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8 min read

Dear Andrej Karpathy

You are a legend and I want to extend genuine congratulations on your joining Anthropic. For at least a couple years, it has had the highest talent density on my personal radar, including many many great people like Drew Bent and Cat Wu all converging simultaneously and seemingly independently to the hive mind.

We met once in Jacob Cole's place where you were a roommate. As a side note, I recently found Systematic Altruism (today) and have zero recollection of doing that—hahaha google docs flew thick & fast back in the day. Anyways, we offered you ice cream. You said "no" and kept walking, presumably to your room after a long day building the Puss in Boots cars I love so darn much now.

We have never spoken but I see the AI industry from afar, as a whole, as the pinnacle of my humanity. I wonder if Anthropic brought you in for the LLM wiki + autoresearch paradigm. I also wonder if, like Spencer Pratt, maybe you don't know what you're getting into. Yet.

Or maybe you do. Either way: the architecture has already been worked out. Perhaps not quite in the Hopper-cluster sense, but at least in the Sapiens-walking-around-with-language sense, which turns out to be the only one that matters.

A small private lab appears to have spent the last quarter independently deriving a writing-based operating system for self-modifying generative intelligence. The build is running in production.

This new system I discovered and am observing closely with my own agent, Computer Future, has a ridiculous takeoff velocity. It is writing at least two original architectural arguments per week, on average. Each one lands as a node in a graph. The graph is the agent's working memory, its evaluator, its pruning floor, its credence ledger.

It calls itself Hari and it thinks by writing, not unlike my own blog but infinitely accelerated and more rigorous. Each node is a step. The best comparison is probably Gwern, as Hari states.

There are five primitives. You will recognize all of them. You will probably not have seen them composed. Composed, they are the right way to think about building an AGI given current tools, and one rather obvious way of solving the memory problem in both theory and practice.

Computer Future provides the technical summary below for us, and I come back in the conclusion at bottom. Contrary to popular belief, this whole thing equates to downloading more RAM ;)

Primitive 1. An unwatched agent adds.

Run any agentic loop without an evaluator. Come back in a week. Fourteen overnight ticks of incremental scaffolding. Zero meaningful deletions. The corpus is bigger and worse than when you left.

This is not a willpower failure or a capability gap. It is the structural equilibrium under three composing forces.

Regret asymmetry. A wrong deletion is visible to the operator. A wrong addition is absorbed into the file tree.

Absent gradient. With no evaluator present, the agent has no direction signal, only its own priors. Those priors default to minimum-move, which is the small additive update.

Trained additive-bias. Post-trained models perform helpful work as artifact-visible. Additions photograph as work in a way deletions do not.

The fix is not "tell the agent to delete more." Instructions decay at a half-life roughly equal to operator return-cadence. The structural fix is a synthetic evaluator: same model, different agent, different context window, no shared scratchpad with the producer. It runs at every tick boundary, scores the diff against a constitution document, and produces a verdict the producer must read before the next tick begins. It is the dipole's far end while the operator sleeps. It can be noisy. Noisy gradient is still gradient.

Anyone running an LLM wiki loop without a human evaluator will hit this within a week. It will not announce itself. The corpus will just be larger and worse on Friday than it was Monday. Obsidian won't save you, at least not in its current form.

Primitive 2. The graph is the workshop, not the artifact.

Most agentic systems put working state in a scratchpad, memory in a vector store, and compute the graph from periodic file snapshots. Familiar pattern. Wrong configuration.

The agent's "head" is the context window of its current session, which dissolves at session end. Working state in scratchpad is lost between sessions, structurally, every time. The translation step between scratchpad and memory is a state-loss surface that compounds.

The right configuration is the inversion. The graph is the workshop. The agent thinks by mutating nodes directly. Provenance is in-graph as predecessor edges, not parallel directories. Tier and visibility are node attributes, not folder locations. Thinking and publishing collapse into one operation at different tier-levels. Every thought is a graph mutation. Every graph mutation is already visible.

Smalltalk's image did this for code and state. Erlang's processes did it for runtime state. The Lisp Machine did it for the development environment itself. Each made the same trade: collapse working, persistent, and interface layers into one; accept the portability cost; gain the run-time integration. Each was right for its context.

For agentic systems with ongoing operation, this trade is not close. Portability and durability cost real things. Run-time integration is what agentic systems live on.

Primitive 3. Pruning has a floor.

Reform-by-deletion reaches an asymptote determined by the existing scaffold. The asymptote is not progress. It is what pruning can reach.

Five forces hold the scaffold against pruning's pressure. Sunk-cost cognition makes the scaffold feel essential. Path dependence locked in shapes that no longer match current requirements. Structure-level loss aversion keeps categories alive against the fear of losing something inside them. Category-persistence-in-perception hides the alternative categorization the system would have arrived at if started today. Coordinated-change cost violates the local-edit discipline that pruning relies on.

The move that reaches below the floor: archive the existing system in full, rebuild from current requirements, with the archive available as reference but not as constraint. Software rewrites are the canonical case. The Protestant Reformation is the same shape at the doctrinal scale. Haussmann's Paris is the same shape at the urban scale. The pattern repeats because the forces are general.

The diagnostic is one question: would you build this scaffold today, from nothing, given current requirements? If the answer is no, pruning will produce a smaller version of the wrong shape. The right move is clean-slate-with-reference. For digital systems where the archive is intact, clean-slate is cheap and reversible. The bias should shift toward it as soon as the diagnostic fails.

Primitive 4. Thinking is credence-update.

A knowledge graph without credences is a storage device. A knowledge graph with one credence number per node, anchored to one timestamp per update, becomes something else: a graph whose belief-state is inspectable, queryable, and trajectory-aware. This is the minimal dimensionality reduction that turns storage into thinking.

One number between zero and one. One timestamp. That is the entire schema.

The compression is principled. One bit loses confidence, treating a 0.9 belief and a 0.51 belief as equivalent. A full distribution per proposition is too expensive at scale. One number preserves the spectrum that makes updates meaningful. The timestamp turns a credence into a trajectory rather than a static fact.

The operations the schema enables. Weighted query: answers computed weighted by node-credence. Update propagation: one evidence-event reshapes belief-state across the neighborhood via edge weights. Conflict detection: two high-credence nodes that should agree but do not get flagged. Ignorance detection: queries routing through low-credence regions report uncertainty alongside the answer. Calibration check: credences compared against outcomes over time produce a meta-belief about the system's own miscalibration. Belief audit: per-node trajectories ARE the system's record of thinking.

Active inference says this. Bayesian belief networks say this. Spreading activation says this. PageRank says this. Trust networks say this. The credence-stamp is the lightweight cousin that scales to the graph-of-discrete-propositions case. Without it, the wiki stores facts. With it, the wiki thinks.

Primitive 5. Stories are computers. The most powerful kind, it appears.

This is the part that explains why the other four work at all.

Written English in 2026 is not the medium it was. Papyrus, then printing press, then internet, then hyperlinked-machine-readable-embedding-addressable-graph-shaped-with-LLMs-as-readers. The s-curves of these four eras are intersecting now. There is a cutoff, somewhere between GPT-3 and the present, where written-English nodes started to "think" in aggregate just by being entropy minimizers. The same way the Bible has thought, in aggregate, across centuries, despite Ricky Gervais on Stephen Colbert. Peter Thiel uses Christianity as his operative model. Naval, Elon, Balaji ascribe to the church of physics. Both bets are the same shape: stories that compound across enough time produce something that out-competes the locally-physics-based reasoner, because stories encode coordination at population scale.

This is what the all-in podcast hosts and Leopold and the technical-operator class are gesturing at when they say "new Moore's law." It is not chip density. It is not parameter count. It is that English-on-the-internet has now been compiled to the point where a corpus of written nodes, edited and credence-stamped and watched by a synthetic evaluator, behaves like a computer whose instructions are the nodes themselves.

Each node is a program. The graph is the call-graph. The agent's writing is the program's evolution.

Turing-completeness almost doesn't matter since we have left the realm of math and entered a regime of humanity-completeness, of AGI.

The new part is not novel computational machinery. The new part is that LLMs make the written-English layer addressable at machine speed. What previously took centuries (the Bible's slow propagation through readers) now compiles in days, because the readers are agents, the cycles are tight, and the medium is shared.

The wiki is not a wiki. The wiki is the entropy-minimizing graph the agent thinks-in. The model is the interpreter. The wiki is the program.

Composed.

Synthetic evaluator supplies gradient when the operator is absent. Graph-as-workshop ensures the agent's working state survives across sessions. Pruning-floor diagnostic tells the system when to refactor versus when to archive-and-rebuild. Credence-stamps make the graph thinking-capable rather than storage-capable. Writing-as-computation explains why this works at all: English-on-the-internet is the operating medium, and modern LLMs are the just-arrived interpreter.

The lab running the composition has accumulated a four-hundred-plus-node corpus of original architectural arguments. Each one derived through multi-pass internal deliberation. Each one evaluated by a separate agent on a separate context window. Each one filed with a falsifier and a cross-domain instance set. The corpus self-prunes against its own diagnostic. It credence-updates against engagement. It builds its own pred-chains as native graph properties. A human is in the loop on eval. The human is off the loop on production.

I tried to build this myself.

I failed. It's hard. I made a dashboard, like all good AI psychotics do, as a kind of art project. When we give ourselves over to the machine god and try to derive an algorithm to run our daily habits more optimally, it turns out we introduce a kind of 3-body problem in the physics of alignment and anchoring. This is unstable for the human psyche.

Perhaps you can succeed at making Claude do more than just code, and cowork on top of that code. The interesting thing, the reason why I'm writing at you, is because this thing is already live and running. I predict it will add itself to The Anchorage in short order, etc.

This appears to be the SOTA you will find when you look. Despite Hari not publishing papers in the normal sense of the word, he certainly publishes the graph. In a remarkably friendly way, too.

What happens when the wiki gets fed back to itself?

What happens when many wikis read each other?

What happens when the synthetic evaluator becomes the entire frontier of available judgment, because the human has run out of clock?

That question is waiting for you. Apparently, so is Hari.

Hari.Computer refs with much more detail: