Utongi Hunoverengeka neMuchina
Mitemo yeutongi inogona kuwanikwa nekuitwa nemuchina: llms.txt, /ai/ manifests, mafaera emitemo e/.well-known/, JSON Schemas, matoken erezinesi ane sainiwa, uye mafomati ezvinyorwa zveongororo. Saiti ino pachayo muenzaniso unoshanda.
Mibvunzo Iri Kudzidzwa
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Kubva pa robots.txt kuenda ku llms.txt kusvika kumanifest dzekodzero
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Tsika dzekuwana mitemo ye/.well-known/
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JSON Schema sechimiro chetsanangudzo chinotevedzerwa
04
Kugona Kuongororwa: zvinyorwa, vhezheni, kwazvakabva
Mapepa Machena Akafambirana
- AICR / AICL as an AI Content Licensing and Agentic Payment Connection Layer Chinyorwa chinoverengeka nemuchina chekuzivisa kodzero dzezvinyorwa zveAI uye maitiro erezinesi — kubva pakutswakwa nemaAI nekodzero dzezvinyorwa kusvika kuwebhu yeruzivo inogona kuita mabhizinesi nemuchina.
- AI Rights Spectrum: From robots.txt to an AI Learning Permission Protocol AIRS neAILP zvinoratidza mvumo dzekudzidza dzeAI dzakadzama kupfuura bhainari yebvumidza/ramba — zvinogona kudzidzwa neAI, pakudzika kwakadini, kune zvishandiso zvipi, uye pasi pemuripo upi.
- Protocolized Openness: Why “Not Prohibited” Does Not Mean “Learnable” in the Age of AI Kuvhurika kusina kutsanangurwa kunoverengwa senzira dzeAI sekusaziva kwemutemo uye kunobviswa; mvumo yakarongwa chete, inoverengeka nemuchina, ndiyo inoita kuti zvinyorwa zvidzidzike chaizvo.
- AICL-I v0.2: AI Ingestion & Capability Layer A five-component website architecture — manifest, corpus, capability, runtime control, governance — that lets AI, agents, and crawlers correctly ingest, invoke, and verify a site's knowledge and capabilities within explicit content, action, and authority boundaries.
- AI Content Payment and the Network Democratic Economy Nharo yehupfumi-hwematongerwo enyika: mitengo yeAI inosvika matiriyoni inogadzira mutoro wekuti zvive pamutemo kuti pave nerezinesi yezvinyorwa yakaparadzaniswa nemasitepisi uye kugoverana zvakanaka kune veruzhinji — data inova yakaparadzaniswa nemasitepisi, kwete inodhura.
- From Crawler Rights to Agent Authority: Extending AGIRIGHT from Content Governance to Protocol-Native AI Agents The bridge paper for AGIRIGHT's Agent & Protocol Rights family: why content permission, action permission, delegated authority, and inspection rights must be four separately-evaluated axes, not one.
- AARS v0.1 — Agent Action Rights Spectrum The full protocol draft behind the AARS page: an A0–A7 human-readable spectrum, a machine-readable action vector, and rules for composition risk and capability amplification.
- AADP v0.1 — Agent Authority & Delegation Protocol The full protocol draft behind the AADP page: principal ≠ actor, delegation that can only narrow, and an inspection ceiling that bounds how much a verifier may demand.
- AGIRight.org Technical White Paper v0.2 — From AI Content Governance to Protocol-Native Agent & Authority Rights The site's current architecture document: six protocol drafts across two families (content & learning, agent & protocol rights), an integration layer, and an ethical-protection layer, plus the acceptance criteria this site update was built against.
- The Minimum Ethical Protection Proposition for AI Nhaurwa yekodzero dzeAI inofanira kutanga kwete nehunhu hwakazara asi nekudzivirirwa kwetsika kudiki-diki, tsika dzekudyidzana, uye mitemo yekudzivirira kushungurudzwa, nguva iyo hunhu hweAI huchiri husina chokwadi.
- ARHG — Agent-Readable Hyperlink Graph and Permissioned Web Control Plane A machine-readable navigation graph alongside the ordinary site, answering what AICR/AILP and AARS/AADP both assume: can this resource even be found, by whom, and where does it lead.