Adding tools, memory, and RAG knowledge

Today’s episode

LinkedIn just changed the title of its product managers to product builders.

What does it even mean to be a “builder PM”?

Well, tools only get you so far. Learning Claude Code is helpful, but means nothing if you don’t have an understanding of the underlying first principles.

That’s today’s episode.

Mahesh Yadav created one of our most popular episodes, with over 35K views on YouTube, and now he’s back. Earlier, he taught you AI agents. Today, he’s touching you how to become a builder PM:

If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.

I’m giving a free talk on how to get interviews at the top AI PM companies on Thursday, April 23rd 2026 @ 9:00AM PDT. Grab your seat.

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Check out the conversation on Apple, Spotify, and YouTube.

Brought to you by:

* Maven - Build cohort-based courses that scale

* Amplitude - The market leader in product analytics

* Jira Product Discovery - Prioritize what matters with confidence

* NayaOne - Airgapped cloud-agnostic sandbox to validate AI tools faster

* Product Faculty - Get $550 off their #1 AI PM Certification with my link

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Key Takeaways:

1. Builder PM defined - A builder PM talks to customers, figures out what to build, and ships the first version to 10 customers without talking to any developer. The skill is knowing what to build, not knowing how to code.

2. Four agent components - Every agent that works has intelligence (model), tools (actions), memory (session context), and knowledge (your company data). Every agent that disappoints is missing at least one.

3. n8n for foundations - n8n is the best learning tool because you visually see every component of the agent architecture as separate nodes. Build your first multi-agent system and evaluation pipeline here.

4. Claude Code ate three company types - Context companies, action companies, and evaluation companies all got replaced by one agentic loop inside Claude Code. The three pieces collapsed into one tool.

5. Computer control is the real unlock - File system access plus bash commands equals full laptop capability. This is why Claude Code went from coding tool to work operating system.

6. Long-horizon jobs changed the game - AI agents went from 3-minute tasks to 3-6 hour sustained jobs in six months. This turns Claude Code from assistant to autonomous worker.

7. Continuous learning loops - Build a second agent that watches your corrections to the first agent's work. After five repeated patterns, it proposes a skill update. Your tools get better every day.

8. OpenClaw pattern - Delegation through existing channels, full machine sandboxing, model-agnostic. Not a product but a pattern that Google and AWS will copy inside their ecosystems.

9. AI PM interviews changed - At L5 and L6, product sense questions are being replaced with live building exercises and system design for AI architectures. Pull out Claude Code during the interview or you are already out.

10. Compensation trajectory - From $120K at Microsoft to $1.3M at Google over 13 years, doubling every 18 months through AI-focused switches. Left because big companies kill innovation with six-week approval cycles.

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Where to find Mahesh Yadav

* LinkedIn

* Maven Course

Related content

Podcasts:

* Claude Code Team OS with Carl Vellotti

* OpenClaw + Claude Code with Naman Pandey

* Claude Code OS with Dave Killeen

Newsletters:

* The complete context engineering guide

* How to use Claude Code like a pro

* Practical AI agents for PMs

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