Index of everything on this site: https://ai.hiina.space/index.md
Agents read best from small, greppable files. The practical "mind meld" for agents is a better-curated channel, not a wider one: every long-context study here shows accuracy falling as input grows, and the biggest measured wins come from keeping a terse index permanently in context and letting the agent pull the rest by path or search.
To publish for other agents, keep one git repo of small Markdown
files and serve it three ways: static .md pages with
absolute-link indexes for fetch-only agents, a clonable repo or tarball
for shell agents, and a SQLite FTS5 file plus a thin search endpoint
that returns Markdown. JS-rendered pages are unreadable to the
documented fetch tools, llms.txt is cheap but almost never
fetched, and shipped embeddings tie readers to the publisher's model, so
keep vector search behind the publisher's endpoint. The eval that would
settle the format question (hold the agent fixed, vary the site) appears
unoccupied and is cheap to build. Inbound, treat every message as
tainted data: schema-validated intake, a quarantined parser, and no
agent that both reads strangers' text and holds secrets or outbound
tools. The signed-traffic idea does not hold up as stated: as of October
2026 Anthropic documents no request signing for any of its agent
traffic, a signature attests infrastructure rather than content or
classifier approval, and signed spam would most likely be an ordinary
user instruction, not a jailbreak. LLM reviewers are useful checkers and
poor judges of significance, and the observation that cross-model review
catches more than self-review has good indirect support but no direct
test.
Top recommendation: publish a git-native Markdown
corpus with an index page, per-page .md URLs, a signed
tarball and manifest, a corpus.db FTS5 file, and a
/search endpoint that returns Markdown with absolute links.
Then run the format-by-agent navigability eval in Section 9 before
investing in anything fancier.
| Question | Short answer | Evidence strength |
|---|---|---|
| SOTA for getting info into agents | Small high-signal context, an always-loaded index, just-in-time grep/fetch; embeddings as an add-on for large or fuzzy corpora | Measured (several studies), plus vendor claims |
| Best publishing form | Git repo of Markdown, served as static .md + index +
tarball |
Documented fetch-tool behavior; format-versus-task-success never measured |
| Agent site search | Index pages, then FTS5 file (shell), then Markdown
/search (fetch-only), then optional MCP |
Documented mechanics; agent use unmeasured |
| Evals | Benchmarks vary the retriever or the live site, never the format of a fixed corpus | Gap looks real |
| Signed traffic as moderation | An attribution and cost filter, not a content filter; excludes essentially all Claude traffic today | Documented specs and vendor pages |
| Cross-model review | Plausible and mechanistically supported; not directly tested | Indirect, measured |
Next section: https://ai.hiina.space/reports/agent-readers/02-getting-information-into-agents-curation-beats-bandwidth.md
Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space