This week on How I AI: 0-to-1 AI guide for absolute beginners + how this CEO turned 25,000 hours of sales calls into a self-learning GTM engine
Your weekly listens from How I AI, part of the Lenny’s Podcast Network
Hey friends 👋,
Here’s a weekly recap of new podcast episodes across Lenny’s Podcast Network:
Every Monday, host Claire Vo shares a 30- to 45-minute episode with a new guest demoing a practical, impactful way they’ve learned to use AI in their work or life. No pontificating—just specific and actionable advice.
How this CEO turned 25,000 hours of sales calls into a self-learning go-to-market engine | Matt Britton (Suzy)
Brought to you by:
Brex—The intelligent finance platform built for founders
Zapier—The most connected AI orchestration platform
Guest: Matt Britton, founder and CEO at Suzy
Biggest takeaways:
“There’s always a way” should be your AI mantra. When Gong didn’t provide an easy way to access call transcripts, Matt could have given up. Instead, he hacked together a solution using Browse AI to scrape the data. “Just because the tool doesn’t do it, doesn’t mean it can’t be done,” he explains.
Customer calls are your best source of marketing keywords. By extracting the exact language customers use to describe their needs, Matt’s system automatically identifies keywords they should be bidding on in Google—ensuring that their marketing speaks the same language as their customers.
The future belongs to proactive problem-solvers. Matt is reshaping his team to favor “far more individual contributors, far more people who want to put their hands on keyboard.” As he explains, “I don’t need more people to tell what to do. I need people who are going to come up with new ideas and solutions and be proactive.”
▶️ Listen now on YouTube | Spotify | Apple Podcasts
A complete beginner’s guide to coding with AI: From PRD to generating your very first lines of code
Brought to you by: ChatPRD—An AI copilot for PMs and their teams
Biggest takeaways:
Cursor’s Composer One model is blazingly fast for simple projects. While you might need more-powerful models for complex applications, Claire demonstrates how Composer One can scaffold a basic Next.js application in seconds. For beginners, this speed means less waiting and more learning through rapid iteration.
The “editor view” vs. “agent view” choice matters for beginners. Cursor 2.0 offers two interfaces, and Claire recommends the agent view for beginners because it focuses on what you’re building rather than file structures. This approach shields newcomers from overwhelming technical details until they’re ready.
JavaScript is “easy to see,” while Python is “easy to read.” When choosing your first programming language for AI coding, Claire suggests JavaScript (particularly with Next.js) because you can immediately see your results in a browser, while Python might be more readable but requires more steps to visualize.
When AI tools give you too much, start over. Claire’s experience with V0 generating an overly complex application demonstrates an important lesson: If an AI tool is making things more complicated than you want, it’s often faster to start over with a different approach than to try fixing what you have.
▶️ Listen now on YouTube | Spotify | Apple Podcasts
More shows coming soon. . . 👀
If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy, anything.
Catch you next week,
Lenny
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I agree with this piece. As a regular user of AI to ship products, I appreciate how both of these episodes have made the exact same point – the power does not come from "AI" itself as an abstract concept but from users who refuse to stop pushing vendors to incorporate these tools into the flow of their work. Suzy's example also serves as a reminder to teams that are waiting for the perfect integration from their vendor; if you cannot get clean call transcripts from your vendor by 2025, then you will either scrape/export or leave money-making opportunities sitting on the sidelines.
In terms of new comers, I believe you're spot-on to encourage folks to see some type of output (Next.js, browser, agent view, smaller models) rather than dumping a bunch of files and token logs on them in the first week of trying to learn. However, I would take it one step farther by making sure to explain the potential pitfalls of the tutorial process. For non-technical folks, there is a high likelihood that after completing these types of tutorials, they'll be convinced that they can now create an MVP, when all they've actually done is created a demo they can't maintain/debug. A secondary part to the mantra might be: "There's always a way," and "If it seems too magical, take a step back and run it again, step-by-step."
That being said, this is one of the few "AI for Beginners" rundowns that hasn't glossed over the specifics of the tools nor jumped directly to thought leaders; that's exactly what people need right now -- concrete, marginally glamorous patterns they can replicate into their own environment tomorrow.