Product for Product Management

We’re excited to welcome Lea Khasidi, product management freelancer, founder and CEO of MaPott, and soon‑to‑be author, for a powerful conversation on gender-based data analytics for product managers.

Lea shares her journey from intelligence work in the IDF, through product roles in EdTech, cybersecurity, and beyond, to founding a health management app for women with chronic conditions. Along the way, one pivotal discovery while consolidating four cybersecurity platforms, seeing men and women use the same filtering feature in completely different ways, sparked her deep focus on how gender shapes behavior in our products, especially in B2B contexts where it’s often ignored.

Join Matt and Moshe as they explore with Lea:How a real-world analytics puzzle revealed stark gender differences in workflow, and changed how she thinks about usage data

The three pillars of context we can’t change (who users are, how they act, where they work) and the one we can: what we give them in the product

How “hacks” and workarounds used by different genders can hint at your next features

A practical system for existing products: using analytics to segment by gender, spotting behavioral differences, then following up with observation and interviews

How to approach gender representation when validating new products, and why balanced samples matter even for seemingly “neutral” tools

Ways to lean on existing research about gender patterns in your domain (health, education, finance, etc.) instead of guessing from scratch

Why early-stage startups should ship faster and learn from usage, while mature products might rely more on A/B tests and targeted outreach

The risk of building solely from our own biases and assumptions, and why gender should be on the PM checklist, even if it’s not always the first lens

Lea’s upcoming book on gender-based analytics for PMs

One piece of advice she has for founders learning to trust their insight while staying open to the data

And much more!

Want to connect with Lea or learn more?LinkedIn: https://www.linkedin.com/in/lea-khasidi/ 
Website: https://leakhasidi.com
MaPott: https://www.ma-pott.com/

You can also connect with us and find more episodes: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

Note: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not represent the products mentioned in any way.

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We’re delighted to welcome Radhika Dutt, product leader, founder, and author of Radical Product Thinking, for a deep dive into her newest work: the OHLA (formerly OHL) Toolkit.

 In this episode, Radhika joins Matt and Moshe to challenge how product teams set goals, measure progress, and use frameworks, proposing a puzzle‑driven alternative to traditional OKRs and “framework-following” culture.

Drawing from her journey from electrical engineering and startups at MIT, through painful “product diseases” like Hero Syndrome and Obsessive Sales Disorder, Radhika shares why recipes and templates alone don’t create real progress. Instead, she introduces OHLA as a lightweight but powerful way to cultivate a Jedi mindset, one that keeps teams grounded in first principles, context, and learning rather than chasing vanity metrics and rigid targets.

Join Matt, Moshe, and Radhika as they explore:Radhika’s path from engineering and founding to Radical Product Thinking and now the OHLA Toolkit

Why classic goal‑setting and OKRs often backfire, creating “alibi progress,” outdated goals, and incentives to hide bad news

OHLA in practice:

Observe – what’s really happening in your product, team, or market

Hypothesize – what might explain it and how you’ll test those ideas

Learn – what the results actually tell you

Adapt – how you’ll change course based on evidence

“Puzzle setting” vs goal setting: defining puzzles with Observation, Open Questions, and an Objective summary to stay longer in the problem space

How OHLA complements design thinking by forcing teams to remain curious and uncomfortable before jumping into solutions

Practical stories, from maritime platforms to enterprise teams, where puzzle thinking led to very different solutions than OKR‑driven targets

How managers can shift conversations from “Did we hit the number?” to “How well did it work? What did we learn? What will we try next?”

A realistic path to transition: starting with your own puzzle, then introducing OHLA within your immediate sphere of influence

And much more!

Want to explore the OHLA Toolkit or connect with Radhika?OHLA Toolkit: https://www.radicalproduct.com/toolkit/#OHLToolkit

Radical Product site: https://www.radicalproduct.com/

LinkedIn: https://www.linkedin.com/in/radhikadutt/

You can also connect with us and find more episodes: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

Note: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not represent the products mentioned in any way.

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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:
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!
You can connect with us and keep following what comes after this AI Tools series:
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
Note: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not represent the products mentioned in any way.

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We’re keeping the AI Tools series rolling with Adir Traitel, entrepreneur, product leader, and early adopter of just about every vibe coding tool out there. Adir joins Matt and Moshe to share hard‑won lessons from building real apps with v0, Bolt, Replit, Figma Make, and more, all while running his own startup and consulting on product builds across industries.

From his early days in project management and mobile app startups, through work with companies like Moovit and across FinTech, AgTech, and credit scoring, Adir has consistently been the “try it first” person for new build tools. In this episode, he breaks down what these platforms actually do well, where they fall short, and how product managers can use them responsibly for experiments, prototypes, and beyond.

Join Matt, Moshe, and Adir as they explore:
Adir’s journey from PM and founder to heavy user of vibe coding tools in his current startup

His 3-layer view of the ecosystem: AI dev assistants (Cursor, Antigravity, Claude Code), front-end mockup tools (v0, Figma Make), and full‑product builders (Lovable, Base44, Bolt, Replit)

V0: where it shines for quickly building functional UIs (like his electricity consumption app) and where it starts to crack

Lovable: great for sites and simple flows, but not ideal for complex SaaS or CRM‑like products

Bolt: fun and fast for concepts, but why it never got him close to production

Replit: stronger agents and capabilities, but weaker UI output and surprising backend defaults that can get very expensive very quickly

Figma Make and Google Stitch: when design quality trumps everything else, especially for SaaS interfaces

The real costs of vibe coding: AI token spend, hosting/pricing traps, and why production economics matter as much as build speed

What his “dream product” would look like, including multi‑agent environments, better security/privacy, and built‑in QA and CI/CD

How all this is reshaping the product management role, and why curiosity and tool fluency are becoming must‑have skills

And much more!
Want to connect with Adir or learn more?
LinkedIn: https://www.linkedin.com/in/adirtraitel/

Website: https://adirtraitel.com/
You can also connect with us and find more episodes:
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
Note: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not represent the products mentioned in any way.

Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️

We’re continuing our AI Tools series with Marcos Polanco, engineering leader, founder, and ecosystem builder from the Bay Area, who joins Matt and Moshe to introduce CLEAR, his method for using AI to build real software, not just demos. 

Drawing on decades in software development and his recent research into how AI is reshaping the way teams ship products, Marcos shares how CLEAR gives both technical and non‑technical builders a production‑oriented way to work with vibe coding tools.Instead of treating AI like a magical black box, Marcos frames it as an “idiot savant”: incredibly capable and eager, but with no judgment.

CLEAR wraps that raw power in structure, guardrails, and engineering discipline, so founders and PMs can go from prototype to production while keeping humans in control of the last, hardest 20%.

Join Matt, Moshe, and Marcos as they explore:
Marcos’s journey through engineering, founding, and AI research, and why he created CLEAR

Why AI tools like Bolt, Cursor, Claude, and Gemini are fabulous for prototypes but risky for production without a method

CLEAR in detail:

C – Context: onboarding AI like a new hire, using stories and behavior‑driven design (BDD) to articulate requirements

L – Layout: breaking work into focused, scoped pieces and choosing a tech stack so AI isn’t overwhelmed

E – Execute: applying test‑driven development (TDD), writing tests first, then having AI write code to pass them

A – Assess: using a second, independent LLM as a QA agent, plus a human‑run 5 Whys to fix root causes upstream

R – Run: shipping to users, gathering new data, and feeding it back into the next iteration of context

How CLEAR lowers cognitive load for both humans and AIs and reduces regressions and hallucinations

Why Markdown (with diagrams like Mermaid) is becoming Marcos’s standard format for shared human–AI documentation

How CLEAR changes the coordination layer of software development while keeping engineers central to quality and judgment

Practical advice for PMs and founders who want to move from “just vibes” to predictable, production‑grade AI development

And much more!

Want to go deeper on CLEAR or connect with Marcos?
CLEAR on GitHub: https://github.com/marcospolanco/ai-native-organizations/blob/main/CLEAR.md

CLEAR slides: https://docs.google.com/presentation/d/1mwwDtr7cCP5jLUyNVgGR5Aj-MBq8xsMlhSc0pvSQDks/edit?usp=sharing

LinkedIn: https://www.linkedin.com/in/marcospolanco

You can also connect with us and find more episodes:
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

Note: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not represent the products mentioned in any way.

Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️