What does business sense for engineers look like in practice? Arnon Cavaeiro joins me to talk about product engineering, product discovery and choosing which customer problems to solve. We get into revenue, costs and product strategy, and how understanding them helps you decide what to build next.

The conversation moves from discovery to delivery, with practical questions engineers can bring to their next project. Why can a feature go unused even when a customer requested it? What is the smallest experiment that would test your riskiest assumption? Arnon shares how to use early signals before revenue catches up, make the business case for technical debt, and navigate the growing overlap between engineering and product management.

In our conversation, we discuss:

1. Why understanding revenue and costs changes what engineers choose to build
2. How to judge customer problems by reach, pain and relative priority
3. Why customer requests need small experiments and clear product positioning
4. How leading metrics help you change direction before revenue catches up
5. How to connect technical debt and engineering decisions to business outcomes

Where to find Arnon Cavaeiro:
• LinkedIn: https://www.linkedin.com/in/acavaeiro/

Where to find Peppe:
• LinkedIn: https://www.linkedin.com/in/peppesilletti/
• X: https://x.com/peppesilletti
• Website: https://peppesilletti.io

Where to find The Product Engineers Podcast:
• Website: https://productengineers.com

In this episode, we cover:
(00:00) Introduction
(03:23) Why engineers need to understand the business
(07:15) Choosing problems worth solving
(09:56) Small experiments and risky assumptions
(13:16) Unused features, customer requests and positioning
(16:57) Building as thinking, prototypes and taste
(26:05) Outcomes, experiments and leading metrics
(35:33) Making the business case for technical debt
(41:40) Who owns product decisions?
(47:08) Closing thoughts and where to find Arnon

Referenced:
• Taste Labs: https://tastelabs.com/
• Linear: https://linear.app/

Robert C. Martin, also known as Uncle Bob, joins The Product Engineers Podcast to discuss why technical expertise still matters when AI writes the code. The author of Clean Code and Clean Architecture explains how those principles help engineers steer AI coding agents towards better software.

We get into Robert's use of static analysis, complexity and coverage metrics, dependency checks and mutation testing. He shares how he stays technically involved while reading less code, and why understanding customers is essential to choosing the right technical constraints.

In our conversation, we discuss:

1. Why clean code and clean architecture still matter when AI writes the code
2. How static analysis and dependency checks help steer agents towards better software
3. How Robert combines acceptance tests, QA tests and mutation testing to check AI-written code
4. How engineers can stay technically in control without reading every line of code
5. Why understanding customers leads to better technical decisions

Episode transcript: https://productengineers.com/episodes/season2/why-hardcore-technical-focus-still-matters-big-time

Where to find Robert C. Martin:
• X: https://x.com/unclebobmartin
• Website: https://cleancoder.com/
• Videos: https://cleancoders.com/

Where to find Peppe:
• LinkedIn: https://www.linkedin.com/in/peppesilletti/
• X: https://x.com/peppesilletti
• Website: https://peppesilletti.io

Where to find The Product Engineers Podcast:
• Website: https://productengineers.com

In this episode, we cover:
(00:00) Why technical expertise still matters with AI
(05:27) Agile, customer contact and organisational change
(08:39) Learning from customers without losing technical depth
(13:19) Connecting technical requirements to customer needs
(18:00) Clean code, static analysis and quality metrics for agents
(23:48) Enforcing architecture and checking behaviour with tests
(28:26) TDD, reading less code and staying technically in control
(34:27) Where deep specialists matter and who can build with AI
(40:30) Where to find Robert

Referenced:
• Clean Code: https://www.informit.com/store/clean-code-a-handbook-of-agile-software-craftsmanship-9780135398579
• Clean Architecture: https://www.informit.com/store/clean-architecture-a-craftsmans-guide-to-software-structure-9780134494166
• Agile Manifesto: https://agilemanifesto.org/
• CRAP4J: the complexity-and-coverage metric discussed in the episode
• Gherkin: the acceptance-test language used in Robert's workflow

Everyone tells engineers to talk to users. But what should you ask, and how do you turn the answers into better product decisions? Sara Iacozza is a Senior UX Researcher and Product Strategist with 7+ years of experience helping teams understand user behaviour and decide what to build.

In our conversation, we discuss:

1. How understanding users helps engineers make trade-offs and find simpler solutions
2. How to practise user interviews with colleagues, an AI coach, and a researcher’s feedback
3. How to avoid leading questions, use plain language, and ask about real experiences
4. How to turn interview notes into shared insights and check AI analysis against the evidence
5. How to get closer to users by shadowing interviews, watching recordings, and talking to colleagues

Episode transcript:

Where to find Sara Iacozza:
• LinkedIn: https://it.linkedin.com/in/sara-iacozza/

Where to find Peppe:
• LinkedIn: https://www.linkedin.com/in/peppesilletti/
• X: https://x.com/peppesilletti
• Website: https://peppesilletti.io

Where to find The Product Engineers Podcast:
• Website: https://productengineers.com

In this episode, we cover:
(00:00) Introduction and what UX research involves
(06:35) Why user research matters for engineers
(12:33) Learning to interview users, with practice and AI
(24:19) Avoiding leading questions and polite answers
(28:39) Using language your users understand
(33:08) Turning conversations into insights and checking AI analysis
(42:00) Making research part of the team’s work
(45:20) Getting closer to users when access is limited
(51:35) Where to find Sara

Referenced:
• Sara’s User Interview Coach (announcement and access link): https://www.linkedin.com/posts/sara-iacozza_chatgpt-user-interview-coach-activity-7296141061372993536-pWZQ
• Continuous Discovery Habits by Teresa Torres: https://www.producttalk.org/continuous-discovery-habits/

Jon Yablonski is a senior product designer at Mixpanel and the author of Laws of UX. In this episode, we explore how software engineers can get better at UX design through observation, practice, and collaboration, starting with the products they already use and love.

In our conversation, we discuss:

✦ Why engineers may already have more design skills than they realise

✦ How to train your design eye by studying products you use and love

✦ How engineering decisions shift complexity between your code and your users

✦ When familiar interface patterns help, and where novelty makes sense

✦ How Jon uses AI for prototypes while working through design ideas with pen and paper

Episode transcript: [ADD EPISODE URL]

Where to find Jon Yablonski:
• Website: https://jonyablonski.com/
• LinkedIn: https://www.linkedin.com/in/jon-yablonski-b4269115/

Where to find Peppe:
• LinkedIn: https://www.linkedin.com/in/peppesilletti/
• X: https://x.com/peppesilletti
• Website: https://peppesilletti.io

Where to find The Product Engineers Podcast:
• Website: https://productengineers.com

In this episode, we cover:

(00:00) Introduction: what UX design really means
(07:01) Why engineers aren’t “bad at design”
(10:22) Bridging design and engineering
(17:45) How to train your design eye
(20:16) Laws of UX: complexity and trade-offs
(27:29) Familiar patterns and when to break them
(31:57) Designing better AI experiences
(37:37) Using AI without outsourcing your thinking
(41:49) Figma, AI prototypes, and design systems
(47:31) Design is a skill you can practice

Referenced:
• Laws of UX: https://jonyablonski.com/work/laws-of-ux/
• Laws of UX, 2nd Edition: https://www.oreilly.com/library/view/laws-of-ux/9781098146955/
• Tesler’s Law
• Jakob’s Law
• Figma
• Claude Code
• Storybook

Drew Hoskins is the author of The Product-Minded Engineer and has spent more than 20 years building products and platforms at Microsoft, Meta, Stripe, and Temporal. His work and writing focus on helping engineers develop a product mindset by understanding their users, deciding what’s worth building, and turning technical expertise into better products.

In our conversation, we discuss:

1. Why great code can fail to win adoption, and how product thinking helps engineers increase their impact
2. How to learn from users through support records, sales calls, GitHub issues, and product metrics
3. How user scenarios reveal “plot holes,” guide prioritisation, and become tests that reflect real usage
4. What Drew learned as a PM at Temporal, and how tools help make product-minded habits last
5. Why AI may create software generalists, how to review AI-generated tests, and practical exercises to start tomorrow

Episode transcript: https://productengineers.com/episodes/season2/how-to-become-a-product-minded-engineer-in-the-ai-era

Where to find Drew Hoskins:
• LinkedIn: https://www.linkedin.com/in/drewhoskins2/
• Substack: https://drewhoskins.substack.com/

Where to find Peppe:
• LinkedIn: https://www.linkedin.com/in/peppesilletti/
• X: https://x.com/peppesilletti
• Website: https://peppesilletti.io

Where to find The Product Engineers Podcast:
• Website: https://productengineers.com

In this episode, we cover:
(00:00) Introduction and what product-minded engineering means
(05:39) Developer experience, adoption, and engineering career impact
(10:47) Getting closer to users through conversations and data
(15:25) User scenarios, plot holes, and meaningful tests
(22:01) Lessons from Temporal and building a product-minded culture
(28:21) AI coding, reviewing tests, and the software generalist
(33:57) Collaboration, daily habits, and exercises to get started

Referenced:
• The Product-Minded Engineer: https://www.oreilly.com/library/view/the-product-minded-engineer/9781098173722/

If you’re a product engineer, engineering manager or PM, we all know how it goes: you go through tons of meetings every day, and only when they end is when real work starts. PRDs, tickets, specs, follow-ups — all the artefact work nobody sees you do. And by the time you’re done, the next meeting is already starting.

One of today’s guests has a name for it: the infinite workday.

And that’s exactly the problem today’s guests set out to fix. Mark Barbir and Sanden Gocka are the founders of Earmark — an AI meeting note taker that turns live conversations into shippable work in real time. We get into how they actually use it day to day, how it helps product teams, where the new bottleneck of product work has moved, and what it looks like when an idea you just said out loud turns into a working prototype before the call ends.

If you’ve been hunting for AI tools for product managers that go beyond generic summaries, I’m walking you through them, and you can decide if it’s worth trying.

GUESTS

Mark Barbir and Sanden Gocka are the co-founders of Earmark. They previously worked together at Mindbody, a large enterprise SaaS company with a 400-person delivery organisation, and later crossed paths again at ProductPlan, where Sanden was an engineering manager and director. After years of watching product and engineering leaders drown in meetings and artefact work, they set out to build the tool they wished they had: an AI meeting assistant that generates the artefacts a conversation implies, in real time, while the meeting is still happening.

Mark’s LinkedIn: https://www.linkedin.com/in/markbarbir/Sanden

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

Earmark: https://www.tryearmark.com

TIMESTAMPS

00:00 The Invisible Second Shift: PM Meetings Problem

01:49 The Infinite Workday: Why PMs Work After Hours

07:49 Vision Pro Pivot: From Presentation Coach to Meeting AI

11:35 Why Vision Pro Failed (and What They Learned)

14:13 Engineering Translator Card: Understanding Technical Talk

16:00 Specs, PRDs & Code in Real-Time During Meetings

19:00 Make Me Look Smart: AI for Shy PMs

28:32 PMs Are Now The New Bottleneck

31:56 Dog Food Success: How Earmark Built Earmark

40:09 How to Get Started with Earmark

REFERENCES

Cursor: https://cursor.com

Linear: https://linear.app

MacWhisper — the local speech-to-text tool Sanden used in his early hacked workflow

AssemblyAI (referenced in the co-marketing case study story): https://www.assemblyai.com

CONNECT WITH PRODUCT ENGINEERS

Host: Peppe Silletti

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

Product Engineers:

Website: https://productengineers.com

Discord Channel: https://discord.gg/4sMtRNSgU4

Share this episode with a teammate who is still writing their PRDs at 10pm.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit

podcast.productengineers.com

AI is reshaping product development and the future of work for every role in the tech industry. Every product team used to work the same way: slow discovery, designers as bottlenecks, agile scrum ceremonies that meant nothing because the rest of the company was still doing waterfall, and a measurement phase everyone skipped. The result was feature factories everywhere. In this episode, I break down what product development looked like before AI and what it looks like now — based on my own experience and conversations with founders and engineers building AI-native companies.

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TIMESTAMPS

00:00 How Product Development Worked Before AI

01:06 Discovery: Slow Data, Slow Analysis

02:17 Design: Always a Bottleneck

03:43 Delivery: Scrum in a Waterfall Company

06:36 Measurement: The Phase Everyone Skipped

07:00 The Feature Factory Problem

07:43 How AI Changed Discovery

09:06 The New PM: Full-Stack Product Builders

12:00 How AI Changed Design: Three Approaches

15:44 How AI Changed Delivery: Four Levels of AI Collaboration

20:00 Tools: Cursor, Claude Code, Codex, Superpowers, nWave

22:22 How AI Changed Measurement

24:16 AI-Native Startups Are Finally Agile

25:37 Daily Sprints and Same-Day Feedback (Andsend)

26:39 The Convergence of Product and Engineering Roles

28:39 What This Means for Your Career

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RESOURCES MENTIONED

- PostHog — product analytics platform with AI-powered insights

- Andsend — AI-native startup with daily sprints

- Cursor — AI-powered IDE

- Claude Code — AI coding agent by Anthropic

- Codex — AI coding agent by OpenAI

- Superpowers — AI agent framework for software development

- nWave — multi-agent framework for the full product development lifecycle

- Lovable, V0, Bolt, Stitch, Replit — AI prototyping tools

- Figma MCP — integration between AI agents and Figma

- MCP Protocol — Model Context Protocol for tool integration

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CONNECT WITH PRODUCT ENGINEERS

Host: Peppe Silletti

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

Website: https://peppesilletti.io

Product Engineers

Website: https://productengineers.com

Newsletter: https://newsletter.productengineers.com

Community: https://discord.gg/4sMtRNSgU4

---

SUPPORT THE SHOW

If you've seen AI change how your team builds products — or you're still stuck in the old way — share this episode with your team. Drop a comment with what phase AI impacted most for you: discovery, design, delivery, or measurement.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit

podcast.productengineers.com

AI software development is changing software engineering forever, but most AI tools produce code that drifts, hallucinates, and needs constant rework. What if test-driven development, refactoring, and product engineering discipline could be baked into every AI-generated line of code? Michele Brissoni, Alessandro Di Gioia, and Marco Consolaro have a combined 60+ years of experience — from Ferrari Formula One to Fortune 500 DevOps transformations to writing the award-winning book on TDD. They built nWave to answer that question.

---

GUESTS

Michele Brissoni - Founder of BrickX Consulting, Fractional CTO, Host of Forge of Unicorns Podcast

Michele has 20+ years in software engineering, starting with behavioural engineering at Ferrari F1 during the Schumacher era. He invented the Software Craftsmanship Dojo, led a DevOps transformation at IBM across 15,000 people, and has hosted 80+ episodes interviewing the people behind 52 unicorn companies.

Alessandro Di Gioia - Software Craftsman, Technical Coach, Co-founder of Alcor Academy

Alessandro co-authored "Agile Technical Practices Distilled" with Marco and Pedro Santos, a multi-award-winning book on TDD, refactoring, and software design. He's been teaching outside-in development at conferences like NDC, DDD Europe, and DevOps Days for years. He's the primary builder behind nWave's deterministic execution system.

Marco Consolaro - Software Craftsman, Technical Coach, Co-founder of Alcor Academy

Marco co-authored "Agile Technical Practices Distilled" and has spent years teaching software craftsmanship and TDD. He brings a focus on the human side of engineering — sustainable pace, team dynamics, and the importance of trust in building quality software.

Find them:

- nWave GitHub: https://github.com/nWave-ai

- Michele's LinkedIn: https://www.linkedin.com/in/michelebrissoni

- Alessandro's LinkedIn: https://www.linkedin.com/in/alessandro-di-gioia/

- Marco's LinkedIn: https://www.linkedin.com/in/consolaro/

---

TIMESTAMPS

00:00 Introduction and Guest Welcome

03:00 The Origin Story: From Early AI Tools to nWave

08:03 The Career Lesson Behind nWave

10:17 Discuss nWave: Understanding What to Build and For Whom

26:16 Design nWave: Architecture with Adversarial Reviews

38:41 DevOps and Distill nWaves: CI/CD and Acceptance Tests Before Code

41:42 Deliver nWave: Outside-In TDD with Fresh Context Every Step

52:51 "Doesn't This Make You Slower?" — The Rework Argument

01:02:25 What Do You Do While nWave Is Working?

01:05:34 Closing and How to Contribute

---

RESOURCES MENTIONED

Link with all research material used to build nWave: https://drive.google.com/file/d/1yvSDNwj-F1amEavoX3Qz4oRZfw-9XKF9/view

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CONNECT WITH PRODUCT ENGINEERS

Host: Peppe Silletti

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

Product Engineers Community:

Website: https://productengineers.com

Discord Channel: https://discord.gg/4sMtRNSgU4

---

SUPPORT THE SHOW

If this episode changed how you think about AI-assisted development, share it with an engineer or team lead who's struggling with AI code quality. Drop a comment below with your biggest takeaway.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit

podcast.productengineers.com