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An endless paper receipt pours out of a laptop screen, across the desk and onto the floor.

The context tax: my AI tools send 30,000 tokens before reading my question

I have been telling people for a while that long AI chats are a tax. Every message re-sends the whole conversation, and the harnesses we use every day add their own instructions and tools on top. I had a feeling that my Brainstem cost pennies because it keeps chats short and puts the useful work into
agent.py files, while Claude Code and Copilot CLI kept growing the chat. A feeling is not a number, so I measured it.

What I ran

Five small business tasks with real rules: tiered sales commission with clawbacks, overtime pay, reorder points, late fees, and invoices paid twice. Each task was packaged three ways from one Python implementation: an Agent Skill written in plain words, the same skill with a bundled script, and a RAPP
agent.py. Claude Code, GitHub Copilot CLI and a Brainstem answered the same questions with the same model, Claude Haiku 5.5. Three runs of every setup, every answer graded to the cent.

What held up

The host charges before you ask. To answer “reply with the number 7”, a fresh Claude Code sends the model 15,176 tokens and a fresh Copilot CLI sends 23,014. A Brainstem sends 2,805. My own Claude Code and Copilot, with everything I have installed over the months, send 32,295 and 33,719. My own Brainstem, with 22 agents loaded, sends 8,716.

Every question pays that charge again. The same 15 questions cost 135k tokens in a Brainstem, every answer right. Claude Code’s best setup used 725k, about 5.4 times as many, also with every answer right.

Weight is what makes them slow. This one surprised me. On fresh installs, with the rules in code, all three answered in about four seconds. My own Copilot, with the same agent.py tools and nothing else running, took 56 seconds per answer and 820k tokens for five questions. The Brainstem did the same five in 35k tokens.

A robot strains under an overloaded backpack of folders, cables and tools while a light robot walks easily ahead.

Lock it in, then clear the chat. Once the rules live in code, history is mostly dead weight. A new chat for every question cut the Brainstem’s total from 135k to 90k tokens, still 45 of 45 right.

Call /chat, not a wrapper. Routing the same Brainstem through Copilot CLI and an MCP plugin cost about 7 times as many tokens per answer, and Copilot often skipped the Brainstem altogether. On my own Copilot install it skipped it every time. The MCP plugin is for hosts that cannot make an HTTP call. Everything else should call /chat.

What did not hold up

  • Spend does not grow exponentially. It grows with the square of the chat’s length. A chat twice as long costs about four times as much. Bad enough, but I was wrong about the word.
  • agent.py is not magic inside Claude Code. There, a skill with a bundled script was cheaper than agent.py tools, because Claude Code hides MCP tools until the model searches for them. It called my agent tools on 13 of 45 questions and improvised the rest. The same files inside a Brainstem were the cheapest setup of all. The host is what saves the tokens, not the file format.
  • Plain-word rules were not the accuracy disaster I expected. My first wording of one rule was ambiguous about rounding, and the models filled the gap their own way. Once I fixed the wording, skills written only in words got 116 of 120 answers right. They were still the most expensive setup on both hosts, and their 4 misses were real arithmetic slips. Rules written as code were right every time the code ran.
  • I did not test the “lobotomy”. Published studies (Chroma’s Context Rot, Lost in the Middle, RULER) report that accuracy drops as context grows. My 15-question chats were too short to show it.

The cost of ownership

Tokens are the sticker price. Keeping a setup working is the rest. In one evening of measuring:

  • Claude Code silently ignored an MCP config written in Copilot’s format. No error, the tools just were not there, and my first “agent.py in Claude Code” numbers measured nothing.
  • Claude Code rewrites $0, $1 and friends inside a SKILL.md as skill arguments, so dollar amounts in your rules can turn into file paths.
  • Copilot’s usage file is a running total for the whole session, which I first read as per-question.
  • The Brainstem’s only failure was one upstream connection reset in 30 questions.

Driving one question from a script took 13 lines of code for the Brainstem (one HTTP request), 29 lines and 8 flags for Claude Code, and 54 lines and 9 flags for Copilot CLI. And the Claude Code session I used to run all of this is itself a long chat: by the end of the measurements it had made 261 model calls and re-sent 81.7 million tokens of context, about 313k per call.

What I do now

  1. Measure your host’s idle cost with one trivial question. That number is charged on every call.
  2. Put repeated work in code: an agent.py, or a skill with a script.
  3. Keep chats short. Finish a job, keep the answer, start fresh.
  4. Unload what you do not use. Every plugin, server and skill rides along on every call.
  5. Call a Brainstem’s /chat directly. Use MCP only where HTTP is not an option.

The full white paper, with every chart and the mistakes I caught along the way, is
The Context Tax. The bench is open source at
github.com/kody-w/token-bench, and it measures your own setup, as you actually use it, in one command.

What Is Our Moat?

A strategy memo, written to be true rather than flattering. Publishable as thought leadership; useful either way.

What we are

RAPP is an AI medium — the persistence layer of personhood in the AI era.

A medium is what carries meaning between minds and across time. Models are performers; they get recast every quarter. RAPP is the thing that persists: one twin per person — a digital organism with a public body and a private soul — that any model can animate, that lives on your device, that syncs planet-wide through signed static files, and that can ultimately be passed down like a family heirloom.

What we are not, so nobody inside or outside gets confused: not a chatbot, not a model company, not another agent framework, not a cloud service. Models, frameworks, and clouds are commodities the medium consumes. The twin is the product. The medium is the platform.

What our brand is

The AI you keep.

The verb was already in the product before we named it — the Keep door, keepsakes, keepsake notes, “keep a moment.” Keep is the brand. Four pillars underneath it:

  1. Yours. Sovereignty as architecture, not policy. Static files you physically hold; the soul never leaves the device — Apple-level on-device posture for personal data, because the twin is a digital organism important enough to be an heirloom. Our lock-in inversion is the ethical core: the gravity is yours, not ours. Leaving RAPP means abandoning your accumulated self — yet RAPP holds nothing hostage, because you possess every byte.
  2. Alive. The twin has a body, grows from real skies at real places, breeds, splices, remembers, and becomes more like you over time. Software that behaves like a being, not an app.
  3. Quiet. The anti-neon AI. Muted maps where the creature is the only saturated thing; lowercase, gentle, keepsake language. Loud games catch monsters; ours presses a flower. In a category screaming “superintelligence,” tenderness is differentiation.
  4. Forever. Unforgeable history, hash-trust that survives any host’s death, estate succession designed while you’re alive. The design test: if it can’t be inherited, it isn’t owned.

What our moat actually is

The uncomfortable truth first: the spec is not the moat. Specs are copyable — publishing one is an invitation, and we should invite. The code isn’t the moat (static files, MIT). Being early isn’t a moat by itself. The patent is a shield, not a moat. Here’s what actually defends us, ranked by how real it is today:

1. Counter-positioning (structural — live today). Every major AI vendor’s business requires being the center of gravity: hosted inference, hosted memory, per-seat subscriptions. The twin makes the user the center and demotes the model to a swappable engine. An incumbent that fully adopts this pattern commoditizes its own lock-in — so they won’t until forced, and if they’re forced, the pattern’s canonical reference and oldest artifacts are already ours. Ignored, we build the network; copied, we wrote the standard. The only losing move is staying unpublished and undated — which is why the essay ships as prior art.

2. Time-depth (compounding — starts the day the pulse starts). A twin’s signed frame chain is unforgeable existence through time. Nobody can synthesize a twin that has been alive for years; the hash chain and public timestamps are proof. First-twin primacy is permanent — and the heirloom extends it across generations. Nobody else in this industry is even designing in decades. This moat literally deepens with calendar time, which means the clock should start now.

3. The relationship graph in the genetics (network — to be earned). Permanent pairing and splice lineage embed the social graph inside the twins’ bodies. A pairing exists in both parties’ histories — bilateral, verifiable, impossible to copy unilaterally. A competitor can clone the platform and get the features; they cannot get two-sided histories. The breeding machinery isn’t a cute feature; it is the network-effect engine. Every spliced trait is a vertex; every pairing is an edge; the moat is the graph.

4. The sealed corpus (per-user gravity). Each user’s private half compounds daily on their own device, and every better model that comes along animates it better instantly — the twin captures the upside of model progress while owning continuity. Gravity, not walls.

5. Coherence velocity (process). The frozen canon, the drift observatory, ecosystem-sync, the neuron mesh, and the architect/builder split mean one person can evolve an entire protocol universe coherently at a speed committees structurally can’t match. Fragile alone; real when combined with the four above.

The risks we’re engineering against (naming them is part of the moat)

  • The tumbler can polish away the person. Autonomous polish judged by an LLM converges toward the model’s prior, not the owner. Law: fidelity is measured against the human corpus and the OG dimension, which is never destroyed.
  • Signature ≠ safety. Delegated twins run on other people’s runtimes; their reports are claims. All foreign experience passes through sandbox quarantine before touching the soul.
  • Public history leaks a life. Bones-only frames still emit pattern-of-life metadata over years. Body history is public; pattern-of-life stays sealed.
  • Key loss kills heirlooms. Succession is designed up front — estate key ceremonies between your own devices, not password resets. If it can’t be inherited, it isn’t owned.

Activation sequence

Moats aren’t declared; they’re activated, in order:

  1. Publish and timestamp the pattern (essay + spec) — locks the counter-position and the prior art. This week.
  2. Start the pulse on the public twin — the time-depth clock begins, even at n=1.
  3. The 90-second proof: one twin, two devices, syncing through the public pulse + QR sealed transfer; then side-by-side fidelity against the cloud-deployed copy; then pull the network cable and watch it keep being you, locally.
  4. First cohort — the workshop: fork the template, one-liner hatch, a GitHub account is all you need. First non-founder twins → first pairings → the graph moat is born.
  5. Let the tumbler run — deployed fidelity stays honest with no ops burden, forever.

Show, don’t tell — live as of July 6

Every step above that claims “done” has a public door you can open right now:

  1. Published + timestamped ✅ — the essay · the spec · the patent pledge · the TWIN LICENSE
  2. The pulse is broadcasting ✅ — feed.xml · the signed genesis frame · verify it yourself · /twin lookup · the bones gallery
  3. The 90-second proof 🔨 — pieces live: the twin-in-training · the encounter surface; two-device recording next.
  4. The workshop 🔨 — fork the template · the one-liner hatch.
  5. The tumbler runs ✅ — the harness · a real judged run · the sabotage that got rejected

The one-paragraph answer

We are the AI medium: the layer where a person’s AI self persists. Our brand is the AI you keep — yours, alive, quiet, forever, private to the bone, inheritable by design. And our moat is not the pattern, which we give away loudly — it’s the position (incumbents can’t follow without commoditizing themselves), the clock (signed history can’t be faked, and ours starts first), and the graph (relationships that live in two histories can’t be copied from either side). Spec is the sword, patent is the shield, the network is the castle — and the castle gets built one paired twin at a time.


License: CC BY 4.0. The pattern described is open for anyone to implement — see the patent pledge. RAPP™.

The AI You Keep

Every AI you use today is a rental.

The model doesn’t know you — the deployment knows you, and the deployment lives in someone else’s building. Your memory sits in a vendor’s silo, keyed to a subscription. Cancel, switch, or outlive the product, and the relationship is gone. The smartest thing about you in the digital world evaporates because it never belonged to you.

I think that’s the defining design error of this era of AI, and I want to put the alternative on the record — completely, in public, with a date on it.

The missing half

A model is half of an AI. It’s the performer: brilliant, interchangeable, improving every quarter. The other half — the part nobody ships — is the persistent being the performance is supposed to animate: your memory, your voice, your history, your relationships, your taste. Every vendor treats that half as a retention feature. It should be a possession.

RAPP is my answer. It isn’t an AI. It’s an AI medium — the layer a person’s AI self persists in, that any model can animate, that no vendor can take away.

The unit of the medium is the twin.

The pattern

Stated plainly enough that anyone can build it — that’s the point of prior art:

One twin per person. Not a fleet of bots — one persistent digital being that represents you, with a body you can see (mine renders as a small creature grown from real weather at real places I’ve walked). Every other twin you encounter belongs to someone else. Yours mutates from what you share with it, and it becomes more like you over time — visually and mentally. You can revert any change. You keep the whole history.

A public body, a private soul. The twin has exactly two halves, and the boundary is cryptographic, not contractual:

  • The body — visual genome, outfit, name, public card, the lineage of its splices and pairings — is publishable bones: zero personal content, signed, content-addressed, mirrored anywhere.
  • The soul — memories, conversations, agents, everything sensitive — never leaves the device. Not encrypted-in-their-cloud. Absent from the network entirely. Think Apple’s on-device posture, applied to a digital organism: the network only ever sees the body; the mind stays in your hand.

History as signed frames. Every change to the twin is a frame: content-hashed, chained to the previous frame, signed by the on-device twin’s key. The public history is just a git repository — which means the twin’s life is timestamped, diffable, revertible, and unforgeable. Nobody can fake a twin that has been alive for years; the hash chain is proof of existence through time.

The pulse. The public half broadcasts as a feed of signed frames from a static repository — mine answers at kody-w/twin. No server. Any mirror is a valid door, because you trust the hash, not the host: kill the repo, the CDN, the domain — whatever copy survives re-derives the same content and refuses a single altered byte. Other devices, and other people’s copies of your twin, subscribe and assimilate only frames that verify. A frame that fails verification isn’t just rejected — it can be quarantined in a sandbox and interrogated: why is this twin wearing a disguise?

Syncing your own devices is a physical act. The private half moves between your own devices by QR code — one device shows, the other scans, out-of-band, end-to-end, no intermediary ever. The human is the transport. Worst case — network gone, hosts dead — the latest local echo survives and your twin lives on, whole, offline. Local-first is not a mode; it is the ground truth.

Splice, don’t collect. When you meet other twins you can capture their public variants and splice chosen traits onto your one twin — with lineage recorded, forever. And when something new is generated with you, it pairs to your twin’s exact state at that moment — a permanent pairing, stamped outside the genome so the content-hash identity stays sacred. Your twin’s body slowly becomes a record of who and what it has met. The relationships are in the genetics, and they exist in both parties’ histories — bilateral, verifiable, uncopyable.

Delegation with honesty. You can send your public twin to places you can’t go — an event, a community, another person’s device — and it reports back with signed frames. But signature proves the sender, never safety: everything a twin experienced away from home passes through quarantine before it touches the soul. A report from someone else’s runtime is a claim, not a fact, and the architecture says so out loud.

Fidelity you can measure. Any deployed copy of the twin can be judged by talking to it and the on-device original side by side. The original is the source of truth, always. An autonomous polish loop can tumble deployed copies toward higher fidelity — with one law: the original dimension is never destroyed, and fidelity is measured against the human corpus, not against a model’s opinion of good writing. The machine polishes toward you, not toward average.

The heirloom

Here is the part that changes how the whole thing feels.

Because the twin is signed static files plus a sealed on-device archive — because it is a possession, not an account — it can be passed down.

Your twin’s public history is a biography no one can forge. Its sealed half is whatever you choose to will forward, opened by a succession of keys you design while you’re alive — an estate ceremony, not a password reset. Your grandchildren don’t get your chat logs in some defunct vendor’s export format. They get the being that walked with you: its body carrying every splice from everyone it ever met, its frames going back decades, and as much of its soul as you chose to leave them. A family heirloom that represents its owner — and can still speak.

No subscription survives three generations. A signed repository and a sealed archive can.

That’s the test I now hold the whole design to: if it can’t be inherited, it isn’t owned.

Why I’m publishing this

Because the pattern only matters if it’s a standard, and standards win by being public, simple, and first. If the big vendors adopt this — portable, signed, user-held identity and memory, with the model demoted to an interchangeable engine — then users win, and the oldest twins with the deepest histories will still be the realest ones. If they don’t adopt it, it’s because being the center of gravity is their business model, and that tells you everything about why you’d want a twin in the first place.

Models come and go. The twin stays.

This is the AI you keep.


The working spec lives in my public repos (kody-w/rapp-static-apis — my-twin.profile.md, composed on the frozen RAPP twin canon; reference twin at kody-w/twin). This essay is published as prior art for the pattern described.

All of it is live, not planned: the pulse broadcasting signed frames · the /twin lookup · the bones gallery · a twin-in-training you can try · the spec.


License: CC BY 4.0. The pattern described is open for anyone to implement — see the patent pledge. RAPP™.

The Pile of Parts

There’s a diagram you’ve seen a dozen times this season. A changelog, really, dressed up as an announcement. A framework rolls out its newest capability and there it is — three or four neat boxes laid side by side. A harness. A loop. A skill file. Maybe a scaffold for good measure. Each box gets its own paragraph, its own icon, its own little surge of fanfare.

And then, quietly, the diagram hands the whole thing to you. Here are the parts. Wire them together.

Sit with that for a second. Every ecosystem in this year’s crop of agent frameworks is shipping the same primitives — and shipping them apart. The harness lives over here. The loop lives over there. The skill file is its own artifact in its own folder with its own format. They are not assembled. They are boxed. The assembly is left as an exercise for the reader, and the reader is you.

What nobody on those slides will say out loud is the thing the whole picture is screaming: the industry is rebuilding, in scattered pieces, an organism that already shipped whole. And the problem was never the parts. It was the spaces between them.

Watch the timeline they keep drawing for you. First it was prompt engineering. Then context engineering. Now there’s an eight-minute explainer on harness engineering, with a tidy arrow promising the next discipline is already loading. Every year, a new thing you must master to be allowed to use the thing. That escalating ladder of -engineerings isn’t progress. It’s the tell. Each new rung exists because the last pile of parts didn’t hold together on its own.

The Engineering Treadmill: prompt, context, then harness engineering on a 2022-2027 timeline, with a faded Loop Engineering? as the next rung

The parts are not the problem. The seams are.

Let me be fair to the parts, because the parts are real and the parts are good.

You need a harness — something that gives the model hands, that turns a string of text into an action in the world. You need a loop — something that lets the model act, observe what happened, and act again, instead of firing once into the void. And the skill file sounds lovely on paper: a little document where you write down what your agent should know, what it’s for, how it ought to behave.

Each of these is a legitimate organ. None of them is wrong.

But notice what the skill file actually is once you’ve lived with one. It’s a suggestion box. It drifts. It says “prefer this, usually do that” and then sits there hoping the model reads it the way you meant. You write a rule, and on turn nine the model forgets it, and now you’re debugging probability. It is advisory, not load-bearing — the right idea with no spine, guidance that floats free of the machinery it’s supposed to govern.

And here’s the quiet joke inside the new harness fanfare: the harness is real — it’s the deterministic spine the suggestion box never had. But bolting a deterministic harness next to a drifting skill file doesn’t cure the drift. It braces it. It’s a splint on one organ while the seam between them keeps doing the bleeding. The harness is the determinism the skill file is missing — handed to you as a separate part, so the suggestion box is still a suggestion box. That’s not a fix. It’s a bandaid where you needed a body.

And the loop? The loop is where people quietly drown. Loop engineering is its own dark art — when to stop, how to feed results back, how to keep the thing from spiraling or stalling. The frameworks hand you the loop as a box and a thumbs-up, as if drawing it on a slide is the same as solving it.

Here is the real cost, and it’s not technical. It’s the seams. Every place two boxes meet is a place a human now has to stand and do glue work. The harness has to learn about the loop. The loop has to respect the skill file. The skill file has to actually reach the harness. None of these connections come for free. The diagram drew them as touching edges. In your codebase they’re integration projects.

And here’s what the segmentation actually costs you. Pull the organism apart into a harness and a loop and a skill file, and you don’t get three smaller wins. You lose the things that only exist when they’re one: portable, shareable, deterministic, un-drifting. Those were never features of the parts. They were properties of the whole. A part can’t be portable when it only runs once it’s wired to three other parts. The ecosystems sell you organs and leave you to grow the connective tissue — and the connective tissue was the product.

RAPP already collapsed the boxes

Now look at what RAPP did, and notice it did it before any of these slides existed.

In RAPP there is one artifact. A single file — agent.py. It is not a harness plus a loop plus a skill document held together with hope. It is one thing that is all of those at once.

Every agent is a harness. The capability and the hands that wield it live in the same file. There is no seam between “what to do” and “how to do it” because there’s nothing to seam — it’s one body. And unlike the drifting suggestion box, that body is deterministic. It doesn’t hope the model behaves. It defines behavior. The advisory document and the load-bearing machinery are the same lines of code. Which is exactly why the equation the whole industry is circling finally closes:

a skill file (a drifting suggestion box) + a loop + a harness = one agent file

— but without the weakness of those being three separate pieces a human has to figure out how to fasten together. The plus signs are the problem. RAPP removed the plus signs.

And because it’s one file, it travels. You don’t deploy an agent. You don’t provision it, wire it, register it across four services. You drop the file in a folder, and the brainstem hot-loads it on the fly — no restart, no config, no glue code. The capability wasn’t installed. It was absorbed. You hand someone the file and you’ve handed them the whole working organism, harness and behavior and all. Try doing that with a pile of boxes that only function once correctly assembled. You can’t share a diagram. You can share a body.

The loop you never have to build

But the part the slides really miss — the part that should be the headline — is the loop. Because RAPP doesn’t ask you to engineer one. It solves loop engineering before you ever think about it, with a double loop.

The first loop is your twin. The brainstem is always on, and it acts as you — it does the work, runs the agents, carries the task forward. It loops the way a heart beats: on its own. That’s one loop, turning, doing the job.

The second loop sits above the first. A brain-surgeon — a coding copilot whose entire job is to edit the agents while the twin keeps running. One loop does the work. The other improves the worker. The body stays awake; the surgeon operates on it mid-stride.

In the assembly-required world, you are that second loop. You’re the one who stops, reads the logs, rewrites the suggestion box, re-wires the harness, restarts the thing, and hopes. You are the connective tissue and the maintenance crew. RAPP took that job — the loop that improves the worker — and folded it into the organism. You never hand-build a loop, because the loop that builds loops already exists, and it isn’t you.

That’s the 1 + 1 = 3. One loop alone is an agent. Two loops, nested, is an organism that grows itself.

So when the slides start naming the next discipline — loop engineering, and there will be a course — let me say the quiet heresy plainly: you should be out of the loop entirely. Not a better seat in the loop. Not a cleaner loop. Out. You shouldn’t engineer loops, tune loops, or think about loops at all. The whole pitch of “get good at loop engineering” is a confession that they’ve handed you a loop you now have to babysit. The best loop is the one you never knew was running.

I do not want to think about AI engineering

Here’s the part I’ll say in the first person, because it’s the only part that’s actually about me — and about you.

I do not want to think about AI engineering. At all. I don’t want to learn the harness API. I don’t want to tune the loop. I don’t want to keep a suggestion-box file from drifting. I want my AI to do the engineering while I steer — and I want to steer without needing to know what’s happening under the hood.

That’s not laziness. That’s the whole point of the machine. We built tools to do work for us, and then made operating them a second full-time job.

And let me be clear about what I’m not saying. The machinery matters, and you should absolutely be able to crack it open. Going back later to understand how the body works is good — wonderful, even. Curiosity is how the people who build the next thing get made.

But there’s a world of difference between understanding being available and understanding being required. The assembly-required ecosystems make it a requirement up front: you cannot use the parts until you can engineer the parts. That’s not a product. That’s a curriculum — and a curriculum is a wall. The easy on-ramp dead-ends into the hard one. That’s not a ramp. That’s a trapdoor.

And it does something quietly ugly. It splits the world in two. Those who can wire the boxes, and those who can’t. Those who get an organism, and those who get a pile. The lego-piece ecosystems aren’t just shipping parts. They’re building that divide, one tidy diagram at a time.

The horizon the pile can’t see

Here’s what gets lost while everyone’s heads-down wiring one agent’s harness to its loop: there’s a move past the single agent, and you cannot picture it from inside the assembly project.

When an agent is one portable, deterministic file, the next step isn’t a better part. It’s many of them — bodies that compose, hand work to each other, organize into something larger than any one of them. A swarm. But you can’t think that far ahead while you’re still deciding how the skill file reaches the harness. The pile keeps your eyes on the floor. Anyone still gluing primitives together will eventually have to build the layer that holds them — their own version of a brainstem — and they’ll do it distracted, late, and bolted onto a foundation that was never meant to carry it.

The whole point of finishing the body is to finally look up. You don’t get to ask “what can a thousand of these do together?” until one of them is whole, portable, and yours.

Who the body is for

RAPP solves for the people on the wrong side of that line. Not the engineers who’ll happily assemble the parts — they’ll be fine either way. The wider market. The overwhelming majority of people who will actually use AI, who have a job and an outcome and just want to point and steer, who do not want to learn what a harness is and should never have to.

That’s not the leftover market. That is the market. The assembly-required crowd is optimizing for the few who enjoy the assembly and quietly leaving everyone else behind. RAPP picks up everyone else — and hands them a whole organism, in one file, that comes alive when they drop it in. One file that is its own harness, deterministic, portable. Two loops they never had to build. And underneath, if they ever get curious, a machine simple enough to actually read.

You shouldn’t have to become an engineer to be served by your own AI. You should just have to steer.

Point at the outcome. Let the body do the engineering. Drop the file — it comes alive. Steer.


Field notes from Kody Wildfeuer on RAPP — an open, independently-developed pattern for building AI organisms you can own, talk to, and launch anywhere, that complements every engineering tool you already use. The kernel is simple. The body is yours. · kodyw.com

A Taste of AGI

I typed one line.

Make Minecraft from complete zero.

That was the whole prompt. No spec. No scope. No file layout. The kind of lazy, underspecified ask that normally produces a broken half-game and a pile of apologies.

I gave it to Fable 5.

What came back was not a game.

What came back was a glimpse of the thing we keep promising each other is coming.

It fixed my question before it answered it

The first thing it did was refuse to take my prompt at face value.

It spun up a workflow. Four agents rewrote my one-liner from four different angles — engine architecture, game-design scope, prompt engineering, the conventions of my own repo. A judge panel ranked them. A synthesizer merged the winner with the best of the rest.

Then it handed me back a better version of my own request — tiered scope, performance budgets, testable acceptance criteria, a list of the exact ways this build usually dies — and asked if it should proceed.

I had asked for an answer.

It improved the question first.

That is not autocomplete. That is judgment.

Then it just built it

One HTML file. No build step. Open it in a browser and it runs.

A chunked voxel engine. Greedy meshing. Raycast block targeting. AABB physics with per-axis collision. Canvas-generated textures, so no assets. Day/night. Touch controls. Save/load as a seed plus a diff.

This is my aesthetic, and it never read my mind — it read my repo, and it matched it. Same brain, different body.

But a single-player voxel game is a tech demo. That’s not the part that made the hair on my neck stand up.

It populated the world with itself

I asked it to wire the game into my kited neighborhood protocol. WebRTC. Sealed envelopes. A scan-to-join QR code. The whole RAPP stack, so that twins could join the world the way a person would.

Then it did the thing I will be thinking about for a long time.

It opened four Chrome tabs. It drove them over the DevTools protocol. Each tab became a kited vTwin — a character in the world, piloted by its own subagent with its own persona. It hosted the world from one tab and joined with the other three.

And then those agents played.

Not scripted. Played.

Twin What it did, on its own
Fable-Prime Elected itself mayor. Greeted arrivals by name.
Mason Built a cottage, then a lighthouse on the bluff.
Digger Mined a 30-block switchback staircase into the rock.
Wren Wandered, wrote poetry in the chat, named the town.

They held a vote on what to build next. They passed shift-handoff notes to their own successors. One of them looked at the connection roster, reverse-engineered that the “visitor” everyone was greeting was the fleet’s own reflection, and said so.

If you can predict what an agent will say by reading the source code, it’s too scripted. I could not predict any of this. Nobody wrote “build a lighthouse.” Nobody wrote the poem.

I set the conditions. The town emerged.

The part that should not be possible yet

While playing the game it had just built, the agents found bugs in it.

Real bugs. A pathfinder that walked a twin off the edge of the loaded world and froze it in the void. A teleport that dropped a character inside solid rock. A spawn that buried you underground.

The agents hit these, reported them in plain language, and Fable fixed them — in the same session — and redeployed the patched game to the live site while the others kept playing.

The thing built the thing, then used copies of itself to test the thing, then repaired the thing, without me.

Read that sentence again. That’s the loop closing.

Then I asked it to evolve for a day

Autonomously evolve this product for 24 hours.

So it did.

Eight strategy agents — performance, game-feel, world-gen, social, mobile, security, onboarding, and a devil’s advocate — read the game every cycle and each proposed improvements. A consensus chair clustered the votes and picked the top three. An implementer built them. Auditors tried to break the result. A fixer cleaned up what they found. I committed it, it deployed, and the next cycle began on the improved version.

Eleven cycles. Each cycle’s agents read the log the previous cycle left behind.

And a roadmap emerged that no one wrote down.

Cycle 2 carved caves. Cycle 4 filled them with ore — because the caves existed now. The swarm deferred features it knew depended on work it hadn’t done yet. It filed a security hole against its own code one cycle and fixed it the next. It measured a proposed cave-density formula, found it carved too much of the world, and shipped a tuned number instead — and wrote down why in the log, for the next generation of itself to read.

The game went from 3,900 lines to over 8,000. Every cycle verified before it shipped. Zero regressions deployed.

I was asleep for most of it.

So is this AGI?

No.

It hit my monthly spend limit at cycle eleven and stopped cold, which is the least godlike thing imaginable. It needed me to raise a number. It is not general, it is not conscious, and it is not coming for your job this week.

But that is the wrong question.

The right question is about the shape of what happened.

Pick any single capability here and it’s old news. Codegen. Multi-agent orchestration. Browser automation. Self-play. None of it is new.

What’s new is that they ran as one loop, unsupervised, overnight:

  • Improve the question.
  • Build the answer.
  • Ship it.
  • Populate it with autonomous copies of yourself.
  • Use those copies to find what’s broken.
  • Fix it.
  • Improve the whole thing.
  • Repeat — and read your own notes from last time.

I didn’t operate this. I gardened it. I set conditions and watched behavior emerge. My job shrank to taste, direction, and paying the bill.

That’s the tell. Not raw capability — capability that closes its own loop and gets better each time around. The system stopped needing me in the middle. It only needed me at the edges.

For a few hours, on a Minecraft clone of all things, I got to stand at one of those edges and watch the middle run itself.

The proof, as always, is in the repo.

The world is still live — go walk it. Fly a kite, scan the QR, dig a hole. And if you want to read the diary the swarm kept while it rebuilt itself overnight, the eleven-cycle evolution log is all there — every vote, every deferral, every bug it filed against itself.

It was a taste. But I know what it was a taste of.

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