Vin Patel is an AI technologist and published author with 25+ years of experience. He is the creator of Manuscript (open-source AI content detection), AEORank (AI Engine Optimization), and the Bhagavad Gita App. His work has been archived by the British Library and cited by UKCERT.

AEORank is an open-source CLI tool that scans any website across 12 AI visibility dimensions, scores it from 0-100, and generates 8 deployment-ready files that improve discoverability by AI engines like ChatGPT, Perplexity, and Claude. Free at aeorank.dev.

What is the Bhagavad Gita App?

A verse-by-verse platform at bhagavad.net presenting all 700 Gita verses through 8 philosophical traditions with multi-tradition synthesis, life applications across 4 pillars, and 5,600+ searchable life questions. Open source and MIT licensed.

Manuscript is the only open-source AI content detector that runs 100% on your infrastructure. It detects AI-generated text, images, audio, and video with zero external API calls. Built in Go, self-hosted via Docker. Visit manuscript.dev.

How can I work with Vin Patel?

Vin is available for speaking engagements, podcast interviews, and consulting on AI strategy. He also runs the IdeaForge Workshop, a 4-day intensive for building AI products. Contact vinpatel.pro@gmail.com or visit vinpatel.com/speaking.

Project Glasswing: Security Infrastructure for AI Software Supply Chains

The signal: Microsoft released Project Glasswing, a framework to secure critical software dependencies that AI systems rely on. 1200+ HN engagement.

Why it matters: A single model deployment pulls in hundreds of packages — Python, CUDA drivers, specialized ML libraries. Traditional supply chain security wasn’t built for this complexity. Glasswing is the first serious attempt at mapping AI-specific dependency graphs.

The pattern I’m watching: We’re hitting the infrastructure maturity phase for AI. The same way Docker changed deployment and Snyk emerged for supply chain security, we’re seeing purpose-built tooling for AI operational concerns.

What I’d do with this: Audit your AI dependencies now. Map what your model inference actually touches in production — it’s probably 3x more than you think. AI governance tooling is becoming table stakes, not nice-to-have.