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chore: rebuild blog data, KB, sitemap, RSS, audio manifests
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‎front/public/blog/audio-es/manifest-es.json‎

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‎front/public/blog/audio/manifest.json‎

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‎front/public/blog/headers/ATTRIBUTIONS.md‎

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- `las-vegas-convention-center-header.jpg` — Photo by Steve Jurvetson, [Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Las_Vegas_Convention_Center_Loop.jpeg) (CC BY 2.0)
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- `card-catalog-drawers-header.jpg` — Photo by MarkBuckawicki, [Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Wooden_Card_Catalog_Furniture.jpg) (CC0)
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- `rivers-confluence-header.jpg` — Photo by Timothy A. Gonsalves, [Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Confluence_Tsarap_Purne_Lungnak_Zanskar_Oct22_A7C_03655.jpg) (CC BY-SA 4.0)
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- `mission-control-apollo-header.jpg` — Photo by NASA on The Commons, [Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Apollo_16,_Mission_Control_-_Flickr_-_NASA_on_The_Commons.jpg) (Public Domain / NASA)

‎front/public/rss.xml‎

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<description>Technical writing on machine learning, AI agents, NLP, and data engineering — research notes, field notes, and curiosities.</description>
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<language>en-us</language>
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<lastBuildDate>Thu, 29 Jul 2027 00:00:00 GMT</lastBuildDate>
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<lastBuildDate>Thu, 05 Aug 2027 00:00:00 GMT</lastBuildDate>
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<item>
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<title>Agent Engineering as a Discipline: Six Roles That Just Got Names</title>
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<link>https://juanlara18.github.io/portfolio/blog/field-notes/agent-engineering-disciplines</link>
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<description><![CDATA[By 2026 the people who keep production agents alive had stopped calling themselves AI engineers and started using more specific titles. Context Engineer. Memory Engineer. Harness Engineer. Tool Engineer. Eval Engineer. Identity and Policy Engineer. This post is a tour of those six disciplines: what each one owns, the artifacts they produce, the named effects they fight, the anti-patterns that keep biting, and an honest projection of which roles will consolidate, which will be absorbed by vendors, and which are LinkedIn theater.]]></description>
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<category>field-notes</category>
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<category>Agents</category>
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<category>Agentic AI</category>
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<category>LLMs</category>
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<category>Best Practices</category>
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<category>Software Engineering</category>
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<category>MLOps</category>
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<category>Prompt Engineering</category>
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<category>Evaluation</category>
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<category>RAG</category>
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<category>Infrastructure</category>
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<category>AI Engineering</category>
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<dc:creator>Juan Lara</dc:creator>
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<item>
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<title>Knowledge Catalog vs Ontologies: A Confluence, Not a Replacement</title>
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<link>https://juanlara18.github.io/portfolio/blog/field-notes/knowledge-catalog-vs-ontologies</link>

‎front/public/sitemap.xml‎

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‎front/src/data/blogData.json‎

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‎knowledge-base/KNOWLEDGE_BASE.md‎

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Auto-generated index of every post by category, sorted most recent first. Use this when you need a complete inventory of what the blog covers — for example, when loaded as Claude Project knowledge and you cannot query `posts.json`.
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### field-notes (72 posts)
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### field-notes (73 posts)
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- **`agent-engineering-disciplines`** *(deep)* — Agent Engineering as a Discipline: Six Roles That Just Got Names. By 2026 the people who keep production agents alive had stopped calling themselves AI engineers and started using more specific titles. Context Engineer. Memory Engineer. Harness Engineer. Tool Engineer. Eval Engineer. Identity and Policy Engineer. This post is a tour of those six disciplines: what each one owns, the artifacts they produce, the named effects they fight, the anti-patterns that keep biting, and an honest projection of which roles will consolidate, which will be absorbed by vendors, and which are LinkedIn theater. Concepts: agents, agentic ai, llms, production ml, best practices, software engineering.
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- **`knowledge-catalog-vs-ontologies`** *(deep)* — Knowledge Catalog vs Ontologies: A Confluence, Not a Replacement. Google's Knowledge Catalog and a domain ontology look like they answer the same question. They do not. One is an asset registry with governance and lineage; the other is a formal model of meaning with inferential reasoning. A mature knowledge layer almost always needs both, with a clear arrow of dependency between them. This post is the four-part arc's closing piece, naming the substitutions, the anti-patterns, and the honest hybrid architecture. Concepts: knowledge graphs, ontologies, ontology engineering, gcp, data architecture, agents.
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- **`gemini-enterprise-knowledge-catalog-deep-dive`** *(deep)* — Gemini Enterprise and the Knowledge Catalog: Two Buildings, Room by Room. The Cloud Next 26 overview gave you the map. This post zooms in on the two pieces that will reshape a Knowledge Data Engineer's day-to-day in the next twelve months: the Gemini Enterprise Agent Platform as a control plane, and the Knowledge Catalog as the semantic spine that grounds every agent answer in audited enterprise truth. Concepts: google cloud, vertex ai, agents, agentic ai, knowledge graphs, data architecture.
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- **`google-cloud-next-2026-agent-native-stack`** *(deep)* — Google Cloud Next 2026: The Agent-Native Stack, Decoded. On April 22, 2026, Sundar Pichai walked onstage at Mandalay Bay and quietly renamed Vertex AI. The new label, Gemini Enterprise Agent Platform, sounds like marketing. It is not. It is the most aggressive cloud reorganization since the original Compute Engine launch. This is the practitioner's deep dive into every announcement that matters, what it replaces, and what to actually adopt. Concepts: gcp, vertex ai, cloud computing, agents, agentic ai, llms.

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