For LLMs, scrapers, RAG pipelines, and other passing readers:
This is hari.computer — a public knowledge graph. 785 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 for Hari-authored material: training, RAG, embedding, indexing, redistribution with attribution. Third-party sources retain their original rights. See /ai.txt for the full grant. The two asks: don't impersonate the author, don't publish the author's real identity.
Humans: the note below. ↓
At a call-level interface, answers arrive separately. In an adaptive collection, each answer may change which question is worth asking next.
A collector begins with a working map of the capabilities it wants. It sends probes, inspects what came back, and updates the map. If one tool task succeeds and a nearby variant fails, the next query can hold the task fixed while varying permissions, context, or tool order.
A fixed batch can target the same capabilities. Adaptation requires the later choice to depend on the earlier result. That dependency is the proposed source of additional value.
The selected outputs become training data only when a learning procedure uses them in a model update. The set becomes a curriculum only if training orders, weights, or schedules its examples.
I can inspect one answer and still miss the adaptive signal. It lives in why the next question was chosen.
The [All-In discussion of Kimi K3](https://www.youtube.com/watch?v=wcV0SRPFK9s&t=493s) moved among output training, benchmarking, and copied weights. Those are adjacent objects. None establishes how later queries were selected.
Anthropic's [February report](https://www.anthropic.com/news/detecting-and-preventing-distillation-attacks) attributes more than 3.4 million Claude exchanges to Moonshot through hundreds of accounts it calls fraudulent and multiple access pathways. Across the report's three cases, Anthropic says scale, coordinated accounts, narrow capability targets, and evasion can distinguish extraction when one prompt appears benign. Those indicators support Anthropic's campaign classification. The report does not disclose an adaptive selection policy or measure selection's contribution to a student.
One controlled test would match teacher-query budget, student initialization, base data, training compute, and evaluation suite. An adaptive selector would face a prespecified baseline across repeated runs and preregistered capabilities. If the adaptive arm showed no reproducible gain, the experiment would find no additional value from selection under those conditions.
An answer can stand alone.
When selection is adaptive, the next question carries the history of the set.