Free2AITools Nexus Index (FNI)

The FNI is the ranking algorithm behind Free2AITools. It evaluates 464,000+ AI entities (models, datasets, papers, tools, agents, spaces, prompts) across five weighted factors to produce a single 0-100 score.

Current version: V2.0

The Formula: S.A.P.R.Q

FNI = 0.35*S + 0.25*A + 0.15*P + 0.15*R + 0.10*Q

Factor	Weight	What It Measures
S — Semantic	35%	Query-time relevance via vector similarity matching and AI-powered reranking
A — Authority	25%	Ecosystem gravity: knowledge mesh centrality, cross-entity citations, source credibility
P — Popularity	15%	Community adoption: downloads, stars, likes (log-scaled to prevent gaming)
R — Recency	15%	Freshness: exponential time decay with type-specific half-lives
Q — Quality	10%	Completeness: README depth, metadata richness, runtime compatibility

Agent Structured Tags (V2.0)

Beyond the five score factors, FNI V2.0 attaches structured metadata for machine-readable model selection:

Tag	Type	Meaning
ollama_compatible	boolean	Has GGUF quantization files — can be run via ollama run
can_run_local	boolean	Locally runnable: ≤13B parameters + GGUF available
license_type	string	Classified as permissive, copyleft, non-commercial, or unknown
hosted_on	string[]	Cloud providers offering this model (Replicate, Together, HF Inference)
hosted_on_checked_at	ISO date	When the hosting data was last verified

These tags power the select_model API — AI agents can filter models by hardware constraints, license type, and deployment target via MCP or HTTP.

Interpreting FNI Scores

Range	Label	Meaning
80-100	Elite	Top-tier: strong across all five factors
60-79	Strong	Well-rounded with 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 factor)
0-19	Low Signal	Minimal community footprint or very stale

Version History

V1.0 (2025) — Originally called “Freshness-Novelty Index.” Four-factor P.V.C.U formula (Popularity, Velocity, Context, Uniqueness). Single-source HuggingFace data. No semantic component.

V2.0 (2026) — Renamed to “Free2AITools Nexus Index.” Five-factor S.A.P.R.Q formula. Multi-source aggregation across 7+ platforms. 464K+ entities evaluated daily. Agent structured tags for select_model API. Factor scores included in every API response.

Anti-Gaming

FNI uses multi-dimensional cross-validation to detect manipulation:

• Anomalous growth: alerts when 7-day metric growth exceeds 10x the category average
• Ratio anomalies: download/star ratios outside reasonable ranges for the entity type
• Content mismatch: high popularity but no substantial documentation or code

Flagged entities are reviewed and scores adjusted. Log-scaling on popularity metrics (P factor) inherently dampens artificial inflation.

For Developers

Every API response includes fni_version and per-factor breakdown:

{
  "fni_score": 72.4,
  "fni_factors": {
    "semantic": 50.0,
    "authority": 85.2,
    "popularity": 67.1,
    "recency": 91.0,
    "quality": 44.3
  }
}

The FNI algorithm is open source: github.com/mosesy5688-cell/ai-nexus

Related

• Methodology — Visual breakdown of the FNI formula and fairness pillars
• Developers — API documentation for select_model and compare
• LLM Benchmarks — Traditional evaluation metrics (MMLU, HumanEval)
• Local Inference — Running models locally with Ollama/llama.cpp
• VRAM Requirements — Hardware needed for different model sizes