Podcast
The Growth Podcast
Hosted by Aakash Gupta · EN
Join 65K+ other listeners in the worlds biggest podcast on AI + product management. Host Aakash Gupta brings on the world's leading AI PM experts.
www.news.aakashg.com
www.news.aakashg.com
37episodes
Episodes
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PM's Guide to Claude - When to use Chat vs Cowork vs Code, with Pawel Huryn
Yesterday01:32:40Summary readyToday’s episodeWhen do you use Claude chat vs Cowork vs Code? No one has created a resource that helps you get the most out of the Claude ecosystem.Until now. I’ve brought back Pawel Huryn, the guest behind our most popular episode ever, the Complete Course on AI Product Management.Today we’re covering everything you need to know to get the most out of the Claude Ecosystem.Most PMs open Claude chat. Ask something. Get an answer. Close the tab. Tomorrow, same thing. Fresh context. Zero memory.The PM who tracked Anthropic’s 74 releases in 52 days stopped doing this entirely. He built a system where Claude organizes its own knowledge, extracts its own rules from data, promotes hypotheses when evidence confirms them, and demotes them when it does not. The system improves without him telling it what went wrong.I sat down with Pawel Huryn, creator of the Product Compass newsletter. He has defined 60+ PM skills, built a PM skills marketplace that hit 10,000 GitHub stars, and runs his entire content operation across Cowork, Claude Code, and Dispatch.In this episode, he walks through every screen live. Real files. Real agent workflows. Real self-improving knowledge bases.----Check out the conversation on Apple, Spotify, and YouTube.Brought to you by:* Bolt - Ship AI-powered products 10x faster* Amplitude - The market-leader in product analytics* Jira Product Discovery - Plan with purpose, ship with confidence* Product Faculty - Get $550 off their #1 AI PM Certification with code AAKASH550C7* Land PM Job - 12-week experience to master getting a PM job----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.I’m accepting applications for my third LandPMJob cohort. Join Me.----Key Takeaways:1. Stop using Claude Chat as your default. Cowork accesses real files, connects to Gmail and Slack via MCP, and runs parallel sub-agents. Chat does none of this.2. Skills are the highest ROI investment. Install marketplace baselines, iterate 5-6 times with specific feedback, and Claude rewrites from first principles until 99% accuracy.3. Progressive disclosure keeps context clean. Agent reads skill names and descriptions first. Loads full instructions only when the task matches. Hundreds of skills, minimal overhead.4. Your CLAUDE.md should route, not store. Project structure and pointers only. Domain knowledge lives in separate files the agent loads on demand.5. Build self-improving knowledge with three types. Rules are confirmed and applied by default. Hypotheses are tracked with evidence. Rejected patterns are kept to avoid retesting.6. The three-line self-improving prompt works for any domain. Review rules before starting. Apply confirmed rules. Update after feedback. Testing, marketing, strategy, whatever.7. Claude Code adds explorer view, hooks, subagents, and local MCP scoping. PMs need it once their system grows past 50 files.8. Every Product Compass infographic was built in Claude Code. HTML generation, component library, iteration through conversation, PNG export. Zero code written by the human.9. Use Agent Browser from Vercel instead of Chrome MCP. Chrome MCP screenshots every 0.5s and burns $100/hr. Agent Browser uses headless mode and is token-efficient.10. Dispatch lets you run multiple tasks from your phone. Start an infographic, check emails, analyze competitors. Each runs as a separate thread. Your system works while you live.----Where to find Pawel Huryn* LinkedIn* Product Compass Newsletter* PM Skills Marketplace on GitHub* [Quadathon - starts May 9th](VERIFY - Quadathon URL)Related contentPodcasts:* n8n Masterclass with Pawel Huryn* Claude Code PM OS with Dave Killeen* Claude Code Team OS with Carl VellottiNewsletters:* The complete Claude Cowork guide* How to use Claude Code like a pro* Build your PM operating system----PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe
Summary
How to Build a Full AI Dev Team in Claude Code | Guide from Google PM Gabor Meyer
2w ago02:15:18Summary readyCheck out the conversation on Apple, Spotify, and YouTube.Brought to you by:* Maven - Get a $675 discount off Gabor’s course with my code* Amplitude - The market-leader in product analytics* Testkube - The leading test orchestration platform* Land PM Job - My 12-week AI PM + Job Search Course starts Monday!* Product Faculty - Get $550 off their #1 AI PM Certification with code AAKASH550C7Today’s episodeHere’s the problem with most Claude Cost demos: they stop at the prototype.Nobody shows what happens next. You try to add a second feature. The first one breaks. The styling reverts to default. The code is so tangled that you spend more time debugging than you saved by generating.Gabor Mayer showed me what happens when you stop treating Claude Code like a magic prompt box and start treating it like a team.He is a PM at Google. He has not written production code in 15 years. But over the past several months, he has been building real mobile apps using 21 specialized Claude Code agents. Not prototypes that live in a demo. Apps that are on the App Store.In today’s episode, he walked through the entire workflow live and share all the resources free.If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.Do you want to become an AI PM? I’ve created a course for you. Starts next week.Newsletter deep diveThank you for having me in your inbox. Here is the complete guide to building a full AI development team in Claude Code:* Why one-prompt vibe coding fails* The 21-agent team architecture* The spec-first workflow * From design to code without touching either* What changes when PMs actually buildSave this. The full 10-step playbook on one page. Everything below is the why and how behind each step. 1. Why one-prompt vibe coding failsEvery PM I know has built something with Bolt, Lovable, or Replit. The prototype looks great. It runs. It impresses people in a Slack message.Then you try to ship it to real users. And you hit a wall.Blocker 1 - Context compression silently destroys your specThis is the failure mode that nobody talks about in tutorials. When you give one agent one massive prompt, the model compresses context. Details get dropped. Not randomly. Strategically. The model decides what is “important” and what is not.In the episode, Gabor defined a complete color palette. Oranges, neutrals, specific accent tones. The agent received everything. The output used none of it. The layout was there. The structure was solid. But every color was a default.The reason is straightforward. When the context window is full, visual styling details are lower priority than functional logic. So the model drops them. Silently. Without warning. Without an error message. You just get generic output and wonder what went wrong.The fix is not better prompts. It is context engineering. Smaller, scoped tasks. Each agent gets only the context it needs for its specific job. The designer agent gets the brand guideline. The CTO agent gets the architecture spec. Neither gets the full 50-page document.Blocker 2 - AI-generated code compiles but is not maintainableA Reddit comment that hit home for Gabor - “Vibe coding is just the rebranding of unmaintainable, low-quality source code.”This is the real prototype-to-production gap. The code works today. You can demo it. You can push it to TestFlight. But the moment you touch it to add a feature, three other features break. No naming conventions. Circular references between modules. Zero comments explaining why anything was built the way it was.The fix is a dedicated code quality agent. Gabor calls his the Spaghetti Agent. It runs after every sprint and checks naming conventions, circular references, comment coverage, and structural debt. When he ran it on his codebase for the first time, it caught issues he never would have found manually.If you are building anything beyond a one-off demo, this agent is not optional. I covered similar quality patterns in my AI testing guide and my AI evals deep dive.Blocker 3 - No dependency mapping means cascading failuresWhen you build without organizing work into sprints, agents try to build features that depend on code that does not exist yet. Front-end components reference API endpoints that have not been created. Database queries call tables that have not been defined.The Atlassian MCP currently cannot create sprints directly in JIRA. That is a real limitation. Gabor uses tags as a workaround. He tags tickets as Sprint 1, Sprint 2, Sprint 3 and maps dependencies between them manually before starting the build. Without this step, the entire multi-agent workflow falls apart.Every PM who has gone from prototype to production with AI agents has hit at least one of these blockers. The ones who shipped figured out the workarounds. The ones who quit assumed the tools were the problem.Here is what the three blockers look like side by side, and what flips the moment you stop one-prompting and start running a team.2. The 21-agent team architectureYou do not need 21 agents to start. Three will get you surprisingly far. But understanding the full architecture shows you where the complexity lives and which roles to add as your projects grow.Here is the full roster: four clusters, 21 roles, and the markdown file pattern that makes them portable across every project you build next.2a. The core agents every PM needsThe System Analyst is the linchpin. It breaks down product requirements into technical specifications. It asks clarifying questions one at a time. It documents decisions in Confluence. It creates tickets in JIRA. Without this agent, every other agent operates on incomplete context.In the episode, the system analyst asked 14 clarifying questions before a single line of documentation was written. Vector DB choice. Usage limit mechanics. Conversation history handling. Search fallback strategy. API provider. Minimum iOS version. Screen count. Naming conventions. Each question one at a time so the answers stay deep.The prompt pattern that makes this work -“Please act like a good system analyst. Ask clarifying questions until you have a complete and comprehensive understanding. Ask questions one at a time. Do not start writing documentation until all questions are answered.”Two critical instructions. “One at a time” prevents the agent from dumping 25 questions at once. “Do not start writing” stops it from jumping ahead before the spec is complete. Different LLMs have different tendencies. Some love to start coding instantly. You need to explicitly constrain them. This is the same principle behind the prompt engineering techniques that work across any AI tool.The Spaghetti Agent handles code maintainability. Naming conventions. Circular references. Comment quality. Structural debt. Born from that Reddit comment. When Gabor ran it on his codebase for the first time, it caught problems he never knew existed.The UX Flow Architect creates clickable prototypes using Figma’s built-in prototyping arrows. This is a small but important detail. The early versions of this agent placed visual drawn arrows between screens instead of using Figma’s actual prototyping connections. The prototype looked like it had navigation. But when you clicked play, nothing happened. It took months of iteration to fix.Each agent has a specific Claude Code agent markdown file that defines its role, its constraints, and its interaction patterns. The setup mirrors how you would build a <a target="_blank" href="https:...
Summary
How to Become a "Builder PM" with n8n, Claude Code, and OpenClaw | Mahesh Yadav (ex-Google, AWS, Meta, Microsoft; Founder LegalGraph AI)
4w ago01:36:50Summary readyToday’s episodeLinkedIn just changed the title of its product managers to product builders.What does it even mean to be a “builder PM”?Well, tools only get you so far. Learning Claude Code is helpful, but means nothing if you don’t have an understanding of the underlying first principles.That’s today’s episode.Mahesh Yadav created one of our most popular episodes, with over 35K views on YouTube, and now he’s back. Earlier, he taught you AI agents. Today, he’s touching you how to become a builder PM:If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.I’m giving a free talk on how to get interviews at the top AI PM companies on Thursday, April 23rd 2026 @ 9:00AM PDT. Grab your seat.----Check out the conversation on Apple, Spotify, and YouTube.Brought to you by:* Maven - Build cohort-based courses that scale* Amplitude - The market leader in product analytics* Jira Product Discovery - Prioritize what matters with confidence* NayaOne - Airgapped cloud-agnostic sandbox to validate AI tools faster* Product Faculty - Get $550 off their #1 AI PM Certification with my link----Key Takeaways:1. Builder PM defined - A builder PM talks to customers, figures out what to build, and ships the first version to 10 customers without talking to any developer. The skill is knowing what to build, not knowing how to code.2. Four agent components - Every agent that works has intelligence (model), tools (actions), memory (session context), and knowledge (your company data). Every agent that disappoints is missing at least one.3. n8n for foundations - n8n is the best learning tool because you visually see every component of the agent architecture as separate nodes. Build your first multi-agent system and evaluation pipeline here.4. Claude Code ate three company types - Context companies, action companies, and evaluation companies all got replaced by one agentic loop inside Claude Code. The three pieces collapsed into one tool.5. Computer control is the real unlock - File system access plus bash commands equals full laptop capability. This is why Claude Code went from coding tool to work operating system.6. Long-horizon jobs changed the game - AI agents went from 3-minute tasks to 3-6 hour sustained jobs in six months. This turns Claude Code from assistant to autonomous worker.7. Continuous learning loops - Build a second agent that watches your corrections to the first agent's work. After five repeated patterns, it proposes a skill update. Your tools get better every day.8. OpenClaw pattern - Delegation through existing channels, full machine sandboxing, model-agnostic. Not a product but a pattern that Google and AWS will copy inside their ecosystems.9. AI PM interviews changed - At L5 and L6, product sense questions are being replaced with live building exercises and system design for AI architectures. Pull out Claude Code during the interview or you are already out.10. Compensation trajectory - From $120K at Microsoft to $1.3M at Google over 13 years, doubling every 18 months through AI-focused switches. Left because big companies kill innovation with six-week approval cycles.----Where to find Mahesh Yadav* LinkedIn* Maven CourseRelated contentPodcasts:* Claude Code Team OS with Carl Vellotti* OpenClaw + Claude Code with Naman Pandey* Claude Code OS with Dave KilleenNewsletters:* The complete context engineering guide* How to use Claude Code like a pro* Practical AI agents for PMs----PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe
Summary
How to Design like OpenAI and Figma
Apr 1000:53:35Summary readyToday’s episodeThe design process you learned is already dead.Most teams still follow the same linear pipeline. Low fidelity to high fidelity to handoff. Sketch it. Spec it. Ship it over the wall. That pipeline was built around a constraint that no longer exists. High fidelity used to be expensive. It is not anymore.I brought in two people who represent both sides of the new design infrastructure.Ed Bayes is a member of the design staff at OpenAI. He leads design on Codex, which just crossed 2 million weekly users with usage surging 3X since the start of the year. He spends 70-80% of his time coding. He still calls himself a designer.Gui Seiz is the Director of Product Design for AI at Figma. He leads design on all their AI features, including the Figma MCP server and Figma Make. His designers are now shipping PRs to production.----Check out the conversation on Apple, Spotify, and YouTube.Brought to you by:* Bolt: Ship AI-powered products 10x faster* Amplitude: The market-leader in product analytics* Pendo: The #1 software experience management platform* NayaOne: Airgapped cloud-agnostic sandbox* Product Faculty: Get $550 off their #1 AI PM Certification with my link----If you are trying to understand the new design workflow, this is the one episode to watch.If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.I’m putting on a free webinar on Behavioral and AI PM interviews. Join me.----Key Takeaways:1. Code vs canvas is a false dichotomy - The best designers use both fluidly. Canvas for exploration, collaboration, and pixel-perfect craftsmanship. Code for interactions, responsive testing, and the last mile of polish. The question is what you are trying to learn, not which tool to commit to.2. High fidelity is no longer expensive - The entire linear design process existed because building something interactive required engineering resources. That constraint is gone. A functional wireframe takes the same time as a paper sketch.3. The Codex-Figma MCP makes handoff lossless - Import screens from a running React app into Figma with exact pixel values. Border radius, padding, shadows, all one to one. It is not a screenshot. It is a responsive, editable design artifact.4. The reverse direction works seamlessly - Make changes in Figma, paste a component link into Codex, and it updates your code automatically. No redline spec, no handoff document.5. Ed spends 70-80% of his time coding and still calls himself a designer - The medium changed but the mandate did not. Designers are still the voice of the user, still upholding craft. The tools expanded, the role stayed.6. Figma designers are shipping PRs to production - Teams that six months ago were AI curious are now banging down the door. Monetization designers who never wrote code are building technically complex prototypes.7. "Prototypes, not PRDs" is the emerging norm - PMs at OpenAI bring working prototypes to design reviews. They ship PRs to stress-test ideas before handing off to engineering.8. You do not need permission to start - Someone from OpenAI's GTM team built an iOS app with zero experience. Download Codex and build something for yourself tonight.9. Curiosity is the defining skill for this era - Not code proficiency, not design talent. The AI is an infinitely patient tutor. Ask questions. Build understanding alongside output.10. Total football is the mental model - Every player can play every position. Roles still have natural spikes. But the tool constraints that enforced rigid boundaries are dissolving.----Where to find Ed Bayes* LinkedIn* OpenAI* XWhere to find Gui Seiz* LinkedIn* Figma* XRelated contentPodcasts:* Xinran Ma - Design with AI* Carl Vellotti - Claude Code PM OS* Codex PM Guide with Carl VellottiNewsletters:* AI prototyping for PMs* The PM guide to Bolt* Codex PM guide----PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe
Summary
How to build a Team OS in Claude Code with Hannah Stulberg, PM @ DoorDash
Apr 701:10:33Summary readyToday’s episodeThe way PM teams are trending, one PM is going to support 20 people.Not just engineers. Designers. Analysts. Strategy partners. GTM. Sales. Support.You cannot answer everyone’s questions about everything. You cannot be in every Slack thread. You cannot be the bottleneck for context that already exists somewhere in a Google Doc no one can find.But you can give them a high-context, well-organized repo.Hannah Stulberg is a PM at DoorDash and a former Google PM. She has spent over 1,500 hours in Claude Code.She wrote the viral Claude Code for Everything series. Her setup is not a personal productivity system. She has structured her entire team’s context into a shared repo that everyone queries.Her strategy partner - completely non-technical - puts up pull requests every day. Her engineers query metric definitions without asking the analyst. Her designers pull product context without waiting on a PM.If you are building a team that runs on AI, this is the episode to watch.----Check out the conversation on Apple, Spotify, and YouTube.Brought to you by:* Bolt: Ship AI-powered products 10x faster* Jira Product Discovery: Plan with purpose, ship with confidence* Kameleoon: Leading AI experimentation platform* Amplitude: The market-leader in product analytics* Product Faculty: Get $550 off their #1 AI PM Certification with my link----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.I’m putting on a free webinar on Behavioral and AI PM interviews. Join me.----1. Build a Team OS, not a personal OS - A shared repo where every function checks in work. Engineers, designers, and analysts self-serve without asking the PM.2. Root CLAUDE.md is everything - Doc index, team roster with Slack IDs, channel map. Keep under one page or you burn context every session.3. Nested indexes save 97% of context - Every folder gets a navigation CLAUDE.md. A customer query used only 3% of the context window.4. Three token tiers - Always-loaded root (~500 tokens), folder indexes on navigation (200-500), content files on demand (1,000-10,000+).5. Split analytics by product area - Metrics, queries, schemas separated. Progressive loading prevents waste.6. Gate launches on repo updates - Feature not shipped until metrics, queries, schemas, and playbooks are checked in.7. Verified playbooks kill hallucinations - Analyst-audited methodology. Claude follows verified steps instead of inventing its own.8. Plan mode makes 10x docs - Shift+Tab twice. Five phases: load context, ask questions, build plan, push thinking, review agents.9. Split long docs across parallel agents - Each writes to a temp file. Orchestrating agent compiles. Prevents context overflow.10. The flywheel compounds daily - Automate one task, free time, improve the repo. After 1,500 hours still iterating every day.----Where to find Hannah Stulberg* LinkedIn* In the Weeds SubstackRelated contentPodcasts:* My Claude Code PM OS with Dave Killeen* Claude Code + Analytics with Frank Lee* Claude Code as PM OS with Carl VellottiNewsletters:* The ultimate guide to context engineering* Build your PM operating system* How to use Claude Code like a pro----PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe
Summary
How to Turn Claude Code into an Operating System with Carl Vellotti
Mar 3001:06:48Summary readyToday’s episodeClaude Code just hit $2.5 billion in annualized revenue in 9 months.It is the fastest B2B software product ramp in history.So why are most people still using it like a chatbot?This is how most people use Claude Code. Type a prompt and get output. The context fills up. It compacts. You lose everything. You start over.The top users flipped it. They built skills that interview through a framework before building anything. They use sub-agents that preserve context. They have operating systems where every file, every person, every project has a home.That shift is what today’s episode is about.I sat down with Carl Vellotti for the third time. His first episode was the beginner course. His second episode was the advanced masterclass. Together they crossed over a million views across platforms.Today is the operating system layer. If you are already an 80 out of 100 on Claude Code, this episode will bring you to a 95 out of 100.This episode covers context management, creating sub-agents to manage your context for you, auto-triggering skills with hooks, trustworthy data analysis with Jupyter notebooks, and building an operating system around it all.If you are living in Claude Code 8 to 10 hours a day and want to stop fighting the tool, this is the one episode to watch.----Check out the conversation on Apple, Spotify, and YouTube.Brought to you by:* Bolt: Ship AI-powered products 10x faster* Amplitude: The market-leader in product analytics* Pendo: The #1 software experience management platform* NayaOne: Airgapped cloud-agnostic sandbox* Product Faculty: Get $550 off their #1 AI PM Certification with my link----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.I’m putting on a free webinar on Behavioral and AI PM interviews. Join me.----Key Takeaways:1. Context management is the real skill - A single web search eats 10% of your context. Run /context to see what is consuming it. System prompt and MCPs take 10-16% before you type one message.2. Sub-agents save 20x context - Delegate research to a sub-agent. Same task costs 0.5% instead of 10%. Your main session only gets the summary.3. Replace MCPs with CLIs - MCPs eat context by existing. CLIs have zero overhead. GitHub CLI, Vercel CLI, Google Workspace CLI are all dramatically more efficient.4. Powerful skills need zero code - Anthropic's front-end design plugin is just a good prompt. No APIs or tooling. Just rules that tell Claude "do not look like AI."5. Give Claude self-checking tools - The make slides skill uses Puppeteer to screenshot output, measure overflow, and fix issues before you see them.6. Repeat prompts for better quality - A Google paper showed pasting a prompt twice helps. Tell Claude to double-check against skill instructions after the first pass.7. Use hooks to auto-invoke skills - A user_prompt_submit hook matches your words against skill keywords instantly. Zero context cost.8. Jupyter notebooks solve data trust - Every analysis shows exact code, inputs, and outputs. Traceable and reproducible.9. Build an operating system - Knowledge folder for people context. Projects folder for task isolation. Tools folder for scripts. CLAUDE.md for identity.10. The people folder compounds - Connect meeting transcription. After every meeting, update each person's dossier. Every prompt gets more specific over time.----Related contentPodcasts:* Claude Code Masterclass with Carl Vellotti (Ep 2)* Claude Code PM OS with Dave Killeen* OpenClaw Setup Guide with Naman PandeyNewsletters:* The ultimate guide to context engineering* How to use Claude Code like a pro* Claude Cowork and Code setup guidePS. Please subscribe on YouTube and follow on Apple & Spotify. It helps! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe
Summary
AI PM at Netflix, Amazon and Meta - Here's How to Become an AI PM (Fundamentals + Job Search)
Mar 2301:12:55Summary readyToday’s episodeStop applying to AI PM jobs until you understand the fundamentals.That is not gatekeeping. That is the MIT finding. 19 out of 20 AI pilots fail. The #1 reason? Picking the wrong problem to apply AI to.Not the wrong model. Not the wrong data. The wrong problem.Jyothi Nookula has spent 13.5 years in AI. 12 patents. AIPM at Amazon (SageMaker), Meta (PyTorch), Netflix (Developer Platform), and Etsy.She has hired AIPMs at three of those companies. Trained 1,500+ PMs to transition into AI roles.If you are trying to break into AI PM, this is the one episode to watch.----Brought to you by* Product Faculty: Get $550 off their #1 AI PM Certification with my link* Amplitude: The market-leader in product analytics* Pendo: The #1 software experience management platform* NayaOne: Airgapped cloud-agnostic sandbox for AI validation* Kameleoon: Prompt-based experimentation for product teams----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.If you want my PM Operating System in Claude Code, click here.----Key Takeaways:1. Two types of AIPM roles exist - 80% are traditional PM roles with AI features added on, where the core product existed before AI. 20% are AI native roles where the product IS AI and the value proposition is impossible without it. Know which type before you apply.2. The AI PM stack has three layers - Application PMs own user experience (60% of roles, easiest entry point). Platform PMs build tools for other builders (30%). Infra PMs build foundational systems like vector databases and GPU orchestration (10%).3. 19 out of 20 AI pilots fail from wrong problem selection - AI makes sense for complex pattern recognition, prediction from historical data, and personalization at scale. If explainability is non-negotiable, rules exist, data is limited, or speed is critical, start with heuristics.4. Most teams overcomplicate their AI technique choice - If you can put the problem in a spreadsheet with inputs and an output to predict, traditional ML is the answer. Perception problems need deep learning. Natural language reasoning needs Gen AI. These are not competitors, they are tools in your toolkit.5. AI products are fundamentally probabilistic - The same input can produce different outputs. AIPMs must think in quality distributions and acceptable error rates, not binary success vs failure. Data is a first-class citizen, not a nice-to-have.6. Agents decide, workflows follow steps - Workflows have predetermined sequences with deterministic outcomes. Agents receive goals and independently decide which tools to use. The live N8N demo showed identical tools producing completely different execution patterns.7. Context engineering is the real production skill - Claude Sonnet has a 200K token context window but that fills fast with knowledge bases, conversation history, and real-time data. Every token costs money. Managing what to load and when directly impacts both quality and cost.8. Follow the hierarchy before fine tuning - Prompt optimisation first, then context engineering, then RAG. 80% of use cases get solved with RAG. Fine tuning should only be considered after exhausting all three.9. Build products not projects - Launch your AI work, get real users, encounter real breakage. That gives you richer interview material than any course certificate. Build an agent, build a RAG system, and build an app that solves a real problem.10. PM culture at big tech shapes who you become - Amazon PMs spend 40-50% of time writing PRFAQs and six-pagers. Meta PMs live in experimentation and statistical significance. Netflix PMs operate with full autonomy through context over control. Each teaches something different.----Where to find Jyothi Nookula* LinkedIn* NextGen Product ManagerRelated contentPodcasts:* Naman Pandey on OpenClaw* Lisa Huang on Gemini Gems* Frank Lee on Amplitude and MCPNewsletters:* The ultimate guide to context engineering* RAG vs fine tuning vs prompt engineering* AI foundations for PMsPS. Please subscribe on YouTube and follow on Apple & Spotify. It helps! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe
Summary
Evals are the new PRD. Here is the playbook with the CEO of the leader in the space (Ankur Goyal, Founder and CEO, Braintrust)
Mar 2000:51:57Summary readyToday’s episodeMost PMs treat evals like a quality gate. Something you run right before shipping, just to check the box.That is backwards.The best AI product teams treat evals as the starting point. They write the eval before the prompt. They iterate on the scoring function before the model. They use failing evals as a roadmap.That shift is what today’s episode is about.I sat down with Ankur Goyal, Founder and CEO of Braintrust. It is the eval platform used by Replit, Vercel, Airtable, Ramp, Zapier, and Notion. Braintrust just announced its Series B at an $800 million valuation.Users are running 10x more evals than this time last year. People log more data per day now than they did in the entire first year the product existed.In this episode, we build an eval entirely from scratch. Live. No pre-written prompts, no pre-written data. We connect to Linear’s MCP server, generate test data, write a scoring function, and iterate until the score goes from 0 to 0.75.Plus, we cover the complete eval playbook for PMs:If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.If you want my PM Operating System in Claude Code, click here.----Check out the conversation on Apple, Spotify, and YouTube.Brought to you by:* Kameleoon: Leading AI experimentation platform* Testkube: Leading test orchestration platform* Pendo: The #1 software experience management platform* Bolt: Ship AI-powered products 10x faster* Product Faculty: Get $550 off their #1 AI PM Certification with my link----Key Takeaways:1. Vibe checks are evals - When you look at an AI output and intuit whether it is good or bad, you are using your brain as a scoring function. It is evaluation. It just does not scale past one person and a handful of examples.2. Every eval has three parts - Data (a set of inputs), Task (generates an output), and Scores (rates the output between 0 and 1). That normalization forces comparability across time.3. Evals are the new PRD - In 2015, a PRD was an unstructured document nobody followed. In 2026, the modern PRD is an eval the whole team can run to quantify product quality.4. Start with imperfect data - Auto-generate test questions with a model. Do not spend a month building a golden data set. Jump in and iterate from your first experiment.5. The distance principle - The farther you are from the end user, the more critical evals become. Anthropic can vibe check Claude Code because engineers are the users. Healthcare AI teams cannot.6. Use categorical scoring, not freeform numbers - Give the scorer three clear options (full answer, partial, no answer) instead of asking an LLM to produce an arbitrary number.7. Evals compound, prompts do not - Models and frameworks change every few months. If you encode what your users need as evals, that investment survives every model swap.8. Have evals that fail - If everything passes, you have blind spots. Keep failing evals as a roadmap and rerun them every time a new model drops.9. Build the offline-to-online flywheel - Offline evals test your hypothesis. Online evals run the same scorers on production logs. The gap between them is your improvement roadmap.10. The best teams review production logs every morning - They find novel patterns, add them to the data set, and iterate all day. That morning ritual is what separates teams that ship blind from teams that ship with confidence.----Where to find Ankur Goyal* LinkedIn* BraintrustRelated contentNewsletters:* AI evals explained simply* AI observability for PMs* How to build AI productsPodcasts:* AI evals with Hamel Husain and Shreya Shankar* AI evals part 2 with Hamel and Shreya* Aman Khan on AI product quality----PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe
Summary
The Complete Guide to OpenClaw for PMs [EXCLUSIVE]
Mar 1701:40:52Summary readyThis is a free preview of a paid episode. To hear more, visit www.news.aakashg.comToday’s episodeEvery PM I talk to is using AI the same way. Open Claude. Type a question. Get an answer. Close the tab.The AI does nothing while you sleep. It forgets everything the next morning. It cannot touch your Slack, your email, your file system.OpenClaw changes that.245,000 GitHub stars. 2 million weekly visitors. Peter Steinberger built it, Sam Altman bought it for over a billion dollars. I covered what OpenClaw is and why it matters when it first went viral. Today’s episode goes deeper. A complete, step-by-step installation and five PM automations you can copy.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by* Jira Product Discovery: Plan with purpose, ship with confidence* Vanta: Automate compliance, manage risk, and prove trust* Mobbin: Discover real-world design inspiration* Maven:* Product Faculty: Get $550 off their #1 AI PM Certification with my link----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.If you want my PM Operating System in Claude Code, click here.----Key Takeaways:1. OpenClaw is a proactive AI agent, not a reactive chatbot - Unlike ChatGPT or Claude, OpenClaw runs as a continuous daemon on your machine. It executes tasks at 3 a.m. while you sleep, maintains persistent memory across sessions, and acts autonomously based on scheduled cron jobs.2. Installation takes three terminal commands - NPM install, openclaw onboard, and hatch the bot. If you do not see red text in the terminal, the installation worked. Yellow warnings are normal and safe to ignore.3. The Slack integration has one critical step everyone misses - Every time you change bot permissions in the Slack API console, you must click Reinstall to Workspace. Without this step, no permission changes persist and the bot appears broken.4. The workspace docs folder is your team's knowledge base - Drop PRDs, FAQs, and product docs into the local .openclaw/workspace/docs folder. Any team member can query the entire repository by mentioning the bot in any Slack channel, and the bot can write back to the docs.5. Cron jobs replace manual PM rituals - Set up a morning stand-up summary that scans Slack channels overnight and posts a brief at 9 a.m. with what shipped, active blockers, and customer complaints. You describe it in English and OpenClaw writes the code.6. Competitive intelligence runs on autopilot - OpenClaw can monitor competitor websites, reviews, and mentions every 30 minutes and post SWOT analyses to a private Slack channel. It tracks changes over time for trend analysis months later.7. Voice of customer reports aggregate every feedback source - Connect Slack support channels, email, Google reviews, Reddit, and more. OpenClaw scans every 30 minutes and synthesizes a weekly report automatically.8. Smart bug routing checks customer tier automatically - OpenClaw reads bug reports, looks up the reporter in a customer CSV, escalates enterprise bugs to engineering immediately, and routes free-tier bugs to design as low priority.9. Security audit is non-negotiable before going live - Tell OpenClaw to analyze its own security vulnerabilities. It will flag unrestricted file access, disabled firewalls, and missing approval gates. Set up a weekly cron job to run the audit automatically.10. Local deployment is safest for most PMs - A VPS gives 24/7 uptime but removes your physical kill switch. A dedicated Mac Mini is the most recommended option. Local deployment on your laptop is the safest because the bot sleeps when you close your laptop.----Related contentNewsletters:* OpenClaw complete guide* My PM Operating System* The AI PM Tool StackPodcasts:* Claude Code PM OS with Dave Killeen* Claude Code + Analytics with Frank Lee* Gemini Gems Masterclass with Lisa Huang----PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail.
Summary
This CPO Uses Claude Code to Run his Entire Work Life | Dave Killeen, Field CPO @ Pendo
Mar 1100:52:48Tap to summarizeToday’s episodeMost PMs start every day like this. Open the calendar. Open the CRM. Open Slack. Open the meeting notes. Open LinkedIn. Piece together what matters. Lose 30 minutes before real work even starts.That is not how the best PMs are working anymore. The best PMs are running one command in the morning and getting everything they need in five minutes. Their calendar, their deals, their market intel, their career gaps, all pulled together automatically.That shift is what today’s episode is about.I sat down with Dave Killeen, Field CPO at Pendo.io. He has worked at BBC, Mail Online, and now runs the field product function at one of the largest product management platforms in the world. He has 25 years in product. Over the last few months, he built a full personal operating system called DEX in Claude Code, open sourced it on GitHub, and it is getting serious traction.In this conversation, Dave walks through his entire system live on screen. You will see how he runs a daily plan, creates PRDs from a backlog, manages parallel workstreams on a Kanban board, and tracks his career goals, all from one terminal window. And you will learn the three building blocks that make it all work.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by* Pendo: The #1 software experience management platform* Jira Product Discovery: Plan with purpose, ship with confidence* Amplitude: The market-leader in product analytics* NayaOne: Airgapped cloud-agnostic sandbox* Product Faculty: Get $550 off the AI PM Certification with code AAKASH550C7----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.----Key Takeaways:1. One command replaces your morning routine - Dave's daily plan slash command pulls from calendar, CRM, Granola, LinkedIn, YouTube, and 120 newsletters in five minutes. No tab switching. No manual assembly.2. MCP servers are the key to connecting everything - Point Claude at any API documentation with your API key and it builds an MCP server for you. MCP provides structured guardrails that make the AI's behavior consistent and deterministic.3. Skills, MCP, and hooks are three different things - Skills are plain English job descriptions for what the AI should do. MCP servers are structured integrations for connecting external services. Hooks are triggers that fire at specific conversation moments.4. Session start hooks make the system compound - Every new Claude Code chat gets injected with weekly priorities, quarterly goals, working preferences, and past mistakes. The AI never starts from scratch.5. Living markdown files are the compounding mechanism - Every project, person, and company gets a markdown file that accumulates context from meetings, messages, and intel over time. The more you use the system, the smarter every file becomes.6. You can build a mobile app in 37 minutes - Dave built the full app with Claude and spent more time in Xcode publishing it. The constraint is taste, not building speed.7. The AI should hold you accountable - Dave's Claude MD file includes "harsh truths for Dave" that the AI wrote after auditing his system. This gets injected into every session to prevent the same mistakes.8. Career planning should compound like product data - A career MCP server collects evidence, runs gap analysis, and calculates promotion readiness. When review time comes, the evidence is already assembled.9. Be precise about your goal, not the path - The kindest thing you can do for the AI is give it a very clear destination. Do not tell it how to get there. Let it figure out the most elegant approach itself.10. Voice-first changes everything - Using Whisperflow or Super Whisper instead of typing fundamentally changes how you interact with Claude. You think out loud. The conversation flows. You build faster.----Where to find Dave Killeen* LinkedIn* Pendo----Related contentNewsletters* The PM operating system guide* How to use Claude Code like a pro* Master AI agent distribution* Claude Cowork and Code setup guide* The AI PM tool stackPodcasts* Frank Lee on Claude Code and MCP workflows* Carl Vellotti on Claude Code operating systems* Rachel Wolan on AI PM workflows* Caitlin Sullivan on building with Claude----PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe
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