82a358f18d
ontoref-derive: #[onto_mcp_tool] attribute macro registers MCP tool unit-structs in
the catalog at link time via inventory::submit!; annotated item is emitted unchanged,
ToolBase/AsyncTool impls stay on the struct. All 34 tools migrated from manual wiring
(net +5: ontoref_list_projects, ontoref_search, ontoref_describe,
ontoref_list_ontology_extensions, ontoref_get_ontology_extension).
validate modes (ADR-018): reads level_hierarchy from workflow.ncl and checks every
.ncl mode for level declared, strategy declared, delegate chain coherent, compose
extends valid. mode resolve <id> shows which hierarchy level handles a mode and why.
--self-test generates synthetic fixtures in a temp dir for CI smoke-testing.
validate run-cargo: two-step Cargo.toml resolution — workspace layout first
(crates/<check.crate>/Cargo.toml), single-crate fallback by package name or repo
basename. Lets the same ADR constraint shape apply to workspace and single-crate repos.
ontology/schemas/manifest.ncl: registry_topology_type contract — multi-registry
coordination, push targets, participant scopes, per-namespace capability.
reflection/requirements/base.ncl: oras ≥1.2.0, cosign ≥2.0.0, sops ≥3.9.0, age
≥1.1.0, restic declared as Hard/Soft requirements with version_min, check_cmd, and
install_hint (ADR-017 toolchain surface).
ADR-019: per-file recipient routing for tenant isolation without multi-vault. Schema
additions: sops.recipient_groups + sops.recipient_rules in ontoref-project.ncl.
secrets-bootstrap generates .sops.yaml from project.ncl in declarative mode. Three
new secrets-audit checks: recipient-routing-coherent, recipient-routing-coverage,
no-multi-vault. Adoption templates: single-team/, multi-tenant/, agent-first/.
Integration templates: domain-producer/, mode-producer/, mode-consumer/.
UI: project_picker surfaces registry badge (⟳ participant) and vault badge
(⛁ vault_id · N, green=declarative / amber=legacy) per project card. Expanded panel
adds collapsible Registry section with namespace, endpoint, and push/pull capability.
manage.html gains Runtime Services card — MCP and GraphQL toggleable without restart
via HTMX POST /ui/manage/services/{service}/toggle.
describe.nu: capabilities JSON includes registry_topology and vault_state per project.
sync.nu: drift check extended to detect //! absence on newly registered crates.
qa.ncl: six entries — credential-vault-best-practice (layered data-flow diagram),
credential-vault-templates (paths A/B/C), credential-vault-troubleshooting (15 named
errors), integration-what-and-why (ADR-042 OCI federation), integration-how-to-implement,
integration-troubleshooting.
on+re: core.ncl + manifest.ncl updated to reflect OCI, MCP, and mode-hierarchy nodes.
Deleted stale presentation assets (2026-02 slides + voice notes).
118 lines
5.3 KiB
Markdown
118 lines
5.3 KiB
Markdown
# Extractions — Steal This Deck (Talisman, KGC 2026)
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Source: Jessica Talisman, "Stop Betting, Start Building", Knowledge Graph Conference 2026.
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Newsletter: Intentional Arrangement (Substack). Received: 9 May 2026.
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Full deck: `Steal_This_Deck.pdf`
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---
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## Framing / Intro hooks
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**Para abrir cualquier presentación de ontoref:**
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> *"AI is a knowledge tool. Not a data tool. That breaks every assumption underneath many AI strategies."*
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Adaptado a ontoref: los agentes AI que trabajan sobre tu repositorio operan sobre correlación estadística si no hay infraestructura de conocimiento. Ontoref es esa infraestructura, pero para proyectos de software.
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> *"Agentic AI is not a model upgrade away. It is a shared language, from which to act."*
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Este cierre del deck es el mejor hook de intro para ontoref: un modelo más grande no sabe qué es tu proyecto, qué decisiones tomaste, ni en qué estado está. Eso requiere conocimiento estructurado, no un token window mayor.
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---
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## Datos citable para el problema
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Todos con fuente verificable — útiles en posts y slides sin necesitar justificación propia:
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| Dato | Fuente |
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| 89% de empresas reportan **cero impacto de productividad** de AI en 3 años | NBER, Feb 2026, n=5.937 execs |
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| Developers experimentados fueron **19% más lentos** con herramientas AI | METR RCT, 2025 |
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| Ganancia neta semanal del trabajador AI-promedio: **−14 minutos** | Foxit/Sapio, March 2026 |
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| 1.7× más issues en PRs escritos por AI | CodeRabbit, 2025 |
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| 46% del código en GitHub escrito por Copilot | Octoverse 2025 |
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**El argumento que emerge:** *AI is not saving time. It is generating volume. Without a knowledge backbone, its only measurable output is noise.*
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---
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## El argumento central — para slides y posts
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**El stack de conocimiento (Talisman lo llama "knowledge infrastructure"):**
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```
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1. Controlled vocabularies — términos con significado único y autoritativo
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2. Taxonomies — organización jerárquica
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3. Thesauri — equivalencias, relaciones cruzadas
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4. Ontologies — compromisos formales: clases, propiedades, constraints
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5. Knowledge graphs — live, queryable, governable
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```
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> *"You cannot skip layers. You cannot start at five and reverse-engineer to one."*
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**Ontoref implementa este stack para proyectos de software:**
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- Schemas NCL → vocabularios controlados
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- `.ontology/core.ncl` → nodos Practice/Concept con edges tipados
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- DAG-formalized knowledge → el knowledge graph
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- ADRs + migrations + `state.ncl` → governance
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---
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## El argumento de precisión — para posts técnicos
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> *"The accuracy gap is not closed by a bigger model. It is closed by a defined schema, an ontology, and a validated query."*
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Con datos:
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- **16% → 72%** en question-answering sobre SQL enterprise con ontology checks (Allemang & Sequeda, data.world AI Lab, 2024)
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- **3.4×** GraphRAG vs vector RAG en 43 queries enterprise (Diffbot KG-LM Benchmark, 2023)
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- Vector RAG colapsa a **0% past 5 entities per query**. KG-grounded retrieval sostiene.
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> *"Defining your terms is the cheapest accuracy and cost lever in the LLM stack."*
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Ontoref es exactamente esto: `ontoref describe` y el sistema Q&A (ADR-003) son el vocabulario controlado que reduce el espacio de alucinación cuando un agente trabaja sobre el proyecto.
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---
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## El argumento de MCP — crítico para el posicionamiento técnico
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> *"MCP and A2A are transport. Not semantics. They move bytes between endpoints. They do not establish shared meaning."*
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>
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> *"Two agents connected by MCP exchange text bytes. They do not truly share context."*
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**Ontoref cierra este gap:** tiene superficie MCP (`ontoref-daemon/src/mcp/`) encima de una capa ontológica. El protocolo mueve bytes; ontoref provee el significado que hace que esos bytes sean conocimiento, no texto.
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Esto es diferenciación directa respecto a "simplemente exponer tu repo por MCP".
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---
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## El argumento cultural — para posts de opinión
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> *"The industry is optimized to ship solutions, not to own problems."*
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| Celebrado | Huérfano |
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| Launching new platforms | Maintaining existing platforms |
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| AI-generated content | Taxonomy work |
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| Ontologies built on the fly | Domain expertise |
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> *"Knowledge work IS the maintenance. Yet it keeps getting cut."*
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Ontoref formaliza exactamente lo que siempre se corta: las decisiones arquitectónicas (ADRs), el estado del proyecto (`state.ncl`), la memoria operacional (`.coder/`). Lo hace queryable y machine-readable para que no dependa de que alguien lo recuerde.
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---
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## El close — para cualquier formato
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> *"The organizations that close the perception/reality gap first won't be the ones with the best models. They'll be the ones who finally did the work."*
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Adaptado: los proyectos donde los agentes AI trabajan mejor no son los que tienen el modelo más grande. Son los que tienen conocimiento estructurado sobre sí mismos.
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---
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## Atribución
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Jessica Talisman, MLS — Semantic Engineer, Information Architect, Knowledge Infrastructure Strategist.
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Newsletter: *Intentional Arrangement* (Substack).
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Talk: "Stop Betting, Start Building", Knowledge Graph Conference 2026, Technology Track, May 6, 2026.
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Al usar cualquiera de estos puntos en público, citar la fuente — es un argumento de autoridad que refuerza, no debilita, la posición de ontoref.
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