The Growth Podcast

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:* 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

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.----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

This 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.

Today’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

Today’s episodeMost PMs are using AI the same way they used Google in 2005.Type something in. Get something out. Move on.That is not how the best PMs are using it. The best PMs have stopped treating AI as a search engine and started treating it as a team member. One that already knows their product, their writing style, their strategy. One that does not need to be briefed from scratch every single time.That shift is what today’s episode is about.I sat down with Lisa Huang, SVP of Product at Xero, an $18 billion finance platform. She built the AI assistant for the first generation Meta RayBan smart glasses. She created Gemini Gems at Google. She has been an AI PM at Apple, Meta, and Google - three of the most demanding AI product environments in the world.She gave us a masterclass across Gemini Gems, building AI into hardware, running AI agents at scale inside a financial product, and what the AI PM career actually looks like from here.In today’s episode, we discuss across three topics.* How to build Gemini Gems and AI projects that actually work.* What she learned building AI into a wearable device.* What the future of the AI PM career actually looks like.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by - Reforge:Get 1 month free of Reforge Build (the AI prototyping tool built for PMs) with code BUILD----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. Stop briefing your LLM from scratch every time - Gemini Gems hold your context permanently. Your role, your company strategy, your writing style. Build it once and it already knows everything the next time you open it.2. Every PM needs 3 Gems - A writing clone trained on your PRDs and emails. A product strategy advisor loaded with your company docs and competitor analysis. A user research synthesizer that ingests raw transcripts and surfaces key themes.3. Vague instructions are the number one mistake - "Help me write better" gets you nothing. Write a full page of context. Your role, your audience, your format preferences. The more specific, the more personalized the output.4. Gemini Gems vs ChatGPT custom GPTs - OpenAI framed GPTs as an app store ecosystem. Google focused on personal productivity instead. First principles beat copying a competitor's framing, and the GPT store never took off.5. On-device AI is the future for wearables - Cloud is the default today but once a device is on your face all day, people want their data staying local. Privacy beats performance when the device is that personal.6. Accuracy is the product in high-stakes AI - LLMs out of the box are not great at math, accounting, or tax. Winning agents combine deep domain knowledge with proprietary data that no general-purpose model can access.7. Measure agents in three layers - Quality first (evals, human annotators, LLM judges). Product metrics second (adoption, retention, CSAT). Business impact third (revenue attribution, ARR). Skip to layer three without the foundation and you are measuring on sand.8. AI will not replace PMs - it will replace the execution work. Writing PRDs, creating mocks, managing roadmaps. What stays is product judgment. The ability to look at ambiguous signals and say this is the right bet and here is why.9. The PM role is becoming a hybrid - PM to engineer ratios will compress. The expectation is that PMs also build. Not just spec and hand off, but prototype, design, and code enough to show what they mean. The tools to do this exist right now.10. Your company's permission is not required - Most companies are not fine-tuning models. They are using the same consumer tools you already have. Build Gems. Build projects. Build small AI products with your personal data. There is nothing stopping you.----Where to find Lisa Huang* LinkedIn* WebsiteRelated contentNewsletters* How to become an AI PM* Practical AI agents for PMs* AI evals explained simply* AI product strategy* The AI PM learning roadmapPodcasts* Claude Code + Analytics - Vibe PMing with Frank Lee* AI evals explained simply with Ankit Shukla* How to become an AI PM with Marily Nika* AI prototyping mastery with Sachin Rekhi----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

Today’s episodeAI prototyping tools are redefining what it means to be a PM.Bolt went from 0 to $40M ARR in 4.5 months. Lovable hit $17M ARR in 3 months. Every forward-thinking product team is starting to prototype earlier, faster, and at higher fidelity than ever before.But most PMs are using these tools wrong.They open Bolt or Lovable, type a vague prompt, get something that looks decent, show it around, and move on. No problem space work. No divergent solutions. No user testing. The prototype dies in a Slack thread and nothing changes.In this episode, we built a LinkedIn sentiment analysis feature from scratch - live - to walk you through the complete workflow. From blank page to multi-page, clickable, high-fidelity prototype. We covered when to prototype, how to prompt, when to go high fidelity, and how to hand off to engineers with zero open questions.If you watch, you’ll also learn why your PRD and prototype need to live together - and why that combination is the new standard for forward-thinking PMs.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Pendo: The #1 software experience management platform* Testkube: Leading test orchestration platform* Gamma: Turn customer feedback into product decisions with AI* Product Faculty: Get $550 off the AI PM Certification with code AAKASH550C7* Mobbin: Discover real-world design inspiration----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. AI prototyping doesn't replace problem space work - it accelerates solution space work. Before opening any prototyping tool, lock down the problem, the user story, and the rough shape of the solution. If you can't write all three in one paragraph, you're not ready.2. Always start from your design system, not a blank page - Drop a screenshot of your existing product and ask the tool to recreate it. Save that as a team template. Every prototype you build from that point looks like it belongs in the product.3. Build 3 to 4 divergent solutions before choosing one - The entire point of AI prototyping is that building a second and third version costs almost nothing now. We built two versions of the sentiment analysis feature live. Neither was perfect. Both were useful. That comparison is the point.4. Use visual editing for fine-tuning, not prompting - Once you've picked the strongest direction, switch to direct visual editing. Move elements, match colours with the eyedropper, adjust spacing. It's faster because the result is immediate.5. Single-page prototypes miss too much - Build the full end-to-end flow. The moment you start connecting pages, edge cases surface automatically. We found two edge cases in minutes that would have cost engineering time in sprint.6. Prompt clarity beats prompt engineering - Any ambiguity in your prompt will get exploited statistically. Before running a complex prompt, paste it into a separate chat and ask it to find the contradictions. Fix those first.7. Use discuss mode before building anything major - Don't ask the AI if it can do something. That always gets a yes. Ask what it thinks the right approach is. The answer is far more honest and useful.8. High fidelity is for selling and usability testing - Low fidelity is for team exploration. Any prototype going in front of users needs to feel real, otherwise you get feedback about the roughness, not the experience.9. The PRD and prototype should live together - The PRD covers edge cases, empty states, error conditions. The prototype covers the 90% flows. Together they leave zero open questions for engineers. If someone reads both and still has a question, something is missing.10. The prototype is already standard code - A functional prototype built in Dazzle is a full server-side and client-side application. Download the project folder, drop it next to the production codebase, and tell Cursor to copy the interaction. Most of the implementation gets handled automatically.----Related contentNewsletters* Product Requirements Documents (PRDs): a modern guide* Ultimate guide to AI prototyping tools (Lovable, Bolt, Replit, v0)* Your guide to AI product strategy* AI PRDs: everything you need to know* AI agents: the ultimate guide for PMsPodcasts* The most powerful AI workflow for PMs with Frank Lee* How to engineer delight into AI products with Nazarin Shenel* AI prototyping tools with Eric Simons, CEO of Bolt----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

Today’s episodeThere is a term Andrej Karpathy coined last year: vibe coding.We have the same for product management: Vibe PMing.You describe the problem. The agent pulls the data. Analyzes the chart. Synthesizes the feedback. Drafts the spec. Files the ticket.That is not theory. That is what I walked through in today’s episode with a principal PM at Amplitude who builds MCP and agent products for a living. He showed it live, on screen, in real time.If you tune in, you’ll learn the full end-to-end workflow:----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.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Amplitude: The market-leader in product analytics* Pendo: The #1 software experience management platform* Testkube: Leading test orchestration platform* Product Faculty: Get $550 off the AI PM Certification with code AAKASH550C7* Bolt: Ship AI-powered products 10x faster----Key Takeaways:1. Claude Code + MCP is the most powerful AIPM workflow today - Connect your analytics tool via MCP, load your product context into a repo, and let the agent do analysis that used to take hours in minutes.2. Deep chart analysis now takes 90 seconds instead of 3 hours - Drop a chart URL into Claude Code, trigger the analyse chart skill, and the agent navigates your data taxonomy, finds anomalies, and hypothesises why metrics changed.3. Automate your entire weekly business review - Point Claude Code at your dashboards Monday morning. Get 3-5 top insights and the one urgent issue to tackle — no manual dashboard scanning ever again.4. Customer feedback synthesis across all channels in one pass - Zendesk, Gong, Salesforce, Slack, app stores all unified. Claude Code navigates the MCP, clusters themes, and surfaces what customers love and hate that week.5. PRDs write themselves from insights - Take the analysis output, point it at your PRD template in Cursor or Claude Code, and get a first draft spec in under 2 minutes. Iterate with command L or command K.6. Skills are the most important Claude Code feature - A skill is just a named prompt with heuristics and tool instructions. It loads only when relevant, preventing context bloat and giving the agent a repeatable workflow.7. The biggest MCP mistake is connecting too many servers - Every tool description burns context. Load only what's relevant to the workflow. Remove or hide tools that aren't being used for a given task.8. MCP is not for complex orchestration — it's for data access - Set the right expectation. MCP connects AI to external systems easily. It's the first step, not the whole pipeline.9. Granola has no MCP, so build a script instead - Frank used Claude Code to write a local script that dumps Granola meeting notes into his product repo. Now he can pull all meeting context with a single at-command.10. The future of PMing is vibe PMing - Chart analysis, dashboard reporting, feedback synthesis, spec writing, and prototyping — all agent-driven. PMs who adopt this workflow now will have a massive advantage in 2-3 years.----Related contentNewsletters:* How to use Claude Code like a pro* Steal 6 of my Claude skills* Context engineering* The AI stack for PMs* Practical AI agents for PMsPodcasts:* How to build an AI-native PM operating system with Mike Bal* AI evals explained simply with Ankit Shukla* Advanced guide to AI prototyping with Sachin RekhiPS. 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

Today’s EpisodeDesigning with AI isn’t about prompting.Most PMs think they understand AI design because they can write a good prompt. They’re wrong.Real AI design is about understanding the entire workflow, the system, the constraints, and the behaviors.Xinran Ma runs Design with AI, one of the top newsletters on AI design. He’s been studying AI design tools for three years. And he hasn’t shared most of this information publicly before.In today’s episode, we’re going live. We’re building real prototypes. We’re showing you the exact workflows that top 1% designers use.By the end of this episode, you’ll know the entire workflow from PRD to prototype to product.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* NayaOne: Airgapped cloud-agnostic sandbox* Pendo: The #1 software experience management platform* Maven: The cohort-based course platform powering the future of learning* Bolt: Ship AI-powered products 10x faster* Gamma: Turn customer feedback into product decisions with AI----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:Key Takeaways:1. AI design covers five areas not just prompts - Prompting, ideation, design/prototyping, workflows, and staying conscious. Most people think better prompts equal better design. That's just 20% of the skill.2. Use Google AI Studio for quick design variations - Upload 2-3 visual references. Describe what you want. Generate three different design directions in 5 minutes. What used to take 3-4 hours now takes 15 minutes.3. Lovable builds functional prototypes in seconds - Describe the experience you want to build. Lovable generates a working prototype in 60 seconds. Not mockups—actual clickable experiences you can test with users.4. Match tools to specific use cases - Custom GPT for effective prompts. Lovable for high-quality prototypes. Magic Patterns for design variations. Google AI Studio for free exploration. Cursor for full-stack experiences. Claude Code as all-purpose best.5. Good design passes four layers not just visual - Visual representation, problem-solving, design principles, and implementation feasibility. Most people stop at layer one. Great design works at all four layers.6. Context matters more than prompt length - Don't say "design a button." Say "design a primary CTA button for B2B SaaS onboarding where users connect calendar. Professional brand." Specificity drives quality.7. Visual references anchor AI output - Upload 2-4 screenshots showing the aesthetic you want. These show AI what "modern and minimal" means to you. The quality difference is massive versus text-only prompts.8. Iteration speed determines final quality - The magic isn't in the first output. It's in the 10th iteration after you've refined and tweaked. Review, identify issues, tell AI how to fix, repeat.9. Always validate with real users - AI tools make generating designs easy. Only users tell you if those designs actually help. Show prototypes to 3-5 users. Watch them try to use it.10. Workflows changed from linear to parallel - Before AI: sequential steps taking weeks. After AI: describe, generate, iterate freely. This is how top 1% designers work now.----Where to Find Xinran* LinkedIn* Newsletter* Maven courseRelated ContentNewsletters:* AI Prototyping Tutorial* AI Prototype to Production* How to Build AI Products* Prompt Engineering* Product Requirements DocumentsPodcasts:* Advanced Guide to AI Prototyping with Sachin Rekhi* AI Prototyping for PMs* How to Become an AI PM* Everything You Need to Know About AI----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

Check out the conversation on Apple, Spotify and YouTube.Brought to you by - Reforge:Get 1 month free of Reforge Build (the AI prototyping tool built for PMs) with code BUILDToday’s EpisodeAnkit Shukla is BACK after his gangbusters episode, that is my #2 most popular of all time. This time he's diving deep on one of the most important new AI skills for PMs: Evals.Whether you're working on AI features now or not, this is a skill you want to have an intuitive understanding of. So, I'm building on my library of eval episodes with today's drop.I've never heard someone explain evals from first principles as intuitively as Ankit has with this one. Hope you enjoy as much as I did!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.Where to find Ankit Shukla* HelloPM* Twitter (X)* LinkedIn* YouTubeRelated ContentNewsletters:* AI Evals* AI Testing* LLM JudgesPodcasts:* How to Do AI Evals Step-by-Step with Real Production Data* The PM’s role in AI Evals* AI Evals LivePS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!email productgrowthppp at gmail dot com for sponsorships. 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

Today’s EpisodeDiscovery might be the most important core PM skill for building great products.But most PMs are unprepared to do discovery in AI. PMs run surveys incorrectly, conduct interviews poorly, and end up with poor insights.Today will give you the roadmap to avoid all those mistakes.Caitlin Sullivan is a user research expert who runs courses teaching PMs how to do AI-powered discovery. And in today’s episode, she shows you exactly how she does it.We’re talking live demos. Step-by-step workflows. Real survey data. Real interview transcripts.This is a masterclass in discovery. The kind that moves the needle.----Brought to you by:Maven: Get 15% off Caitlin’s courses with code AAKASHxMAVENPendo: The #1 software experience management platformJira Product Discovery: Plan with purpose, ship with confidenceKameleoon: AI experimentation platformAmplitude: The market-leader in product analytics----Key Takeaways:1. Replicate the human process - Good AI analysis mirrors how experienced researchers work: comb through data first, then synthesize. Never jump straight to "give me themes."2. Use multi-step prompting - Load context in one prompt, run per-participant analysis in the next, then verify. Cramming everything into one prompt degrades quality.3. Code before you count - For surveys, apply inductive coding labels to every response before asking for patterns. Skipping this step leads to miscategorized, unreliable results.4. Always audit AI's work - Force the model to re-check its own analysis. It catches contradictions, overexaggerated intensity ratings, and miscoded responses regularly.5. Claude wins on nuance, Gemini wins on frequency - Claude gives more thorough, complete analysis by default. Gemini surfaces top-frequency themes faster but misses smaller patterns.6. Define everything explicitly - Quotes, ratings, emotional intensity levels, contradiction types. If you assume the model shares your definitions, you'll get inconsistent results.7. Markdown files beat raw transcripts - Converting transcripts to structured markdown improves accuracy and helps you work around token limits on non-Max plans.8. Parallelize with Claude Code agents - Set up agent markdown files for interview and survey analysis, then run both simultaneously. Cuts total analysis time in half again.----Related ContentNewsletters:How to Do Product Discovery RightAdvanced Techniques: Continuous DiscoveryCustomer Interviews: Advanced TechniquesPodcasts:Teresa Torres’ Guide to AI DiscoveryComplete Course: AI Product DiscoveryUltimate Guide to Knowing Your Users as a PM----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

Today’s EpisodeMost PMs are drowning in tools.You log into JIRA. Then Figma. Then Confluence. Then Notion. Then Google Analytics. Then Slack.Twenty different tabs. Twenty different logins. Zero flow state.Mike Bal runs product at David’s Bridal, a company undergoing massive digital transformation.And he operates from a single interface.Cursor and Claude Desktop sit at the center. Everything else connects through MCP and custom integrations.Research? Manus feeds into Claude. Analytics? Clarity exports into Cursor. Design? Figma pulls directly into his projects.This isn’t a tool stack. It’s an operating system.Today, Mike shows you exactly how to build it.----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.Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job. Only 9 seats left.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by Linear: Plan and build products like the best.----Key Takeaways:1. Operating systems beat tool stacks - Stop logging into 20 different UIs. Build one central interface through Cursor and Claude Desktop that connects to everything. The composable mindset adapts to your needs.2. MCP changes PM workflows forever - Model Context Protocol lets you connect JIRA, Figma, GitHub, Notion, Confluence through natural language. Check ticket status without opening JIRA. Compare designs without manual cross-reference.3. Design validation takes 30 seconds now - "Find my Confluence doc about Feature X, load this Figma design, compare them and tell me what I missed." Used to take 1-2 hours of manual comparison work.4. Manus dominates heavy research - Gives you multiple file outputs: sample CSVs, combined datasets, data sources report, quick start guide, markdown summary. All traceable back to sources. ChatGPT just gives responses.5. Research must stay external until vetted - The "conspiracy theorist LLM" problem is real. If you automatically feed everything into your system, AI anchors to wrong information. Vet research separately, then bring validated context in.6. PMs can build what required engineers - Mike built a colorization app for e-commerce in one morning. Migrated content to Sanity CMS in a few hours. All from natural language prompts in Cursor.7. Context switching kills productivity - Every time you open a new tab, you lose flow state. The operating system keeps you in one interface. The AI handles the context switching for you.8. Corporate IT restrictions become irrelevant - You already have Cursor or Claude Desktop. You already use JIRA, Figma, GitHub. Connect them through a better interface. No new tool approvals needed.9. Analytics workflows save massive time - Export Clarity data, upload to Cursor, prompt "analyze drop-offs and create visualizations." Takes 10 minutes vs hours of manual Excel work.10. AI native PMs think in prompts - "What do I need to do? What are the steps? What tools will help?" Treat AI as an extension of yourself, not a separate tool to learn.----Where to Find Mike* LinkedIn* Youtube* Website----Related ContentNewsletters:* AI Product Strategy* How to Build AI Products* AI Agents for PMs* Product Requirements DocumentsPodcasts:* AI Prototyping for PMs* How to Become an AI PM* Everything You Need to Know About AIPS. 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

Today’s EpisodeChatGPT just made huge waves with its Atlas browser. Perplexity made waves before that with its Comet browser. And Atlassian just spent a billion dollars to buy Dia.Big companies are making big moves in the AI browser space.But should you use an AI browser? Is it safe? Will it make you more effective as a PM?I asked this question at Berkeley last month during my keynote. Out of 500 PMs in the room, literally two hands went up.That needs to change.Naman Pandey has tested these browsers more extensively than anyone else. He runs the Ready Set Do podcast and has spent hundreds of hours finding the real use cases that actually work.Today, we’re putting all three browsers head-to-head. Same prompts. Same tasks. Live demos.You’ll see which browser wins for each use case, where they fall over, and the exact workflows to use them as a PM.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Jira Product Discovery: Plan with purpose, ship with confidence* Mobbin: Discover real-world design inspiration* Pendo: The #1 Software Experience Management Platform* Product Faculty: Get $550 off the 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, DeepSky, Relay.app, Magic Patterns, and Mobbin - for free, grab Aakash’s bundle.Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job. Only 14 seats left.----Key Takeaways:1. AI agent browsers are underhyped for PMs - Only 2 out of 500 PMs at Berkeley were using them. If you're doing web research, competitor analysis, or data scraping, you're leaving hours on the table every week.2. The three browsers serve different purposes - ChatGPT Atlas for deep research across multiple pages. Perplexity Comet for real-time quick lookups. Arc Dia for workflow automation. They're not competing head-to-head.3. Atlas dominates data extraction - Scrape YC companies, find recruiters on LinkedIn, build competitor comparison tables. What took 2-3 hours now takes 10 minutes with one prompt.4. Comet wins on speed for real-time info - Stock prices, sports scores, breaking news. It's the fastest by far. Perfect for quick research sprints across Reddit, Twitter, and news sites.5. Dia automates repeated workflows - Monitor competitor pricing weekly. Document onboarding flows. Generate recurring reports. Set it once, let it run on schedule.6. Tab context is the hidden superpower - Open 5 competitor sites. Ask "What's the common pricing strategy?" The AI reads all tabs and synthesizes insights. Eliminates copy-paste friction.7. The job seeker use case is mind-blowing - "Find 20 PMs at Google, get their LinkedIn profiles, draft personalized DMs." Atlas does this in 15 minutes. Used to take 2-3 hours manually.8. Onboarding analysis becomes trivial - "Go through Notion's signup flow, capture screenshots, document each step." Dia does this in 5-10 minutes. Perfect for competitive analysis.9. Don't log into sensitive accounts - Banking, email, social media with private data - keep these in your regular browser. Use AI browsers only for public research and data extraction.10. The slowness matters less than you think - Yes, they're slow compared to Google. But if the alternative is 2 hours of manual work, waiting 10 minutes is a massive win. Batch requests and walk away.----Related ContentNewsletters:* AI Product Strategy* How to Build AI Products* AI Prototyping Tutorial* How to Become an AI PM* Ultimate Guide to OnboardingPodcasts:* How to Build ChatGPT Apps* AI Prototyping for PMs* Everything You Need to Know About AI* AI Product Management Course----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

This is a free preview of a paid episode. To hear more, visit www.news.aakashg.comToday’s EpisodeWhen you first start using AI prototyping tools, you get wowed.You type “create me a CRM application” and boom a fully functioning app appears in 60 seconds.But here’s the problem.It looks generic. The styling is basic. The features are vanilla. You’d never ship this to customers.This is AI slop.Sachin Rekhi was the former Head of Product at LinkedIn Sales Navigator. He’s now teaching thousands of PMs at Reforge how to master AI prototyping.----Brought to you by - Reforge:Get 1 month free of Reforge Build (the AI prototyping tool built for PMs) with code BUILD----Key Takeaways:1. Product shaping changes everything - Anthropic builds multiple prototypes for every problem, launches internally, sees what people use, then productionizes winners. This used to only be possible at Apple with massive labs.2. AI slop is real - Type "create a CRM" and you get generic styling, vanilla features, basic scenarios. Looks magical but you'd never ship it. The challenge is going from slop to production-grade prototypes.3. The 15-skill mastery ladder - Apprentice level: prompting, editing, design consistency. Journeyman: versioning, debugging, diverging. Master: functional prototyping, product shaping, analytics integration.4. Design consistency starts with baselining - Take screenshot of your product. Recreate it. Iterate until perfect. Save as template. Now every prototype inherits your design system automatically.5. Diverging is the secret weapon - Generate 4 design variants instead of 1. Magic Patterns has this built in. Or use multiple tools to get 8 options. Evaluate alternatives like designers do.6. Functional prototypes unlock real validation - Integrate OpenAI API for actual responses. Add PostHog for session recordings and heatmaps. Build surveys. Track clicks. Test with real data, not mockups.7. The tools face-off: which to actually use - Bolt for speed. V0 for beautiful UIs. Replit for full-stack. Magic Patterns for product teams with diverging. Reforge Build for context integration. Cursor for technical PMs.8. The $5/month unlimited execution hack - Host n8n on Hostinger instead of paying per execution. Get unlimited runs. Build workflow that backs up to Google Drive for version history.9. PMs can build what used to require engineering - Calendar integration. Email agents. Analytics dashboards. Multi-model comparison. Survey collection. All from prompts. No code required.10. Traditional workflows beat agents for production - Workflows save tokens, run faster, and are more reliable. Use agents only when tasks need real decision-making. For known processes, use workflows.----Where to Find Sachin* LinkedIn* Newseltter* X* Youtube* Reforge cources----Related ContentNewsletters:* AI Product Strategy* How to Build AI Products* AI Prototype to Production* AI Prototyping Tutorial* Product Requirements DocumentsPodcasts:* AI Prototyping for PMs* How to Become an AI PM* Everything You Need to Know About AI----PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail.

This is a free preview of a paid episode. To hear more, visit www.news.aakashg.comToday’s EpisodeChatGPT Apps might be the next billion-dollar opportunity.Or they might be another ChatGPT feature that gets abandoned in 6 months.I genuinely don’t know yet.But when people say “this could be the new App Store,” my ears perk up. I spent four years building an iOS app in the early days of the App Store. The distribution was incredible. We grew fast purely because of where we were.So when OpenAI announced the ChatGPT App Store, I needed to understand it.I brought in Colin Matthews to break it down. Colin is one of my go-to sources for technical product topics. Our AI prototyping collaborations have been some of your favorite episodes.Today, we’re exploring ChatGPT Apps and what they mean for you as a product builder.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Maven: Get $500 off with my code on Coil Build ChatGPT Apps course* Vanta: Automate compliance, Get $1,000 with my link* Land PM job: 12-week experience to master getting a PM job* Mobbin: Discover real-world design inspiration* NayaOne: Airgapped cloud-agnostic sandbox----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, and Mobbin - for free, grab Aakash’s bundle.Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job. Only 23 seats left.----Key Takeaways:1. ChatGPT apps = MCP + widgets - The Model Context Protocol (invented by Anthropic) lets AI agents call external tools. OpenAI added UI widgets on top to create embedded app experiences directly in chat.2. 900M weekly active users = massive distribution opportunity - This is the new SEO. Early data shows 26% higher conversion from AI traffic vs traditional search. Every enterprise will eventually build here.3. You're building for multiple platforms - MCP works across ChatGPT, Claude (coming soon), Cursor, and other AI tools. Build once, distribute everywhere. Gemini doesn't support it yet.4. Apps get called based on tool descriptions - Your metadata matters. Like SEO but for LLMs. Run evals to test if correct prompts trigger your tools. Iterate on descriptions to improve discovery.5. Three eval categories: direct, indirect, negative - Direct: user names your app. Indirect: user describes outcome. Negative: irrelevant request shouldn't trigger your tool. Test all three systematically.6. PMs should prototype but engineers ship production - Use tools like Chippy to prototype quickly and test concepts. Show stakeholders real interactions. Engineering team builds the production version.7. Enterprise-first, solo builders second - Large companies (Target, Uber, Canva) are early adopters chasing distribution. But huge opportunity for indie builders once public marketplace launches.8. Best opportunities: embedded collaboration tools - Spreadsheets, task lists, whiteboards where ChatGPT can partner with you. Not just search results—actual interactive experiences.9. Error analysis on observability logs is critical - Track what prompts triggered which tools with what parameters. Look for mismatches between expected and actual behavior. Iterate tool descriptions.10. Marketplace launching by end of 2024/early 2025 - Currently only launch partners can publish. Public marketplace coming soon means anyone can ship apps and reach ChatGPT's massive user base.----Where to Find Colin* LinkedIn* NewsletterRelated ContentNewsletters:* AI Prototyping Tutorial* How to Build AI Products* AI Product Strategy* Complete Course: AI Product ManagementPodcasts:* AI Prototyping for PMs* How to Become an AI PM* Everything You Need to Know About AI----PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail.

Today’s EpisodeEveryone’s demoing AI features. Few are shipping them to production reliably.The gap? Evals.Not the theoretical kind. The real-world kind that catches bugs before users do.Hamel Husain and Shreya Shankar train people at OpenAI, Anthropic, Google, and Meta on how to build AI products that actually work. Their Maven course is the top-grossing course on the platform.Today, they’re walking you through their complete eval process.----Brought to you by:* The AI Evals Course for PMs & Engineers: You get $800 with this link* Vanta: Automate compliance, Get $1,000 with my link* Jira Product Discovery: Plan with purpose, ship with confidence* Land PM job: 12-week experience to master getting a PM job* Pendo: the #1 Software Experience Management Platform----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, and Mobbin - for free, grab Aakash’s bundle.Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job. Only 23 seats left.----Key Takeaways:1. AI evals are the #1 most important new skill for PMs in 2025 - Even Claude Code teams do evals upstream. For custom applications, systematic evaluation is non-negotiable. Dog fooding alone isn't enough at scale.2. Error analysis is the secret weapon most teams skip - Looking at 100 traces teaches you more than any generic metric. Hamel: "If you try to use helpfulness scores, the LLM won't catch the real product issues."3. Use observability tools but don't depend on them completely - Brain Trust, LangSmith, Arise all work. But Shreya and Hamel teach students to vibe code their own trace viewers. Sometimes CSV files are enough to start.4. Never use agreement as your eval metric - It's a trap. A judge that always says "pass" can have 90% accuracy if failures are rare. Use TPR (true positive rate) and TNR (true negative rate) instead.5. Open coding then axial coding reveals patterns - Write notes on 100 traces without root cause analysis. Then categorize into 5-6 actionable themes. Use LLMs to help but refine manually.6. Product managers must do the error analysis themselves - Don't outsource to developers. Engineers lack domain context. Hamel: "It's almost a tragedy to separate the prompt from the product manager because it's English."7. Real traces reveal what demos hide - Chat GPT said the assistant was correct but missed: wrong bathroom configuration, markdown in SMS, double-booked tours, ignored handoff requests.8. Binary scores beat 1-5 scales for LLM judges - Easier to validate alignment. Business decisions are binary anyway. LLMs struggle with nuanced numerical scoring.9. Code-based evals for formatting, LLM judges for subjective calls - Markdown in text messages? Write a simple assertion. Human handoff quality? Need an LLM judge with proper rubric.10. Start with traces even before launch - Dog food your own app. Recruit friends as beta testers. Generate synthetic inputs only as last resort. Error analysis works best with real user behavior.----Where To Find Them* LinkedIn:* Hamel: Hamel’s LinkedIn* Shreya: Shreya’s LinkedIn* AI Evals Course: World’s best AI Evals Course (You get $800 off with this link)Related ContentNewsletters:* AI Evals* AI PM Observability* AI Testing* LLM Judge* AI Prototype to Production* AI Product Strategy* How to Build AI Products* Prompt EngineeringPodcasts:* AI Evals: Everything You Need to Know to Start* Everything you need to know about AI (for PMs and builders)* Carl Vellotti on Claude Code* Marily Nika on Google AI PM Tool Stack* Pawel Huryn on n8n 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

Today's EpisodeCarl Vellotti is back.His first episode hit 30k views. My most popular episode in months.Today, he’s giving you the advanced playbook for Claude Code.Claude Code hit $1 billion ARR in December. Fastest product ever to reach that milestone. Under 6 months.Carl has been building with Claude Code every single day. He’s made all the mistakes. He’s learned all the expert workflows and tips.In today’s episode, he shares everything.----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, and Mobbin - for free, grab Aakash’s bundle.Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job.----Brought to you by:Amplitude: The market-leader in product analyticsPendo: the #1 Software Experience Management PlatformJira Product Discovery: Move discovery and roadmapping out of spreadsheetsMiro: The innovation workspace----Key Takeaways1. Claude Code hit $1B ARR in 6 months - fastest product ever. Anthropic optimized for power users doing deep work. Fewer users, insanely deep usage. Carl: "I spend all day in Claude Code. It gets to the point where you never really have to leave."2. MCPs connect Claude to everything in 2 minutes - Model Context Protocol invented by Anthropic. One command to install Linear, Google Workspace, Slack. Essential stack: documents first, task management second, communication third, data sources fourth.3. End-to-end PM workflow takes one morning instead of one week - Survey creation → analysis → PRD → presentation → 19 tickets. All through MCPs. Carl didn't use templates. Opus 4.5 handled everything.4. Skills are pre-built templates but don't always auto-trigger yet - Solution: specify explicitly. "Create presentation using your presentation skill." Build Skills for anything you do more than twice. That's the compound effect.5. Real Google Slides, not images - Carl's presentation skill created 19 fully editable slides from PRD. Takes a few minutes because Claude creates slides in parallel. "This is saving you hours. We used to spend entire afternoons turning PRDs into decks."6. GitHub turns Claude into remote worker - Create issues from your phone. @mention Claude. It works while you're away. Carl's journal is a private GitHub repo. Voice transcriptions → issues → Claude updates markdown.7. Workflows beat agents for production - Level 1 workflow: 5,000 tokens, 40 seconds. Level 3 agent: 90,000 tokens, 90 seconds. Carl's rule: "If something can be expressed as code, code it. Leave agents for cognitive work."8. Production best practices separate hobbyists from pros - Set error workflows. Add retry logic (3 attempts, 1-second delays). Pin data during development. Use reasoning effort appropriately. Write detailed tool descriptions.9. Real challenge is building reusable workflows - Learning basics takes a day. First workflow takes hours. But having 50 workflows built over months means everything is automated. Carl: "Because I have workflows for all these things already built, I can do it all in one shot."10. Tools improve faster than you can learn them - Carl: "By the time you see this, there might be Claude 5." Your workflows from today will 10x when better models drop. PMs who started 6 months ago aren't just 6 months ahead, they're 100x ahead because systems compound with every improvement.----Where to Find Carl Vellotti* Linkedin* X (Twitter)* Instagram* NewsletterRelated ContentNewsletters:* How to Use Claude Code Like a Pro* Steal 6 of My Claude Skills* Claude Skills Tutorial* AI Agents: The Ultimate Guide for PMs* Practical AI AgentsPodcasts:* The Ultimate Guide to n8n for PMs, with Pawel Huryn* This is what a Google AI PM’s Tool Stack Looks Like, with Marily Nika* How to Use Claude Code Like a Pro (Carl’s First Episode)----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

Marily Nika and I filmed my 7th most popular episode a year ago.She broke down everything on how to become an AIPM because she’s done it. She is an AIPM at Google with 11 years of experience.Today, she’s back with a completely new episode. She’s dropping every AI tool she uses daily as an AIPM.Not theory. Not hype. The actual 6 tools she uses multiple times every single day.Today she’s dropping all the expert tips and tricks. She’s made the mistakes so you don’t have to.----Today’s guide covers:* The AI PM Tool Stack* Google AI Studio for Prototyping* Opal for Mini Apps* Notebook LM for Domain Expertise* Perplexity for Reddit Research* ChatGPT for Your Voice* Fireflies and Getting Tools at Work* The 18-Month AIPM Roadmap* The AIPM Interview Red Flags----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Jira Product Discovery: Move discovery and roadmapping out of spreadsheets* Vanta: Automate compliance across 35+ frameworks* Maven: Improve your PM skills with awesome courses. Discount with my link* Mobbin: Discover real-world design inspiration* Product Faculty: Get $500 off the AI PM Certification with code AAKASH25----Key Takeaways1. Marily's stack is just 6 tools - Google AI Studio for prototyping, Opal for mini apps, Notebook LM for learning, Perplexity for user research, ChatGPT for PRDs, and Fireflies for meetings. Each maps to one PM workflow. No tool hopping.2. Prototype before writing PRDs. Marily builds a working app in AI Studio first, then brings engineers in to debate the actual thing. Saves weeks of back-and-forth on documents. PRDs are now for complex cross-functional work only.3. Notebook LM learned a 4-hour video in 15 minutes. Marily had an interview the next day. She uploaded the job description and a 4-hour investor relations video. It gave her 15 key points. She memorized them and crushed the interview.4. Perplexity's Reddit filter is your secret weapon. Turn off web search, turn on discussions and opinions. Ask "would young professionals be interested in a fitness ring?" Get 20+ Reddit sources instantly. Know what users actually want.5. Marily's PRD generator has 10,000+ users. She trained a ChatGPT custom GPT with her voice and old PRDs. It asks probing questions before generating. Gets PRDs done in days instead of weeks. She's not embarrassed to use AI at work.6. Tool selection has 4 rules. Does it save 10x time (not 2x)? Does it work across contexts? Does it work with your company's limitations? Does it compound over time? If not all 4, delete it.7. "Be like a crab" for AIPM roles. Move adjacent to your experience. Hearing aids → AirPods PM. ESPN journalism → Meta sports AI PM. Don't ignore your past experience. It's your competitive advantage.8. Red flag in AIPM interviews - missing PM craft. Ex-ML scientists dive into algorithms without asking why, who, and how to measure success. AI changes HOW you fulfill use cases, not the use cases themselves.9. Who replaces PMs? Not AI. PMs who use AI when you're not. Don't be embarrassed about using AI at work. It's not cheating. The gap is widening between PMs who use these tools and those who don't.10. Every PM will become an AIPM by 2026. Show Marily any product and she'll find the AI use case. Even retail stores use cameras and AI to monitor which areas get traffic. The AIPM title will just become PM.----Where to Find Marily* LinkedIn* Twitter* Website* YouTube* Newsletter* Maven - Her course is part of my special curation with Maven----Related ContentNewsletters* AI Foundations for PMs* How to Become an AIPM* AI Prototyping for PMs* AI PRDs* The Complete AI PM Course* AI Product Strategy* ChatGPT for PMsPodcasts* Pawel Huryn on n8n* Alex Danilowicz on AI Prototyping* Hamza Farooq on Landing AIPM Jobs* Todd Olson on Upskilling to AIPM* Rachel Wolan on AI Product Leadership----If you want access to AI tools for free, get Aakash’s bundle and access to 9 AI products for an entire year.Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job.----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

This is a free preview of a paid episode. To hear more, visit www.news.aakashg.comPawel Huryn is the guest behind my most popular episode ever (52K+ views).Today, he’s back to give you a masterclass in one of the most exciting AI tools out there: n8n.n8n is the most powerful workflow automation tool that combines two things: traditional workflow automation and building AI agents.And Pawel has been knee-deep in n8n more than almost anyone else in the world.He’s tried everything. He’s made all the mistakes. He’s learned all the expert workflows and tips and tricks.In today’s episode, he walks through building real n8n workflows from scratch.-----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Amplitude: The market-leader in product analytics* Vanta: Automate compliance across 35+ frameworks* Testkube: Leading test orchestration platform* Kameleoon: AI experimentation platform* Pendo: The #1 software experience management platform-----Key Takeaways1. N8N combines traditional workflow automation AND AI agent building in one platform - making it more powerful than Zapier or Make for complex automation needs.2. Real use cases span from simple business workflows to chatbots, automatic competitor monitoring, multi-agent research systems, and inbox workers that take actions based on emails. Sky is the limit.3. Pavel's competitor monitoring workflow costs $1-2/week using the FREE version of N8N. Just needs Perplexity API ($1-2 for hundreds of calls) and OpenAI credits. Enterprise tools charge $500+/month.4. Pin your data during development. N8N caches API responses so you don't burn credits while testing workflows. Click the pin icon and N8N uses cached data instead of making new API calls.5. N8N automatically loops through items - no need to write for-loops or while-loops. When you connect a node with 6 items, N8N repeats the action 6 times automatically.6. Compress context before sending to LLMs. Pavel cuts 70% of tokens by extracting only summary content and citation URLs from Perplexity results, ignoring irrelevant snippets and metadata.7. Use ChatGPT to write N8N code snippets. Pavel never writes code blocks himself - just takes a screenshot of the data and asks GPT "how do I compress this information?"8. Traditional workflows are more efficient (saves tokens, very reliable) for predictable tasks. AI agents are more flexible but use more tokens and can make mistakes. Use workflows when you know the steps.9. Set GPT reasoning effort to "low" for simple tasks. When you just need formatting or summarization (not complex thinking), low reasoning effort saves tokens significantly.10. Best practices: Set dedicated error probes to catch errors before they break workflows. Use max iterations to prevent infinite loops. Set retry on fail to 3x attempts. Pin data during development.-----Where to Find Pawel* LinkedIn* Newsletter* YouTube-----Related ContentNewsletters:* Ultimate Guide to AI Prototyping Tools* AI Agents: The Ultimate Guide for PMs* Practical AI AgentsPodcasts:* AI Agents Demo, with CEO of Relay.app* How to Build AI Agents (and Get Paid $750K+)* 5 AI Agents Every PM Should Build, with CEO of LindyIf you want access to a n8n competitor for free, get Aakash’s bundle and access to Relay.app.-----If you want to advertise, email productgrowthppp at gmail.

Today’s EpisodeLaura Burkhauser started as an IC PM at Descript, the $550M AI video editing platform.Three years later, she’s CEO.She shipped AI features that worked.In today’s episode, you’ll hear the exact features Laura built, the eval framework she used, and her complete IC → CEO path.This might be the most actionable career episode you listen to all year.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Maven: Improve your PM skills with awesome courses* Pendo: #1 Software Experience Management Platform* Vanta: Automate compliance across 35+ frameworks like SOC 2 and ISO 27001* NayaOne: Airgapped cloud-agnostic sandbox* Kameleoon: Leading AI experimentation platform----Key Takeaways1. Map your user journey BEFORE picking AI features. DeScript identified pain points (retakes, eye contact, rambling), then asked "what just became possible with LLMs?" Build that intersection.2. Build prepackaged buttons, not blank chat boxes. Each DeScript AI tool is a carefully crafted prompt behind a single button that delivers reliable results every time.3. Use human evals on production data before shipping. Test on real customer data, ask "would I use this as a customer?" If yes, ship. If no, don't.4. The ultimate metric is export rate. If users apply your AI feature then remove it before exporting, it didn't meet their quality bar.5. Switch from buttons to chat when you hit 30+ parameters. When users wanted topic selection, speaker choice, and platform optimization, chat became better than buttons.6. Match your eval data to actual use case. DeScript failed with Studio Sound because they tested on terrible audio (vacuuming, jackhammers) when real users had laptop microphones. Different models handle different quality levels.7. Test agents with real customer language early. Don't use toy data or employee terminology. Mix sophistication levels—some advanced at video and AI, some complete beginners—to understand how real people prompt.8. Launch AI agents to new users first. Video editing is hard and many people quit. DeScript tested Underlord on activation and it won, so new users got it first before existing users.9. Choose breadth over depth for product-wide agents. DeScript chose breadth—Underlord works across all features because "we're not a point solution." Requires more context, tool coverage, and evals but serves the product vision.10. Earn founder trust by getting command, not by being strategic. Use the product extensively. Talk to customers constantly. When you speak, people think "Smart" and invite you to more rooms. Ship features before focusing on strategy.----Where to Find Laura Burkhauser* LinkedIn* Company----Related ContentPodcasts:* AI Product Leadership Masterclass* Conversation with the CEO and Founder of Bolt* This $20M AI Founder Is Challenging Elon and Sam Altman | Roy Lee, CluelyNewsletters:* Ultimate Guide to AI Prototyping Tools* AI Agents: The Ultimate Guide for PMs* How to Land a $300K+ AI Product Manager JobPS. 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

Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Jira Product Discovery: Move discovery and roadmapping out of spreadsheets* Vanta: Automate compliance across 35+ frameworks like SOC 2 and ISO 27001* Kameleoon: Leading AI experimentation platform* NayaOne: Airgapped cloud-agnostic sandbox* Product Faculty: Get $500 off the AI PM Certification with code AAKASH25Today’s EpisodeClaude Code hit $1B ARR in 6 months. But OpenAI is not just giving up.ChatGPT’s new Codex is the most powerful way for product managers to build prototypes. And it’s a far better way to use ChatGPT than the browser.So every PM should know how to use it.Today, I brought back the man behind my Claude Code tutorial, Carl Vellotti, for a full guide on how to use ChatGPT Codex for PMs:This might be the most important podcast you watch all year. (And not a single other PM podcast has even talked about this tool)Your Newsletter Subscriber BonusFor subscribers, each episode I also write up a newsletter version of the podcast. Thank you for having me in your inbox.Today’s guide covers:* Getting Started: The Non-Technical PM’s Guide to Codex CLI* Classic PM Tasks: Documents, Meetings & PRDs with Codex* Advanced Prototyping: From Vibe Coder to Vibe EngineerThis is your complete Codex roadmap.1. How to Get Started - The Non-Technical PM’s Guide to Codex CLIIf you’ve never touched a terminal, this is for you.1a. Open Codex in an IDECarl opens Cursor and shows files Codex creates in real-time.Workflow: Open project folder in Cursor → Terminal (Control + backtick) → Type codex.Now you see files created, preview documents, navigate visually.As Carl Says:“Open it in an IDE. Easiest way to see what it’s actually doing.”1b. The YOLO Mode HackCodex asks for permission constantly—every website, every command.Solution:codex --yoloFull access mode. No prompts. Just execution.“Haven’t broken my computer yet. We’re in this directory so it won’t leave.”1c. Codex vs Claude CodeSame task on both: Search web, summarize differences.Claude Code: 3 searches simultaneously, ~2 minCodex: One site at a time, asks permissions, ~4 minClaude Code is faster, hides details. Codex shows every command—more verbose.“It’s Apple versus Microsoft. Claude does things in a nicer interface. Codex shows you everything.”2. How to Handle Classic PM Tasks with CodexCodex shines at daily PM work.2a. File Analysis Without UploadingRun Codex from a folder, it accesses everything. No uploading.Carl has demo folder (Taskflow) with interviews, notes, PRDs.“What user interviews completed?” → Codex lists them“Top 3 pain points?” → Returns: voice input reliability, integration gaps, template workflowCreates document with direct quotes—no manual file providing.2b. Template-Driven WorkflowsSolution: Create /templates folder with markdown files.Example: Discussion Points, Action Items, Risks & Blockers, Next StepsUse: Summarize @meeting-notes using @templateSame format every time. Works for PRDs, one-pagers, research summaries.2c. Socratic Questioning for Better PRDsProblem: Asking Codex to write a PRD produces garbage without proper thinking.Solution: Socratic template makes Codex ask YOU questions first.Questions: “Why is this helpful? Data, feedback, or strategic?” “What must work for V1?” “Edge cases?”You answer. AI embeds your thinking.Then Codex reviews context, templates, example PRDs, and writes.Carl: “Goes from kind of there to a really good PRD almost right out of the box.”3. Advanced Codex Techniques for Future Vibe EngineersThis is where Codex separates from the browser.3a. Design Systems with StorybookVibe coder problem: “Make title pink” → wrong shade → 20 iterations → still jankyVibe engineer solution: Storybook (npm run storybook)See components visually, change colors instantly, see changes live without redeploying.Carl changes recipe title to pink: read-only mode to see plan, then YOLO mode to execute. Updates in Storybook immediately.Pre-built components: Use Shadcn UI for production-grade React components. Calendar, date picker, dropdown—all done.“All the logic and difficult things, you get for free.”3b. Test-Driven Development is The UnlockProblem: AI says “Done!” but it’s broken. AI will lie and set variables to true to pass tests.Solution: Write tests BEFORE building.Red-Green-Refactor: Tests fail → Build until pass → Improve codeCarl’s macro calculator:Tests: API returns calories, empty recipe returns null, missing data shows “N/A”, API fails retries 3x, divide by servings correctlyTold Codex: “Build to make tests pass.”35 minutes. Zero touch. All tests green. Feature worked.Carl: “If you write tests FIRST, they can’t cheat.”3c. An Example - The TikTok Recipe BotCarl built this for his girlfriend: TikTok recipe extractor.Problem: Recipe videos on TikTok. No written recipe. Just “comment and I’ll DM.”Solution: Paste URL → Codex downloads video → Sends to Gemini (only model that processes video) → Gemini extracts recipe → Codex formats to PDFHow: Detailed implementation spec: architecture, data flow, APIs, error handling, retry logic, tests.Gave to Codex in GPT-5 Codex mode. Left 35 minutes.Built entire feature. Tests passed. Worked first try.Carl: “First time I saw the dream—give it a medium-sized feature and it just builds it.”Where to Find Carl Vellotti* Linkedin* X (Twitter)* Instagram* NewsletterRelated ContentPodcasts:* Claude Code Tutorial* Windsurf Tutorial* AI Prototyping TutorialNewsletters:* Ultimate Guide to AI Prototyping Tools* AI Agents: The Ultimate Guide for PMs* How to Land a $300K+ AI Product Manager JobPS. 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