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Updated 2026-02-20HistoryData
Page StatusContentIndex
Edited 2 months ago130 words
14QualityStub13ImportancePeripheral15ResearchMinimal
Content3/12
SummaryScheduleEntityEdit history1
Tables1Diagrams0–Int. links1/ ~5Ext. links0Footnotes0References0Quotes0Accuracy0RatingsN:3 R:2 A:1 C:1
Change History1
Remove low-value validation rules and insights system#1752 months ago

Audited last 20 PRs for unnecessary complexity. Removed 4 low-value validation rules (entity-mentions, mermaid-style, quality-source, human-attribution) and the entire insights data layer (18K lines YAML, 6 data files, CLI commands, components, internal page). Reduces rule count from 40 to 36 and eliminates an underused data subsystem.

Issues1
StaleLast edited 67 days ago - may need review

Insights Index

This page collects discrete insights from across the project, calibrated for AI safety researchers/experts.

DimensionQuestionScale
SurprisingWould this update an informed AI safety researcher?1-5
ImportantDoes this affect high-stakes decisions or research priorities?1-5
ActionableDoes this suggest concrete work, research, or interventions?1-5
NeglectedIs this getting less attention than it deserves?1-5

Types: claim (factual), research-gap, counterintuitive, quantitative, disagreement, neglected

See the Critical Insights framework for the theoretical basis.

Adding Insights

Insights are stored in src/data/insights.yaml. Be harsh on surprising - most well-known AI safety facts should be 1-2 for experts.

- id: "XXX"
  insight: "Your insight here - a compact, specific claim."
  source: /path/to/source-page
  tags: [relevant, tags]
  type: claim  # or: research-gap, counterintuitive, quantitative, disagreement, neglected
  surprising: 2.5  # Would update an expert? (most should be 1-3)
  important: 4.2
  actionable: 3.5
  neglected: 3.0
  compact: 4.0
  added: "2025-01-21"

Prioritize finding: counterintuitive findings, research gaps, specific quantitative claims, and neglected topics.