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 His work has been archived by the British Library and cited by UKCERT.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 AI visibility platform that scans any website across 36 criteria, scores it from 0-100, and generates 9 deployment-ready files that improve discoverability by AI engines like ChatGPT, Perplexity, Claude, and Gemini. Includes a free CLI, dashboard at app.aeorank.dev, GitHub App, and 13 framework plugins. Visit 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.

MCP vs Skills: The AI Agent Architecture Battle Lines Are Drawn

The signal: Developers are increasingly picking Model Context Protocol (MCP) over traditional skills-based approaches for building AI agents.

Why it matters: MCP gives you standardized tool interfaces that work across different models and platforms, while skills lock you into vendor-specific implementations. You’re betting on infrastructure that’ll outlast the current wave of proprietary agent frameworks.

The pattern I’m watching: We’re seeing the same standardization battle that happened with APIs, containers, and cloud interfaces. The winners will be the protocols that reduce vendor lock-in and increase portability across the AI stack.

What I’d do with this: Start building your agent tooling on MCP now—it’s the closest thing we have to a universal standard for AI tool integration. If you’re shipping production agents, MCP’s structured approach will save you months of custom integration work when you need to switch models or add new capabilities.