The Growth Podcast

Today’s episode
The 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.
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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
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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.
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Key Takeaways:
1. Code vs canvas is a false dichotomy - The best designers use both fluidly. Canvas for exploration, collaboration, and pixel-perfect craftsmanship. Code for interactions, responsive testing, and the last mile of polish. The question is what you are trying to learn, not which tool to commit to.
2. High fidelity is no longer expensive - The entire linear design process existed because building something interactive required engineering resources. That constraint is gone. A functional wireframe takes the same time as a paper sketch.
3. The Codex-Figma MCP makes handoff lossless - Import screens from a running React app into Figma with exact pixel values. Border radius, padding, shadows, all one to one. It is not a screenshot. It is a responsive, editable design artifact.
4. The reverse direction works seamlessly - Make changes in Figma, paste a component link into Codex, and it updates your code automatically. No redline spec, no handoff document.
5. Ed spends 70-80% of his time coding and still calls himself a designer - The medium changed but the mandate did not. Designers are still the voice of the user, still upholding craft. The tools expanded, the role stayed.
6. Figma designers are shipping PRs to production - Teams that six months ago were AI curious are now banging down the door. Monetization designers who never wrote code are building technically complex prototypes.
7. "Prototypes, not PRDs" is the emerging norm - PMs at OpenAI bring working prototypes to design reviews. They ship PRs to stress-test ideas before handing off to engineering.
8. You do not need permission to start - Someone from OpenAI's GTM team built an iOS app with zero experience. Download Codex and build something for yourself tonight.
9. Curiosity is the defining skill for this era - Not code proficiency, not design talent. The AI is an infinitely patient tutor. Ask questions. Build understanding alongside output.
10. Total football is the mental model - Every player can play every position. Roles still have natural spikes. But the tool constraints that enforced rigid boundaries are dissolving.
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Where to find Ed Bayes
* LinkedIn
* OpenAI
* X
Where to find Gui Seiz
* LinkedIn
* Figma
* X
Related content
Podcasts:
* Xinran Ma - Design with AI
* Carl Vellotti - Claude Code PM OS
* Codex PM Guide with Carl Vellotti
Newsletters:
* AI prototyping for PMs
* The PM guide to Bolt
* Codex PM guide
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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 episode
The 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.
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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
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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.
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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.
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Where to find Hannah Stulberg
* LinkedIn
* In the Weeds Substack
Related content
Podcasts:
* My Claude Code PM OS with Dave Killeen
* Claude Code + Analytics with Frank Lee
* Claude Code as PM OS with Carl Vellotti
Newsletters:
* The ultimate guide to context engineering
* Build your PM operating system
* How to use Claude Code like a pro
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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 episode
Claude 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.
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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.
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Key Takeaways:
1. Context management is the real skill - A single web search eats 10% of your context. Run /context to see what is consuming it. System prompt and MCPs take 10-16% before you type one message.
2. Sub-agents save 20x context - Delegate research to a sub-agent. Same task costs 0.5% instead of 10%. Your main session only gets the summary.
3. Replace MCPs with CLIs - MCPs eat context by existing. CLIs have zero overhead. GitHub CLI, Vercel CLI, Google Workspace CLI are all dramatically more efficient.
4. Powerful skills need zero code - Anthropic's front-end design plugin is just a good prompt. No APIs or tooling. Just rules that tell Claude "do not look like AI."
5. Give Claude self-checking tools - The make slides skill uses Puppeteer to screenshot output, measure overflow, and fix issues before you see them.
6. Repeat prompts for better quality - A Google paper showed pasting a prompt twice helps. Tell Claude to double-check against skill instructions after the first pass.
7. Use hooks to auto-invoke skills - A user_prompt_submit hook matches your words against skill keywords instantly. Zero context cost.
8. Jupyter notebooks solve data trust - Every analysis shows exact code, inputs, and outputs. Traceable and reproducible.
9. Build an operating system - Knowledge folder for people context. Projects folder for task isolation. Tools folder for scripts. CLAUDE.md for identity.
10. The people folder compounds - Connect meeting transcription. After every meeting, update each person's dossier. Every prompt gets more specific over time.
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Related content
Podcasts:
* Claude Code Masterclass with Carl Vellotti (Ep 2)
* Claude Code PM OS with Dave Killeen
* OpenClaw Setup Guide with Naman Pandey
Newsletters:
* The ultimate guide to context engineering
* How to use Claude Code like a pro
* Claude Cowork and Code setup guide
PS. 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 episode
Stop 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.
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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
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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.
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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.
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Where to find Jyothi Nookula
* LinkedIn
* NextGen Product Manager
Related content
Podcasts:
* Naman Pandey on OpenClaw
* Lisa Huang on Gemini Gems
* Frank Lee on Amplitude and MCP
Newsletters:
* The ultimate guide to context engineering
* RAG vs fine tuning vs prompt engineering
* AI foundations for PMs
PS. 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 episode
Most 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
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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.
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Where to find Ankur Goyal
* LinkedIn
* Braintrust
Related content
Newsletters:
* AI evals explained simply
* AI observability for PMs
* How to build AI products
Podcasts:
* AI evals with Hamel Husain and Shreya Shankar
* AI evals part 2 with Hamel and Shreya
* Aman Khan on AI product quality
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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