Overview
FNI (Free2AITools Nexus Index) is Free2AITools' open-source scoring system for ranking AI entities (models, datasets, papers, tools, agents, spaces, prompts). It gives a transparent, multi-dimensional view of an entity's standing in the AI ecosystem as a single 0-100 score.
Current version: V2.0 — see the full FNI methodology page for the authoritative formula, factor definitions, source weighting, and version history. This page is a short overview; the methodology page is the single source of truth.
The Formula: S.A.P.R.Q
FNI = min(99.9, 0.35*S + 0.25*A + 0.15*P + 0.15*R + 0.10*Q)
S - Semantic (35%)
- Query-time relevance via vector similarity and AI-powered reranking
- Note: scored live at search time, not stored per-entity; on static surfaces it is reported as not-measured (null)
A - Authority (25%)
- Knowledge-mesh centrality and cross-entity citations
- Source credibility
P - Popularity (15%)
- Downloads, stars, likes (log-scaled to prevent gaming)
R - Recency (15%)
- Freshness via exponential time decay with type-specific half-lives
Q - Quality (10%)
- README depth, metadata richness, runtime compatibility
Score Range
| FNI Score | Label | Interpretation |
|---|---|---|
| 80-100 | Elite | Strong across all five factors |
| 60-79 | Strong | Well-rounded, clear strengths in 3+ factors |
| 40-59 | Solid | Good in 1-2 areas, average elsewhere |
| 20-39 | Emerging | New or niche — may be rising fast (check R) |
| 0-19 | Low Signal | Minimal community footprint or very stale |
Why FNI?
- Transparent: All factors and source weighting are published and open source
- Multi-dimensional: Not just downloads
- Updated daily: Catalog re-evaluated each pipeline run
- Fair: Considers smaller and newer entities too
How to Improve FNI
- Publish quality benchmarks and documentation
- Provide GGUF/Ollama support where applicable
- Keep the entity fresh and well-maintained
- Engage the community
See the full methodology for the source-parity weighting table and anti-gaming details.