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EP 149 - AI Tools: Summary with Matt & Moshe
We’re wrapping up our AI Tools series with a special episode featuring just the two of us—Matt and Moshe—looking back at what we really learned (and where we’re still confused) about AI in product management.
Across this conversation, we revisit the core themes that emerged with our guests and in our own experiments: from “vibe coding” and no‑code builders, to LLM assistants, enterprise privacy, agentic workflows, and the evolving role of the product manager.
We share candid stories of using tools like Google Stitch, Figma/Figma Make, FlutterFlow, Base44, and others to design and prototype a real mobile app; what worked, what broke, and why credits, pricing, and model limits matter far more than the glossy demos suggest.
Join Matt and Moshe as they explore:
Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️
Across this conversation, we revisit the core themes that emerged with our guests and in our own experiments: from “vibe coding” and no‑code builders, to LLM assistants, enterprise privacy, agentic workflows, and the evolving role of the product manager.
We share candid stories of using tools like Google Stitch, Figma/Figma Make, FlutterFlow, Base44, and others to design and prototype a real mobile app; what worked, what broke, and why credits, pricing, and model limits matter far more than the glossy demos suggest.
Join Matt and Moshe as they explore:
- How our AI Tools series evolved, from “let’s review tools” to “AI is not one thing, it’s many different problem spaces”
- Why “vibe coding” is a misleading umbrella term, and how it means something different to devs, PMs, and designers
- Lessons from using AI for design and prototyping: inconsistent outputs, beta‑stage rough edges, and the pain of credit-based models
- Build vs. buy for AI: integrating foundation models vs. building your own, and what that means for pricing, UX, and reliability
- Enterprise realities: privacy, security, and why tools like Copilot/Gemini have such an advantage where data and IT policies matter
- How conversations with our guests (Sani, Eva, Elena, Stav, Yaron, Marcos and Adir) shifted our thinking about workflows, orchestration, and agents
- The future of agent-to-agent interactions: what happens when AIs negotiate purchases and workflows with minimal human prompts
- Why first principles and business outcomes still matter more than any single AI tool
- How the PM role is changing: less tool‑chasing, more orchestration, strategy, and clarity about what problem we’re actually solving
- What topics we’d tackle next, like pricing, packaging, and credit models for AI products, and how this series is shaping our own careers
- And much more!
- Product for Product Podcast:
- http://linkedin.com/company/product-for-product-podcast
- Matt Green:
- https://www.linkedin.com/in/mattgreenproduct/
- Moshe Mikanovsky:
- http://www.linkedin.com/in/mikanovsky
Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️
View all episodes
By Matt Green & Moshe Mikanovsky
5
66 ratings
We’re wrapping up our AI Tools series with a special episode featuring just the two of us—Matt and Moshe—looking back at what we really learned (and where we’re still confused) about AI in product management.
Across this conversation, we revisit the core themes that emerged with our guests and in our own experiments: from “vibe coding” and no‑code builders, to LLM assistants, enterprise privacy, agentic workflows, and the evolving role of the product manager.
We share candid stories of using tools like Google Stitch, Figma/Figma Make, FlutterFlow, Base44, and others to design and prototype a real mobile app; what worked, what broke, and why credits, pricing, and model limits matter far more than the glossy demos suggest.
Join Matt and Moshe as they explore:
Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️
Across this conversation, we revisit the core themes that emerged with our guests and in our own experiments: from “vibe coding” and no‑code builders, to LLM assistants, enterprise privacy, agentic workflows, and the evolving role of the product manager.
We share candid stories of using tools like Google Stitch, Figma/Figma Make, FlutterFlow, Base44, and others to design and prototype a real mobile app; what worked, what broke, and why credits, pricing, and model limits matter far more than the glossy demos suggest.
Join Matt and Moshe as they explore:
- How our AI Tools series evolved, from “let’s review tools” to “AI is not one thing, it’s many different problem spaces”
- Why “vibe coding” is a misleading umbrella term, and how it means something different to devs, PMs, and designers
- Lessons from using AI for design and prototyping: inconsistent outputs, beta‑stage rough edges, and the pain of credit-based models
- Build vs. buy for AI: integrating foundation models vs. building your own, and what that means for pricing, UX, and reliability
- Enterprise realities: privacy, security, and why tools like Copilot/Gemini have such an advantage where data and IT policies matter
- How conversations with our guests (Sani, Eva, Elena, Stav, Yaron, Marcos and Adir) shifted our thinking about workflows, orchestration, and agents
- The future of agent-to-agent interactions: what happens when AIs negotiate purchases and workflows with minimal human prompts
- Why first principles and business outcomes still matter more than any single AI tool
- How the PM role is changing: less tool‑chasing, more orchestration, strategy, and clarity about what problem we’re actually solving
- What topics we’d tackle next, like pricing, packaging, and credit models for AI products, and how this series is shaping our own careers
- And much more!
- Product for Product Podcast:
- http://linkedin.com/company/product-for-product-podcast
- Matt Green:
- https://www.linkedin.com/in/mattgreenproduct/
- Moshe Mikanovsky:
- http://www.linkedin.com/in/mikanovsky
Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️
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