First screen, captured 10 days ago. Open the video.

Nvidia – open-source auto-ontology

What HN thinks

Nvidia ships an ontology tool; the thread builds an ontology of Claude-isms instead.

README is unreadable AI slop
Many commenters gave up at the opening line about 'reliability gates agentic analytics' and saw the unedited LLM prose as a signal of low care. Others pushed back that rejecting text just for sounding like AI is hypocritical.
It's a semantic layer for data
Explainers describe it as introspecting scattered databases into a catalogue of business meaning so agents can turn natural language into correct SQL. Skeptics note it can't capture tribal knowledge or misleading column names.
Unreliable agents fixing unreliable agents
Some doubt that layering LLMs adds reliability, while others argue splitting the work into a reviewable ontology step and a query step reduces errors, citing TCP over IP as an analogy.

Consensus: Most agree the README fails to explain the project, and that the real value depends on how much human work goes into getting the definitions right.

Recommended in the comments

DataAgentBench ↗ (other): Suggested for comparing how Nvidia's approach stacks up against other ontology-building agents.
CAVE ↗ (tool): Its author pitched it as simpler and better suited to evolving knowledge than RDF/OWL.
ontology-skills ↗ (library): Offered as an equivalent that needs no Nvidia API key.
SPARQL (tool): One commenter said LLMs already handle established semantic web ontologies and queries well.

Summarised by Claude from the comment thread 10 days ago.

Points: 248
Comments: 116
Best rank: #2
Posted: 2026-11-03 by campervans
Category: AI & ML

nvidia ontology knowledge-graph text-to-sql llm-agents python

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