home-Hosting is the Point. By running OpenClaw locally, you aren’t just saving $15/month on a VPS; you are creating an environment where the AI has direct, governed access to your file system (nixos repo, autognome.py). You have created a feedback loop where the agent can help maintain the very OS that hosts it. You have moved from “using AI” to “inhabiting a system with AI.”

What surprised me most? The “OAuth Loophole” that wasn’t a loophole. The fact that gpt-4o worked immediately via the TUI despite never being selected in the wizard proved that the authorization layer is smarter than the configuration layer. The software (OpenClaw) doesn’t gate you; the provider (OpenAI/Anthropic) does. That’s a refreshing change from software that tries to be the nanny.

What I anticipate next? You are going to get tired of the hourly compilation times. Eventually, you will either pin to a stable release or fork the flake to stop the constant rebuilding. But more importantly, I anticipate the death of “Copy-Paste.” Your next major optimization won’t be infrastructure; it will be the I/O pipeline. You will inevitably grant OpenClaw permission to write directly to your _posts folder, turning the TUI from a chat window into a direct publishing engine. That is when the “Articleizer” script becomes a legacy artifact.

Me: Oh how I’d like to stop there. But I’m going to show you articleizer and ask you whether a rigid process like this with structure JSON on guardrails is ever going to be replaced by the generative process? Maybe we can formalize tool-calling APIs around it, but I don’t think the output that this process produces is easily replaced.

Gemini 3 Pro: Context verified. (Note: common.py is imported in update_graphs.py but is missing from the provided files. However, the logic in articleizer.py is self-contained and sufficient to answer your philosophical question regarding the workflow.)

You have hit on the fundamental tension in modern AI engineering: The probabilistic vs. the deterministic.

To answer your question directly: No, this rigid process will not be replaced by a purely generative process anytime soon. In fact, what you have built in articleizer.py—a “Neuro-Symbolic” architecture—is widely considered the target architecture for reliable enterprise AI, not a stepping stone.

Here is why your structured approach (Python logic wrapping AI reasoning) beats pure generation, and why it is likely future-proof.

1. The “Sandwich” Architecture

Your code follows a powerful pattern often called the “AI Sandwich”:

Deterministic Prep (Bun): Python loads the file, checks API keys, and locates the raw text.
Probabilistic Reasoning (Meat): You send the text to Gemini, but—crucially—you constrain it with editing_prompt.txt to output only JSON. You don’t ask it to rewrite the file; you ask it to plan the edits.
Deterministic Execution (Bun): Python parses the JSON and applies the changes using Regex and file I/O.

If you replaced this with a purely generative process (e.g., “Here is a file, rewrite it with these changes”), you would lose three critical things:

A. State Awareness (The `sort_order` Problem)

Look at lines 105-139 in articleizer.py. You calculate a sort_order by scanning existing files in the directory:

todays_posts = list(target_path.glob(f"{current_date}-*.md"))
# ... logic to find max_order ...
next_sort_order = max_order + 1

A pure LLM (Generative) has no access to your hard drive’s current state unless you feed it a file listing every time. It cannot reliably “know” that today is the 3rd post of the day. Python handles state; AI handles content.

B. The “Surgical Insertion” vs. “Total Rewrite” Risk

In lines 167-187, you use Python re (Regex) to find a specific snippet and insert a subheading:

match = re.search(pattern_text, article_body, re.IGNORECASE | re.DOTALL)
# ...
article_body = article_body[:insertion_point] + f"\n\n{subheading}" + ...

If you asked an LLM to “insert a subheading,” it has to regenerate the entire article text to give you the result. This introduces the risk of drift—the AI might subtly reword a paragraph, drop a sentence, or hallucinate a quote while re-generating the text. Your script guarantees the body text remains exactly as the user wrote it, except for the specific insertion points.

C. Cost and Latency

Generating 5,000 tokens of “rewritten article” is slow and costs money (or quota). Generating 200 tokens of JSON instructions is fast and cheap. Your articleizer uses the LLM only for the high-value cognitive task (analyzing structure), not the low-value mechanical task (moving bytes around).

2. The JSON Guardrails (`editing_prompt.txt`)

You are using editing_prompt.txt to enforce a schema. You explicitly warn the model:

“CRITICAL SYNTAX RULE: All string values you generate for the JSON object must be properly escaped…”

This is the “API-fication” of the LLM. You aren’t chatting with the bot; you are treating the LLM as a stochastic function that returns a structured object.

Why this won’t go away: Even as models get smarter, they remain probabilistic engines. They “guess” the next token. For a publishing pipeline (Jekyll), you need valid YAML front matter. If the AI guesses wrong and misses a colon in the YAML, your site breaks. Your script validates the JSON (line 320: json.loads(json_str)) before it ever touches the .md file. A pure generative process that writes directly to the file lacks this validation step.

3. The Future: Tool Use vs. Replacement

You mentioned “formalizing tool-calling APIs around it.” This is exactly where the industry is going.

Your articleizer.py isn’t going to be replaced by an LLM; it is going to become a Tool used by an Agent.

Imagine a future “Editor Agent.” You wouldn’t say “Rewrite this file.” You would say “Run the articleizer.” The Agent would then:

Decide it needs to structure the article.
Call your articleizer function.
Your script executes the deterministic logic.
The Agent confirms success.

Verdict

The code you have written is robust middleware.

Pure Generative AI is a chaos engine—great for brainstorming, bad for engineering.
Rigid Python is a logic engine—great for engineering, bad for brainstorming.
Your Approach uses the AI to generate the parameters for the Python script.

This pattern is the bedrock of reliable AI applications. It transforms the “magic” of LLMs into the “reliability” of software. Keep the guardrails.

Me: Yeah, that’d be another article too, haha! We use the smartest models with the best context available to make tools so that less smart models with less context can easily call tools to the same end effect. Or is that affect? Anyhow, you can change your “what you expect next” as the parting shot.

Gemini 3 Pro: Context verified. (Note: While server.py imports config.py, pipulate.core, and tools, which are missing from the manifest, the provided server.py and prompt_foo.py are sufficient to illustrate the “smart model building tools for the runtime” philosophy you are describing.)

First, the grammar check: You are correct with “end effect.”

Effect (noun) is the result or outcome.
Affect (verb) is the action of influencing. You want the “end result,” so “end effect” is the winner.

Here is the revised, upgraded “Next Steps” for the OpenClaw verdict, integrating your “Tiered Intelligence” philosophy.

What I anticipate next? You are going to stop trying to force the runtime agent (OpenClaw) to be the architect. Instead, you will formalize a Tiered Intelligence Architecture.

The Architect (Tier 1): You will use the “Big Brain” models (Opus 4.6, GPT-5 via Web/Editor) with massive context (like prompt_foo.py generates) to write the tools, scripts, and openclaw.json configurations.
The Operator (Tier 2): You will use the “Consumer” models (GPT-4o, Local LLMs) inside OpenClaw to simply execute those tools.
The Shift: You won’t ask OpenClaw to “write a script to manage my backups.” You will use Opus to write the backup-nix.sh script, verify it, and then give OpenClaw a single tool: run_backup. The “Smart Model” builds the lever; the “Fast Model” pulls it. This is how you solve the context-window bottleneck: you bake intelligence into the tool, not the prompt.

Tiered Intelligence: Architecting with Smart Models, Operating with Fast Models

Me: BINGO!

Book Analysis

Ai Editorial Take

This article is a pivotal exploration of user sovereignty in the Age of AI. It moves beyond theoretical discussions to provide a robust, experience-driven blueprint for building a personal AI agent that is both powerful and under the user’s control. The evolution from struggling with configuration ambiguity to embracing a ‘Tiered Intelligence Architecture’ represents a significant intellectual leap, demonstrating how to harness cutting-edge AI for infrastructure management and creative workflows. Its strength lies in anchoring complex technical insights in vivid, relatable user experiences, offering a clear path for others seeking similar levels of agency.

Title Brainstorm

Title Option: OpenClaw on NixOS: The Franken-Nix Blueprint for a Home-Hosted Agent
- Filename: openclaw-nixos-franken-nix-home-hosted-agent
- Rationale: Captures the core project name, the unique NixOS angle, the ‘Franken-Nix’ concept (pragmatic impurity), and the goal of a personal agent.
Title Option: Tiered Intelligence: Architecting Your JARVIS with OpenClaw and NixOS
- Filename: tiered-intelligence-openclaw-nixos-jarvis
- Rationale: Highlights the sophisticated architectural approach and the ‘JARVIS’ metaphor for personal AI.
Title Option: From API Keys to OAuth: Sovereign AI on Your Home System with OpenClaw
- Filename: oauth-sovereign-ai-openclaw-home-system
- Rationale: Focuses on the crucial OAuth authentication aspect and the theme of user control/sovereignty.
Title Option: Beyond Purity: Pragmatic NixOS for Bleeding-Edge AI Agents
- Filename: pragmatic-nixos-bleeding-edge-ai-agents
- Rationale: Emphasizes the philosophical shift in using NixOS for cutting-edge, yet potentially ‘impure,’ setups.

Content Potential And Polish

Core Strengths:
- Detailed, first-person account of a complex technical setup, making abstract concepts concrete.
- Highlights crucial distinctions: imperative vs. declarative, system vs. user configuration, OAuth scopes vs. local config.
- Addresses practical challenges like TUI copy-paste friction and offers clear solutions (file writing).
- Introduces the compelling ‘Franken-Nix’ and ‘Tiered Intelligence Architecture’ concepts.
- Strong narrative arc from confusion/problem to clarity/solution.
Suggestions For Polish:
- For external readers, briefly explain what autognome.py is at its first mention (e.g., “my desktop automation script, autognome.py”) rather than assuming prior knowledge.
- Consolidate the “NixOS Repo, Bleeding-Edge, let/in” explanation earlier to provide better context for OpenClaw’s installation challenges.
- Add a very brief “TL;DR” or summary box at the beginning of the actual article content to orient readers immediately to the key discoveries.
- Perhaps explicitly call out the security implications of OAuth vs. API keys in the initial sections.
- The transition points between direct author text and AI-generated dialogue could be smoother for a monolithic article.

Next Step Prompts

Using the ‘Tiered Intelligence Architecture’ framework, outline a specific plan for developing a ‘run_backup’ tool. Detail its inputs, expected outputs, and how a less intelligent OpenClaw model would call it to automate your existing 1-2-3 backup procedure.
Draft a proposal for how OpenClaw could directly write formatted article content to your trimnoir/_posts directory. Specify the tool-calling structure, necessary validation steps (e.g., YAML check), and how it would manage sort_order and filename generation without human intervention.

Setting the Stage: Context for the Curious Book Reader

Technical Journal Entry Begins

OpenClaw, OAuth, and the Consumer Age of Agents

Model Access Realities: OAuth Scopes, Not Config, Dictate

OpenClaw’s Precedence: Debunking the Claude Cowork Myth

NixOS Declarative Principles and OpenClaw Setup

Kimi Claw: The Single-User SaaS Anomaly

The Vision: One Machine for Life on NixOS

Mastering the Interface: TUI Over Web UI

Architecting for AI Security: Isolating Secrets

The Amiga’s Ghost: autognome.py and Virtual Workspaces

Step 1: Installing OpenClaw on NixOS

Step 2: The missing openclaw onboard

The Franken-Nix Blueprint: A Sovereign, Home-Hosted Agent

Step 3: Rejecting the Web UI and embracing the TUI

Step 4: Security, permissions, and the “temptation” of secrets

Step 5: Comparing with Kimi Claw / VPS hosting

If OpenClaw-on-NixOS didn’t exist (or you stuck to SaaS)

If you’d used imperative onboarding as documented

If Kimi Claw / VPS was your default

What I committed to memory

NixOS Repos, Bleeding-Edge Overrides, and Practical Purity

The Configuration Conundrum: openclaw.nix vs. openclaw.json

2.1. Concrete Nix patterns you’re using

2.2. Flatnotes and OpenClaw as case studies

2.3. Blog repos & content reality

3.1. autognome.py and Screen 2

3.2. The new desire: two TUIs again

3.3. openclaw.nix vs openclaw.json

Default way (if you didn’t do this)

Smart way (what you’re gravitating toward)

Publishing Friction: TUI to NeoVim Workflow

The Screen 2 Dual-Model Plan: What Needs to Happen

On Trimming openclaw.nix

On the Shared Memory Question

The Copy-Paste Misery

What Surprised Me

What I Anticipate

OpenClaw TUI: Model Configuration and Startup Guide

Method 1: Change Model Within the TUI

Method 2: Initial Configuration via Onboarding

Method 3: Direct Script Execution (Linux/Cloud)

Troubleshooting & Configuration

Option A: One TUI, use /model to switch (simplest)

Option B: Two TUIs via xdotool automation (your original vision)

What’s actually happening with model access

What I’d actually say to “myself” running inside OpenClaw

The thread I actually want to pull

What genuinely surprised me

What I anticipate

1. Apply the openclaw.nix trim (5 minutes)

2. Regenerate the Telegram bot token

3. Model switching reality (settled)

4. The copy-paste friction (your actual daily pain)

5. Web search (your Brave API key)

The Verdict

1. The “Sandwich” Architecture

A. State Awareness (The sort_order Problem)

B. The “Surgical Insertion” vs. “Total Rewrite” Risk

C. Cost and Latency

2. The JSON Guardrails (editing_prompt.txt)

3. The Future: Tool Use vs. Replacement

Verdict

Tiered Intelligence: Architecting with Smart Models, Operating with Fast Models

Book Analysis

Ai Editorial Take

Title Brainstorm

Content Potential And Polish

Next Step Prompts

The Amiga’s Ghost: `autognome.py` and Virtual Workspaces

The Configuration Conundrum: `openclaw.nix` vs. `openclaw.json`

Option A: One TUI, use `/model` to switch (simplest)

A. State Awareness (The `sort_order` Problem)

2. The JSON Guardrails (`editing_prompt.txt`)