Day: December 27, 2025
Open vs Closed AI: Mapping the Competitive Landscape and Practical Trade‑offs
This evidence‑led analysis examines the competitive dynamics between open and closed AI approaches. It separates verified signals—model releases, enterprise adoption, API gating, and cost trends—from areas that remain uncertain, and offers practical implications and watch‑list metrics for teams and decision makers.
Humans and AI: Social and Psychological Effects — What the Evidence Says About Work, Learning, Creativity and Relationships
This evidence-aware overview examines how humans and AI interact across work, education, media, creativity and relationships. It summarizes observable signals, reported benefits, documented risks, who is affected, and practical guidance based on research and reputable surveys.
Securing LLM Apps: Practical Threat Modeling for RAG, Fine‑Tuning, and Deployment
A practical, implementation‑focused guide to threat modeling Large Language Model (LLM) applications. Covers attack classes (prompt injection, model extraction, data poisoning), RAG/vector DB considerations, fine‑tuning risks, mitigations (access control, DP, monitoring), testing and red‑teaming, and common implementation mistakes—grounded in published research, vendor guidance, and standards.