Product Growth Podcast

Brought to you by Customer.io - Send smarter messages using your product data Kameleoon - Leading AI experimentation platform Bolt - Ship AI-powered products 10x faster Jira Product Discovery - Plan with purpose, ship with confidence Product Faculty - Get $150 off their #1 AI PM Certification via code AAKASH150 Today's Episode n8n was the most written-about AI tool in this newsletter last year. I covered it 25 times and ranked it A tier, above Zapier and Make. Then Claude Code and Cowork arrived, the Google Trends line bent downward, and a tweet went around declaring n8n irrelevant. So today I went to the person with the most to lose. Jan Oberhauser is the founder and CEO of n8n, last valued at $5.2 billion after SAP's strategic investment. He says n8n has been declared dead roughly a thousand times, including the week OpenAI shipped its own agent builder, which turned out to be one of his best growth weeks ever. His argument is not that Claude Code is bad. He uses it. His argument is that they are different products and you need both. Claude Code is where you prototype in ten minutes. n8n is where the thing goes once it has to run every night, survive a model outage, pass an audit, and be handed to someone who did not build it. So he opened the product and showed me. A live agent build, an eight-minute AI assistant run, an approval gate that stops an agent mid-action, and an execution log that shows every decision the agent made. Then we got into the growth story. 10x revenue in a year. No lead gen target. No per-seat pricing. A goal of a billion users with fewer than a thousand employees. 10 Key takeaways 1. Claude Code and n8n are different products, and you need both - Claude Code runs Anthropic models in your terminal as an agentic tool. n8n is the orchestration layer connecting your tools, models and data sources on a visual canvas. Jan's own users prototype in Claude Code and migrate to n8n once the workflow has to be reliable. 2. Reliability is fallback models, self-hosting and maintained integrations - Every provider goes down, so you set a fallback model inside the same workflow. You can self-host next to your own data. Each integration is code n8n writes and maintains, so an API change gets fixed once for everyone instead of separately by every builder. 3. Auditability is the thing generated code cannot give you - With code you see the input and the output and nothing in between. n8n replays every past execution step by step, with the data going in and out of each node, and lets you rerun half a workflow from a single data point. 4. AI plus deterministic logic plus human in the loop - An if statement is cheaper, faster and 100% reliable, so it belongs next to the model rather than being replaced by it. Destructive actions sit behind an approval gate, so the agent asks before it sends the email or books the meeting. 5. The AI assistant builds the workflow for you - Jan prompted it the way you would prompt Claude Code. It asked clarifying questions, thought for eight minutes, and returned a working multi-tool agent. Extending it with one-on-one scheduling and Google Contacts took another five. 6. Start small, and skip AI when you do not need it - Companies that try to transform everything at once spend weeks building the wrong thing. Jan's favorite example is a company that automated employee password resets and saved multiple full-time employees a year. The small wins are also the ones that get colleagues interested. 7. n8n wins wherever the work is business-critical - Security orchestration, compliance, employee onboarding and offboarding, DevOps, monitoring. The rule Jan gives is that the more reliability and security matter, the better n8n fits, which covers nearly everything that is not a personal use case. 8. Sprinkling AI on top gets 10 to 30%, being in the value chain gets 10x - Adding an AI button somewhere is not a strategy. n8n's bet was that when someone decides to build an agent, they build it in n8n. That choice is what produced 10x growth in a year. 9. Deleting the metrics that would make money faster - No lead gen target and no per-seat pricing, because profitability means n8n can think in years instead of quarters. The company does not push free self-hosted users onto paid hosting either. The internal goal moved from a billion in ARR to a billion users so nobody confuses the mission with money. 10. Talent density over headcount - The target is a billion users with fewer than a thousand employees, which keeps the hiring bar high. Jan wants tinkerers who run home automation and understand scale, evals and reliability. Live problem-solving beats take-home tasks now that AI can do the take-home for you. Go Deeper Build your first n8n workflow with Pawel Huryn’s guide. Then follow Mahesh Yadav’s learning path in How to Become a Builder PM. Prototype your next agent there, and promote it into n8n through the MCP server. Write 20 test cases and run them as an eval. *** You can get my podcasts on Apple, Spotify, and YouTube. *** If you want to advertise, please email productgrowthppp[at]gmail[dot]com. 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

Brought to you by Arize - Ship AI agents and features faster with fewer regressions. Get a full year of Arize’s paid plan, along with 8 other AI tools, at bundle.aakashg.com. Today’s episode I wrote a PRD using ChatGPT, with a considerable number of iterations, and gave it to the CEO of Zapier to grade. In 2024, that PRD would have killed in any product review. But Wade Foster called it a little old-fashioned. In 2026, the work that made you look smart in 2024 is now the bare minimum. Shopify, Meta, and Microsoft have all tied AI to employee evaluations. Zapier took it a notch higher and published a whole rubric on AI fluency. Then reintroduced another one just 10 months later! Read the complete update here. Wade co-founded Zapier and took it to a $5B valuation on roughly $1.4M of venture money. His rubric is the one a lot of companies have modeled their own on. So I got him to share his grading structure live. If you want to know where you’d be when your company starts grading, I got you. Key takeaways 1. Basic AI use is the floor, not the bar - Summarizing, drafting, looking things up and generating a basic PRD are table stakes at Zapier in 2026. Everything above that line needs structured, reusable systems for specs, prototypes and research. Wade is looking for people operating at a meaningfully higher level than the tool's default. 2. Adaptive is where the curve should sit - Zapier wants most PMs at adaptive most of the time, with one transformative example to point to. Living at transformative full time means you are tuning your systems instead of shipping. Gaps in the capable band are normal, even for strong PMs. 3. Transformative means an n of one - Wade's bar is that you are the first person doing a new thing and figuring it out as you go. On a team of 30 to 40 PMs, that is roughly one rating a half. He grades his own fluency as adaptive in most places and admits he steals his transformative tactics from his team. 4. You should be ahead of your CEO inside your own discipline - Wade's rule of thumb is that he should be behind his PMs on PM-specific AI fluency. When he spots a technique that is obvious to him and new to the PM, he gets nervous. You breathe this every day and he does not. 5. Never stop at the PRD - A document alone reads old-fashioned in 2026. Fire up a coding agent and build the prototype, even though production code is not the PM's job. A picture is worth a thousand words and a prototype is worth ten of those. 6. Evidence got cheap, so there is no excuse - Sift Gong calls, Zendesk tickets, subreddits, LinkedIn and X before asking anyone for buy-in. AI makes a mountain of sources searchable in the time it used to take to read a handful. The buy-in conversation now happens after you have proof, not before you start. 7. Judgment separates a turbo brain from a slop cannon - AI plus judgment looks like watching a new species get invented. AI without judgment generates so much noise that it is harder to work with than a teammate who uses no AI at all. At least that person is quiet. 8. Label the effort level when you share AI-assisted work - Say whether you skimmed it once or stand behind every sentence. Handing over unreviewed output just moves the judgment onto the reader. Speed is a fine reason to share something rough, as long as you say it is rough. 9. The job shifts from doing tasks to auditing the factory - When agents handle spec, prototype, eval and first-draft code, your job is to ask why the machine produced a bad batch, not to fix widgets by hand. Where the human gates go is the design question nobody has solved. Wade sees teams still scratching their heads on the loop design. 10. The new no-code is code, and the user is now the agent - Wade's daily brief runs on generated code he has never read and does not care about. Zapier's bet is deterministic execution in the cloud, so workflows run reliably without burning tokens or keeping a laptop open. If your software cannot run headless, you are swimming against the current. Go Deeper If you ask me, start by shipping your first pull request. Then learn loops to automate your repetitive tasks. Give your system a memory on Claude Code or Hermes or OpenClaw. Then version everything you build for PMs on GitHub. You can also bring the whole org on the same page with an all-in-one team OS. And once the system is running, build your first eval, as auditing is the whole point here. I’ll see you in the next one :) If you want to advertise, please email productgrowthppp[at]gmail[dot]com. PS. Please subscribe on YouTube and follow on Apple and 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 90% of teams have adopted AI, yet 56% of CEOs say they saw no major financial benefit. Both metrics are accurate, and I’m pretty sure you fall somewhere between them. With new AI tools flooding the market every week, it is safe to say that every team has its own AI setup by now. But are any of those setups in sync with each other? I’ve been building this argument in stages. First, Carl Vellotti showed you how to build a personal OS, and Hannah layered it with building a team OS. Jiaona and Mikhail vouch for a full-blown company OS as well. So, today you get a screen share. My guests are the product team at Together AI that raised $800M at an $8.3B valuation and sells inference and fine-tuning to developers. Charles Zedlewski is their CPO who brought Necoline, Pavneet, and Hassan on the call to talk about how to build a shared context repo. Their customers are already agents and their engineers are already agent-first. Their product team had no choice but to catch up. Brought to you by Arize - Ship AI agents and features faster with fewer regressions. Get a full year of Arize, Bolt.new, Airtable, Speechify, Descript, Magic Patterns, Linear, Dovetail and Mobbin at bundle.aakashg.com. 10 Key takeaways 1. Individual productivity can move a company backwards - The team's starting question was not how to make each person faster. It was whether everyone generating unlimited code and content actually added up to progress. Charles called the failure mode flooding your coworkers' context windows, where everyone launches slop at each other. 2. The shared repo holds context and skills, not code - Markdown and YAML files covering customer intelligence, sandboxes, and the output of strategy meetings broken down by mission and milestone. Anything tied to a specific codebase stays out of it. The point is that a PM can read another team's context and draft a real proposal before taking up that PM's time. 3. Skills live closest to the work they touch - If a skill references code inside one team's repo, it stays colocated there. Everything else goes to a personal or shared repo. Test it on a branch, use it a few times, and only push to main once it proves repeatable. Niche ones never get pushed. 4. Shared context is a hierarchy, not a flat pool - The team abandoned the idea that everyone should carry everyone's context. Most people have no motivation to learn the depth of someone else's area. They want the one answer they came for. Some people live at the bottom of the hierarchy, most just traverse the top. 5. The PRD stopped being a gate - Historically it was the document everyone aligned on before building started. Together treats it as a trigger for ideation and problem solving instead. One to two pages, defining the customer problem, a few solution options, and a sample user journey. That is enough to argue about whether the thing is worth building. 6. A prototype replaces the bulk of the long document - A separate skill takes the one pager and produces a prompt for a design tool, and that visual is where the sharpest feedback shows up, from engineering and marketing alike. 7. Discovery collapsed from half a day to five minutes - The research agent pulls from the support platform, the project tracker, and internal docs at once. It surfaced 19 tickets filed in two months, flagged that the feature had been partially built and abandoned, and gave verbatim quotes with sources. The value is not the summary. It is not duplicating work someone already started. 8. Automate execution, keep decisions human - Defining the feature, the API surface area, and the abstraction layer stay hands on. Code writing is the part that runs on its own. The PRD skill is explicitly instructed to challenge the PM's assumptions rather than accept them. 9. Agents are already the majority user, so validate for them - Agent evals spins up a sandbox, gives an agent a real task against the product, and watches. It caught that agents could not find the fine-tunable models page because it was not linked from the quick start. Dozens of docs fixes came out of this. Charles calls agent success the new bar for UX. 10. They refused to oversell the gains - No story points, so no proof, but velocity is up more than 5%. Charles finds 3x claims suspicious, because discovery, debate, and coordination do not get magically better with AI. Costs stayed sane partly through open weight models, partly because optimizing for collective output never produced the runaway token budgets others report. Go Deeper As promised, I put together this section to help you become the best version in your product management journey. If you’re starting from zero on this, begin with How to build a Team OS in Claude Code with Hannah Stulberg, then scale it up with How to build a Company Operating System with Hermes and OpenClaw. For the skills layer specifically, 3x CPO Oji Udezue on the Essential Claude Skills for PMs is the one to read. And if section 3 sold you on agent evals, go to How to Build Frontier-Lab Quality Evals with Daniel McKinnon. Finally, Pavneet’s take on the one-page PRD lines up almost exactly with what Srini Raghavan showed from Freshworks. You can get my podcasts on Apple, Spotify, and YouTube. If you want to advertise, please email - productgrowthppp[at]gmail[dot]com. 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 have automated something with AI by now. A PRD review. A weekly status update. The problem is that every session starts from scratch. Loops are what fix that. A skill that runs the same way forever is just a skill. A skill that takes the log of what happened and rewrites itself is a loop. Everyone keeps saying loops are the new prompts. Almost nobody shows you how to build one. Tyler Folkman is Chief AI Officer and Head of Product at JobNimbus. In this episode he builds a loop live on screen. He runs a preflight check on his own podcast recording, spins up three prototype variants while answering questions, writes a decision skill from scratch, and closes the loop on camera. He also covers the loops every PM should be running, the hooks that stop Claude from doing real damage, and the point where vibe PMing breaks. Brought to you by Customer.io - Send smarter messages using your product data Ariso - Ship AI agents and features faster with fewer regressions Viktor - AI employee connected to 3k+ tools with every action approved by your team Bolt - Build a complete design system from your code Product Faculty - Get $150 off their #1 AI PM Certification with code AAKASH150 10 Key takeaways 1. A skill becomes a loop when you feed the learning back. A static skill sits there and stays the same forever. The loop closes when you hand the AI the log of the whole session and ask what to improve. Skip that step and you are just running a skill.2. The gate is the most important part of the loop. Fetch inputs, do work, pass a gate, write the artifact. The gate is where you validate the work was correct, and making it deterministic matters more than anything else in the chain. 3. Agents make decisions, prompts wait for you. The difference is not the model. An agent gets a task plus a way to verify itself and executes as far as it can alone. Prompting keeps you in the loop, which caps how far you can scale yourself. 4. Write the first version of a skill by hand. Human authored skills tend to beat AI authored ones because you know more about what you actually want. Once you are on the AI loop it moves fast enough that injecting your own thinking gets hard, like promising you will still pedal on an ebike. 5. In product, the gate is a customer, and that breaks the loop. Code loops run fast because the gate is a test. You cannot lock customers in a room and iterate on them. The workaround is mining the research calls and transcripts you already have to build a cheap first filter. 6. Prototypes are free now, so generate variants instead of one answer. The internal standard is at least three variants per idea. One minimal, one full featured, one creative. Then narrow a hundred ideas down to five before anyone talks to a real customer. 7. Synthetic customers filter, real customers decide. Customer research transcripts loaded into a warehouse let AI inspect a prototype as your customer. It is not a high bar, but it catches low hanging failures fast, which is the whole point of a gate. 8. Write docs for AI, keep human docs to three pages. Part of onboarding is not written for people to read. It is context so the AI can answer questions. Anything a human is expected to read should be one to three pages, visual, and cut down by hand. 9. Hooks add determinism that a prompt cannot. Telling Claude never to delete everything or never to share credentials only works if it reads that instruction today. A hook fires on the bash command itself. Session close hooks can also force the improvement step you would otherwise forget. 10. Ship AI output you have not thought about and it costs you. Passing unreviewed AI work up the chain just moves the effort to someone busier than you. Answering a question in a meeting with what Claude said is the failure mode. Use AI to push your thinking, not to replace the part where you do it. Related Content If you don't know where to start on Claude loops, I got you covered with my ultimate guide on Loops for PMs. And then you can follow it up with The Complete PM Guide to /goal in Claude Code. Tyler mentioned the evolving role of PMs and how it is shifting into a Product Builder role, so you can read all about it in my How to Become a Builder PM deep dive. If you want to advertise, please email productgrowthppp[at]gmail[dot]com. P.S. Please subscribe on YouTube and follow on Apple and 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 Today I’m showing you how to build a company operating system, with Mikhail Shcheglov, CPO at OLX Classifieds. After 5 months of continuous building on OpenClaw and Hermes, his entire product team now runs on it. His knowledge graph covers 54% of the company's product, business and customer context. That is a number he tracks as a personal KPI. At that level the agent already makes backlog decisions. Stakeholders pitch feature requests to it before they’re allowed near a PM. It runs his email, his calendar, his recruiting funnel and his design system. He opens his IDE on camera and shows all of it. 2 findings cut against everything you’ve been told. Summarizing your meeting transcripts costs you 20-25% recall, so he stores every single one raw. And letting Hermes write its own skills off repeated tasks produced a 31% accuracy lift in controlled testing. We also get into something heavier than architecture. What happens to PM headcount when one PM covers four domains? And what does a CPO actually screen for now when hiring? Don't miss.... Brought to you by Bolt.new - Ship AI-powered products 10x faster Product Faculty - Get $150 off their #1 AI PM Certification: code AAKASH150 Customer.io - Send smarter messages using your product data Land PM Job - 12-week live course to master the PM job search Viktor - Use $100 in starting credits to get 5x more done with this AI employee If you want access to my AI tool stack including Airtable, Speechify, Descript, Magic Patterns, Linear, Dovetail, Arize and Mobbin, that's $27,000 of value for $150, grab https://bundle.aakashg.com/. Key Takeaways Context coverage is a CPO level KPI - Mikhail tracks what percentage of the company's industry, business model and customer knowledge his agent actually holds. It sits at 54%. That is enough for it to operate like a junior to mid PM and make backlog calls. At 70 to 90% he expects strategy level work. The real problem AI solves is knowledge leakage - A domain expert leaves and takes five years of context with them. Every company has this hole and almost nobody measures it. One store of business, customer, product and technical knowledge closes it, and the better your AI knows that context the more you can hand it. Do not summarize your transcripts - Summarization cost them 20 to 25% recall. You lose the granular detail where the answer usually lives, and you force every conversation into a template it was never shaped like. Store everything raw. Memory needs three layers, not one - A knowledge graph for structure, a vector database for fuzzy retrieval, and raw daily transcripts in MD files. Exact keyword matching fails on most real queries because real questions are ambiguous. The vector layer carries the load. Auto generated skills lifted recall by 31% - Hermes watches what you keep asking for and decides on its own that a skill is worth writing. Tested across five core topics with ten questions each, control group against treatment group. Plus 31% accuracy. Imperatives matter more than prompts - Their rules file runs 700 lines. No fabrications. Think before you act. Facts over guesswork. And a ban on what he calls fake helpful, where the agent can't do the thing so it explains how you could do it yourself. CLAUDE.md stays short, SOUL.md goes long - CLAUDE.md holds under 100 lines and carries the highest priority. SOUL.md runs 800 and sits second. Some of those 800 lines contradict each other and it still produces his most accurate output, because every imperative gets tested against real queries. Make the agent the gatekeeper - Stakeholders are trained to pitch the agent first. It asks clarifying questions, checks the request against priorities already set, declines politely if it doesn't clear the bar, and routes it to the right PM if it does. The org chart is mapped internally so it knows who owns what. Half of PM time is process, not thinking - Weekly reports, stakeholder updates, demos. Delegate that layer and one PM does the work of two, pointed entirely at discovery. He now runs one PM across three or four customer facing domains, and only keeps dedicated owners on monetization and search. Own the agent yourself or lose the advantage - Feedback arrives daily and he pushes changes from his phone straight into the repo. Hand it to an AI ops hire or an engineering team and you keep the tool but lose the speed. Nobody without skin in the game iterates fast enough. Related content I’ve already created all of the resources Mikhail mentioned in this episode. But I never mapped them all to one place. That changes today in this edition. PFA the map sorted by the parts you want to build. Memory first, I Built You Memory for Claude Code, Hermes and OpenClaw Once you've got a memory layer, put it in version control. GitHub for PMs has the 3-repo setup I use for skills and eval The Hermes Agent Guide for PMs is a 20-minute setup and a 30-day rollout with persona and skill templates included For OpenClaw, start with Mahesh Yadav on becoming a Builder PM For the org-wide version, watch my episode with Jiaona Zhang, CPO at Laurel, on building a Company OS My conversation with Hannah Stulberg of DoorDash on building a Team OS is the same thing one floor down, with a free starter kit 👨‍💻 Where to find Mikhail Shcheglov: LinkedIn: https://www.linkedin.com/in/scheglovm1/ Substack: https://corpwaters.substack.com/ 👨‍💻 Where to find Aakash: Twitter: https://x.com/aakashgupta LinkedIn: https://www.linkedin.com/in/aagupta/ Newsletter: https://www.news.aakashg.com/ If you want to advertise, email productgrowthppp@gmail.com. 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 * Customer.io - Send smarter messages using your product data * Ariso - The AI operating partner for every manager and team * Product Faculty - Get $150 off their #1 AI Builder Fellowship. Code AAKASH150 * Land PM Job - Join me for Cohort 5 starting November 16th * Amplitude - Custom agents that monitor your funnel and file the ticket Today’s Episode Freshworks is a $3.4B giant of SaaS. They’ve been around since 2010. They have over 75,000 customers and 4,000 employees. Their 2026 revenue will be $960M. They’re a colossus. When Srini Raghavan joined them as Chief Product Officer, they had a 6 month release process. Under his tenure, they moved to a 2 week release cycle. They embraced a new way of working powered by AI. In today’s episode, he breaks down everything: * The AI PDLC they embraced * The AI Harness they created in Cursor, so it can run on any models * How the PM role shifted to Product Builder If you’re a product leader, this is a great example of how to become AI-native. If you’re a PM, it’s a great harness (using Grok models!) to learn from. I hope you enjoy it as much as I did: Apple | Spotify. | YouTube I’ve written up the key takeaways for newsletter subscribers as well. 1. The AI PDLC they Embraced How do you go from shipping every 6 months to every 2 weeks? It’s not about becoming AI first! Most teams try that: they open Cursor or Figma Make and start prompting. Srini made the point that it actually all begins with being Data First: When you’re data first, you create the right foundation to actually be AI first. At Freshworks scale, with 300 million end users, you can’t afford a hallucination. For Freshworks, that was three things: * A design system structured so agents can parse it * Coding standards written down explicitly * A single repo as the source of truth They wrapped that all into a system they call Prism. It’s a knowledge hub that knows the product and its dependencies inside out, plus a context hub that passes feature context between phases, and a central library of what they call AI builder artifacts. These are the skills, rules, commands, and agents that describe how Freshworks specifically builds. The AI PDLC sits atop all of that. And it looks like this: It’s the same lifecycle every SaaS company runs from discovery through release, with two changes. First, there’s a governed AI agent working inside each. Second, there’s an evals phase at the end. And that’s how their release cycle went from 6 months to 2 weeks. But even in the episode, there were some cracks. Figma Make skipped a few design system components. Srini noted that those are the places where humans still have a role. 2. The AI Harness in Cursor they created I began in engineering. After 14 years of not touching code, now I’m spending a lot of time in Cursor. Srini is now spending lots of time in Cursor, and he showed us. Everything starts with a slash command, /fw-innit. Here’s what that kicks off: The harness asks for the business unit, Epic ID, and feature team. Then it runs 12 phases, all the way from idea brief through to prototyping and QA. Srini did the whole demo on Grok models! He explained: I used to use Claude, but Grok seems to be working really well, and it’s really really fast. Nothing takes more than 10-15 seconds. And indeed, that’s what we found. If you really need speed, consider Grok. He live built a performance dashboard for their new EX Agent Studio. After he described the feature in plain English, the agent came back with questions a good PM would ask: * How deep should the drill down go? * Who is the primary persona? * What does success look like in 6 months? It then wrote its own SQL against Bel, their Databricks data lake with usage from all 75,000 customers. It assembled this into quantitative evidence in the PRD: It looked across all 4,358 active ITSM accounts and showed its work. At the end of the process is a step called CPO check which reviews the draft the way Srini would. His team named it after him. From there, the PRD goes straight into Figma Make. Because of being data-first, they have a design system built into it. And after a few prompts we had a working prototype: That’s the end-to-end process. No Claude Code or Codex. Just Cursor + any model. 3. How the PM role shifted to product builder Which brings us back to where we started: what roles will execute work like this in the picture? Srini manages 200+ people with titles like PM, UX researcher, and designer. His prediction is: The titles Product Manager, Product Designer, and Engineer will go away in the next 5 years. He didn’t say that to get clicks on Social Media. That’s how he’s running Freshworks. When he joined, Freshworks ran 1 PM and 1 designer for every 10 to 20 engineers. Now, their new workflow is 1 PM to 1 engineer. Some teams have no dedicated designer at all. Here’s why: those titles marked stations on an assembly line. The PM would write the PRD, hand it to design, and design would hand it to engineering. Now, like in section 2, one person can do the whole assembly line. If you want to prepare for this future, I’ve put together the roadmap: Pay close attention to step 8. Since one person can now run this assembly line, that person also owns whether the thing made any money. In the full episode, he also covers how Freshworks’ agent builder works, how CPOs can replicate his playbook more. Check it out: Apple, Spotify., YouTube. Get more of Srini Freshworks is hiring and you can find Srini on LinkedIn. He also generously shared his deck. Since the episode was recorded, Srini actually left Freshworks. With his tenure as CPO at RingCentral, SVP at Five9, and Director at Cisco, I’d expect you see him at another high-powered role soon. Go Deeper * Hear from more CPOs on becoming AI native: Rachel Wolan, Jiaona Zhang * Learn more about becoming a Builder PM: Mahesh Yadav * Explore other Coding Harnesses: Codex, Claude Code 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

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 Whether you’re using a PM OS, or using regular Claude, or using another AI harness (like ChatGPT Work or Codex)… One of the most important parts of any PM’s AI setup is the skills you have. Today’s guest Oji Udezue, CPO at Typeform, Calendly, and Parsable, and he goes into a masterclass of what skills PMs should have, how to build them, and how to use them. Brought to you by: Bolt.new - Ship AI-powered products 10x faster Product Faculty - Get $150 off their #1 AI PM Certification: code AAKASH150 Customer.io - Send smarter messages using your product data Ariso - Ship AI agents and features faster, with fewer regressions Land PM Job - 12-week live course to master the PM job search Get More of Oji LinkedIn ProductMind The open-source skills repo Shipyard Building Rocketships Go Deeper I’ve done extensive testing on what makes a good skill and how to build a PM OS for paid newsletter subscribers (that go much deeper than this podcast). → Get the founding plan to get access. I also have several free podcasts on Claude Code that you may find helpful: my 3 part series with Carl Vellotti, PM OS with Dave Killeen, Team OS with Hannah Stullberg, Company OS with Jiaona Zhang, and Claude Code for CPOs. → To never miss an episode, subscribe on YouTube and follow on Apple & Spotify. Finally, in my live course, I teach you how to do extremely advanced PM automation Claude Code. → Join us.

Today’s Episode A developer posted this workflow in March, and it is the clearest picture of where PM is heading that I’ve seen all year. Rasty Turek spent the past year building with coding agents, and he mapped how his process changed over that time. He reckons he now spends around 90% of his time on evals. His eval started as QA, and then it became the spec. Great, now everyone agrees evals are important and will become indispensable for PMs going forward. But there is very little on how to write one. That changes today. I’ve now done 6 episodes on evals, and all of them start with an agent that is running and failing. So what do you do on day 0? Daniel McKinnon was a PM on the Llama models at Meta, a boomerang who spent around 7 years there in total. He sat on Facebook’s central AI team for the entirety of its existence. He wrote enterprise evals for Gemini, Llama, and Ray-Ban Meta. His first job at Meta was on the speech recognition team. He had to figure out how to check whether the models were any good. They weren’t called evals back then. But he’s been writing them for his entire career anyway. In this episode you’ll learn: * How to build an eval set from nothing * The floor-and-ceiling method for calibrating * How to score it and make the shipping call Check it out: Please fill out this short survey on PM salaries. 🆓 I’m doing a free webinar Thursday on getting AI PM interviews. Join me: The next cohort of my LandPMJob program starts in August. If you want my 1:1 coaching, sign up. ---- Check out the conversation on Apple, Spotify, and YouTube. Brought to you by: * SerpApi - Get started with SerpApi using 250 free credits. * Product Faculty - Get $550 off their #1 AI PM Certification with code AAKASH550C7 * Ariso - Ship AI agents and features faster, with fewer regressions * Land PM Job - 12-week experience to master getting a PM job * Pendo - The #1 software experience management platform ---- Key Takeaways: 1. An eval is a trivia question for the model - At its core, an eval is a prompt with a correct or plausibly correct answer plus a way to score whether the output is good. It is the clearest way to communicate what your product should do in the AI era. 2. Offline evals catch problems before you ship - Test the model offline against a fixed prompt set before pushing to production. If it fails, you change the model, the prompt, or the approach before real users ever see it. 3. The best eval sits between too easy and too hard - An eval that scores 100% gives your engineering team nothing to optimize. An eval that scores 0% is equally useless. Aim for a 25% to 50% success rate so there is room to run. 4. Old benchmarks are already saturated - MMLU, HellaSwag, ARC and the rest were built for a simpler question-and-answer world. Frontier models now score effectively 100% on them, which is why you have to keep building new evals and throwing away old ones. 5. Writing an eval is mechanical once you understand the problem - Come up with roughly 100 prompts that match the real distribution of tasks. The hard part is not the writing. It is deeply understanding the domain first. 6. Subject matter expertise drives everything - The cystic fibrosis and congenital heart disease evals worked because Daniel understood the genetics, not because of any template or tool. There is no eval template the way there is a PRD template. 7. Modern evals are agentic, not just Q&A - The genetics eval hands the agent a file with billions of variants and asks it to find the cause of a disease. This is a task, not a lookup, and it mirrors how real AI products now work. 8. Find the model ceiling on purpose - The easy cystic fibrosis case gets solved by most models. The harder digenic congenital heart disease case exposes where even strong models fail. Knowing the ceiling is the point of the exercise. 9. Sample multiple times before you trust a result - Models are non-deterministic. Run the same task several times so you understand the real distribution of outcomes rather than a single lucky or unlucky pass. 10. Meta and Google build products very differently - Google is seen as more engineering-led, Meta as more product-led and far more aggressive culturally. Daniel worked on both Gemini and Llama and saw everything from Llama 3 highs to Llama 4 lows. ---- Where to find Daniel McKinnon * LinkedIn * X * Gamow Labs Related content Podcasts: * AI Evals with Hamel Husain and Shreya Shankar * How to Run Evals in Claude Code with Aparna Dhinakaran * Evals are the New PRD with Ankur Goyal Newsletters: * AI Evals for PMs: Everything You Need to Know to Get Started in 2026 * AI PM’s Guide to LLM Judges * AI Evals Explained Simply ---- 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

Check out the conversation on Apple, Spotify, and YouTube. Brought to you by * Land PM Job - 12-week experience to master getting a PM job * Jira Product Discovery - Plan with purpose, ship with confidence * Amplitude - The market-leader in product analytics * Bolt - Ship AI-powered products 10x faster * Product Faculty - Get $550 off their #1 AI PM Certification with code AAKASH550C7 ---- Today’s episode Every startup founder picks up Zero to One. Reads the chapter on Delaware C-Corps. Files the paperwork. Moves on. That paperwork will outlast every product decision they ever make. I sat down with Eric Ries, the man who created Build, Measure, Learn. NYT bestselling author of The Lean Startup. Co-founder of Answer.AI with Jeremy Howard. Founder of the Long-Term Stock Exchange. Dario Amodei called him before Anthropic’s seed round. His new book Incorruptible drops May 26. It is the blueprint for building a company that the financial system cannot capture. He also demoed live how he wrote the book using Solve It, the AI platform from Answer.AI. Not prompting. Not generating. Editing the model’s responses directly. If you are building anything you want to outlast the next funding round, this is the one episode to watch. Check out the episode on Apple Podcast and Spotify. If you want access to my AI tool stack, grab Aakash’s bundle. ---- Key Takeaways: 1. Governance has four dimensions - Compliance is table stakes. Purpose, coherence, and integrity are the three most boards ignore. Companies that nail all four outperform the market over decades. 2. Financial gravity destroys good companies - The unconscious reflex to comply with the values of those who have more than you. Jim Senegal called it heroin. You compromise once and it gets baked into the forecast. 3. Costco's governance fortress is the blueprint - Staggered board terms, poison pills, fiduciary hierarchy. $10K at the Costco IPO is worth $8.7M today versus $151K in the S&P 500. 4. Stone does not enforce itself - Johnson & Johnson carved values into limestone. Asbestos ended up in the baby powder. $10B settlement. Structure protects ethos but does not create it. 5. Mission lock vehicles create 6x survival - A separate entity holding the for-profit board accountable. Novo Nordisk, IKEA, Patagonia, Hershey, Vanguard all use this structure. 60% survival to year 50 versus 10%. 6. Anthropic's LTBT took two years to defend - AI safety experts appoint board seats. The trust gains power as the company hits milestones. Structural protection is why Anthropic can afford to be courageous. 7. Public Benefit Corporations write mission into the charter - Legal permission to pursue purpose over shareholder value. Not the B-Corp certification sticker. A legal structure. 8. LLMs are conformity machines - They produce the center of the outcome distribution. For competitive advantage you must change how you use AI. 9. Solve It enables human-in-the-loop writing - Edit the model's responses directly. 600 test readers, 10K structured comments, Python scripts organizing feedback per chapter. 10. Build Measure Learn works at any timescale - Not about absolute speed. Relative velocity versus your industry convention. The AI labs that release more quickly create decisive trust advantages. ---- Where to find Eric Ries * LinkedIn * Incorruptible * Answer.AI / Solve It * Long-Term Stock Exchange * Lean Startup ---- Related content Podcasts: * AI Product Strategy with Aman Khan * The Marty Cagan Episode on Product Management * How to Succeed as a Head of Growth with Dan Olsen Newsletters: * AI product strategy in 2026 * How Cursor grows * PLG in 2026 ---- 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 “This one’s too complex. I’m still stuck on ChatGPT.” I get some version of that DM every week, usually right after I publish something on the PM OS or the Team OS. And every time, I feel it, because those guides do assume you’re already up and running. So I made this episode for the person sending the message. Jyothi Nookula has been an AI PM since before that was a title, through Netflix, Meta, and Amazon, and I asked her to take a PM from zero to eighty on the entire Claude stack in one sitting. She walks through: The five-layer Claude stack, so you finally know which surface and which model to reach for and when A chief of staff you build in Claude Code that reads your meetings and quietly learns your org, your people, and your politics The self-improving agent loop she used to beat 30 engineering teams at an internal hackathon, as a PM Everything she shows is something she runs at work, which is the only reason any of it holds up. Send this to the PM in your life who keeps saying they’re behind. Two hours from now, they won’t be. ---- Brought to you by: Hyper Agent: Turn your recurring PM work into reusable agents ---- If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, Relay.app, Magic Patterns, Speechify, Bolt.new and Mobbin - become an annual subscriber ($150), and grab Aakash’s bundle. If you want access to my AI PM customizations - PM OS, Job Search OS, and Prompt Library - become a founding subscriber ($250). ---- Key Takeaways: 1. Match the model to the job - Sonnet handles ninety percent of PM work at the best cost. Save Opus for genuinely hard reasoning, and hand fast bulk jobs to Haiku. Defaulting to the smartest model for everything just burns time and money. 2. Stop skipping the knowledge layer - Projects, skills, and memory are what make Claude know your actual work instead of guessing from a blank slate. Almost everyone underinvests here, and it is the difference between a chatbot and an assistant that knows you. 3. A skill beats a prompt - A skill is a saved playbook Claude picks up on its own when it fits the task. It only loads when needed, so it never clogs the context window. Build one once and stop re-explaining the same task forever. 4. Write your skills yourself - Human-written skill files consistently beat AI-written ones. Draft with Claude to move fast, then layer in the domain knowledge only you have. That last step is what makes it actually work. 5. Automate your time based work - A morning brief, a standup summary, and an end-of-day wrap can all run on a schedule while you sleep. You walk in already knowing what needs your attention. It clears the busywork that eats your mornings. 6. Give your automations guardrails - Cap the length, tell them to stick to facts, and never let them hallucinate. Left unchecked, an AI brief will pad itself and invent things. A few hard rules keep it sharp and trustworthy. 7. Build a chief of staff that learns your org - Point Claude at your meeting notes and let it build a picture of your people, your priorities, and your politics over time. Feed it transcripts first, since they carry the richest signal. It compounds into something no generic chatbot can match. 8. Keep that knowledge base on your own laptop - Your most personal work data does not belong in someone else's cloud. When you leave a company, it walks out with you. You keep full control of your most sensitive context. 9. The PM job is changing fast - The ratio is shifting from one PM per eight engineers toward two PMs per one. Building is becoming part of the role, and the PMs who can ship are pulling ahead. 10. Building is easy now, taste is scarce - When anyone can build, the edge moves to knowing what is worth building and what good actually looks like. That judgment is the one skill you cannot download. ---- Related content Github repo: https://github.com/fibbonnaci/ai-builder-skills Podcasts: How to Become an AI PM - YouTube | Spotify | Apple How a VP Uses Claude Without Producing Slop - YouTube | Spotify | Apple This CPO Uses Claude Code to Run His Entire Work - YouTube | Spotify | Apple Newsletters: I Built You Memory for Claude Code, Hermes, and OpenClaw I spent 100s of hours building a PM OS for you How to build a Team OS in Claude Code ---- Where to find Jyothi Nookula: LinkedIn: https://www.linkedin.com/in/jyothinookula/ NextGen Product Manager: https://nextgenproductmanager.com/ Where to find Aakash: Twitter/X: https://x.com/aakashgupta LinkedIn: https://www.linkedin.com/in/aagupta/ Newsletter: https://www.news.aakashg.com --- 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 Today I’m showing you, advanced Codex workflows with Meng To, founder of Design+Code. He told me early on that he’s barely touched Claude. He’s been living inside Codex every day since launch, running a setup most PMs have never seen up close. Plan mode and a fleet of 20 agents running at once while he steps away from his desk entirely. Slides, charts, and full brainstorms generated inside the same chat window, reviewed, regenerated, and shipped, all without writing a line of code. We also get into something heavier than workflows. Why PMs are losing their jobs to layoffs right now? And how to future proof your career? Don’t miss.... ---- Brought to you by: Arize: Trace, evaluate, and fix your AI agents before broken behavior ships to users ---- If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, Relay.app, Magic Patterns, Speechify, Bolt.new and Mobbin - become an annual subscriber ($150), and grab Aakash’s bundle. If you want access to my AI PM customizations - PM OS, Job Search OS, and Prompt Library - become a founding subscriber ($250). ---- Key Takeaways: 1. Codex is a fleet operator - Meng runs 20 agents at once while stepping away from his desk entirely. Each one works on something different, slides, charts, brainstorms, while he does something else completely. 2. Plan mode isn't optional - Skipping it means paying twice, once to build the wrong thing, once to undo it. Codex returns a full breakdown, architecture, steps, and open questions, before touching anything. 3. A screenshot beats a paragraph every time - It shows the AI what you mean instead of what you think you mean. A simple two-key shortcut drops any window straight into the chat as context. 4. The taste skill is the real differentiator - Without it, AI design defaults to generic. With it, the output looks like something a senior designer with years of experience actually made. 5. Trust is earned in tiers - Read only first, then supervised access, then full access. Skipping straight to full access before learning where the AI tends to fail is how people get burned. 6. HTML beats Figma for speed - Every extra tool is a login, a subscription, and a context switch the AI can't do for you. Keep your blast radius small. 7. UGC won because audiences are tired of corporate polish - A synthetic version of you, used honestly, reads as more human than a generic message. Ten old photos is all it takes to build a digital twin. 8. Technical PMs aren't surviving layoffs because they write code - Meng hasn't written a single line in six months. They're surviving because they're fluent enough to direct a fleet of agents and catch a wrong output before it ships. 9. Meng builds his own tools when nothing off the shelf fits - His own video editor, his own SaaS templates, his own design brainstorming app. The tool built for your exact workflow beats the popular one every time. 10. The bar isn't five star anymore - Five star is just the floor everyone clears by default now. The real question is what six, seven, all the way to eleven star looks like, because that ceiling rises exactly as fast as the floor does. ---- Related content Podcasts: How to Design with AI - YouTube | Spotify | Apple How to Use Codex Like an OpenAI PM - YouTube | Spotify | Apple The Ultimate Guide to ChatGPT Codex - YouTube | Spotify | Apple Newsletters: OpenAI’s Codex is the Best Way to Use ChatGPT I spent 100s of hours building a PM OS for you How to build product strategy in the age of AI ---- 👨‍💻 Where to find Meng To: LinkedIn: https://www.linkedin.com/in/mengto Design+Code: https://designcode.io Aura: https://aura.build 👨‍💻 Where to find Aakash: Twitter/X: https://x.com/aakashgupta LinkedIn: https://www.linkedin.com/in/aagupta/ Newsletter: https://www.news.aakashg.com --- 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 Product teams have figured out AI for engineering. The PMs are using Claude. The engineers are in Cursor. If you’ve been reading this newsletter from the start, you’ve seen how the top 1% are using AI to 10x their output. But there are still teams at companies like Adobe, teams in sales, customer success, finance, who don’t have access to any of these tools. You ask them what AI to use for their next task, and you get a blank stare. That gap is the real problem. And it compounds every day. Jiaona Zhang “JZ” has built the fix. She is the CPO at Laurel, which just raised $100M in Series C, and she has led product at Airbnb, Dropbox, Webflow, and WeWork. Today she runs a product team that ships frontend and backend features end to end, without any engineering handoff. In this episode, she screen-shares everything. Laurel’s full Company OS built in GitHub, with skill files for every function from CS to legal to finance. The playbook to agent pipeline that turned 50-page docs into automated workflows. The daily Slack briefing that tells every person exactly what to do and which skill to use when. And a ton more. This is one of the densest episodes I’ve ever recorded. The knowledge per minute is as high as it gets. Don’t miss..... ---- Brought to you by: Ariso - Ship AI agents and features faster, with fewer regressions Bolt - Ship AI-powered products 10x faster Pendo - The #1 software experience management platform Product Faculty - Get $550 off their #1 AI PM Certification with code AAKASH550C7 Customer.io - Send smarter messages using your product data ---- If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, Relay.app, Magic Patterns, Speechify, Bolt.new and Mobbin - become an annual subscriber ($150), and grab Aakash’s bundle. If you want access to my AI PM customizations - PM OS, Job Search OS, and Prompt Library - become a founding subscriber ($250). ---- Key Takeaways: 1. Every company has a 1% who are AI-native and a 99% who do not know what to use when. The Company OS closes that gap by encoding the 1%'s workflows into skills that anyone can use when they open Claude.2. Build the ontology before you build the OS. Map every team's work to categories and tasks first. Color-code what should get more human time vs what gets automated. The OS is built from that work map.3. Even the friction of going to a different interface kills adoption. A separate agent tool in a new tab will not get used consistently. Deliver skills and automations inside Slack and email, where people already are.4. When AI adoption is everyone's responsibility, it is no one's responsibility. Dedicate one person full-time to AI Operations. Start with one person who demonstrates value. Every other function will want their own version within months.5. The Company OS turns a 50-page playbook into a set of agents. Write the playbook first. Then audit it. What requires a human? What can be automated? Build the skill files from what remains.6. The captain model replaces the handoff chain. Every feature has one owner end-to-end. The captain is whoever has the most critical skill for that feature's hardest problem.7. PMs at Laurel ship front-end and back-end features. Not just growth experiments or copy changes. Core product features deeply integrated with billing systems and time entry logic. One PM who identifies as a designer shipped one of these end-to-end last month.8. JZ went from hundreds of reports to 5 PMs and 4 designers. They ship more than ever. Adding people adds coordination cost. In a world where one PM can take a feature from discovery to production in a day, large teams cancel out their own capacity gains.9. The new PM interview is a screen share. JZ asks every candidate to show their actual screen. In 60 seconds she knows their level of AI skills.10. The PM fundamentals never changed. Problem space first. Know why and for whom you are building before you build. The speed changed dramatically. What you are supposed to be doing at the heart of it did not. ---- Related content Podcasts: How a VP Uses Claude Without Producing Slop - YouTube | Spotify | Apple How to Build a Team OS in Claude Code - YouTube | Spotify | Apple How to Become a Builder PM - YouTube | Spotify | Apple Newsletters: I spent the last week building an OS in Claude Code I spent 100s of hours building a PM OS for you How to build product strategy in the age of AI ---- Where to find Jiaona Zhang LinkedIn - https://www.linkedin.com/in/jiaona/ Reforge - https://www.reforge.com/profiles/jiaona-zhang Laurel - https://www.laurel.ai/ Where to find Aakash: X - https://x.com/aakashgupta LinkedIn - https://www.linkedin.com/in/aagupta/ Newsletter - https://www.news.aakashg.com --- 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 There are hundreds of guides on writing PRDs with Claude. Dozens on running user interviews. Almost nothing on how a VP of product actually uses it. Matt Wensing is VP of Product and Design at Customer.io. They crossed $100M ARR, just shipped an AI agent, and are one of the fastest growing companies in B2B SaaS right now. I asked him to show me his actual documents, his actual Slack threads, and the exact sessions where Claude helped him produce leadership grade output. What he showed me changed how I think about AI for leaders. Claude has the instincts of a brilliant new hire, it wants to deliver before it fully understands what you need, and at the VP level that gap shows up fast. Matt has spent months figuring out how to manage it, and in this episode he shows you everything. ---- Brought to you by: LogRocket - Find the bugs killing your conversion before your users do. I ran a head-to-head eval to see if that's true. ---- If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, Relay.app, Magic Patterns, Speechify, Bolt.new and Mobbin - become an annual subscriber ($150), and grab Aakash’s bundle. If you want access to my AI PM customizations - PM OS, Job Search OS, and Prompt Library - become a founding subscriber ($250). ---- Key Takeaways: 1. Take inventory before you open Claude - Before building anything, list every piece of raw material you already have. Zoom recordings, strategy docs, past presentations. The quality of what you feed Claude determines the quality of what comes out.2. Pivot content, do not write from scratch - Claude's best use case is transformation, not creation. Give it two inputs and ask it to reorganize one into the shape of the other. Matt calls this matrix multiplication.3. Build slides first - Build the visual story first. Screenshot the finished slides and feed them back into the same Claude session. Ask it to write a talk track that adds depth using all the context it already has, not one that just repeats the slide.4. Kill eager suggestions immediately - The moment Claude asks if you want it to generate the next thing, say stop. You control the pace. A 200-iteration session with a great deliverable beats saying yes to the first draft every time.5. Start sessions in the abstract - If you reveal the domain too early, Claude pattern matches to the nearest template. Keep it abstract. Build a clean mental model first. Reveal the domain only when the framework holds up on its own.6. Layer complexity in slowly - Start with the simplest version of the framework. Let Claude stabilize on the basics before you add exceptions. Dumping everything in at once produces a lost in the woods experience for both of you.7. AI alignment decks always backfire - When you one-shot an alignment deck, you flatten the problem. Senior executives have spent months living with the real complexity. They feel the thinness immediately, even when they cannot say why.8. Decompose the problem before building anything - Challenge yourself to explode a nasty problem into all its pieces before you touch Claude. Put those observations into the context window first. Then assemble the solution.9. The Slack scanner keeps leaders close to the ground - Customer.io built an AI scanner that monitors dozens of Slack channels and surfaces threads where a product person should be involved. It runs continuously without overwhelming. 10. Chiefys audits your strategy docs automatically - Chiefys is a Slack bot that holds Customer.io's ratified company documents and checks new work against all of them. It flags contradictions and stale documents so nothing goes invisible after you ship something new. ---- Related content Podcasts PM’s Guide to Claude - YouTube | Spotify | Apple How to Become a Builder PM - YouTube | Spotify | Apple We Built an AI Product Manager in 58 mins - YouTube | Spotify | Apple Newsletters How to Use Claude for Work How to Build Product Strategy with Claude Code The PM OS ---- Where to find Matthew Wensing: 1:1 Video Consultation: https://intro.co/MattWensing LinkedIn: https://www.linkedin.com/in/wensing/ X: https://x.com/mattwensing Where to find Aakash: X: https://x.com/aakashgupta LinkedIn: https://www.linkedin.com/in/aagupta/ Newsletter: https://www.news.aakashg.com --- 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 Six months ago, I told you Codex is the best way to use ChatGPT for PM work. Most of you tried it. Some of you stuck with it and very few of you are running it the way the people who built it actually run it. Today we get that inside look. Abhi Muchhal is an International Growth PM at OpenAI. Before that, Meta, Nubank, and a founder building on the OpenAI API. He is one of the people responsible for ChatGPT’s growth in India, Brazil, and Japan, markets that are now driving a meaningful share of OpenAI’s 900 million weekly active users. He opened his actual setup on camera. The harness. The automations. The prompts that actually work. And the ones that failed before he figured it out. ---- Brought to you by: Bolt.new - Ship AI-powered products 10x faster Product Faculty - Get $550 off their #1 AI PM Certification with code AAKASH550C7 Customer.io - Send smarter messages using your product data Ariso - Ship AI agents and features faster, with fewer regressions Jira Product Discovery - Plan with purpose, ship with confidence ---- If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, Relay.app, Magic Patterns, Speechify, Bolt.new and Mobbin - become an annual subscriber ($150), and grab Aakash’s bundle. If you want access to my AI PM customizations - PM OS, Job Search OS, and Prompt Library - become a founding subscriber ($250). ---- Key Takeaways: 1. The harness is what separates Codex users from Codex runners - The connectors, the permissions model, and the skills layer are the three components that make Codex a system rather than a chat tool. Without all three, you are using an expensive autocomplete. 2. Generic prompts hit the wrong data - Abhi's team had separate B2C and B2B tables that both matched "tell me about weekly active users." The generic query returned the wrong answer every time. Specificity is the skill, name the exact dashboard and the exact metric, looks simple but saves a lot of time when you scale. 3. Three permission levels - Read tasks get full autonomy. Synthesis and drafts get full autonomy. Anything going to another human gets your eyes first. Treating permissions as binary, all control or all autonomy, breaks. 4. The person who cares most builds the skill - One OpenAI growth team built a skill that automates their entire experiment review process. It writes the hypothesis, monitors the run, and prepares the review doc. 5. Real automations run without you - Abhi runs three automations before he opens a single dashboard: a Slack triage, a 9:30AM self-refreshing growth dashboard pulling from 7-8 sources, and a weekly stakeholder update that writes its own first draft. He reviews, makes edits if needed, and sends. 6. Prototype before you document - Build the working prototype first, then write the 10-question companion FAQ. Showing engineers something that runs changes the conversation from whether to build to how to build it. 7. India is OpenAI's second largest market and under 10% of working adults are knowledge workers - The ChatGPT use case that drove US growth does not reach the same share of people in the markets driving the most new users. Building for the world means knowing how different the world actually is. 8. The WhatsApp computer use loop ran in 68 seconds - Point Codex at the WhatsApp desktop app. It reads what you missed, identifies action items, checks your calendar, and types the draft in the composer. One tap to send. Every PM building for international markets should run this workflow at least once. 9. Speaking evals is the key to breaking into a frontier lab - Name a capability you care about. Describe how you would measure it. Say how you would know if the model improved. You do not need 50 evals under your belt. You need to understand why they exist and what a good one measures. 10. Building something real is non-negotiable for frontier lab applications - Abhi had a live Chrome extension running on the OpenAI API at the time of his application. ---- Related content Podcasts: The Ultimate Guide to ChatGPT Codex How PMs Ship 100K Lines of Code at OpenAI Evals are the new PRD Newsletters: OpenAI’s Claude Code Killer AI Agents Guide for PMs How to Land a $300K+ AI PM Job ---- Where to find Abhi Muchhal: LinkedIn: https://www.linkedin.com/in/abhimuchhal/ OpenAI:LinkedIn: https://www.linkedin.com/company/openai/W here to find Aakash: X: https://x.com/aakashgupta LinkedIn: https://www.linkedin.com/in/aagupta/ Newsletter: https://www.news.aakashg.com --- 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 Most companies are still debating whether PMs should ship code. OpenAI is already debating the best ways for PMs to ship code. They’re living in the future. The builder behind a lot of that harness engineering is Ryan Lopopolo. He wrote the OpenAI post on harness engineering and runs a frontier team where PMs, designers, and engineers all ship using the same system. The wild part for me? His PMs shipped around 100K lines of production code. Did they open the IDE? Hell no! Their coding happened through PRDs, tests, docs, and harness rules. The model did the typing. As someone who spent a decade in PM growth roles, I’ve seen how long it takes to move a feature from PRD in a doc to code in prod. For most companies, that latency is weeks. In Ryan’s world, it can be days, and the PM is inside the loop instead of watching from Jira. So I wanted to get to the bottom of this: * What does the harness look like when PMs can ship like that? * How do engineering teams set PMs up so they don’t ship slop? * What changes in the EPD trio when code is cheap, and validation is the bottleneck? That’s today’s episode, and I come with receipts as Ryan goes deep. ---- Check out the conversation on Apple, Spotify, and YouTube. Brought to you by: * Product Faculty - Get $550 off their #1 AI PM Certification with code AAKASH550C7 * Bolt - Ship AI-powered products 10x faster * Customer.io - Send smarter messages using your product data * Ariso - Ship AI agents and features faster, with fewer regressions * Pendo - The #1 software experience management platform ---- * If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, Relay.app, Magic Patterns, Speechify, Bolt.new and Mobbin - become an annual subscriber ($150), and grab Aakash’s bundle. * If you want access to my AI PM customizations - PM OS, Job Search OS, and Prompt Library - become a founding subscriber ($250). ---- Key Takeaways:1. Code is a liability, not an asset - Every engineering org was built around the assumption that code is expensive to produce, validate, and deploy. Codex inverts this. Code is now the cheapest part of the stack and the constraint moves to how clearly you describe the problem.2. The new constraint is product decisions per week - With code generation effectively free and parallel, the bottleneck is no longer keystrokes. It is the quality of the brief, the clarity of the architectural boundaries, and the speed of verification.3. A billion tokens a day is the new floor - Ryan's claim is that if you are not running this volume you are negligent. The math comes out to roughly $2K to $3K per engineer per month, which is trivial against the headcount cost of human-only execution.4. A single PR can burn 350 million tokens - One refactor that would have taken Ryan three weeks ran on Codex for 60 hours straight across three days. He gave it two prompts total after the initial spec. The output matched what he would have produced himself.5. The harness is the actual product - Codex CLI is the surface. The harness is everything that gets the agent the right context at the right phase. Pre-work, messy middle, and close. Each phase needs different context, different tools, and different verification.6. agents.md is forcibly injected context - This file lives in the repository root and is always loaded into the agent's context. Use it for the operating model and the non-negotiable rules. Everything else gets pulled in dynamically because context is a hard, scarce resource.7. The painted-door technique works inside the codebase - Ryan's team enforces package boundaries so a designer can paint a fake UI on top of stubbed APIs. Real usage signal, no backend cost. This only works because the architecture refuses to permit a ball of mud.8. The PM's PRD can become a shipped PR in one week - In Ryan's setup, the PM wrote a markdown PRD, the team reviewed it in a Monday meeting, and a working feature shipped to customers by the following week with zero PM-to-engineer back-and-forth.9. The Monday morning roadmap starts with legibility - The first move is making the repository legible to the agent. Write the implicit team decisions down in a documentation tree. Use @-mention Codex to keep that tree updated whenever a Slack thread surfaces a new guardrail.10. One agent beats multi-agent handoffs - The lossy friction of agent-to-agent handoffs costs more than it saves. The right answer is one agent with full addressability over design, backend, and frontend, powered by a model good enough to hold the whole task in context. ---- Where to find Ryan Lapopolo * X * LinkedIn * OpenAI Related content Podcasts: * How to Run Evals in Claude Code with Aparna Dhinakaran * How to Build a Full AI Dev Team in Claude Code with Gabor Mayer * This CPO Uses Claude Code to Run His Entire Work Life with Dave Killeen Newsletters: * PM’s Guide to Claude with Pawel Huryn * How to Become a Builder PM with Mahesh Yadav * How to Build a Team OS in Claude Code with Hannah Stulberg ---- 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 Many of the smartest AI teams I know are running their evals on Arize. Teams at Uber, Booking.com, Pepsi, and others. It’s become one of the most important skills for PMs. I already had on the CEO of Braintrust, Hamel Husain and Shreya Shankar, and Ankit Shukla. Today I’m adding to this knowledge base on evals with a masterclass on evals in Claude Code. Aparna Dhinakaran is the founder of Arize. She’s also their CPO. And she gives a masterclass in how to run all of your evals through Claude Code. So if you want to do AI evals like the best, like Uber, like Booking.com, check out this episode. For anyone in building in Claude Code, it’s a doozy. If a candidate did this in an interview, Aparna said she would hire them on the spot. ---- Check out the conversation on Apple, Spotify, and YouTube. Brought to you by: * Superhuman - The fastest email experience ever made * Sign up and get 1-month free of Superhuman Mail with my link: superhuman.com/akash (given by brand - Kartik) * Land PM Job - My 12-week AI PM + Job Search Course, first 10 enrollees get a FREE 30-min 1:1 consultation * Vanta - Automate your compliance. Close deals faster * Product Faculty - Get $550 off their #1 AI PM Certification with code AAKASH550C7 * Bolt - Ship AI-powered products 10x faster ---- If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle. Do you want to become an AI PM? I’ve created a course for you. Starts soon. ---- Key Takeaways: 1. Trace before you eval - A trace is the full step-by-step playback of what your agent did. Without it, you have no evidence base for evals. Every LLM call, every tool call, every intermediate output needs to be visible before you write a single eval. 2. A span is your unit of evaluation - A span is one discrete step inside a trace. Evals run at the span level, not the trace level. "Did this specific scoring step get the priority right?" is a more useful question than "was the whole run good?" 3. Instrumentation is now a one-command job - Claude Code's instrumentation skills can set up observability for your agent automatically. Arize Phoenix's skill looks at your codebase, identifies the LLM calls and tool calls, and wires them to the tracing layer. No engineering support required. 4. The vibe eval is a draft, not a verdict - An LLM can suggest what your evals should test by looking at your traces. That suggestion will not know your bug-first policy, your comp logic, or your definition of "critical." Treat it as v0 and refine against your actual judgment. 5. When evals fire, two things could be wrong - The agent produced a bad output. Or the eval is miscalibrated. Reading the flagged span yourself is the only way to know which one needs fixing. Both are normal. Both are good news. 6. Evals drift and need regular realignment - Your priorities change. Your bug policy changes. Your product changes. An eval calibrated to last quarter will start misfiring this quarter. Regular alignment to human feedback is maintenance, not a failure. 7. The self-improvement loop is already running at the best teams - Fetch all spans where evals fired. Group by failure category. Propose a specific prompt fix. Review and approve. Ship the new version. This loop runs on a schedule and requires a human at the approval step. 8. Enterprise PMs: start with one internal agent - Not a customer-facing product. An internal tool that takes four hours off your week. Once you have it, you will naturally want to trace it. That is when observability starts to matter to you personally. 9. The context graph is the enterprise unlock - Agents are only as useful as the context they have. Enterprise data lives in silos. The teams breaking through are building unified context layers that give one agent access to CRM, Gong, analytics, GitHub, and Slack. 10. Product taste is still the alpha - Code is cheap now. Shipping speed is table stakes. The PMs who pull ahead are the ones with the sharpest judgment about what to build, and the loops that make their agents better every day. ---- Related content Podcasts: * AI Evals with Hamel Husain and Shreya Shankar * Evals are the new PRD with Ankur Goyal * AI PM Crash Course with Aman Khan Newsletters: * AI Evals for PMs: Everything You Need to Know to Get Started in 2026 * Your Complete AI PM Course & Career Roadmaps * AI PM’s Guide to LLM Judges ---- 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 The market is looking tough for non-technical PMs. Every single week, my comments look exactly the same: brilliant product managers who have the vision, specs, and roadmap in mind, but have zero coding skills. They want to build, and while thousands of technical resources exist online, they make a flawed assumption: that you already know how to code. So when I invited Andre Albuquerque on my podcast, I had to ask him to share his setup. Andre is the founder of Builders Camp, a product school with 4,000+ students across 30 countries, who runs five businesses with Claude Code and has never been a developer. Live on the episode, he built a fully functional product from scratch to show how easily a non-technical PM can go from 0 to 1. He also walked me through CLAUDE.md architecture, custom multi-agent skills, and the bridge between Lovable and Claude Code (which, by the way, not many people are talking about). If you have been putting off Claude Code because it feels too technical or intimidating to set up, this episode is absolutely for you. ---- Check out the conversation on Apple, Spotify, and YouTube. Brought to you by: * Customer.io — * Amplitude — The market-leader in product analytics * Bolt — Ship AI-powered products 10x faster * Arize — Ship AI agents and features faster, with fewer regressions * Product Faculty — Get $550 off their #1 AI PM Certification with code AAKASH550C7 ---- * If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, Relay.app, Magic Patterns, Speechify, Bolt.new and Mobbin - become an annual subscriber ($150), and grab Aakash’s bundle. * If you want access to my AI PM customizations - PM OS, Job Search OS, and Prompt Library - become a founding subscriber ($250). ---- Key Takeaways: 1. Non-technical PMs are stuck in Jira, Linear, and PowerPoints - Most European PMs are still product owners in disguise, paper-shuffling between strategy and engineering teams. The way out is to actually start building, not to lobby for more autonomy. 2. Start with Lovable on a personal project - Build something for your family, your friends, yourself. The codebase does not need to be pretty. The point is the safety to make mistakes without breaking anything that matters. 3. The Lovable + Claude Code bridge nobody documents - Connect both tools to the same GitHub repo. Write code in Claude Code with all its depth. QA visually in Lovable with its hosted preview. Publish from Lovable's button. The perfect transition layer. 4. Lovable, Cursor, and Vercel are not competitors - Lovable bundles the IDE, the hosting, and the deployment in one product. Vercel exposes the hosting layer so you can run real branches with real preview URLs. Cursor is just an IDE with a generous free tier. 5. Cursor has a free debugging agent - When Claude Code breaks, open a Cursor agent and paste the error. The free agent unsticks you instead of leaving you stuck at step zero. 6. CLAUDE.md is your team's culture - Loaded automatically every session. The first rule should be "for every task, call the PM agent." When you notice yourself fixing the same issue twice, update CLAUDE.md so it never happens again. 7. The PM agent never writes code - The PM orchestrator's only job is to decide which other agent should handle the work. The researcher investigates. The designer proposes. The engineer architects. The implementer writes. 8. Do not copy famous people's skills wholesale - Going on LinkedIn and downloading 100 skills from product celebrities creates more confusion than value. Look at how your real team works. Write each role down as an agent. 9. Fix the agent, not the feature - When something ships wrong, do not patch the output. Identify which agent in the pipeline failed, update its instructions, and run the pipeline again. The next session inherits the fix. 10. The Monday morning move is exactly three steps - Get added as a collaborator on a low-risk repo. Pick the oldest ticket in the backlog. Push a branch and demo by Friday. ---- Related content Podcasts: * Claude Code and agents with Gabor Meyer * n8n, Claude Code, and OpenClaw with Mahesh Yadav * Claude Code with Hannah Stulberg Newsletters: * How to Build a Full AI Dev Team * How to Become a Builder PM * How to build a Team OS in Claude Code P.S. Reply with “CLAUDE” and I’ll send you Andre’s actual CLAUDE.md template. He said we could share it. PS 2. 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 When do you use Claude chat vs Cowork vs Code? No one has created a resource that helps you get the most out of the Claude ecosystem. Until now. I’ve brought back Pawel Huryn, the guest behind our most popular episode ever, the Complete Course on AI Product Management. Today we’re covering everything you need to know to get the most out of the Claude Ecosystem. Most PMs open Claude chat. Ask something. Get an answer. Close the tab. Tomorrow, same thing. Fresh context. Zero memory. The PM who tracked Anthropic’s 74 releases in 52 days stopped doing this entirely. He built a system where Claude organizes its own knowledge, extracts its own rules from data, promotes hypotheses when evidence confirms them, and demotes them when it does not. The system improves without him telling it what went wrong. I sat down with Pawel Huryn, creator of the Product Compass newsletter. He has defined 60+ PM skills, built a PM skills marketplace that hit 10,000 GitHub stars, and runs his entire content operation across Cowork, Claude Code, and Dispatch. In this episode, he walks through every screen live. Real files. Real agent workflows. Real self-improving knowledge bases. ---- Check out the conversation on Apple, Spotify, and YouTube. Brought to you by: * Bolt - Ship AI-powered products 10x faster * Amplitude - The market-leader in product analytics * Jira Product Discovery - Plan with purpose, ship with confidence * Product Faculty - Get $550 off their #1 AI PM Certification with code AAKASH550C7 * Land PM Job - 12-week experience to master getting a PM job ---- If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle. I’m accepting applications for my third LandPMJob cohort. Join Me. ---- Key Takeaways: 1. Stop using Claude Chat as your default. Cowork accesses real files, connects to Gmail and Slack via MCP, and runs parallel sub-agents. Chat does none of this. 2. Skills are the highest ROI investment. Install marketplace baselines, iterate 5-6 times with specific feedback, and Claude rewrites from first principles until 99% accuracy. 3. Progressive disclosure keeps context clean. Agent reads skill names and descriptions first. Loads full instructions only when the task matches. Hundreds of skills, minimal overhead. 4. Your CLAUDE.md should route, not store. Project structure and pointers only. Domain knowledge lives in separate files the agent loads on demand. 5. Build self-improving knowledge with three types. Rules are confirmed and applied by default. Hypotheses are tracked with evidence. Rejected patterns are kept to avoid retesting. 6. The three-line self-improving prompt works for any domain. Review rules before starting. Apply confirmed rules. Update after feedback. Testing, marketing, strategy, whatever. 7. Claude Code adds explorer view, hooks, subagents, and local MCP scoping. PMs need it once their system grows past 50 files. 8. Every Product Compass infographic was built in Claude Code. HTML generation, component library, iteration through conversation, PNG export. Zero code written by the human. 9. Use Agent Browser from Vercel instead of Chrome MCP. Chrome MCP screenshots every 0.5s and burns $100/hr. Agent Browser uses headless mode and is token-efficient. 10. Dispatch lets you run multiple tasks from your phone. Start an infographic, check emails, analyze competitors. Each runs as a separate thread. Your system works while you live. ---- Where to find Pawel Huryn * LinkedIn * Product Compass Newsletter * PM Skills Marketplace on GitHub * [Quadathon - starts May 9th](VERIFY - Quadathon URL) Related content Podcasts: * n8n Masterclass with Pawel Huryn * Claude Code PM OS with Dave Killeen * Claude Code Team OS with Carl Vellotti Newsletters: * The complete Claude Cowork guide * How to use Claude Code like a pro * Build your PM operating system ---- PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps! If you want to advertise, email productgrowthppp at gmail. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe

Check out the conversation on Apple, Spotify, and YouTube. Brought to you by: * Maven - Get a $675 discount off Gabor’s course with my code * Amplitude - The market-leader in product analytics * Testkube - The leading test orchestration platform * Land PM Job - My 12-week AI PM + Job Search Course starts Monday! * Product Faculty - Get $550 off their #1 AI PM Certification with code AAKASH550C7 Today’s episode Here’s the problem with most Claude Cost demos: they stop at the prototype. Nobody shows what happens next. You try to add a second feature. The first one breaks. The styling reverts to default. The code is so tangled that you spend more time debugging than you saved by generating. Gabor Mayer showed me what happens when you stop treating Claude Code like a magic prompt box and start treating it like a team. He is a PM at Google. He has not written production code in 15 years. But over the past several months, he has been building real mobile apps using 21 specialized Claude Code agents. Not prototypes that live in a demo. Apps that are on the App Store. In today’s episode, he walked through the entire workflow live and share all the resources free. If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle. Do you want to become an AI PM? I’ve created a course for you. Starts next week. Newsletter deep dive Thank you for having me in your inbox. Here is the complete guide to building a full AI development team in Claude Code: * Why one-prompt vibe coding fails * The 21-agent team architecture * The spec-first workflow * From design to code without touching either * What changes when PMs actually build Save this. The full 10-step playbook on one page. Everything below is the why and how behind each step. 1. Why one-prompt vibe coding fails Every PM I know has built something with Bolt, Lovable, or Replit. The prototype looks great. It runs. It impresses people in a Slack message. Then you try to ship it to real users. And you hit a wall. Blocker 1 - Context compression silently destroys your spec This is the failure mode that nobody talks about in tutorials. When you give one agent one massive prompt, the model compresses context. Details get dropped. Not randomly. Strategically. The model decides what is “important” and what is not. In the episode, Gabor defined a complete color palette. Oranges, neutrals, specific accent tones. The agent received everything. The output used none of it. The layout was there. The structure was solid. But every color was a default. The reason is straightforward. When the context window is full, visual styling details are lower priority than functional logic. So the model drops them. Silently. Without warning. Without an error message. You just get generic output and wonder what went wrong. The fix is not better prompts. It is context engineering. Smaller, scoped tasks. Each agent gets only the context it needs for its specific job. The designer agent gets the brand guideline. The CTO agent gets the architecture spec. Neither gets the full 50-page document. Blocker 2 - AI-generated code compiles but is not maintainable A Reddit comment that hit home for Gabor - “Vibe coding is just the rebranding of unmaintainable, low-quality source code.” This is the real prototype-to-production gap. The code works today. You can demo it. You can push it to TestFlight. But the moment you touch it to add a feature, three other features break. No naming conventions. Circular references between modules. Zero comments explaining why anything was built the way it was. The fix is a dedicated code quality agent. Gabor calls his the Spaghetti Agent. It runs after every sprint and checks naming conventions, circular references, comment coverage, and structural debt. When he ran it on his codebase for the first time, it caught issues he never would have found manually. If you are building anything beyond a one-off demo, this agent is not optional. I covered similar quality patterns in my AI testing guide and my AI evals deep dive. Blocker 3 - No dependency mapping means cascading failures When you build without organizing work into sprints, agents try to build features that depend on code that does not exist yet. Front-end components reference API endpoints that have not been created. Database queries call tables that have not been defined. The Atlassian MCP currently cannot create sprints directly in JIRA. That is a real limitation. Gabor uses tags as a workaround. He tags tickets as Sprint 1, Sprint 2, Sprint 3 and maps dependencies between them manually before starting the build. Without this step, the entire multi-agent workflow falls apart. Every PM who has gone from prototype to production with AI agents has hit at least one of these blockers. The ones who shipped figured out the workarounds. The ones who quit assumed the tools were the problem. Here is what the three blockers look like side by side, and what flips the moment you stop one-prompting and start running a team. 2. The 21-agent team architecture You do not need 21 agents to start. Three will get you surprisingly far. But understanding the full architecture shows you where the complexity lives and which roles to add as your projects grow. Here is the full roster: four clusters, 21 roles, and the markdown file pattern that makes them portable across every project you build next. 2a. The core agents every PM needs The System Analyst is the linchpin. It breaks down product requirements into technical specifications. It asks clarifying questions one at a time. It documents decisions in Confluence. It creates tickets in JIRA. Without this agent, every other agent operates on incomplete context. In the episode, the system analyst asked 14 clarifying questions before a single line of documentation was written. Vector DB choice. Usage limit mechanics. Conversation history handling. Search fallback strategy. API provider. Minimum iOS version. Screen count. Naming conventions. Each question one at a time so the answers stay deep. The prompt pattern that makes this work - “Please act like a good system analyst. Ask clarifying questions until you have a complete and comprehensive understanding. Ask questions one at a time. Do not start writing documentation until all questions are answered.” Two critical instructions. “One at a time” prevents the agent from dumping 25 questions at once. “Do not start writing” stops it from jumping ahead before the spec is complete. Different LLMs have different tendencies. Some love to start coding instantly. You need to explicitly constrain them. This is the same principle behind the prompt engineering techniques that work across any AI tool. The Spaghetti Agent handles code maintainability. Naming conventions. Circular references. Comment quality. Structural debt. Born from that Reddit comment. When Gabor ran it on his codebase for the first time, it caught problems he never knew existed. The UX Flow Architect creates clickable prototypes using Figma’s built-in prototyping arrows. This is a small but important detail. The early versions of this agent placed visual drawn arrows between screens instead of using Figma’s actual prototyping connections. The prototype looked like it had navigation. But when you clicked play, nothing happened. It took months of iteration to fix. Each agent has a specific Claude Code agent markdown file that defines its role, its constraints, and its interaction patterns. The setup mirrors how you would build a Claude Code Team OS for a human team. 2b. The real blockers nobody warns you about The Figma MCP color problem. When you connect Claude Code to Figma through the MCP and pass it your full specification, the screens look structurally correct but the colors are wrong. Not slightly wrong. Completely wrong. The model compressed the context and dropped your entire visual identity. The fix is to pass the brand guideline as a separate, focused input to the Designer Agent. Never … 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 LinkedIn just changed the title of its product managers to product builders. What does it even mean to be a “builder PM”? Well, tools only get you so far. Learning Claude Code is helpful, but means nothing if you don’t have an understanding of the underlying first principles. That’s today’s episode. Mahesh Yadav created one of our most popular episodes, with over 35K views on YouTube, and now he’s back. Earlier, he taught you AI agents. Today, he’s touching you how to become a builder PM: If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle. I’m giving a free talk on how to get interviews at the top AI PM companies on Thursday, April 23rd 2026 @ 9:00AM PDT. Grab your seat. ---- Check out the conversation on Apple, Spotify, and YouTube. Brought to you by: * Maven - Build cohort-based courses that scale * Amplitude - The market leader in product analytics * Jira Product Discovery - Prioritize what matters with confidence * NayaOne - Airgapped cloud-agnostic sandbox to validate AI tools faster * Product Faculty - Get $550 off their #1 AI PM Certification with my link ---- Key Takeaways: 1. Builder PM defined - A builder PM talks to customers, figures out what to build, and ships the first version to 10 customers without talking to any developer. The skill is knowing what to build, not knowing how to code. 2. Four agent components - Every agent that works has intelligence (model), tools (actions), memory (session context), and knowledge (your company data). Every agent that disappoints is missing at least one. 3. n8n for foundations - n8n is the best learning tool because you visually see every component of the agent architecture as separate nodes. Build your first multi-agent system and evaluation pipeline here. 4. Claude Code ate three company types - Context companies, action companies, and evaluation companies all got replaced by one agentic loop inside Claude Code. The three pieces collapsed into one tool. 5. Computer control is the real unlock - File system access plus bash commands equals full laptop capability. This is why Claude Code went from coding tool to work operating system. 6. Long-horizon jobs changed the game - AI agents went from 3-minute tasks to 3-6 hour sustained jobs in six months. This turns Claude Code from assistant to autonomous worker. 7. Continuous learning loops - Build a second agent that watches your corrections to the first agent's work. After five repeated patterns, it proposes a skill update. Your tools get better every day. 8. OpenClaw pattern - Delegation through existing channels, full machine sandboxing, model-agnostic. Not a product but a pattern that Google and AWS will copy inside their ecosystems. 9. AI PM interviews changed - At L5 and L6, product sense questions are being replaced with live building exercises and system design for AI architectures. Pull out Claude Code during the interview or you are already out. 10. Compensation trajectory - From $120K at Microsoft to $1.3M at Google over 13 years, doubling every 18 months through AI-focused switches. Left because big companies kill innovation with six-week approval cycles. ---- Where to find Mahesh Yadav * LinkedIn * Maven Course Related content Podcasts: * Claude Code Team OS with Carl Vellotti * OpenClaw + Claude Code with Naman Pandey * Claude Code OS with Dave Killeen Newsletters: * The complete context engineering guide * How to use Claude Code like a pro * Practical AI agents for PMs ---- PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps! If you want to advertise, email productgrowthppp at gmail. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe

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. ---- Check out the conversation on Apple, Spotify, and YouTube. Brought to you by: * Bolt: Ship AI-powered products 10x faster * Amplitude: The market-leader in product analytics * Pendo: The #1 software experience management platform * NayaOne: Airgapped cloud-agnostic sandbox * Product Faculty: Get $550 off their #1 AI PM Certification with my link ---- If you are trying to understand the new design workflow, this is the one episode to watch. If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle. I’m putting on a free webinar on Behavioral and AI PM interviews. Join me. ---- Key Takeaways: 1. Code vs canvas is a false dichotomy - The best designers use both fluidly. Canvas for exploration, collaboration, and pixel-perfect craftsmanship. Code for interactions, responsive testing, and the last mile of polish. The question is what you are trying to learn, not which tool to commit to. 2. High fidelity is no longer expensive - The entire linear design process existed because building something interactive required engineering resources. That constraint is gone. A functional wireframe takes the same time as a paper sketch. 3. The Codex-Figma MCP makes handoff lossless - Import screens from a running React app into Figma with exact pixel values. Border radius, padding, shadows, all one to one. It is not a screenshot. It is a responsive, editable design artifact. 4. The reverse direction works seamlessly - Make changes in Figma, paste a component link into Codex, and it updates your code automatically. No redline spec, no handoff document. 5. Ed spends 70-80% of his time coding and still calls himself a designer - The medium changed but the mandate did not. Designers are still the voice of the user, still upholding craft. The tools expanded, the role stayed. 6. Figma designers are shipping PRs to production - Teams that six months ago were AI curious are now banging down the door. Monetization designers who never wrote code are building technically complex prototypes. 7. "Prototypes, not PRDs" is the emerging norm - PMs at OpenAI bring working prototypes to design reviews. They ship PRs to stress-test ideas before handing off to engineering. 8. You do not need permission to start - Someone from OpenAI's GTM team built an iOS app with zero experience. Download Codex and build something for yourself tonight. 9. Curiosity is the defining skill for this era - Not code proficiency, not design talent. The AI is an infinitely patient tutor. Ask questions. Build understanding alongside output. 10. Total football is the mental model - Every player can play every position. Roles still have natural spikes. But the tool constraints that enforced rigid boundaries are dissolving. ---- Where to find Ed Bayes * LinkedIn * OpenAI * 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 ---- 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. ---- Check out the conversation on Apple, Spotify, and YouTube. Brought to you by: * Bolt: Ship AI-powered products 10x faster * Jira Product Discovery: Plan with purpose, ship with confidence * Kameleoon: Leading AI experimentation platform * Amplitude: The market-leader in product analytics * Product Faculty: Get $550 off their #1 AI PM Certification with my link ---- If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle. I’m putting on a free webinar on Behavioral and AI PM interviews. Join me. ---- 1. Build a Team OS, not a personal OS - A shared repo where every function checks in work. Engineers, designers, and analysts self-serve without asking the PM. 2. Root CLAUDE.md is everything - Doc index, team roster with Slack IDs, channel map. Keep under one page or you burn context every session. 3. Nested indexes save 97% of context - Every folder gets a navigation CLAUDE.md. A customer query used only 3% of the context window. 4. Three token tiers - Always-loaded root (~500 tokens), folder indexes on navigation (200-500), content files on demand (1,000-10,000+). 5. Split analytics by product area - Metrics, queries, schemas separated. Progressive loading prevents waste. 6. Gate launches on repo updates - Feature not shipped until metrics, queries, schemas, and playbooks are checked in. 7. Verified playbooks kill hallucinations - Analyst-audited methodology. Claude follows verified steps instead of inventing its own. 8. Plan mode makes 10x docs - Shift+Tab twice. Five phases: load context, ask questions, build plan, push thinking, review agents. 9. Split long docs across parallel agents - Each writes to a temp file. Orchestrating agent compiles. Prevents context overflow. 10. The flywheel compounds daily - Automate one task, free time, improve the repo. After 1,500 hours still iterating every day. ---- Where to find Hannah Stulberg * LinkedIn * In the Weeds 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 ---- 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. ---- Check out the conversation on Apple, Spotify, and YouTube. Brought to you by: * Bolt: Ship AI-powered products 10x faster * Amplitude: The market-leader in product analytics * Pendo: The #1 software experience management platform * NayaOne: Airgapped cloud-agnostic sandbox * Product Faculty: Get $550 off their #1 AI PM Certification with my link ---- If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle. I’m putting on a free webinar on Behavioral and AI PM interviews. Join me. ---- Key Takeaways: 1. Context management is the real skill - A single web search eats 10% of your context. Run /context to see what is consuming it. System prompt and MCPs take 10-16% before you type one message. 2. Sub-agents save 20x context - Delegate research to a sub-agent. Same task costs 0.5% instead of 10%. Your main session only gets the summary. 3. Replace MCPs with CLIs - MCPs eat context by existing. CLIs have zero overhead. GitHub CLI, Vercel CLI, Google Workspace CLI are all dramatically more efficient. 4. Powerful skills need zero code - Anthropic's front-end design plugin is just a good prompt. No APIs or tooling. Just rules that tell Claude "do not look like AI." 5. Give Claude self-checking tools - The make slides skill uses Puppeteer to screenshot output, measure overflow, and fix issues before you see them. 6. Repeat prompts for better quality - A Google paper showed pasting a prompt twice helps. Tell Claude to double-check against skill instructions after the first pass. 7. Use hooks to auto-invoke skills - A user_prompt_submit hook matches your words against skill keywords instantly. Zero context cost. 8. Jupyter notebooks solve data trust - Every analysis shows exact code, inputs, and outputs. Traceable and reproducible. 9. Build an operating system - Knowledge folder for people context. Projects folder for task isolation. Tools folder for scripts. CLAUDE.md for identity. 10. The people folder compounds - Connect meeting transcription. After every meeting, update each person's dossier. Every prompt gets more specific over time. ---- Related 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. ---- 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 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 ---- 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 * 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 ---- 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.com Today’s episode Every 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 content Newsletters: * OpenClaw complete guide * My PM Operating System * The AI PM Tool Stack Podcasts: * 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 episode Most 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 content Newsletters * 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 stack Podcasts * 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 episode Most 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 * Website Related content Newsletters * How to become an AI PM * Practical AI agents for PMs * AI evals explained simply * AI product strategy * The AI PM learning roadmap Podcasts * 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 episode AI 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 content Newsletters * 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 PMs Podcasts * 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 episode There 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 content Newsletters: * 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 PMs Podcasts: * 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 Rekhi 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 Designing 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 course Related Content Newsletters: * AI Prototyping Tutorial * AI Prototype to Production * How to Build AI Products * Prompt Engineering * Product Requirements Documents Podcasts: * 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 BUILD Today’s Episode Ankit 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 * YouTube Related Content Newsletters: * AI Evals * AI Testing * LLM Judges Podcasts: * How to Do AI Evals Step-by-Step with Real Production Data * The PM’s role in AI Evals * AI Evals Live PS. 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 Episode Discovery 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 AAKASHxMAVEN Pendo: The #1 software experience management platform Jira Product Discovery: Plan with purpose, ship with confidence Kameleoon: AI experimentation platform Amplitude: 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 Content Newsletters: How to Do Product Discovery Right Advanced Techniques: Continuous Discovery Customer Interviews: Advanced Techniques Podcasts: Teresa Torres’ Guide to AI Discovery Complete Course: AI Product Discovery Ultimate 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 Episode Most 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 Content Newsletters: * AI Product Strategy * How to Build AI Products * AI Agents for PMs * Product Requirements Documents Podcasts: * 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! 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 ChatGPT 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 Content Newsletters: * AI Product Strategy * How to Build AI Products * AI Prototyping Tutorial * How to Become an AI PM * Ultimate Guide to Onboarding Podcasts: * 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.com Today’s Episode When 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 Content Newsletters: * AI Product Strategy * How to Build AI Products * AI Prototype to Production * AI Prototyping Tutorial * Product Requirements Documents Podcasts: * 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.com Today’s Episode ChatGPT 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 * Newsletter Related Content Newsletters: * AI Prototyping Tutorial * How to Build AI Products * AI Product Strategy * Complete Course: AI Product Management Podcasts: * 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 Episode Everyone’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 Content Newsletters: * AI Evals * AI PM Observability * AI Testing * LLM Judge * AI Prototype to Production * AI Product Strategy * How to Build AI Products * Prompt Engineering Podcasts: * 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 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 Carl 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 analytics Pendo: the #1 Software Experience Management Platform Jira Product Discovery: Move discovery and roadmapping out of spreadsheets Miro: The innovation workspace ---- Key Takeaways 1. 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 * Newsletter Related Content Newsletters: * 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 Agents Podcasts: * 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 Takeaways 1. 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 Content Newsletters * 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 PMs Podcasts * 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.com Pawel 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 Takeaways 1. 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 Content Newsletters: * Ultimate Guide to AI Prototyping Tools * AI Agents: The Ultimate Guide for PMs * Practical AI Agents Podcasts: * 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 Lindy If 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 Episode Laura 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 Content Podcasts: * AI Product Leadership Masterclass * Conversation with the CEO and Founder of Bolt * This $20M AI Founder Is Challenging Elon and Sam Altman | Roy Lee, Cluely Newsletters: * Ultimate Guide to AI Prototyping Tools * AI Agents: The Ultimate Guide for PMs * How to Land a $300K+ AI Product Manager Job 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

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 AAKASH25 Today’s Episode Claude 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 Bonus For 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 Engineer This is your complete Codex roadmap. 1. How to Get Started - The Non-Technical PM’s Guide to Codex CLI If you’ve never touched a terminal, this is for you. 1a. Open Codex in an IDE Carl 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 Hack Codex asks for permission constantly—every website, every command. Solution: codex --yolo Full 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 Code Same task on both: Search web, summarize differences. Claude Code: 3 searches simultaneously, ~2 minCodex: One site at a time, asks permissions, ~4 min Claude 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 Codex Codex shines at daily PM work. 2a. File Analysis Without Uploading Run 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 workflow Creates document with direct quotes—no manual file providing. 2b. Template-Driven Workflows Solution: Create /templates folder with markdown files. Example: Discussion Points, Action Items, Risks & Blockers, Next Steps Use: Summarize @meeting-notes using @template Same format every time. Works for PRDs, one-pagers, research summaries. 2c. Socratic Questioning for Better PRDs Problem: 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 Engineers This is where Codex separates from the browser. 3a. Design Systems with Storybook Vibe coder problem: “Make title pink” → wrong shade → 20 iterations → still janky Vibe 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 Unlock Problem: 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 code Carl’s macro calculator: Tests: API returns calories, empty recipe returns null, missing data shows “N/A”, API fails retries 3x, divide by servings correctly Told 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 Bot Carl 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 PDF How: 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 * Newsletter Related Content Podcasts: * Claude Code Tutorial * Windsurf Tutorial * AI Prototyping Tutorial Newsletters: * Ultimate Guide to AI Prototyping Tools * AI Agents: The Ultimate Guide for PMs * How to Land a $300K+ AI Product Manager Job 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 AI PM jobs pay 30-40% more than regular PM jobs. But here’s the problem: You can’t just slap “AI PM” on your resume. Todd Olson has spent 28 years in product management, VP of Product at a public company, then founder of Pendo, now a $2.5B product management platform working with everyone from American Cancer Society to Zendesk. ---- 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 ---- Key Takeaways 1. AI PM market exploded - Last year 10% of PM jobs were AI PM jobs. This year it's 20%. They pay 30-40% more because of scarcity and skill level. But Todd warns: "You better damn well be good and know what you're talking about if you're gonna call yourself an AI PM because we are going to interrogate the hell out of it."2. Real requirement is production at scale - Not "I built prototype at 1-person startup." Hiring managers want 20,000 paying B2B customers experiencing your AI feature successfully. To get there: upskill internally at current company by shipping AI features on your roadmap.3. The 5-layer technical pyramid - Foundation: AI/ML fundamentals, data pipelines, prompt engineering. Middle: Observability (trace analysis), cost optimization, evals. Top: Product strategy, stakeholder management, leadership. You need to climb all 5 layers. Most PMs stop at layer 1.4. RAG is table stakes - "RAG is the de facto way to build." You ingest data, create embeddings, feed into vector database, look up relevant context, pass to LLM. Todd: "If you put too much in context window, just like a human, you get confused. You want to give the right context."5. PM-engineering tension is real - At startups, PMs do trace analysis. At large companies, engineering managers push back: "This is my world. I don't want some PM shadowing me." Similar to Data Dog—most PMs don't have login. Know the line. Be fluent but respect boundaries.6. But evals are YOUR domain - Unlike trace analysis, evals are where PMs are the expert. "The PM is probably the best-suited human being to author and manage eval sets." You understand user and business needs. Engineers don't have that context. This is must-have competency now.7. Cost optimization will matter - Some AI companies have sub-15% gross margins. Traditional software is 70-80%. Todd: "It's not a business at sub-15%." Eventually you'll rearchitect systems because infrastructure is too costly. Rule: when something's faster, it's cheaper (both buying compute).8. Solve hard problems, not shiny objects - Todd's test: "Are we gonna do much better job than ChatGPT out of box? Why would we just wrap that and slap Pendo logo on it?" His discovery agent example: hard part isn't interviewing customers—it's finding which to interview, prioritizing, scheduling. Automate that workflow.9. Kill bad features ruthlessly - Todd shipped features couple years ago that weren't great and turned them off. "Too often we hold on to something. Turn them off. Be unafraid. The more stuff in your product, the worse the experience is by default."10. Control the narrative with boards - Don't show up with no story and get crushed with random requests. Todd: "Show them how you actually run your business. I want to see what you're looking at, not something just made for me." Think deeply about how each bet drives shareholder value. ---- Where to Find Todd Olson * LinkedIn * Company * X ---- Related Content Podcasts: * How to Become, and Succeed as, an AI PM | The Marily Nika Episode * If you only have 2 hrs, this is how to become an AI PM * Complete Course: AI Product Management Newsletters: * How to Become an AI Product Manager with No Experience * How to Write a Killer AI Product Manager Resume * How to become an AI Product Manager ---- 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 There are tons of tutorials about Claude Code and Cursor for IC PMs. But what about leaders? Today’s episode is a masterclass on both sides of AI product leadership. How to be a productive AI leader and how to ship AI-native features at scale. Rachel Wolan is the Chief Product Officer at Webflow, the $4 billion company powering TED Talks, SoundCloud, and Reddit. Rachel walks through building her agentic Chief of Staff live, sets up a LinkedIn post generator from scratch, and shares the brutal lessons from launching Webflow’s AI app generator. ---- Brought to you by: Linear: The task management platform dethroning Jira ----- Key Takeaways: 1. IC CPO means self-serving answers - "As a leader, you are able to get your own answers to practically any question." No waiting on data scientists. No back-and-forth with analytics. You have tools to self-serve insights, make analysis, automate workflows. Model behavior for your team to inspire them. 2. Calendar agent analyzes time - Runs weekly with prompt: "Analyze my calendar for last two weeks. Where could I delegate?" Returns delegation opportunities, red flags (double bookings, context switching), what to cut next week. Rachel gives output to EA. Spot on when shown live. 3. Email agent watches behavior - Complete inbox access. Runs triage, archives junk (calendar notifications, marketing), pins important messages, creates draft replies. Twist: watches behavior. If email sits too long, it notices. Caught meeting missing link. Rachel's rule: agent recommends, she approves. No autonomous sending. 4. Analytics agent via MCP - Connected Claude Code to Snowflake via MCP servers (not officially supported repos, just fed them to Claude Code). Ask natural language questions, get SQL executed real-time. "How many sites does Shirts.com have?" Claude writes query, authenticates via SSO, returns answer. Data scientist in pocket. 5. Accept the adoption curve - Your org follows standard curve: early adopters, early majority, late adopters, laggards. Create pathways for everyone to ascend ladder at their pace. Don't force everyone to be you. Rachel to team: "I only want to see prototypes when you have meetings with me." Creates culture investing in prototype quality. 6. Builder Days strategy - Give everyone access: Claude Code licenses, MCP to Snowflake/Tableau, Figma Make, Cursor with design system. Run Builder Days where champions help others through technical hurdles. Everyone demos something outside comfort zone. Results: 0% to 30% of designers using Cursor weekly after first Design Builder Day. 7. Rewrite career ladder - Webflow rewriting career ladder to make AI-native work an expectation, not nice-to-have. Create right incentives. Make sure people supported. Avoid AI for AI's sake. Example: Two designers built similar prototypes. Director caught early: "Go harmonize your prototypes now." Easier now than late in product cycle. 8. MVO before MVP framework - Most teams: Feature → PRD → Design → Ship. Rachel flips it. MVO (Minimal Viable Output) before MVP. Get model's output right FIRST using RAG, prompt engineering, context engineering. Only then build feature. "If you don't have desired outputs, don't spend time productizing the AI feature." 9. Evals are now your job - Brutal story: Webflow's AI app generator 2 weeks from launch. Rachel tested it. Agent kept dying. Realized: changed underlying model, evals didn't have coverage. Evals = test cases for models. Want dream evals (should pass) and edge cases (should fail). Use BrainTrust. Teaching PMs to write evals is part of AI PM toolkit now. 10. Build on your strengths - Framework: See trend → Is it applicable to customers? → What's YOUR core competency? Webflow's strength: bringing visitors to front door via CMS. Built production-grade app generator (not prototype like Lovable). Uses your brand, CMS, hosting, security. "We're bringing a way to prompt an app to production." Don't copy trends, leverage unique strengths. ----- Where to Find Rachel Wolan * LinkedIn * Website * X ---- Related Content * Claude Code Tutorial for AI PMs * AI Agents for PMs in 69 Minutes, with IBM VP * 5 AI Agents Every PM Should Build, with CEO of Lindy Newsletters: * AI Evals Guide for PMs * Prompt Engineering for AI Agents * AI Agents: The Ultimate Guide for PMs ---- PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps ---- If you want to advertise, email productgrowthppp at gmail. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe