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If you only get asked about feasibility, this episode is for you. Kent talks with Sam Julien - Director of Product at CopilotKit, author of Agentic GraphRAG - about moving past ticket-taking, collaborative product-engineering dialogue, and frameworks that help you push back with vocabulary. They cover leverage / neutral / overhead triage, Kagan's four big risks, communicating intent with AI agents, and why engineers need product models even when they have a PM. (00:00) - Meet Sam Julien (01:26) - Dev to DevRel to product (03:37) - Product, design, and engineering overlap (06:00) - Durable skills as agents write code (12:01) - Get closer to customers (15:34) - Building a product-minded engineering team (18:46) - When technical work was the whole job (25:28) - Talk through the problem first (33:26) - Sales-driven development and gatekeeping (36:26) - Beyond feasibility (39:35) - LNO, RICE, and friends (42:42) - Continuous discovery and agents (46:44) - Communicating intent to AI (51:27) - Homework: triage your backlog Sam Julien went from self-taught web developer to developer relations to product. He is Director of Product at CopilotKit (generative UI across front ends, Slack, Teams) and author of the O'Reilly book Agentic GraphRAG. The through-line is product-minded engineering as a durable skill. Implementation-only careers are getting thinner as agents take more code work. Sam's article on building a product-minded engineering team name-drops jobs-to-be-done, Kagan's four big risks, continuous delivery, LNO (leverage / neutral / overhead), DHM, RICE, and North Star - and he lands on LNO as especially practical for backlog triage. Leverage work: small effort, outsized outcome. Neutral: necessary, so scope it. Overhead: automate or drop - often a fit for AI. Engineers need these models so they can push back with shared vocabulary, especially when the PM is checked out. Feasibility alone is not enough; value, usability, and business viability belong in the dialogue. They also talk collaborative product-engineering conversations, gatekeeping sales-driven development, and communicating intent clearly when agents execute. Homework: spend ten minutes on your backlog and apply at least one triage framework (LNO or similar). Extra credit: watch something recent from Teresa Torres. Homework Open your backlog once this week.Spend about ten minutes sorting items with a triage lens (LNO - leverage, neutral, overhead - or another framework from the episode).For each item, ask why you are doing it, what the effort is, and what the value or impact is.Resources Sam JulienBuilding a Product-Minded Engineering TeamCopilotKitSam on XSam on GitHubSam on LinkedInAgentic GraphRAG (O'Reilly)Guest: Sam Julien Company: CopilotKitGitHub: @samjulien𝕏: @samjulienHost: Kent C. Dodds Website: kentcdodds.com𝕏: @kentcdoddsGitHub: @kentcdoddsYouTube: kentcdodds-plusPodcast: epicproduct.engineerSee on Epic Product Engineer

If you ship features and never hear what users actually said, this episode is for you. Kent talks with Ben Vinegar - co-founder of Modem, formerly founding engineer and VPE at Sentry - about aggregating feedback, eliminating adoption barriers, and keeping human judgment in the loop as AI speeds up shipping. They cover FreshBooks-era product engineering, Modem's approach to capturing feedback across Slack, Discord, GitHub, and support tools, and why five real conversations beat a median LLM opinion. (00:00) - Meet Ben Vinegar (01:29) - Career startup guy (03:48) - What Modem is for (06:59) - Building products for builders (12:58) - Aggregating feedback at scale (15:26) - FreshBooks and early product loops (21:34) - Onboarding and getting users unstuck (25:06) - Emerging technology at Sentry (27:59) - Buy a laundromat - go deep on users (33:56) - Support culture and intentionality (39:35) - Human connection in the feedback loop (42:27) - Hunk and accelerating taste with AI (45:12) - Human judgment vs median products (49:43) - Homework: five people talking Ben Vinegar calls himself a career startup guy - early at FreshBooks, Disqus, and Sentry (first five people through ~400 employees), then head of product engineering, VPE, and VP of emerging technology. He founded Modem (modem.dev) to give product engineers faster access to what users are saying so they can iterate. The through-line is closed-loop feedback. Successful products Ben has worked on followed make-a-change, get-it-in-front-of-users, adjust, repeat. Modem aggregates signals from Slack Connect, Discord, GitHub issues, Intercom, Zendesk, and similar channels so teams are not hunting screenshots in five tools. They talk adoption barriers, FreshBooks' support culture, and why human judgment still differentiates when agents can ship median products. Ben's Hunk terminal diff tool story shows taste accelerating with AI: he built what he needed as a "norm core" Mac user who wanted agents running while he closed the laptop. Extreme product bets rarely come from averaged LLM taste. Homework from Ben, sharpened by Kent: get five people to try your software - and talk with them about their experience using it. Homework Pick something you shipped (or are about to ship).Get five people to try it.Talk with those five people about their experience using your software - not just whether they clicked install.Resources ModemBen VinegarBen on XBen on BlueskyBen on GitHubBen on LinkedInHunkGuest: Ben Vinegar Company: ModemGitHub: @benvinegar𝕏: @bentlegenHost: Kent C. Dodds Website: kentcdodds.com𝕏: @kentcdoddsGitHub: @kentcdoddsYouTube: kentcdodds-plusPodcast: epicproduct.engineerSee on Epic Product Engineer

If your backlog is full of tickets and zero customer contact, this episode is for you. Kent talks with Teresa Torres - product discovery coach, author of Continuous Discovery Habits, and host of Just Now Possible - about why engineers belong in discovery, what "just now possible" really means, and how curiosity beats solution advocacy. They cover opportunity solution trees, confirmation bias in decision-making, the Mom Test trap of pitching your solution, and homework that starts with one real conversation (or observing one). (00:00) - Meet Teresa Torres (02:03) - Just now possible (04:14) - Why coding agents are not enough (08:07) - Engineers in the data model conversation (15:10) - Continuous discovery habits (19:01) - Prove the null hypothesis (21:22) - Where engineers should show up more (24:06) - Solving your own problem (27:27) - AI unlocks software for one (30:04) - The Mom Test trap (33:26) - Opportunity solution trees (39:19) - Confirmation bias vs status quo (42:07) - Informed opinion vs preference (43:16) - Start with curiosity (46:01) - Homework: talk to a customer Teresa Torres has spent about twenty years coaching product teams to make better decisions about what to build. She is a maker at heart: product discovery exists so makers ship useful things instead of crickets. The through-line is engineer involvement. Marty Cagan's line sticks: engineers know best what is just now possible. Teresa's Google Maps API story makes it concrete - a PM would never have invented map pins for customer connection without an engineer who saw the new API and understood the business. AI accelerates that dynamic. Her podcast Just Now Possible interviews teams building AI products for that reason. Coding agents write a lot of code; they do not replace judgment about scalability, security, maintainability, or a data model that will survive the next iteration. They dig into continuous discovery habits: weekly customer contact, opportunity solution trees that separate outcomes from solutions, and the confirmation-bias trap of comparing every idea only to the status quo. The Mom Test warning is sharp - showing someone your solution teaches you whether they are polite, not whether the problem is real. When leadership wants ticket-takers, Teresa's answer is not "quit immediately." Start with genuine curiosity: "I'm curious, can you tell me more?" Opinionated why-questions create defensiveness. Informed opinions matter after you have customer context - personal preference dressed up as technical expertise usually misses the mark. Homework: talk to someone who matches your customer segment (even casually over coffee) and ask about the last time they experienced the problem your product exists to solve. Or sit in on interviews your PM, designer, researcher, sales, or support teams already run. Homework Identify who uses your product (or who has the problem it solves) and find one person in your network who matches that segment.Have a casual conversation (coffee, LinkedIn second-degree, whatever fits) and ask: tell me about the last time you experienced [the problem your product solves]. Learn about them as a human - not about your product.If customer contact is blocked, ask to observe an interview your PM, designer, researcher, sales, or support team is already running.Resources Product TalkProduct Discovery BasicsContinuous Discovery HabitsJust Now Possible podcastTeresa on BlueskyTeresa on LinkedInTeresa on XGuest: Teresa Torres Company: Product Talk𝕏: @ttorresHost: Kent C. Dodds Website: kentcdodds.com𝕏: @kentcdoddsGitHub: @kentcdoddsYouTube: kentcdodds-plusPodcast: epicproduct.engineerSee on Epic Product Engineer

If you still build for an audience of one and get defensive when users "use it wrong," this episode is for you. Kent talks with Mark Techson - staff developer relations engineer on Angular at Google - about empathy as a career differentiator, listening without defense, and why knowing what to build still beats vibe-coding forever. They cover internal tools as real products, a fitness-app failure that forced better questions, Google Developer Experts as a feedback loop, durable expertise in the AI era, and why attention is the scarce resource when anyone can ship. Mark Techson (legal name Mark Thompson) has spent about twenty years building software - teaching, senior engineering roles, a fitness startup, American Express fraud tooling, healthcare exchange apps, and now staff DevRel on Angular at Google. The views in this episode are his own; he notes he works for a major frontier-model provider. The through-line is empathy. Mark's number-one product-engineering skill is putting yourself in the end user's seat before you ship. Engineers who only implement tickets miss the concert of perspectives PMs, EMs, and builders bring when everyone aims at the best user experience. He compares great teams to a film set where talent still suggests a shoulder turn that makes the scene land. Listening without defense is the mechanism. Feedback means either you built it wrong or they cannot see how it works - both are actionable. Mark's hard lesson was a fitness app he built for himself: even his wife chose another app. Surveys flipped his assumption from "customize everything" to "just tell me what to do." At Google he leans on Angular GDEs and a simple reply to angry comments: tell me more. They also talk AI. Expertise is not going out of style - Mark uses a guitar-tuner analogy: auto-tune does not erase your ear. Knowing what to build, who to build for, and how to orchestrate agents matters more as code gets cheaper. Kent adds that human attention stays finite even if tokens get cheap. Mark closes against the "you're left behind" narrative: you do not have to live on the bleeding edge, but ignoring AI is the only wrong move. Homework: write a prompt for the first basic version of a product you have been postponing, run it in any AI tool, and notice what you failed to specify. Homework Pick a product idea you have postponed for lack of time or skill.Write a prompt that describes only the first basic version - not the million-dollar finished product.Run that prompt in any AI coding tool, then list what was missing from your prompt and iterate toward that first version.Resources Mark TechsonMark on XMark on GitHubMark on LinkedInAngularGoogle Developer ExpertsGuest: Mark Techson Company: GoogleGitHub: @MarkTechson𝕏: @MarkTechsonHost: Kent C. Dodds Website: kentcdodds.com𝕏: @kentcdoddsGitHub: @kentcdoddsYouTube: kentcdodds-plusPodcast: epicproduct.engineerMark's views are his own. He notes he works for a major frontier-model provider (Google).

If execution got cheap and you still wait for someone to tell you what to build, this episode is for you. Kent talks with Joyce Er about five years owning features on VS Code, the jump to product at Cerebras, and why trust is the metric that tells you the work is working. They cover daily stand-ups that roast your feature in public, friction logs as taste practice, dogfooding when you cannot be the user, qualifying customers instead of taking every check, and why a sales book belongs on a product engineer's shelf. (00:00) - Meet Joyce Er (01:18) - A dinner-plate AI chip (02:47) - Engineering as we knew it shrunk (04:38) - Ownership on the VS Code team (07:33) - Why ownership matters more with AI (10:23) - Keep a friction log (12:18) - Judgment from lived experience (15:33) - Invalidate old priors (17:41) - VS Code's daily stand-up roast (20:35) - When you cannot dogfood (22:52) - Getting partner teams to help (29:30) - Curiosity and time-boxing AI (32:20) - Selling inference to coding tools (34:00) - Qualify the right customer (38:47) - Trust as the success metric (40:11) - Homework: friction log and a sales book Joyce Er spent five years as a software engineer on VS Code at Microsoft, with a front-row seat to GitHub Copilot and the shift from typing code to owning the why. About a year before this conversation she moved into product at Cerebras, the company behind dinner-plate wafer-scale chips for ultra-fast AI inference. The views in this episode are her own. A major theme is ownership. On VS Code, every engineer owned the end-to-end definition of what shipped, not just the implementation. That only works with trust, dogfooding, and rituals that force you to defend the experience. Joyce describes a daily stand-up where you get five minutes to walk the feature, then the team roasts padding, consistency, and whether it belongs in the product. The lighter version is a bug bash. If people inside the company will not use the feature, that is a signal. They also talk about how you grow judgment when nobody assigns the work. Joyce's first practice is a friction log: five minutes at the end of the day listing paper cuts in any software you used. Her more controversial homework is to read a sales book - she recommends The Qualified Sales Leader - so you understand how business value actually closes, especially when the product is not a credit-card checkout. Trust, she says, is the end-to-end metric: if customers and teammates trust you, you are doing the job. Homework: keep a daily friction log, and read a book about sales. Homework Spend five minutes at the end of each day writing a friction log of paper cuts in software you used.Read a sales book - Joyce recommends The Qualified Sales Leader - and notice how value gets sold at your company.Use those notes the next time you decide what is worth building. Resources Joyce Er on XJoyce on GitHubJoyce on LinkedInCerebrasVisual Studio CodeThe Qualified Sales Leader by John McMahon Guest: Joyce Er Company: CerebrasGitHub: @joyceerhl𝕏: @joyceerhl Host: Kent C. Dodds Website: kentcdodds.com𝕏: @kentcdoddsGitHub: @kentcdoddsYouTube: kentcdodds-plusPodcast: epicproduct.engineer See on Epic Product Engineer The views expressed by Joyce Er are her own and do not necessarily represent those of her employer.

If you ship a feature and never click through it yourself, this episode is for you. Kent talks with Rita Kozlov, VP of Product for Cloudflare's developer platform, about the line between product managers and product engineers, why ownership still matters when prototypes look real, and how far upstream an engineer should insert themselves. They cover holding the pager vs throwing away a prototype, sitting in customer calls until someone will not click the button, primitives vs paved paths, and what to do when your PM is running a feature factory. (00:00) - Meet Rita Kozlov (01:36) - Engineering taste in a PM role (04:05) - Where PM and product engineer blur (07:53) - You are not holding the pager (09:48) - What AI changed on both sides (13:40) - Sit in the user's seat (17:04) - The art of the possible (18:53) - Trade-offs and why not both (23:22) - Primitives vs paved cow paths (26:43) - Insert yourself upstream (29:40) - When the PM is a feature factory (33:17) - Deep-stack work still reaches users (37:58) - Homework: go use the product Rita Kozlov is VP of Product for Cloudflare's developer platform - Workers, KV, Pages, and the rest of the stack Kent uses constantly. She started as a software engineer, and that background shows up in the details: taste for developer experience, intuition for patterns that do not sit right, and a refusal to treat implementation as someone else's problem. A major theme is how AI blurred the PM and engineer line. Prototypes are worth a million words, and they create a temptation to ship the facade. Rita's reminder to her product team: you are not the one holding the pager. Accountability is not about having someone to call at 2 a.m. It is about living with the code for the next two years. Product engineers close that gap by translating implementation trade-offs, filling the holes a PRD will never list, and sitting in the user's seat - including the customer call where nobody clicks the button. They also get into Cloudflare-shaped product judgment: most doors swing both ways if you wait; early customers are worth delaying a launch for; primitives can serve many jobs, and sometimes you still pave a cow path. Rita wants PMs to define the problem, not the solution. Engineers should get closer to customers, challenge a feature-factory PM with why, and remember that even work deep in the stack - cache, TTL, the interface between systems - is user experience. Homework is basic on purpose: go use the product, including the feature you just shipped, and push it before you throw it over the fence. Homework Use the product you work on this week - not as a demo path, as a real user.After you ship a feature, try it in the different modalities and push the limits before you hand it off.Write down what you found that you would have missed from the ticket alone. Resources Rita KozlovRita on XRita on BlueskyRita on GitHubRita on LinkedInCloudflare WorkersCloudflare Guest: Rita Kozlov Company: CloudflareGitHub: @rita3ko𝕏: @ritakozlov Host: Kent C. Dodds Website: kentcdodds.com𝕏: @kentcdoddsGitHub: @kentcdoddsYouTube: kentcdodds-plusPodcast: epicproduct.engineer See on Epic Product Engineer

If you are merging beautiful AI PRs without being able to say why the change exists, this episode is for you. Kent talks with Erin Fox about treating every feature like a new product, asking why before you write code, and rebuilding the trust contract that agents quietly broke. They cover circling back to metrics, using community Slack as a feedback loop, telling a stakeholder story that is not just a migration estimate, and going slower with agents so they do not rewrite your patterns. (00:00) - Meet Erin Fox (01:40) - A new feature is a new product (05:03) - Finding the why before you build (07:36) - AI PRs broke the trust contract (10:57) - Metrics, research, and circling back (14:25) - Community Slack as a feedback loop (16:17) - Tell the story stakeholders can hear (20:49) - Communications skills as an engineering edge (24:12) - Ask your boss their goals (27:22) - Go slower with your agents (37:50) - Homework: ask why on every PR Erin Fox is a full-stack engineer who has shipped a React Native MLS soccer app, creator-focused email tools, and plenty of features that felt like launching a product each time. In this conversation, she and Kent dig into what that framing changes: you do not just implement a ticket. You figure out the date, the why, and whether the real fix is a new feature or a two-hour margin change. A major theme is trust. Erin used to assume a teammate's PR already had the why baked in. AI-generated PRs look great and pass tests, but the why is often missing. Her red flag is simple: if you cannot explain why this change is needed, do not treat passing tests as permission. That same question belongs in the PR template, in stakeholder conversations, and in how you brief an agent - because "fix this bug" will happily change a million things. They also get practical about feedback loops. Look at the metrics you instrumented. Lurk in community Slack, including accessibility channels, until user pain is not abstract. Then tell the story in language stakeholders care about: not "this migration takes a quarter," but what it unlocks. Erin's communications master's degree shows up here. She asks her manager what their goals are this quarter and treats that as the job. Homework is small on purpose: on every PR you open or review, write why this change matters. Homework On the next PR you open, add a Why section that a teammate could understand without the ticket.On the next PR you review, ask why this change is needed - especially if it looks AI-generated.If you cannot answer why, stop and find out before you merge. Resources Erin Fox on XErin on BlueskyErin on GitHubErin on LinkedInKit Guest: Erin Fox GitHub: @erinfox𝕏: @erinfoox Host: Kent C. Dodds Website: kentcdodds.com𝕏: @kentcdoddsGitHub: @kentcdoddsYouTube: kentcdodds-plusPodcast: epicproduct.engineer See on Epic Product Engineer

If shipping got cheap and you are still not sure you are building the right thing, this episode is for you. Kent talks with Peppe Silletti, independent product engineer and host of The Product Engineers Podcast, about why customer discovery still comes first when AI can write the code in a day. They cover startup vs scale-up trade-offs, raising the MVP baseline without adding noise, how PostHog treats product engineers vs product managers, and the durable skills that stay valuable when agents take more of the implementation. (00:00) - Meet Peppe Silletti (01:13) - Startup exploration vs scale-up bottlenecks (04:04) - Trade-offs before you paint yourself into a corner (06:07) - AI raises the MVP baseline (09:47) - Do not add so much that the data gets noisy (11:13) - AI is a tool, not a silver bullet (13:47) - Product engineers are not just managers (16:06) - How you know you built too much (18:49) - Prioritizing after product-market fit (24:30) - PM as compass, engineer as slice owner (27:08) - What Peppe learned hosting a podcast (29:25) - Durable skills as agents take the code (33:06) - Product engineering for backend engineers (38:26) - Homework: ally with your PM Peppe Silletti is an independent product engineer and host of The Product Engineers Podcast. In this conversation, he and Kent dig into what changes when you move from a startup hunting for product-market fit to a scale-up fixing bottlenecks - and what AI does and does not change about that work. A major theme is that writing code got cheap, so the old lean-startup constraint loosened. You can ship more than a classic MVP, then sculpt features away like Instagram dropping everything but photos. The risk did not go away. It got worse. You can feel productive while running in the wrong direction, because AI will happily agree with you. Discovery, customer interviews, and a tight feedback loop still decide whether you are learning or just shipping. They also unpack the product manager vs product engineer split. Peppe points to PostHog: product engineers owned decisions for years before a PM showed up to hold the bigger picture. His model is a PM as compass, a product engineer owning one outcome end to end. For listeners who still take tickets over the wall, the durable skills are framing the problem, interviewing without jumping to solutions, and treating UX as more than pixels - including APIs and neighboring layers of the system. Peppe's homework is deliberately social: shadow customer interviews, watch session replays with your team, and get into discovery before the roadmap is locked. Homework Ask your PM if you can shadow customer interviews and listen for how people describe the pain.Set a weekly half hour to watch session replays with your team and name the friction you see.Once those two are happening, ask to join discovery earlier - when requirements and the roadmap are still being shaped. Resources Peppe SillettiThe Product Engineers PodcastPeppe on LinkedInPeppe on GitHubThe Product Engineers Podcast on YouTubeProduct management is broken. Engineers can fix it - PostHogContinuous Discovery Habits by Teresa TorresLaws of UX by Jon Yablonski Guest: Peppe Silletti Company: The Product Engineers PodcastGitHub: @peppesilletti𝕏: @peppesilletti Host: Kent C. Dodds Website: kentcdodds.com𝕏: @kentcdoddsGitHub: @kentcdoddsYouTube: kentcdodds-plusPodcast: epicproduct.engineer See on Epic Product Engineer

If you walk into cross-team asks with a prescription instead of curiosity, this episode is for you. Kent talks with Shaundai Person about privacy UX at Netflix, the micro-sales skills she brought from a decade in sales, and why AI should accelerate good engineering judgment instead of replacing it. They cover putting yourself in the user's seat before you push back, selling the conversation instead of the ticket, what happens when agents fill a repo with band-aids, and homework that gets you out of your usual domain: try a CSS animation course without AI. (00:00) - Meet Shaundai Person (00:51) - Privacy and consent at Netflix (04:30) - What privacy engineering actually involves (08:38) - Push back with user scenarios (11:10) - UX so intuitive a toddler can use it (13:43) - Working through technical constraints (18:45) - Micro-sales from a sales career (22:32) - Curiosity is not manipulation (25:47) - AI's impact on software engineering (29:42) - Agent band-aids and architecture gaps (35:40) - New criteria for senior engineers (37:35) - AI accelerates bad practices too (40:51) - AI as a supplement, not a replacement (45:09) - Homework: try animations without AI Shaundai Person is a senior software engineer at Netflix working full stack on privacy and consent for Netflix.com. In this conversation, she and Kent dig into what product engineering looks like when the work is mostly invisible when you do it right - and when your job is piping the right experience to the right person under different laws, profiles, and countries. A major theme is how she communicates across teams. After more than a decade in sales before engineering, Shaundai treats collaboration as a series of micro-sales: get someone into the conversation, then into a small piece of work, then into teaching you how they solved it. She argues smart people do not want prescriptions. They want curiosity, a clear goal, a map of where they fit, and an invitation to dissent. They also talk about AI's impact on software engineering. Shaundai sees AI elevating more engineers toward architecture and product thinking, while warning that unchecked agents create pattern-free codebases full of band-aids. Her take: humans stay in the loop because software is for humans, and AI is a supplement - not a replacement. Her homework is specific: try Josh Comeau's Whimsical Animations (or its CSS-based starter path) and do the first stretch without AI. Homework Start Josh Comeau's Whimsical Animations (or the free CSS-based starter path).Do the first stretch without AI - refresh your own problem-solving muscle.Notice how far you get on your own, then decide where a tool would actually help.Resources Shaundai PersonShaundai on BlueskyShaundai on XShaundai on GitHubShaundai on LinkedInTypeScript for JavaScript DevelopersWhimsical Animations by Josh ComeauNetflixGuest: Shaundai Person Company: NetflixGitHub: @shaundai𝕏: @shaundaiHost: Kent C. Dodds Website: kentcdodds.com𝕏: @kentcdoddsGitHub: @kentcdoddsYouTube: kentcdodds-plusPodcast: epicproduct.engineerSee on Epic Product Engineer

If you are building something people want but still cannot grow it, this episode is for you. Kent talks with Michael Grinich, founder of WorkOS, about cold-emailing product managers, closing the feedback loop with real users, and why live demos beat polished decks. They cover the missing step after "make something people want," how durable problem spaces survive market shifts (including auth for agents), and why storytelling is part of the product. (00:00) - Meet Michael Grinich (02:00) - Choosing the enterprise-ready niche (04:31) - Building for problems that are not yours (06:34) - Cold-emailing PMs who already shipped enterprise (10:44) - Questions that get past compliments (12:52) - Patience and durable problem spaces (15:57) - Beyond make something people want (20:25) - Auth for agents and MCP (23:57) - Talk to users, not just customers (28:42) - Live demos as tech marketing (31:39) - Test your story like Comedy Cellar material (39:10) - Monkey see, monkey do and Julia Child (43:03) - Homework: build GPT from scratch (46:10) - Where to follow Michael Michael Grinich founded WorkOS to help software companies become enterprise-ready - SSO, directory sync, permissions, audit logging, and the rest of the stack that unlocks bigger customers. In this conversation, he and Kent dig into how that idea came from lived pain at a previous startup, and how he validated the market by cold-emailing and meeting PMs at companies like Dropbox, Slack, Asana, and Airtable. A major theme is that "make something people want" is incomplete. Michael argues you also need an economic engine: who pays, why they pay continuously, and how the business model stays in harmony with the product. He ties that to picking durable problem spaces - WorkOS started in classic B2B auth, and the same foundation now matters even more for agents, MCP, and agent registration via auth.md. They also get practical about product discovery. Talk to users, not only customers and dollar signs. Use live demos as the pinnacle of tech marketing. Treat small meetups like a Comedy Cellar set where you test material before the big stage. And when you show people how something works, you put yourself on the same side of the table - the Julia Child model of teaching by doing. Michael's homework is concrete: spend an afternoon with Andrej Karpathy's "Let's build GPT from scratch" video so you understand the underpinnings of the systems you are building on. Homework Watch Andrej Karpathy's "Let's build GPT from scratch" video (a few hours, one afternoon is enough).Hack along enough to rebuild a simple ChatGPT-like interface on top of a transformer you understand.Notice how that deeper mental model changes how you talk about agents, models, and product bets at work.Resources Michael Grinich on XMichael on GitHubMichael on LinkedInMichael on BlueskyWorkOSauth.mdauth.md on GitHubWorkOS events on LumaLet's build GPT from scratch (Andrej Karpathy)The Mom TestGuest: Michael Grinich Company: WorkOSGitHub: @grinich𝕏: @grinichHost: Kent C. Dodds Website: kentcdodds.com𝕏: @kentcdoddsGitHub: @kentcdoddsYouTube: kentcdodds-plusPodcast: epicproduct.engineerSee on Epic Product Engineer

If you ship features before you can explain the process, this episode is for you. Kent talks with Ronan Berder about building consumer products at Nike, Hilton, and Burberry scale in China, why engineers need to leave the IDE, and why a process you have not written down is not a process. They cover aligning teams on product vision, benchmarking competitors before you invent, design boundaries that unlock creativity, consulting failures from expectation gaps, and the homework that sounds too simple: write down what you are struggling with. (00:00) - Meet Ronan Berder (01:53) - Shipping at China consumer scale (03:42) - Partner vs vendor: strategy before build (09:02) - Aligning engineers on product vision (15:56) - Benchmark competitors before you invent (17:35) - Design systems, boundaries, and process (22:42) - If it is not written down, you do not have a process (28:40) - Process scale: five people vs 160 (33:21) - Horizontal skills beyond the IDE (34:15) - Abstraction layers in the business (39:44) - Talking with VPs and C-suite (44:49) - Consulting failures and expectation gaps (50:48) - When clients insist on bad bets (53:39) - Large companies lack creativity (58:23) - Homework: write down your struggles Ronan Berder founded Wiredcraft in Shanghai, grew it past 160 people, and sold it after years of shipping localized consumer apps for brands like Nike, Hilton, Burberry, and Adidas - often to tens of millions of users on day one. In this conversation, he and Kent dig into what product engineering looks like when you are a partner to VPs and C-suite, not a ticket-taking vendor. A major theme is process as a creative constraint. Ronan argues designers and engineers need boundaries, shared definitions, and written playbooks - not because bureaucracy is fun, but because you cannot improve what you never articulated. He is blunt about engineers who never benchmark competitors, teams that chase cool campaigns over boring work that moves sales, and consulting failures that come from expectation gaps more often than from bad code. They also talk about AI making pure implementation more replaceable, why understanding one layer above and below your work still matters, and how large companies rarely invent the creative bets that startups do. Ronan's homework is simple: whatever you are struggling with right now, write it down and organize your thoughts. Homework Pick one thing you are struggling with in your work right now.Write it down and organize your thoughts until the gaps are visible.Share the written version with a teammate and use it to align on what 'done' means.Resources Ronan BerderRonan on XRonan on GitHubRonan on LinkedInRotsuWiredcraftGuest: Ronan Berder Company: RotsuGitHub: @hunvreus𝕏: @hunvreusHost: Kent C. Dodds Website: kentcdodds.com𝕏: @kentcdoddsGitHub: @kentcdoddsYouTube: kentcdodds-plusPodcast: epicproduct.engineerSee on Epic Product Engineer

If you build internal tools, AI enablement, or platform work, this episode is for you. Kent talks with Ben Ilegbodu about treating developers as customers, measuring success without a checkout funnel, and the durable skills that still matter when agents write more of the code. They cover agentic workflows and skills at Netflix, closing the agent loop for TV UI development, verification and harness engineering, and why deciding what to build beats shipping three times more features. (00:00) - Meet Ben Ilegbodu (01:06) - From React speaking to Netflix AI enablement (02:56) - Training engineers for agentic workflows (04:17) - Skills, context, and insulating teams from churn (08:41) - Product engineering for internal tools (10:06) - How to measure success without a checkout funnel (12:07) - Closing the agent loop for TV UI (18:34) - Durable skills: what to build, specs, verification (23:45) - Harness builders and agent experience (26:33) - Agent-to-agent PR review (28:21) - Intent docs and harness engineering (29:36) - Do users want 3x more features? (32:00) - Retrospective skills that improve the system (36:56) - AI is here to stay (39:11) - Homework: turn repeated prompts into skills Ben Ilegbodu has spent years helping other engineers move faster - first through React education and UI tooling, and now on Netflix's TV UI productivity team focused on AI enablement. In this conversation, he and Kent talk about what product engineering looks like when your customers are other developers, not the people paying for Netflix. A major theme is that internal tooling still needs product judgment. Ben argues the product is what you deliver to developers, and that feedback can be even more direct than consumer product work because your users Slack you when something breaks. Measuring success means observability, usage, and silence that is not always golden. On the AI side, they dig into skills as reusable context for agents, the hard problem of closing agent loops for TV apps that are not web browsers, and why durable skills like deciding what to build, writing specs, and verification will outlast any particular harness. They also talk about agent-to-agent workflows, retrospective skills that improve the system from real usage, and the temptation to turn 3x throughput into 3x feature spam. Ben's homework is practical: notice the prompts and workflows you repeat while developing with an agent, and turn those into skills so you stop retyping the same guidance every session. Homework While you develop with an agent, notice the prompts or workflows you repeat over and over.Turn one of those repeated instructions into a skill (or part of a larger skill) so the agent can reuse it.Run with that skill on your next task and notice what details you can stop retyping every session.Resources Ben IlegboduBen on XBen on GitHubNetflixGuest: Ben Ilegbodu Company: NetflixGitHub: @benmvp𝕏: @benmvpHost: Kent C. Dodds Website: kentcdodds.com𝕏: @kentcdoddsGitHub: @kentcdoddsYouTube: kentcdodds-plusPodcast: epicproduct.engineerSee on Epic Product Engineer

Kent talks with Robert C. Martin - Uncle Bob - about what AI agents change, what they do not change, and why software architecture, design sense, and customer empathy still matter. They cover why engineers may need to "walk away from the code" while still caring about structure, how agents can be guided with quality tools, why beginners still need to learn the material agents manipulate, and what product engineering looks like when implementation gets cheaper. (00:00) - Meet Robert C. Martin (02:53) - What has changed and what has not (05:41) - The rising abstraction line (08:00) - Walking away from the code (11:15) - Agents and larger systems (14:16) - Clean code when agents write code (20:09) - Design sense and agent judgment (22:36) - Why beginners still need code (34:57) - What product engineering means (41:18) - Homework: try SwarmForge Robert C. Martin has watched software move through layers of abstraction for decades: binary, assembly, high-level languages, frameworks, and now AI agents. In this conversation, he and Kent talk about the next layer up and the durable engineering judgment that still belongs to humans. A major theme is that the low-level work keeps changing while the high-level rules of design and architecture stay remarkably stable. Bob argues that if engineers want the real benefit of agents, they will eventually need to stop treating code as the primary surface and start reviewing module structure, dependencies, data flow, and system behavior. But that does not mean code quality stops mattering. It means humans need better feedback loops, better tools, and enough design sense to know what to ask the agents to improve. They also talk about new engineers, education, and the danger of skipping the code too early. Agents are power tools, and Bob's advice is that engineers still need to understand the material those tools are shaping. The episode lands on product engineering as the marriage of deep technical skill and deep customer understanding: the product engineer lives partly in the technology and partly in the customer's world. Homework Try Robert C. Martin's SwarmForge project locally and follow the setup instructions far enough to run it.Use it to experiment with coordinated agents passing tasks or information to each other.If you find a problem or a useful improvement, leave an issue on the GitHub repo so the product feedback loop closes.Resources Robert C. MartinClean CodersSwarmForgeAgile ManifestoClean CodeGuest: Robert C. Martin Company: Uncle Bob Consulting LLCGitHub: @unclebob𝕏: @unclebobmartinHost: Kent C. Dodds Website: kentcdodds.com𝕏: @kentcdoddsGitHub: @kentcdoddsYouTube: kentcdodds-plusPodcast: epicproduct.engineerSee on Epic Product Engineer

Kent talks with Annie Sexton, developer educator at ngrok, about the durable skills engineers need as AI changes the shape of software work. They cover how to teach complex technical ideas without assuming too much, why curiosity keeps engineers from becoming button pushers, how pacing and storytelling make educational content work, and why UX and product judgment are becoming harder to ignore. (00:00) - Meet Annie Sexton (02:15) - Teaching for the mid-level developer (05:48) - Jobs to be done for technical education (08:19) - Which fundamentals still matter with AI? (12:13) - Tricking people into learning (18:27) - Measuring educational content (23:13) - Pacing, hooks, and retention (30:12) - Education as trust-building (35:23) - Curiosity and engineering joy in the AI era (40:11) - UX as a differentiator (43:40) - Homework: make users awesome and audit a product Annie Sexton is a developer educator, software engineer, and technical video creator whose work has focused on making difficult infrastructure and software concepts feel approachable. In this conversation, she and Kent talk about teaching developers in an AI-heavy world, where syntax and implementation details are cheaper but curiosity, taste, pacing, and user empathy matter more. A major thread in the episode is education as product work. Annie describes writing for the mid-level JavaScript developer, using curiosity to pull people into topics they did not expect to care about, and treating comments, retention, and repeated confusion as feedback loops. Kent connects that to jobs to be done, teaching with agents enabled, and the need to understand what learners actually need now instead of preserving old rituals just because they were once necessary. They also dig into the emotional and practical shift AI has created for engineers. Annie talks honestly about losing some of the old joy of hands-on coding while finding a new place for that energy in product thinking, UX, and education. The episode closes with two concrete pieces of homework: read Badass: Making Users Awesome, and practice noticing every little point of friction in a product you almost chose but did not. Homework Read Badass: Making Users Awesome by Kathy Sierra and pay attention to the shift from making an impressive product to making users feel capable.Pick a product you considered using but rejected, then go through its onboarding or core flow again.Write down every specific point of friction, confusion, hidden error, awkward field, or moment where your eyes glaze over.Resources Annie SextonngrokFly.ioBadass: Making Users AwesomeWebcomic StudioGuest: Annie Sexton Company: ngrokGitHub: @anniebabannieHost: Kent C. Dodds Website: kentcdodds.com𝕏: @kentcdoddsGitHub: @kentcdoddsYouTube: kentcdodds-plusPodcast: epicproduct.engineerSee on Epic Product Engineer

Kent talks with Michael Shimeles - Rasmic - about building products in the AI era without losing the engineering judgment that makes software useful. They cover AI consulting, why clients often ask for the wrong thing, how to push conversations back to the real problem, why simplicity is a product advantage, and why developers are more valuable when they combine technical skill with taste, domain knowledge, and user empathy. (00:00) - Meet Michael Shimeles (01:16) - Product studios and AI consulting (04:35) - Training teams and building custom agents (07:37) - Uncovering the real problem (12:33) - Questions that cut through AI hype (16:47) - Do it the hard way first (19:01) - Finding ideas in products you use (21:03) - Taste, quality, and care (24:10) - Simplicity beyond design (28:04) - Domain knowledge and feedback (31:11) - Developers are expected to own more (35:35) - Do not fall in love with the solution (39:14) - Homework: read an old engineering book Michael Shimeles is a full-stack engineer, DevX engineer at Convex, creator, and the person behind a product studio and AI consultancy. In this conversation, he and Kent talk about what happens when every company suddenly wants agents, automation, and custom AI tools - and why the right answer is sometimes "you do not need AI yet." A major thread in the episode is problem discovery. Michael describes clients who come in excited about subagents, custom harnesses, or the newest tool from a YouTube video, while the real job is to slow down, ask what problem they are trying to solve, and decide whether a simple workflow, an off-the-shelf tool, or a custom product is actually warranted. Kent connects that to The Mom Test and the value of doing things the hard way first so you know where existing solutions fall short. They also dig into taste and simplicity. Michael argues that AI has made it easier to ship software, but not easier to care. Taste comes from shipping, noticing products you wish you had made, learning from feedback, and making the path from a user's problem to a solved problem as short as possible. The episode closes with a practical challenge for engineers using agents: go back to the old engineering books. Syntax is cheaper now, but engineering, architecture, first-principles thinking, and simplicity make you better at directing AI. Homework Pick one classic engineering book you have ignored or dismissed and read it with AI-assisted development in mind.Notice how the engineering principles from that book change the way you prompt, review, and steer coding agents.Treat agents as code-writing accelerators, not replacements for architecture, simplicity, and first-principles thinking.Resources Ras Mic - Michael ShimelesConvexFabrikaThe Mom TestClean CodeGuest: Michael Shimeles Company: ConvexGitHub: @michaelshimeles𝕏: @rasmicHost: Kent C. Dodds Website: kentcdodds.com𝕏: @kentcdoddsGitHub: @kentcdoddsYouTube: kentcdodds-plusPodcast: epicproduct.engineer