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by Aakash Gupta
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- 4 days ago1h 32m
PM's Guide to Claude - When to use Chat vs Cowork vs Code, with Pawel Huryn
Today’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 p
- 2 weeks ago2h 15m
How to Build a Full AI Dev Team in Claude Code | Guide from Google PM Gabor Meyer
Check 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 Red
- 4 weeks ago1h 36m
How to Become a "Builder PM" with n8n, Claude Code, and OpenClaw | Mahesh Yadav (ex-Google, AWS, Meta, Microsoft; Founder LegalGraph AI)
Today’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:<
- Apr 10, 202653 min
How to Design like OpenAI and Figma
Today’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 somethin
- Apr 7, 20261h 10m
How to build a Team OS in Claude Code with Hannah Stulberg, PM @ DoorDash
Today’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 SubstackRela
- Mar 30, 20261h 6m
How to Turn Claude Code into an Operating System with Carl Vellotti
Today’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.</p
- Mar 23, 20261h 12m
AI PM at Netflix, Amazon and Meta - Here's How to Become an AI PM (Fundamentals + Job Search)
Today’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:</str
- Mar 20, 202651 min
Evals are the new PRD. Here is the playbook with the CEO of the leader in the space (Ankur Goyal, Founder and CEO, Braintrust)
Today’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.</p
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