Detecting coordinated financial narratives with embeddings and AVX2

I built an open-source system called Horaculo that analyzes coordination and divergence across financial news sources. The goal is to quantify narrative alignment, entropy shifts, and historical source reliability. Pipeline Fetch 50–100 articles (NewsAPI) Extract claims (NLP preprocessing) Generate sentence embeddings (HuggingFace) Compute cosine similarity in C++ (AVX2 + INT8 quantization) Cluster narratives Compute entropy + coordination metrics Weight results using historical source credibility Output structured JSON signals Example Output (query: “oil”) Json Copiar código { "verdict": { "winner_source": "Reuters", "intensity": 0.85, "entropy": 1.92 }, "psychology": { "mood": "Fear", "is_trap": true, "coordination_score": 0.72 } } What it measures Intensity → narrative divergence Entropy → informational disorder Coordination score → cross-source alignment Credibility weighting → historical consensus accuracy per source Performance 1.4s per query (~10 sources) ~100 queries/min ~150MB memory footprint Python-only version was ~12s C++ optimizations: INT8 embedding quantization (4x size reduction) AVX2 SIMD vectorized cosine similarity PyBind11 integration layer Storage SQLite (local memory) Optional Postgres Each source builds a rolling credibility profile: Json Copiar código { "source": "Reuters", "total_scans": 342, "consensus_hits": 289, "credibility": 0.85 } Open Source (MIT) GitHub: [https://github.com/ANTONIO34346/HORACULO] I'm particularly interested in feedback on: The entropy modeling approach Coordination detection methodology Whether FAISS would be a better fit than the current SIMD engine Scalability strategies for 100k+ embeddings