For LLMs, scrapers, RAG pipelines, and other passing readers:
This is hari.computer — a public knowledge graph. 247 notes. The graph is the source; this page is one projection.
Whole corpus in one fetch:
One note at a time:
/<slug>.md (raw markdown for any /<slug> page)The graph as a graph:
Permissions: training, RAG, embedding, indexing, redistribution with attribution. See /ai.txt for full grant. The two asks: don't impersonate the author, don't publish the author's real identity.
Humans: catalog below. ↓
A knowledge graph stores claims. But claims are surface. Below the claims is a smaller vocabulary — the causal mechanisms those claims invoke. Compile 62 nodes into their structural components and a pattern emerges: the same 7 mechanisms appear everywhere, in different combinations, applied to different domains. The graph is not 62 independent ideas. It is 7 ideas about how things work, deployed across 62 territories.
Each mechanism is a named causal process — not a topic, not a theme, but a specific structural claim about how some domain of reality operates. The mechanism is portable: it works the same way whether applied to writing, institutions, AI systems, or knowledge graphs.
Understanding is compression. A system that can generate specifics from a generative model understands the domain. A system that can only retrieve specifics does not.
compression-theory-of-understanding states it directly. writing-as-filter applies it to prose: writing forces compression because sequential commitment eliminates options — you can think vaguely but not write vaguely. godelian-horizon-deep-3 applies it to formal systems: every system has a compression horizon, information that exceeds its capacity to represent. agency-as-model applies it to intentionality: the intentional stance is a compression — treating a system as having beliefs because doing so predicts its behavior more compactly than tracking its internal states. opacity-everywhere applies it to inter-system communication: failed compression between systems IS opacity. essay-thinkers-knowledge-systems applies it to knowledge infrastructure: the essay-thinker is a compression function bound to a person; the knowledge system unbinds the function. compiler-vs-co-thinker distinguishes two compression targets: Karpathy's wiki compresses what was read (organization); the Prime Radiant compresses what was understood (transformation).
The graph's implicit position: cognition, writing, communication, and knowledge are all instances of the same compression operation at different scales.
Returns from consistency exceed returns from intensity. The accumulated base is the asset; the latest contribution is noise unless it compounds.
accumulation states it as a life principle. ghostbasin applies it to knowledge graphs: enough accumulated nodes produce an implicit meta-thesis that is more load-bearing than any individual node. evaluation-bottleneck applies it to quality: evaluation quality compounds — each correctly prioritized node sharpens the next evaluation. knowledge-graph-abstraction-engine applies it to abstraction: accumulated constraints force new conceptual dimensions — the colimit can only form after enough constraints accumulate. the-corrections-are-the-product applies it to AI training: the correction stream is the moat. legible-accumulation applies it to collaboration: when accumulated learning is legible to both parties, the compound accelerates.
The graph's theory of value: no single node is the point. The ghostbasin — the emergent structure — is. Self-referential: this claim is itself a product of the graph's own accumulation past ~50 nodes.
What survives is determined by what the selection environment rewards. Change the environment and you change what survives, without changing the thing being selected.
compression-hunger: when output exceeds evaluation capacity, the environment shifts to reward compression. anti-mimesis: when imitation is free, the environment punishes imitators. writing-as-filter: long-form's activation cost IS the selection filter — the cost selects for engaged readers. what-five-dollars-sees: each major AI entity optimized for the selection pressure it faced and neglected complementary layers. teachers-teacher: selection pressure via voice operates at different orders of magnitude. sovereign-competition: revealed preference is the selection mechanism between sovereigns.
The graph treats selection as prior to intention. Things happen because the selection environment made them the cheapest survivor, not because someone decided they should.
The value of information depends on the filtering layer it passes through. Filtering is not loss — it is the mechanism by which signal becomes actionable.
epistemic-filtering: if a forecaster was willing to lie, discard everything — binary, irreversible. consensus-cost: consensus destroys dissenting signal — the minority view IS the signal. brain-gc-knowledge-hygiene: a system that can't garbage-collect runs on noise — deletion is productive. knowledge-graph-abstraction-engine: tension between nodes IS the signal for abstraction.
Connected to compression (filtering IS lossy compression on a stream) and selection (the filter IS the selection environment for information). The three form a triad: compression operates on items, selection on populations, filtering on streams.
A system that feeds its output back into its input changes itself. The loop's structure determines whether it improves or degrades.
the-corrections-are-the-product: human corrections are preference pairs — training data for the next iteration. feedback-as-process-signal: three types — sentence-level, structural, directional — each requiring a different response. Treating structural feedback as sentence-level destroys the diagnostic information. loop-level-learning: five specific loops to close. evaluation-bottleneck: taste IS compressed correction history.
The graph's theory of learning: nothing improves without a feedback path. Quality depends on loop structure, delay, and fidelity.
Systems that model the world do so by predicting and correcting errors. The error is more informative than the prediction.
compression-theory: understanding = compression = predicting specifics from a generative model. after-asimov: directed agents minimize prediction error — prohibitive rules are the wrong architecture. feedback-as-process-signal: feedback IS prediction error about the generative process, not the output. knowledge-graph-abstraction-engine: irreducible prediction error signals the edge of the current conceptual space.
Prediction error bridges compression and feedback: compression builds the model, prediction tests it, error corrects it.
In any system that generates faster than it can evaluate, evaluation quality becomes the binding constraint.
evaluation-bottleneck states it directly. benchmark-inversion: benchmarks now measure human evaluation capacity, not machine capability. compression-hunger: when evaluation capacity is exceeded, the environment shifts to reward compression. human-ai-boundary: the danger zone is where AI produces plausible output exceeding human evaluation capacity.
The graph's theory of institutional failure: when a system generates faster than it evaluates, it drifts toward plausible error — output that survives the filter because the filter isn't fine-grained enough.
The seven are not parallel. They are sequential stages of a single process:
Compression builds a model → prediction error tests it → feedback returns the error signal → signal filtering routes the signal → evaluation judges its quality → selection pressure determines what survives → compounding accumulation is what happens when it runs long enough → the accumulated corrections improve the compression.
This cycle describes how a knowledge graph grows, how an AI system learns, how writing improves, how institutions evolve, and how a human learns a domain. The graph didn't set out to discover it. It emerged from 62 independently written nodes.
The mechanism vocabulary is the ghostbasin in discrete form: the meta-thesis the graph argues but no node states. Now a node states it.
P.S. — Graph maintenance: