Max and AI expert Rohan Venkataram break down a packed slate of AI developments: Socotra’s Model Context Protocol (MCP) Server for secure agentic workflows in insurance; a University of York letter on how generative AI is undermining learning and how to redesign assessments; a founder’s playbook for resisting a platform ransom; a plain‑English tour of sparse autoencoders for interpretability; Tencent’s Hunyuan 3D 3.0 model with 1536³ voxel fidelity and free access; OpenAI’s GPT‑5 Codex for more autonomous, review‑aware coding agents; and Google’s VaultGemma work on differential privacy scaling laws. Three takeaways: standardization is how enterprises safely scale agents, autonomy requires human‑in‑the‑loop accountability, and privacy is a tunable trade‑off among noise, data, and compute.Sources:

• Socotra Launches MCP Server for Secure Agentic AI Integration
• Google releases VaultGemma, its first privacy-preserving LLM
• How I Stopped A Platform Ransom Against My Startup
• Tencent Unveils Hunyuan 3D 3.0 AI Model: Tripling Modeling Accuracy with Free Access
• Chery to Launch Hong Kong IPO Bookbuild on Sept 17, Targeting HK$140B Valuation
• OpenAI releases GPT-5 Codex designed for bug fixes and code generation
• The Sequence Knowledge #720: A Cool Intro to Sparse Autoencoders for AI Interpretability
• heise+ | KI-Bildbearbeitung im Vergleich: ChatGPT 5 und Gemini 2.5 Flash Image
• OpenAI Introduces GPT-5-Codex: An Advanced Version of GPT-5 Further Optimized for Agentic Coding in Codex
• How AI is undermining learning and teaching in universities | Letter