u/vishal_jaiswal

I wanted to share two completely free resources I have put together to help product managers transition into AI-native workflows and prep for modern AI product roles.

The first is a comprehensive, open-source tutorial that walks you through using Claude Code as a working PM tool. Instead of just giving you a folder of copy-paste prompts, it teaches you how to build actual agents and automated pipelines that run inside your terminal.

The second is a massive, searchable database of 1,200 real AI PM interview questions asked recently at top-tier tech companies.

Here is a quick breakdown of what you will find in both:

Part 1: The AI-Native PM OS (Course & Workspace)

This is a practical build program containing 63 self-paced lessons (about 40 to 50 hours of total content). Everything is built around a fictional B2B SaaS company called Meridian (with pre-built user research, OKRs, and competitive landscapes) so your outputs feel like actual work products rather than classroom exercises.

• Spec & Sync: Write and stress-test PRDs with Claude, then auto-generate Jira epics via MCP.
• Competitive Intelligence: Build a Competitive Intelligence Agent that sweeps the web and summarizes rival threats.
• Feedback Loop: Cluster customer feedback at scale using a Jobs-to-be-Done framework.
• UX Prototyping: Generate clickable HTML prototypes from rough user flows and deploy them to Vercel instantly.
• Exec Updates: Draft stakeholder-aware OKR updates with tailored tone adjustments.

No paid tools are required beyond a Claude subscription to run the terminal client.

Part 2: The AI PM Question Bank

If you are gearing up for a tough interview loop, I synthesized 1,200 interview prompts from AI Product Manager loops at 73 companies, including Meta, OpenAI, Anthropic, Google DeepMind, Stripe, Perplexity, and Mistral.

• Sourced from public reports and real candidate feedback.
• Categorized into 13 specific PM focus areas so you can filter exactly what you are prepping for.
• Searchable and updated for May 2026 interview patterns.

Links to the Project

• Main Site & Course: https://ai-productmanagers.vercel.app/
• Searchable Question Bank: https://ai-productmanagers.vercel.app/question-bank.html
• GitHub Repository: https://github.com/vishalmdi/ai-native-pm-os

If you find this course or the question bank useful, a star on GitHub helps a lot to keep the project growing. I am happy to answer any questions!

Google recently published official guidance on how to optimize pages for AI-powered search features like AI Overviews and AI Mode - https://developers.google.com/search/docs/fundamentals/ai-optimization-guide

Most of the advice floating around GEO / AI search optimization is still pretty hand-wavy, so I wanted something more concrete.

So, I converted Google’s AI search guidance into an open-source Claude Code skill:

https://github.com/vishalmdi/goog-geo

The skill audits any live URL and turns the guidance into a scored report:

• Checks whether Googlebot can crawl the page
• Checks indexability and snippet eligibility
• Detects noindex, nosnippet, max-snippet, canonicals, robots.txt issues
• Uses a live browser to inspect rendered DOM and JSON-LD schema
• Reviews headings, semantic HTML, answer blocks, FAQs, tables, author/date signals
• Checks whether AI crawlers like GPTBot, PerplexityBot, ClaudeBot, and Bingbot are allowed
• Produces a 100-point GEO / AI search readiness score
• Gives a prioritized action plan instead of vague SEO advice

The main idea is simple - Google’s AI search features are not a totally separate SEO system. They still depend on crawlability, indexability, snippet eligibility, helpful content, and structured/extractable pages.

So instead of guessing what “AI optimization” means, this skill audits against the actual signals Google documented. I also added a “what not to do” section because Google explicitly says some popular AI SEO advice is useless or misunderstood, like treating `llms.txt` as a Google AI ranking lever.

Would love feedback from anyone working on SEO, content, SaaS landing pages, docs, or AI search visibility.

If you find it useful, a GitHub star would help:

Repo Link: https://github.com/vishalmdi/goog-geo

I put together a free, open-source GitHub tutorial that walks you through using Claude Code as a working PM tool - not just for generating text, but for building actual workflows you’d use on the job.

It’s called the AI-Native PM OS. The goal is simple: by the end, you have a set of agents and pipelines that handle the repetitive, time-consuming parts of PM work.

What the tutorial covers:

• Writing and stress-testing PRDs with Claude, then auto-generating Jira epics via MCP
• Building a Competitive Intelligence Agent that runs sweeps and summarizes threats
• Clustering customer feedback at scale using a Jobs-to-be-Done framework
• Generating clickable HTML prototypes from rough product flows and deploying to Vercel
• Drafting executive OKR updates with stakeholder-aware tone adjustments

How it’s structured:

Everything is built around a fictional B2B SaaS company called Meridian, with pre-built personas, OKRs, user research, and a competitive landscape. That gives every exercise real context, so the outputs feel like actual work product, not homework.

• 11 modules, roughly 40–50 hours total
• 30-minute, self-paced lessons
• No paid tools required beyond a Claude subscription

If you’ve been curious about Claude Code but didn’t know where to start as a PM, this is designed for exactly that. Each module builds on the last, so you end up with something functional rather than a collection of one-off prompts.

Repo: https://github.com/vishalmdi/ai-native-pm-os

If you find it useful or think you might come back to it later, a star on GitHub helps a lot — and I’m happy to answer questions in the comments.

I put together a free, open-source GitHub tutorial that walks you through using Claude Code as a working PM tool - not just for generating text, but for building actual workflows you’d use on the job.

It’s called the AI-Native PM OS. The goal is simple: by the end, you have a set of agents and pipelines that handle the repetitive, time-consuming parts of PM work.

What the tutorial covers:

• Writing and stress-testing PRDs with Claude, then auto-generating Jira epics via MCP
• Building a Competitive Intelligence Agent that runs sweeps and summarizes threats
• Clustering customer feedback at scale using a Jobs-to-be-Done framework
• Generating clickable HTML prototypes from rough product flows and deploying to Vercel
• Drafting executive OKR updates with stakeholder-aware tone adjustments

How it’s structured:

Everything is built around a fictional B2B SaaS company called Meridian, with pre-built personas, OKRs, user research, and a competitive landscape. That gives every exercise real context, so the outputs feel like actual work product, not homework.

• 11 modules, roughly 40–50 hours total
• 30-minute, self-paced lessons
• No paid tools required beyond a Claude subscription

If you’ve been curious about Claude Code but didn’t know where to start as a PM, this is designed for exactly that. Each module builds on the last, so you end up with something functional rather than a collection of one-off prompts.

Repo: https://github.com/vishalmdi/ai-native-pm-os

If you find it useful or think you might come back to it later, a star on GitHub helps a lot — and I’m happy to answer questions in the comments.