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 His work has been archived by the British Library and cited by UKCERT.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.

AI Is Making Developers Write Code More Slowly

The signal: The top HackerNews thread this week isn’t about AI making developers faster — it’s about using AI to write better code more slowly, and the community is eating it up.

Why it matters: Every VC pitch deck says AI gives you 10x developer velocity, but practitioners are pushing back hard. If you’re shipping a real product, slow and deliberate beats fast and brittle — and developers are finding that AI works best as a thinking partner, not a code cannon.

The pattern I’m watching: We’re entering the backlash phase of AI-assisted coding. The “just vibe-code everything” narrative is colliding with production reality, and the builders who are winning are treating AI as a forcing function for clearer thinking, not a replacement for it. The redundant reasoning research from arXiv reinforces this — more LLM “thinking” isn’t always better, it’s about quality of reasoning.

What I’d do with this: Stop measuring AI’s value by lines of code generated per hour — start measuring by bugs caught before production and design decisions made explicitly. Build a personal workflow where AI slows you down at architecture decisions and speeds you up only at implementation.