We’ve now crossed 240K subscribers. And it’s been over a year since I talked about what I’m doing here, or gave you a way to explore what I’ve done recently.

So that’s today’s post.

If you’re new here and mostly want the reading list, skip to section 5. That’s where the four roadmaps live.

Today’s Post

I wanted to give you a peek behind the curtain:

1. What I’ve been up to this year

2. What I plan to cover over the next year

3. Growth lessons in hitting 1M followers

4. How I hit $2M/year in revenue, and where I go from here

5. Four roadmaps to explore my content, with all the links to explore them

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1. What I’ve been up to this year

Part 1 - The Newsletter

People might be surprised by this. But I actually give 75% of my time to the newsletter deep dives. It’s my main focus, and everything else I run exists to feed it.

That focus produced 67 deep dives this year. I know it can be hard to navigate, so here’s a quick way to click through them all:

Explore this Year's Archive

In addition, I launched two new programs for paid subscribers who commit annually:

1. Aakash’s Bundle

2. The Founding Plan with quarterly digital product drops

The goal with both is to create a long-term relationship with you, rather than a subscription you reconsider every month.

I’m super proud of the tools we’ve partnered with to give you 1 year of their paid plans in the bundle. These are tools I personally use and like:

1. Linear for task management

2. Mobbin for real-world design inspiration

3. Dovetail for AI customer intelligence

4. Arize for AI evals and observability

5. Bolt.new for full-stack AI building

6. Descript for AI video editing

7. Magic Patterns for front-end AI prototyping

8. Speechify for dictation at 3x typing speed

I’m also quite happy with how the digital product drops in the founding plan went:

1. PM Prompt Library

2. PM OS

3. Job Search OS

4. Content OS

The PM OS was the runaway favorite, so I’m already cooking its follow-up. The plan is to keep dropping regular bundle partners and products so that this continues to be the most no-brainer yearly subscription in your life.

Part 2 - Everything Else

I also built two new lines to the business:

1. I created the AI by Aakash newsletter to help you stay up to date with AI

2. I launched the Land a PM Job cohort program to give you a higher touch outlet

Both of these launches were designed to make this newsletter better:

1. For AI by Aakash, I used it as an opportunity to move out news content from this newsletter. I found that you guys engaged with that type of stuff least.

2. For the Cohort program, I used it as an opportunity to get closer to how real job search candidates are experiencing the market, so I can turn around and coach you. Now my articles on job search have even more real world grounding.

Across everything new this year, I also hired very specific, very talented people that help me with each business line. There are now 8 of us. So the only time of mine pulled away from the newsletter is time that comes back to it.

2. What I plan to cover over the next year

My plan for the next year is to not add any more business lines, but to grow and optimize what I have.

I also want to slightly shift the aperture of focus. The past year, AI has been all the rage. But I’ve sensed the pendulum swing back, and so I’m going to go back to more classical growth, PM, and career content.

You saw that last week with my piece on Stakeholder Management. Here’s what else is already in motion:

1. The State of the PM Hiring Market

2. PM Salary Benchmarks (fill out my survey!)

3. Guide to Trust & Safety Product Management

4. PM’s Guide to Design Taste

AI won’t disappear. But it also won’t be everything.

The bar I’m holding myself to is simple. If you could get the same take by asking ChatGPT or Claude, I haven’t done my job. So expect more original research, more first-hand guests, and more of my time spent on the writing itself.

3. Growth lessons in hitting 1M followers

Growth across my portfolio has been strong this year:

• LinkedIn: 250K → 321K

• X: 116K → 331K

• YouTube: 17.2K → 45.8K

• Product Growth: 184K → 240K

• AI By Aakash: 0 → 140K

In total, I went from 567K followers to 1.08M followers. Here’s how I did it.

Lesson 1 - Flow with the algorithm

The most important principle for growing my followers has been to treat the follower count like a product metric.

I consistently look back at a dashboard of what’s working. This has been by far the most effective tactic available (‘Meta’ in gamer-speak). Because only your account data shows you the map of what works in your niche, right now.

Paying extra close attention to what topic, post content style, and length is working was how I managed to create this massive, 2,700 like LinkedIn post last week.

And it’s how I tripled my X with timely explainer quote tweets like this yesterday that received 1.3K likes.

Lesson 2 - Bring AI in (the right amount)

I did this all inside my Content Operating System built in Claude Code. It's been a big win for me this year.

Now here’s the caution. The output from the OS is good enough to publish, and that’s exactly the trap, because people don’t like to read “AI slop.”

So I take extra care to consistently:

1. Play a huge role in the original idea for any piece of content, usually via a “dictation ramble”

2. Always go in with my human touch to further edit

On social media, I use the Claude Code outputs a bit more readily. But here, in the newsletter, since this summer, I strive to be all human:

Lesson 3 - Don’t Overstretch

I was, at a time, trying hard to increase my following on Apple Podcasts, and Medium, and Instagram, and TikTok.

I since abandoned those pursuits.

I realized that I probably could grow my following on those platforms, but I’d need to spend a lot of time learning them.

In my experience, learning any particular platform requires a really long dedicated push to learn how to succeed on that platform. Typically a year per platform.

That’s why I have 5 platforms at the top of this section. I could do one a year. But it’s very hard to do more than that.

4. How I hit $2M/year in revenue, and where I go from here

It was a pretty good year. My cost basis nearly doubled moving from Durham to LA, and revenue managed to keep pace. How did I do it?

The Creator Revenue Equation

One thing I’ve found as a creator is that how hard you work + how well you cultivate your brand + how good a team you hire = your revenue.

This is quite different from a normal corporate job where, even if you vastly outperform your OKRs for a quarter, your income will only be increased a portion of that on a lag.

What this has meant is that I’ve been able to, in particular, work hard to increase my revenue to ~$2M, 89% above the $1.05M I was at last year.

What’s Next

My eventual dream goal 5 years from now is to be positively impacting 5x the people I am now.

And I have a specific vision for how it plays out as a lifestyle:

• I want to have an office

• I want it to be filled with smart, fun people

But to have an office itself costs a minimum of $200K in LA. Having a team of American staff costs about $1M. So I actually want my revenue to go up, but margins to go down.

My hope is, with that, the quality of content you get is even better.

5. Four roadmaps to read my content, with all the links to explore them

The most frustrating thing with a newsletter is it’s hard to search the content. So I wanted to end today’s newsletter with four roadmaps to help you get the most out of the content:

Roadmap 1 - The PM Course

Get all the PM Links Here

Roadmap 2 - The AI PM Course

Get all the AI PM Links Here

Roadmap 3 - Two Years of the Podcast

Get all the Podcast Links Here

Roadmap 4 - Five Years of the Newsletter

Get all the Newsletter Links Here

Final Words

Thanks so much for being part of this journey. It means the world that I can share this space in your inbox. Reply with any feedback you have, and tell me what you want covered next year. I read every one.

Talk to you tomorrow 👋

Aakash

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Today’s episode

2023: Prompt Engineering
2024: Context Engineering
2025: Harness Engineering
2026: Loop Engineering

As the founder of OpenClaw said:

Loops are all the hype now. I’ve already written the web’s deepest guide to loops for PMs. Today, I bring you the practical screenshare.

My guest is , Chief AI and Product Officer at JobNimbus, which just raised Utah’s largest Series B ever.

And they are operating in the future. Design is building design loops that meet product loops that PMs build that meet engineering loops that engineers build.

Today, Tyler shows you everything:

Today’s deep dive

As a thank you for having me in your inbox, I wrote up my key takeaways and expanded upon some of Tyler’s key insights. Here’s your guide to loops for PMs:

1. What makes a loop a loop?

2. How to write your first loop in 10 minutes

3. 5 Loops every PM should build

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1. What makes a loop a loop?

The first thing to understand is why do we care about loops? Tyler put it very well:

Where prompting got hard is you always had to keep yourself in the loop, so you didn’t feel the gains from what you get from Agentic.

So that’s why we have loops. What is a loop?

Anyone can easily say ‘build a loop in claude code’ or run an agent forever and call it a loop. But are those legit? Probably not.

Tyler visualizes a legit loop like this:

1. Fetches its own inputs

2. Does the work

3. Has a gate

4. Writes a named artifact

And goes back to fetching its own input again.

That’s basically it. An agent or prompt become a loop when you feed the session back in and let it rewrite itself.

2. How to write your first loop in 10 minutes

Here’s some real alpha for you: write your first loop by hand, not with AI.

The advice genuinely surprised me, but Tyler was firm on it:

I’ve read some papers that suggest that skills authored by humans are often better than the ones authored by AI. You know more and can express more of what you want this thing to do.

And then he gave a second reason, too:

People are like, yeah, I got an ebike, but don’t worry, I still want to get exercise and we’ll pedal. Once you get on the ebike, you’re not pedaling.

Once you start using AI to write loops, it’s like an ebike. You only use the AI for pedaling (writing the loops).

So he opened an empty file and typed a skill by hand live. This is roughly it:

# Decide

Help me make good decisions.

I struggle with decision fatigue. Push me toward a decision once enough
thinking has been done.

Use contrarian thinking. Question me, use the Socratic method, and call
my BS. Bring in the latest research where it's relevant.

Be succinct.

That’s a skill. Then you save it, test it, and improve it. When that’s done, it’s one easy command to make it a loop:

Can you set this up as a loop and ask me what you need including a learning hook before closing out sessions that learns from the log.

It’s as simple as that. You set up a skill. You test it. You improve it. Then, you set it up as a loop.

You can also close the loop by hand, which is worth doing once so you see what it's actually doing:

Based on this conversation, how should we improve our skill to be better and more effective next time? 

It works in regular Claude Code. Tyler uses herdr, a terminal tool for running several Claude agents side by side, which further simplifies the process because it will set them off with the keyword ‘agent’ in your Claude Code prompts:

3. 5 Loops every PM should build

Tyler recommends PMs build at least these 5 loops:

Loop 1 - Customer outreach

This is the one Tyler says he gets the most value out of himself:

It’s a really good way to keep close to the customer without one of the biggest challenges, which was just the time to go find who to reach out to and send them a message.

Loop 2 - Project status

This is the boring one that saves the most hours. Pull the tickets, find what’s stale, find who’s blocked. Nothing clever about it.

Loop 3 - Thinking partner

This is the opposite of what everyone’s building. So I want to highlight it.

Tyler’s team writes skills that push back on the person instead of doing the person’s work:

It’s less about doing and more about pushing the thinking of the people on the team. Did you talk to customers? Did you look at the benchmarks in the industry? Where does the data come from?

The reason is that the treadmill is genuinely addictive:

You get all this stuff back and you’re like, I can’t possibly go and evaluate this, so I might as well just send it up the chain and see what happens. That’s not doing product work. That’s outsourcing your thinking.

That’s career damage for a PM in 2026. A year ago a huge deck made you look like a machine. Now people recognize it on sight and know you didn’t think.

Loop 4 - Divergent prototypes

While we were talking, his product skill was building prototypes in the background off a prompt this thin:

Create a crew schedule prototype for a contractor in a product like JobNimbus.
Build three variants: A minimal, B full-featured, C creative.

And this led to 3 divergent prototypes in just a few minutes. From Tyler’s point of view, you should always do this:

Never skip it, in my opinion. We used to talk about what your test coverage is, and now it should just be 100%. It’s basically free. Why not do it?

And then his product loops actually uses a synthetic customer to review the prototypes before any human sees them.

Loop 5 - Quality Watchdog

This is one to ask engineering for, not build yourself.

Here’s the math Tyler walked me through, and it’s the thing most teams are getting wrong right now:

If you ship twice as fast with AI and your quality rate maintains the same rate, your customer experiences twice as many bugs.

That’s his read on why uptime at big companies has been so bad lately. So build this loop to avoid low uptime!

And with these 5 loops, you’ll be ahead of most PMs out there.

Final words

A large part of the PM job used to be production for the majority of the last decade. That work was slow and deliberate and hence the most valued. It is now close to free.

So where does that leave you? Loops. My prediction is that they will be the fastest way to hand over the production half of your job to something that never gets tired of it.

And with the hours won back you can focus on calibration and discovery. So build the loops and then go spend the time in a room with a customer.

I will see you in the next issue.

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Where to find Tyler

• LinkedIn

• X

• YouTube

• Website

• AI Architect

Go Deeper

If you don’t know where to start on Claude loops, I got you covered with my ultimate guide on Loops for PMs. This includes my library of 12 top PM loops.

Then follow it up with The Complete PM Guide to /goal in Claude Code.

Finally, Tyler mentioned the evolving role of PMs and how it is shifting into a Product Builder role, so you can learn more in my podcast with Mahesh Yadav on How to Become a Builder PM.

Finally, please subscribe on YouTube and follow on Apple and Spotify. I have much more content like this coming!

Share Product Growth

Today we’re talking about one of the most classical skills in PM and product leadership.

I want to show you how to see this classical skill differently.

This is What You Don’t Want

Story Time

When I was VP of Product at Apollo.io (recently valued at $2.5B), one PM on the team was great at moving the metrics.

But there was a problem that started to bubble up through feedback channels about his work:

• A designer questioned whether he was bringing the right ideas. I took it as healthy disagreement.

• Then the CEO made a comment on his product area: “I don’t have visibility into what’s going on there.” It made me do a double take. Did he just make a dig at this PM?

It seemed like the designer and CEO respected this guy’s metrics moving. So I didn’t look too much into it.

But I kept getting small signals. Then I realized:

So, I spent some extra time with him on the area.

If I’m honest, things didn’t really turn around much for 2 quarters. But 3 quarters in, the compounding really started to kick off.

The CEO was mentioning his area’s success at a leadership offsite. The design team was praising his product area.

And that crystallized this learning for me:

So I want to share some thoughts on how to do this better.

The Best Stuff

I’ve spent the past week reading up on this topic in prep for this post, and what I found is that the PM literature isn’t the best place for thoughts here.

There’s some good stuff, surely, but the best insights I got were from 2 timeless books:

1. Never Split the Difference by Chris Voss

2. Influence Without Authority by Allan Cohen and David Bradford

So I’m bringing you some of the best of what those resources, and my own experience, have to offer to this critical PM skill.

Today’s Deep Dive

Here’s what we’ll cover today:

1. Stakeholder mapping

2. Move up the pyramid of trust

3. How the human brain works in relationships

4. How to create buy-in so your good ideas aren’t killed

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As Cohen and Bradford say:

Would anyone argue with these simple ideas? Of course not.

And yet, in the workplace with our colleagues, when it matters most for our professional success, so many of us fail to keep these ideas in mind or to use them as the foundation of our actions.

1. Stakeholder mapping

Remember that PM at Apollo.io? One of the first things we did was to do a stakeholder mapping exercise.

You see, the org chart doesn’t tell you everything. In reality, some random staff engineer and sales SME really mattered:

So, instead of relying on the org chart, what we did is to create a map of the actual situation. And that’s what you should do.

Just the exercise of building a map helps. Choose whatever format you like.

Ours looked something like this:

Starting from the inside:

1. In the innermost circle, we focused on his reporting line: me the VP of PM, plus our CPO.

2. Then we looked at the core product team including his designer and engineering manager.

3. Then we defined his third layer as 2 parts:

   • Leaders - Design Skip, Engineering Skip, CEO

   • Larger Product Team - Analyst, Legal, Compliance, Product Marketing, Sales Ops

4. Finally, we identified the influencers that matter:

   • PM Org - Other Directors of Product, Other PMs

   • Decision-Makers - SME in Sales who knows everything, CRO, CTO, CFO

Choose your own categorization that applies to your role (though it will likely be pretty similar to this).

Once you’ve mapped out everyone, you want to understand the current state of your trust equation. For that, we need to introduce a new framework…

2. The pyramid of trust

Level 1 - Not make mistakes

No matter how senior we get in our career, sometimes the work day can get the best of us. When we…

• Show up to meetings unprepared

• Ship AI slop in our communications

• Or don’t do the work expected of us on time

We start the game of respect and trust behind the starting line.

The first level to building trust back is to do your core job mistake-free. If you want to sway someone with your analytical findings, they should have trust in your track record of shipping that in the past.

In practice, what this often means is getting good at saying no to doing too much, so that what you do, you can do well.

It’s sometimes easy to forget that every meeting you attend, every email you receive, is basically an interview. Did you show up to the interview present? Did you respond to the communication on time?

Simply building out the systems to avoid making simple mistakes on these tasks gets you a long way on the trust pyramid. Many people aren’t hitting this basic bar.

Just the exercise of identifying mistakes together with my Apollo.io PM led to so many wins for us. This level really is the important first step of reflection.

Level 2 - Do good work

The next level up from not making mistakes is doing a good job at what each of those individuals on your stakeholder map considers is a good job for your job.

It’s worth thinking about that. What is the sum total of the exposure this person gets of my work?

• For a director of design in another part of the org, it might just be product reviews and all-hands.

• For your designer, it may be everything including product trio meetings.

There’s this funny lack of overlap between the actual work you do and the exposure people get to it:

Even your designer, who’s in the trenches with you day in and day out probably doesn’t see: your product team meetings, all your customer calls, the late night pings from execs, the extra meetings with product marketing and legal, plus a bunch more of the other things you do.

It’s not that you have to tell them, “oh look at all this work I’ve done.” But it’s worth thinking about: of the exposure they have to my work, have I given them a complete picture of what they want?

When we did this with my PM at Apollo.io, here’s some of what we found:

1. His designer expected him to be really close to the customer, and there wasn’t a place the designer learned about the PM’s customer interactions

2. The CEO mainly heard from PMs via OKR reviews and all-hands meetings, and the person hadn’t presented at all-hands

Then we systematically went about plugging those gaps:

1. We added a slack bot that posted his weekly discovery call takeaways to their trio channel when he submitted them on Dovetail

2. He presented his next big win at the all-hands meeting, going through the formal process with BizOps to get listed as a topic

That’s really the power of this exercise. You previously maybe didn’t have a reason to prioritize a Dovetail Slackbot or speaking at all hands.

But after going through this exercise, you do.

At this point, I should say: Doing level 1 + 2 is really enough for many good PMs. Levels 3 and 4 are not always necessary.

But, they represent the “advanced” levels of stakeholder management.

Level 3 - Treat stakeholders like customers

As PMs, we are trained in discovery.

So it’s worth running that same customer skillset on your stakeholders.

The basis of it all is to ask them really good questions and then listen.

As Chris Voss says:

The centerpiece of this book is called Tactical Empathy. This is listening as a martial art, balancing the subtle behaviors of emotional intelligence and the assertive skills of influence, to gain access to the mind of another person.

So to gain access to the mind of a stakeholder, ask questions like:

• To the Sales SME that holds the keys to the CEO’s heart about your big rock feature for next quarter: do you think this product solves the core problem we need to?

• To your design partner: what did you think of the PM artifacts you saw from me last quarter, and how can I improve them next quarter?

You want to show tactical empathy with the questions you ask, so you can really hear their answers and follow up with tangible work after. Here’s a bank of more:

Juicy Stakeholder Question bank

Each question in the bank is powered by one of two techniques from Voss:

1. The calibrated question. It starts with “how” or “what” and hands the other person the problem.

2. Labeling. You say what they said labelled, then you stop talking. People correct you or confirm you, so you can learn where they actually are.

These are the most powerful questions.

Now, don’t go too far with this. Treat your stakeholders like customers to the extent it makes sense. Understand their needs, take tangible steps to address them. Like when our customers make unreasonable demands, be able to have a backbone with stakeholders as well.

Level 4 - Create the failed simulation effect

Cal Newport has this very powerful framework called “The Failed Simulation Effect,” and your colleagues are running it on you.

Basically, everything they see from you they’re asking:

A failed simulation is when they couldn’t see themselves making something as good as you have. A failed simulation, then, makes you the right person (because you have the most expertise) to be doing that job.

This is especially tricky with PM in the AI era, where anyone can create a long document with lots of charts. The old PM tricks don’t work anymore. And it just has to be that when they see your roadmap, your PRDs, your feature results writeups, they should think:

Wow, this is better than I can do!

When you are able to create that failed simulation effect, then you’ll truly win more trust to do more.

And this is the top of the trust pyramid. You aren’t making mistakes, you’re doing good work, you’re meeting stakeholder needs like customers, and they can’t seem to figure out how you do it all so well.

Then you can really solve hazy stakeholder problems.

3. How the human brain works in relationships

Now that we’ve done the highly tactical stuff around stakeholder mapping, understanding gaps, and plugging them with a pyramid of trust, let’s get psychological.

As Chris Voss said:

In my short stay I realized that without a deep understanding of human psychology, without the acceptance that we are all crazy, irrational, impulsive, emotionally driven animals, all the raw intelligence and mathematical logic in the world is little help in the fraught, shifting interplay of two people negotiating.

There are things about how the human brain works in relationships that are worth advanced study for people hoping to improve their stakeholder management.

Read more

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Today’s Episode

I’ve showed you how to build a PM OS, team OS, and company OS in Claude Code. Today I show you how to build a Company OS on Hermes and OpenClaw.

Because Hermes and OpenClaw get you:

• Something that lives on Slack and Telegram

• Something that runs 24/7

• + Self-improvement

These are unique features that really take your Company OS to the next level. And if you’re the PM to lead the project, they give you tremendous impact.

Mikhail Shcheglov is the CPO at OLX Uzbekistan, and he’s had one in production for the last 5 months, so he’s showing you how to build one, mistakes he made along the way, and what it can do for you.

Enjoy:

Today’s Deep Dive

I’ve written up the key takeaways for newsletter subscribers as well:

1. Where to grab the open sourced version

2. How to create your own Company OS with this

3. What the future of product development and management look like in this world

1. Where to grab the open sourced version

Mikhail open-sourced the structural skeleton of the agent that runs his working life at OLX.

Go grab it for yourself:

Saul Goodman

The main caveat is: it’s still a skeleton. You’ll have to supply the vital organs, i.e., context, to watch it come to life.

Let’s show you how.

2. How to create your own Company OS

Now it’s time to really understand the system you’re building better. We’re using a blend of two frameworks:

• OpenClaw is the base runtime because it comes with everything out of the box: Slack and Telegram gateways, scheduler, persistent identity

• Hermes drives automated skill generation and self-improvement. It watches which tasks you request most often and builds them as skills

These come together in 4 system layers, with 3 layers of memory.

As the graphic shows, there are 5 steps to build it. Let’s cover each:

Step 1 - Measure First

Instead of jumping into the scaffolding, in classic PM fashion, let’s start with a metric. It’s called product context coverage, and here’s what it looks like when Mikhail maps it:

Pretty cool, huh? Here’s a prompt you can use to generate it. You can steal it:

The visualization is a separate two-liner afterward. Mikhail one-shotted his in Fable:

Once you have this dashboard, you now have something to goal on.

Mikhail’s screenshotted agent currently reads 54%. At 54%, the Company OS operates like a capable junior-to-mid PM that can make backlog decisions. Once you get to 70-90%, it can actually start doing strategy-level work.

Mikhail keeps that as a personal OKR.

The graph doubles as a management tool. You can drill into any team to see how much context they hold. Silos show up on sight.

For instance, his clusters revealed barely any overlap between the buyer and seller experience product teams:

That means those teams aren’t talking, and a leader can help create that connective tissue.

Step 2 - Scaffolding

The next step is to set up the scaffolding with Claude Code. Once you have forked the repo from Section 1, you’ll see the whole anatomy:

Now we need to fill it in. Hit it with this prompt:

It’ll quiz you as you asked, and then you’ll have everything built out:

Step 3 - Wire Memory

Now we’re ready for memory. Memory has 3 layers. Let’s build them bottom up:

Hit it with a prompt like this:

This will get your memory wired up.

Step 4 - Imperatives

Now let’s talk imperatives. Here’s the underlying problem, in Mikhail’s words:

LLMs are focused on the resemblance of good output rather than the actual results.

The fix is a long, tested list of “imperatives.” You can organize them like this:

So hit it with this prompt:

Step 5 - Put in Repo

Now you’ve done all the setup work, and it’s time to publish. Read my guide to GitHub if this is unfamiliar to you.

Otherwise, hit it with the prompt:

Since every change now lives in GitHub, you can tweak the agent from the Claude app on your phone, the way Mikhail runs his.

Then begin testing with your team!

3. What it means for your job

Here are some use cases you can test this on:

1. Stakeholder gatekeeper - ask stakeholders to ping Saul before a PM

2. Board skill - ask Hermes to abstract each board member’s mental model and give feedback on your next strategy

3. Chief of staff - ask it to give you a morning digest of your email and calendar

4. Recruiting - ask it to build a pipeline for outreach

What you’ll see once this is up and running is…

We’re slowly realizing with AI that the job of product manager is more important than ever.

You can build infinite tools like this, and they’re even more effective on the coding side, but you can’t replace true discovery, coordinating with stakeholders, true strategy, and the accountability to make sure things work as they were planned.

That’s where a PM lives, and that’s your wedge.

Use this to automate all the other work.

Get the Transcript

Get More of Mikhail

• X

• LinkedIn

• Corporate Waters

• Saul the AI Chief of Staff

Go deeper

Start with my guides to Hermes and OpenClaw. Then learn how to build Memory and a Company OS.

You might also enjoy my collaboration last year with Mikhail on What PM Interviewers Look For.

PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!

Meanwhile, there are 16,420 AI product manager job listings.

I’ve been pinging these searches for the last few years. And the results are astounding.

In February 2024, AI PM was 2% of open PM jobs. Today it’s 46%.

And what are hiring managers consistently looking for in these roles?

Experience having shipped AI products to production at scale.

But if you’re trying to break in, you face a catch-22:

That’s the subject of today’s post.

Introducing Nancy Li

Believe it or not, I resisted writing much about AI well into 2024. It was then that I published How to Become an AI PM with No Experience with Dr. Nancy Li. The post’s outperformance helped set off this newsletter’s heavier percentage focus on AI.

Now, Nancy is back. And today she’s sharing real case studies of how her students have nabbed AI PM roles without AI PM experience. You see, she runs the PM Accelerator and does this for a living (with free webinars and a paid cohort).

So she’s just the right person to help.

Today’s Roadmap

We’ve split the piece into the following 5 topics:

1. Why you need synthetic experience

2. Real case studies of synthetic experience

3. The AI PM synthetic experience pyramid

4. How to showcase synthetic PM experience

   • Resume

   • LinkedIn

   • Portfolio

5. The AI PM Synthetic Experience Pack

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1. Why you need synthetic experience

A few factors are conspiring to make synthetic AI PM experience so important. The first starts at a market, or economics, level.

The competition is tougher than ever

In the same span that AI PM went from 2% to 46% of open PM jobs, PM jobs themselves fell 18%.

That’s 7,887 PM roles gone since the August 2025 peak. And then if you look inside that “flat” year. AI PM listings more than doubled (+120%) while non AI-PM listings shrank.

I’ve been tracking this shift for a while. In The State of AI Product Management last year, I used different data, Live Data rather than LinkedIn, to come to similar conclusions.

What’s compounding the mess from a candidate point of view is the supply side. Layoffs keep feeding the supply:

• Per layoffs.fyi, 127,180 tech employees have been laid off this year - most recently, 200 at Apple.

• Mind the Product estimates roughly one in five product people out of a role by mid-2026 (survey + LinkedIn estimate, so treat it as directional).

And it has been running like this for two straight years:

So there’s fewer seats. And more people are chasing them.

Basically, the power of hiring managers is greater than job seekers’ right now. That’s not always the case. 2021 was the opposite.

And the seats that remain are increasingly AI seats:

Experience is a form of de-risking

Put yourself in the hiring manager’s shoes now. Why do they want prior AI experience so badly?

It’s about de-risking. As product leaders, we’re taught to de-risk product risk, de-risk business risk, and, in the case of an experienced PM, de-risk team risk.

If you can prove that you’ve had experience shipping AI features that work at scale, they assume you get it. You know how to work with AI engineers and researchers. You can write great evals. You won’t be surprised by working with product marketing to get the launch to go viral on X.

The 113-listing test

I worked with Claude to pull 113 AI PM listings off LinkedIn. Then I classified each of them by hand based on their stated requirements:

Check out the Database

Here’s what I learned:

You can call that seven in ten. Here’s what my process looked like to identify those:

I classified conservatively. But 40 of the rows were judgment calls. A softer reading might put the number at 55%. Either way, the majority of the market wants proof.

A few more things I learned from the analysis:

You don’t need AI in the title to demand AI PM experience. Generic “Product Manager” titled listings demanded AI experience almost as often as listings titled “AI Product Manager” (66% vs 70%).

“Production” is rarer than you fear. Only 14 of 113 listings (about one in eight) demanded AI experience specifically in production or at scale. The other listings that ask for AI experience leave the venue open.

The ask is for senior on paper, junior on AI. The median listing wants 5 years of product management experience. But when a listing separates PM years from AI years, the AI slice shrinks fast:

• 9fin wants 5+ years of PM with 2+ on ML or GenAI

• Deloitte wants 10+ years with 1+ year of GenAI work

• Goodera wants 2-4 years with 1-2 shipping AI.

What “synthetic experience” means

From here on out, we’ll refer to “synthetic experience” to mean AI product work you manufacture for yourself, ahead of any employer giving you the title.

You build something real, get it used, and document the product decisions along the way. Then it goes on your resume, portfolio, and LinkedIn as work.

Because it is work.

I’ve taught the same play for breaking into PM itself with no experience for years. This is that play, pointed at AI.

It is emphatically NOT fabricated experience.

Fabricated experience collapses at the first technical screen, or the first time someone asks you about your eval set. From here on out, we’re focused on producing artifacts a hiring manager can click, question you about, and verify.

Let’s have a look at some examples.

2. Real case studies of synthetic experience

Nancy and I have run the synthetic experience playbook with many candidates. Here, we show you the real results.

Case Study 1: Brian Luc

Brian spent roughly 15 years in hardware engineering at Apple. He started as a hardware engineer and then moved into a Senior Program Manager role.

He didn’t have a software product manager title, or a title with AI.

So he built Renosmarter.ai:

He used that to update his LinkedIn headline for his AI PM experience. He also posted about the project on LinkedIn. 5 months later, he was an AI PM at Cisco:

AI Product Manager @ Cisco | Building Applied AI Products That Save Time & Reduce Costs | Multimodal AI, RAG, LLM Orchestration | ex-Apple

He managed to use the synthetic experience to land the AI PM role at a ~$444B market cap company.

Case Study 2: Katherine Sao

Katherine is the case for everyone reading this post after a layoff.

🔒 The rest of this post is for paid subscribers only: 3 more case studies, the pyramid of what type of experience helps, and step by step how to show case it.

Plus, The AI PM Synthetic Experience Pack, with checklist and Claude skills to speed up your journey.

Read more

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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:

1. The AI PDLC they embraced

2. The AI Harness they created in Cursor, so it can run on any models

3. How the PM role shifted to Product Builder

I hope you enjoy it as much as I did:

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:

1. A design system structured so agents can parse it

2. Coding standards written down explicitly

3. 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:

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.

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!

I’ve never shared how.

I’ve done it all inside of a Content Operating System built in Claude Code.

But What about AI Slop?

You might be asking yourself: but I don’t want to use AI in my content. And you’d be right to think that way.

LinkedIn is trying to remove AI slop content:

Substack is helping users detect AI slop as well. You can expect others to follow. That’s why…

This System is About Getting Jobs (and Customers)

This system is about automating all the work around content so you don’t generate slop.

In particular, it helps you with:

• Finding the right topics to post about

• Giving you inspiration from other creators

• Helping you create infographics that go viral

• Automatically collecting analytics on your performance

• Analyzing your funnel to help you drive job offers and customers

None of this involves drafting posts for you, or automating comments (ICK!).

It’s about automating the infrastructure around writing posts and comments so you have time to write yourself.

How it Generates Jobs

I got together with one of the fastest growing creators on LinkedIn in the PM niche, Basia Kubicka. And she shared her entire setup in this exclusive episode:

She uses a similar Claude Code operating system to receive 5 inbound opportunities per week 😮:

How it Generates Customers

I used this OS to publish this post a few days ago that drove 784 website visitors, 64 free subscribers, 21 LinkedIn followers, 3 paid subscribers to the newsletter, and 1 application to my cohort:

With an average cost per click on Meta ads between $0.40 and $1.20, most companies have to spend between $313.60 and $940.80 for those results.

I paid $0 for this (because it fit within my existing Claude Max plan).

The Content Operating System chose the topic of the infographic, assembled all the links in that post, and even made the infographic.

I just wrote the text.

How to Get It

Here are your options to get the Content Operating System:

1. Buy it on Gumroad for $49

2. Become a Founding Subscriber for $250 to get it + my other products

Not a founding subscriber and don’t want to buy? The video above is today’s alpha for subscribers. Believe me, it’s THE roadmap to get jobs from LinkedIn.

What’s Inside

Here’s a preview (not everything because people will copy it) of what you get:

And the content inside that context-library? Entirely populated by the system based on your profile link + those of creators you admire.

What’s especially powerful, and worth the money on its own, is the post lab, where you get to see the performance of other creators on your platforms:

Can You Build This on Your Own?

I used Claude Code to build this.

You could build this in Claude Code by yourself, too, if, like me, you iterate on the system for 8 months.

But if you buy the product, you get those 8 months of iterations in the price.

($49 on Gumroad or $250 as part of the Founder’s plan which also gets you my PM OS, Job Search OS, Prompt Library, and Bundle).

Today’s Deep Dive

Once you’ve got the thing, here’s how to use it well:

1. The 2-minute install

2. Then 10-minute setup

3. How to use the Content OS well

4. How I personally used it to grow

   • On X: from 117K to 320K followers

   • On LinkedIn: from 241K to 320K followers

5. Pro tips, mistakes, and FAQs

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1. How to Install the Content OS

It hardly takes 2 minutes and runs in your harness of choice:

Read more

So do PMs all need to learn graphs now?

I spent a few weeks testing them for you to find out. The answer is, perhaps unfortunately, yes.

But the good news is: they’re extremely easy to learn and prompt.

So let’s walk through it.

Today’s Post

1. What is a graph and how did we end up here

2. The graph lego blocks to build from

3. Graph construction in practice

4. The PM graph library

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1. What is a Graph, and how did we get here?

The five hype cycles we’ve seen in AI actually build upon each other.

Prompt → Context → Harness → Loop → Graph

That means everything we learned in the past still matter:

• Prompts define what a model should do

• Context defines what the model should know

• The harness is what the AI can access or change

• A loop is how one agent keeps improving

• Graphs are how the whole system coordinates

For simple tasks, a prompt with some context is enough. But for most high-leverage PM tasks, like writing a PRD, or a feature results writeup, you’re much better off placed with coordinating a whole graph.

(More proof on that in just a minute.)

2. The graph lego blocks to build from

Just like prompts spawned a sub-field of prompt engineering, context spawned context engineering, and harnesses spawned harness engineering, graphs have created the sub-field of graph engineering.

The best way I’ve found think of graph engineering is like putting together legos. If you learn the major pieces of legos, you can basically build anything. The same is true for graphs.

There are 5 most commonly used lego blocks:

• A sequence means one step feeds the next

• A router sorts each request to the right lane

• Parallel blocks do several things going at once

• An evaluator scores the output of another agent

• A review loop reviews an output till it passes a bar

You can combine in any order you’d like.

An extremely common pattern that people will build is an orchestrator + parallel + evaluator to create a diamond:

None of this is particularly new. Us software engineers were taught graphs back in college. Now, with prompting AI, they’re back.

3. Graph construction in practice

Now let’s see what this looks like in practice.

The easiest way to use Graphs is to build them with Claude Code workflows. It will even do the work of selecting the right pattern for you. Start with a prompt like this:

The keys to this prompt are:

1. Define the outcome you want

2. Explain the graph types you want it to use (combos of the types of above)

3. Identify the models you want to use for each task (you can see why I use Opus so much now!)

Then you can see it will go and build the prompts and do deep work, setting up parallel workflows where possible.

You can use /workflows to manage your graphs:

We basically defined a double diamond graph:

You can then monitor specific agents as they work. For instance, here’s the discover analytics agent:

Eventually, you get an output that is the result of many agents working for you, instead of a single agent.

Now onto the main course….

4. The PM graph library

So this brings up 3 really important questions :

What PM tasks deserve a complex graph?

The followup question to that is: What graph should they have?

And then: What’s the best way to prompt such a graph?

So, I used a bunch of graphs and spent 100M tokens on Fable 5 to test everything for you.

Step One - I identified 40 PM tasks are the best fits for AI

First I thought deeply: what tasks are best suited for AI out of the PM tool kit?

I found 40 tasks across 3 buckets:

Step Two - I built the strongest simple prompt possible for each

Then I created a great simple prompt (no graph) for each of those 40 tasks (using my PM Prompt Library and PM OS).

Because here’s the principle I ascribe to:

If a graph can’t beat a great prompt, the graph is just theater.

The idea was to get the single best one-shot prompt possible.

Step Three - I built a strong testing environment

Then I built an environment to test multiple graphs vs simple prompts.

I built a PM OS for a hypothetical PM at Instagram. It had everything: templates, files, even MCP connections.

But I didn’t make it “clean.” I planted issues:

• An action item that got canceled 20 minutes later in a meeting transcript

• A research claim marked “UNVERIFIED, do not cite”

• A sprint that overbooked an engineer on PTO…

Step Four - I ran the tests on the best graphs

Then I went about testing the simple prompts vs graphs. But I didn’t use any graphs.

I theorized about the best graph construction for each type, and tested up to FOUR structurally different graphs for each use case.

Three independent graders graded the simple + 4 graph outputs.

Graphs ultimately won 25 of 40 tasks. These 12 were the biggest winners:

That’s the essence of graph engineering. Find the tasks, write the graphs, test the results.

I also wanted to give you more than a newsletter today.

I’ve put all 25 graph winners, plus the winning 15 simple prompts, into the PM graph library.

Plus, I’ve created a skill that scans your entire repo for tasks and prompts, runs the graph engineering on them, and hands you updated prompts.

You have two options to get the package:

1. Buy the product on Gumroad for $49

2. Upgrade to a paid subscriber and you’ll be able to download it below:

Read more

Check out the conversation on Apple, Spotify, and YouTube.

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5. Land PM Job - 12-week live course to master the PM job search

Today’s Episode

Whether you’re using a PM OS, or using regular Claude, or using another AI harness (like ChatGPT Work or Codex)…

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.

Enjoy:

Apple Podcasts

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The landscape of PM automations is changing rapidly. Between graphs, loops, scheduled tasks, cowork, claude code, lindy, relay… it’s hard to break it all down.

I’m giving a free webinar on Claude Automations next Tuesday. Join me:

Join the Webinar

Do you want to get 1:1s with me to talk AI, PM, and landing a job? If so, join the next edition of my Land PM Job cohort program:

Join the Cohort

Today’s Deep Dive

Thanks for having me in your inbox. Here’s your hand-written key takeaways:

1. Why PMs need a broad range of skills

2. The top skills every PM should have and use

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1. Why PMs need a broad range of skills

Oji is currently running ProductMind, where he works with companies to become AI-native. And one thing he has observed is:

We see PMs who are not speeding up their product judgment, speeding up their orchestration skills to match the new speed of the engineers

There are loads of repos out there for tokenmaxxing, or engineering, but there are almost no good repos for PM skills (one exception: mine).

The way Oji conceptualizes a business in his book Building Rocketships, every company has three layers:

1. Business

2. Product

3. Code

Only having skills at the bottom most layer is a huge gap. That’s why Oji has generously open-sourced business and product skills:

Install Library

2. The top skills every PM should have and use

Oji’s system has 17 skill files:

The most important thing about these skills is what they cover. They cover the stages of product development that actually require PM judgment: finding the problem, deciding what to build, getting people to value, making money, setting direction, and running the work.

In addition, there are two unique things about how Oji set up his skill system:

1. The project scaffolding skill

2. The concept of gates

Let’s walk through each.

The Project Scaffolding Skill

The way Oji has orchestrated the system is it has a new project scaffolding skill at the heart of it all.

What executes is an 11-step workflow, and the first 4 steps never touch code.

By the end you have a market research doc, a product brief, a project-specific CLAUDE.md, a folder structure, a tests folder, CI wired up, and secrets already in .gitignore.

The Concept of Gates

LLMs very rarely tell you no. And this skill will tell you absolutely no, don’t do this.

Everything in Oji’s skills is built around this fact.

Take vet-a-feature. It’s built on the idea of the sharp problem test:

Other skills similarly work with gates. And what this does is it helps you build trust in the LLM’s response and replicate real PM judgment.

You want to build skills with gates that tell you no.

Final Words

With AI where it is today, the scarce skill is no longer building something. It’s the judgment to say this one and not that one. I call it Taste at Speed.

You cannot prompt your way to this skill, and you can’t hire an agent to hold it for you. You have to own it as a PM.

As much as these automations help…

Get the Transcript

Get More of Oji

• LinkedIn

• ProductMind

• ProductMind Substack

• 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.

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• Behavioral questions might have an AI flavor

• Technical questions might have an AI flavor

• And cases might be AI-tinged: AI product design, AI product sense, and AI system design

The fundamental skillset you use to succeed in these interviews is mostly the same.

That breaks when it comes to Vibe Coding interviews.

In these interviews, you often have to complete an entire product sense or product design round, along with vibe coding during it.

To get through so much fast is very tough.

And there’s not a good mock interview on YouTube about it:

So today, I’m releasing the first mock, and going deep on how to succeed.

Today’s Post

Watch the mock:

And here’s the deep dive. This is a successor piece to my guide on the round last year, updated for what I’ve learned the past year coaching 160+ students through my cohort:

1. Who’s actually using the vibe coding round now

2. What real hiring managers are saying about what they look for

3. Breaking down my mock: what I did well and what I could’ve done better

4. Putting it all together into a repeatable framework to take into these rounds

5. The vibe coding round PM harness: a toolkit you can take to your next interview

Subscribe now

Speaking of the cohort, my interviewer in this video was Ankit Virmani, the guest instructor for the AI PM course in our upcoming cohort.

Join us for a free webinar on the fundamentals of AI tomorrow:

Join the FREE Webinar

And apply to get our live coaching, resume review, and more in the cohort:

Join the Cohort

Now into today’s piece…

1. Who’s Using the Vibe Coding Round

The first question I had when writing this was: who is actually using this right now?

There’s shockingly little good, verified information on the internet. The best stuff is frustratingly from anons on places like Reddit and Blind.

It’s not completely trustworthy. But here’s what the internet breadcrumbs reveal:

• Lovable launched in November 2024 and kicked off the AI prototyping trend

• Vibe coding the term was invented in February 2025 by Andrej Karpathy

• A PM reported a vibe coding round at Google in July 2025, though it was disputed

• In February 2026, a PM reported Meta changed its PM process to add a Product Sense with AI round with 30 mins product sense followed by 30 mins vibe coding, however Glassdoor reports don’t always mention vibe coding

• In February 2026, a PM reported a vibe coding round at Adobe

• In March 2026, A PM reported a vibe coding round at Microsoft

  • In the same thread, a commenter confirmed a Google vibe coding round

• Most recently in May 2026, another PM reported a vibe coding round at Microsoft

In short:

Of course, I went deeper than the internet. I asked my cohort members where they’ve seen the interview.

They shared:

• United States: candidates faced it at Figma, Shopify, v0, Perplexity, and NBCUniversal

• India: candidates faced it at Paytm, Flipkart, and Zomato

• Europe: candidates faced it at Airwallex, Bolt, and Kraken

On my podcast, product leaders at Airtable, Laurel, and Freshworks (episode out in August) have also reported it as an area they test for.

Putting it all together:

This follows the general trend I’ve seen with case interviews (like product sense): they’re concentrated at the big, public companies. Smaller companies focus more on behavioral questions.

And the AI piece is natural: the more AI-native a company, the more likely to test it.

2. What Real Hiring Managers are Saying They Look For

I’ve had several of these hiring managers on my podcast. Here’s a few anecdotes of what they shared:

I do ask people to screen share. What I found is it is so easy to say, I'm AI-native, we do a bunch of stuff with AI, but as soon as you really peek under the hood, you're like, actually I think you're what I call level one... Getting to see that live on screen is really interesting because otherwise it's really easy to just say, this is what I do, and you've pulled it from LinkedIn.

—Jiaona Zhang, CPO at Laurel AI

I think just jumping straight into vibe coding something is not what I'm looking for in a PM right now... Ideas are so important. Knowing what to build is so important. So if the first thing you do is jump straight into execution mode, that would worry me.

—Jaclyn Konzelmann, Director of PM at Google AI

You also have an AI round as well now, where you’re asked to code your idea. So, in product sense, whatever idea I would have come up with, they’re like, could you pull up Cursor or your favorite IDE, and let’s start coding. And through the coding they are able to see how I think through, like, why did I choose this option versus this other option... am I just taking the first thing that the AI tells me as, this is great, and wrapping it up, or am I looking through things to say, okay this is good, but what about this edge case... how am I coaching and shepherding my AI to work with me to get it to where I want.

—Jyothi Nookula, Sr Dir of PM at DataRobot

I’ve also been talking to hiring manager for my upcoming book on the AI PM interview.

Putting all the first-hand research together, here’s how you can think about the rubric areas you want to demonstrate in the interview:

🔒 The rest of this post is for paid subscribers only. You get the full rubric, mock breakdown, framework, and my vibe coding interviews harness (worth >$50) that you can bring into interviews to excel.

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5. Pendo - The #1 software experience management platform

Today’s Episode

Rasty Turek spent the past year building with coding agents, and he mapped how his process changed over that time.

His eval started as QA. Then it became the spec. He reckons he now spends around 90% of his time on evals. And the difference is “indescribable.”

Just about every developer and builder has experienced a similar pattern.

Just as the value of a long PRD was waning, evals sprang up. And PMs are often considered the best owner.

So how do you do them well?

I’ve now covered evals basics, excel implementation, advanced theory, Braintrust implementation, and Arize implementation.

Today, I cover how to practically write a great one to hand off to another team.

And I do it with the perfect person.

I’ve brought in Daniel McKinnon, who was a PM on the Llama models at Meta. He wrote the viral piece “Show, Don’t Tell” where he said:

When a partner team wants Llama to do something, I ask them to please not send me a product spec or PRD or description of the problem. This just adds additional work for the team to parse and understand. Cut the middleman and send me an eval directly.

So what would a good eval to send him look like? Today, we show you.

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Today’s Deep Dive

This is the guide to building your first eval before you have a single user.

1. Why your first eval works differently

2. How to build your eval set in Claude or ChatGPT

3. How to score it

4. How to read the number

5. How evals can help your career

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This post was written entirely by hand:

I use AI in my editing and ideation phases, but am here to give you HUMAN content.

1. The cold-start problem

Everything I’ve taught you about evals so far has the same first step: Look at your data.

That’s the error analysis method, and it’s still the most calibrated way to build evals over time. But it has a prerequisite. You need outputs to analyze.

This is the gap Daniel’s method fills. He builds the eval before the product exists, out of domain knowledge instead of production data. I’m calling it the cold-start eval, and it’s the one most PMs never learn.

When a product is expected to do nearly everything, a normal PRD usually folds because it goes into specifics like how it behaves in particular situations and how the user gets value.

So examples become the specification.

“It’s really just like a trivia question for the model.”

Offline vs Online Evals: An Explainer

Say you run a recipe site and you want to generate ice cream recipes.

The bet you’re making is that a good offline score predicts a satisfied online user. It doesn’t always hold, yet it’s still the best predictor available.

Sometimes the offline eval tells you the thing you want to build is not possible with today’s models, and you find that out in week one instead of after launch.

Then you make changes accordingly.

One note before we build, Daniel ran his live demo in Codex. The method has nothing to do with the tool, so I’ve written every step below in a way that could work in Claude Code or ChatGPT Work also.

2. Step-by-step, how to build your eval set

You have a feature idea and no data. So this is how you get to a working eval set in one sitting. The shape of what you’ll build will kinda look like this ↓

Step 1 - Write the problem in one sentence

You have to understand exactly what the feature will do in one sentence. For example:

Step 2 - Validate your domain expertise

You will not write a good eval for a space you don’t know. So, if you’ve never shipped an AI feature, Daniel’s advice is blunt.

And that’s where I come in :D

Step 3 - Set up the workspace

Open Claude or ChatGPT and create a Project. Upload whatever your feature reads, like, sample support tickets, example transcripts, a product spec, your scoring guidelines. Then paste this into the project instructions:

Step 4 - Find your floor

Pick the easiest genuine case you can think of and check the model can do it at all. Daniel picked cystic fibrosis - a disease with a known genetic cause and a canonical gene behind it.

While Daniel’s version was pretty niche, your version can look like:

Now run it on the model you’ll use. Call this the floor test. It’s where you find out that your easy case sits above the ceiling of the model. If your floor fails, you can make the case easier.

Step 5 - Find your ceiling

Now the opposite move. Find a case you’re fairly sure nothing can solve. But why bother testing something you expect to fail? Because if you can’t find a case that fails, your eval is already saturated and it will never tell you anything again.

Step 6 - Fill in the middle

You have a floor and a ceiling. Everything else is a binary search between them.

Run that a few times and keep going until you have a set. How many? Around 100 is typical. And yes, you use AI to build the test for the AI. That’s the only way this takes 90 minutes instead of two weeks.

3. How to score it?

Daniel described the scoring step but didn’t write one out on the recording, so I built a version for you all. It’s built on the binary pass/fail principle from the eval guide.

Calibrate the judge before you trust it. Do this once, at the start. It would take around 30 minutes, but it will make a huge difference between a score and a guess.

4. How to read the number

Say your eval comes back at 50%. Is that shippable? Probably not.

So you go one level down and ask which slices you’re good at. Then you put guardrails on the product so it only answers the kinds of questions it gets right 80% of the time.

That’s a PM call. Everything below the line goes back to the research or engineering team with a specific ask. You hand them a target instead of a feeling.

5. How evals can help your career

Daniel estimates there are probably fewer than 100 PMs building frontier models worldwide.

And, as he said:

The average PM is a motivator and an analyst, and all of that is getting easy to do with AI.

Evals, on the other hand, are purely built on judgment. So, this is the place to go deep and differentiate. As you will be one of the few who can.

P.S. Many of you are looking for a great AI builder role. Daniel’s hiring.

Here’s a handy infographic summarizing what we covered:

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Where to find Daniel McKinnon

• X

• LinkedIn

• Gamow Labs

• His excellent blog

Go Deeper on Evals

1. AI Evals for PMs: Everything You Need to Know to Get Started in 2026

2. How to Run Evals in Claude Code with Aparna Dhinakaran

3. AI Evals with Hamel Husain and Shreya Shankar

4. Evals are the New PRD with Ankur Goyal

5. AI PM’s Guide to LLM Judges

6. AI Evals Explained Simply with Ankit Shukla

To never miss an episode, subscribe to the podcast on YouTube and follow on Apple & Spotify

Last Few Things

I’m doing a survey on PM salaries. Please fill out this 3-minute survey. I’m giving away 10 copies of Eric Ries “Incorruptible” to participants.

• To get access to my AI tool stack - become an annual subscriber ($150), and grab Aakash’s bundle.

• To get access to my AI PM customizations - PM OS, Job Search OS, and Prompt Library - become a founding subscriber ($250).

And, as I mentioned earlier, my PM cohort starts next month with a free Webinar Thursday.

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Since then, we’ve added two more of my favorite tools: Speechify (which I use for dictation) and Bolt.new (which I use for building websites and prototypes).

Today, we add another. And, for many, it will be the most exciting one yet.

It’s one of the most powerful platforms out there. Let me show what it can do.

Today’s Post

I’ve put together a complete guide to Airtable for PMs and Product leaders:

1. What is Airtable

2. Intro to Airtable lingo

3. How to set up Airtable

4. Airtable vs the competition

5. How to redeem it in the bundle

6. Putting it all together into Aakash’s stack

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1. What is Airtable

For PMs and product leaders, the best way to think of Airtable is as a place to run your product work.

It’s like a combination of Jira, Zapier, and Notion Calendar in one place - with connected Claude MCP too.

I had on their VP of Product last year if you want to see him show you:

You might be thinking: we have sheets, we have Claude, what else do we need?

The honest answer is:

But as soon as you hit some level of scale, you’ll want to standardize things. You’ll want your roadmps in one place. You’ll want your automations centrally editable after an employee leaves.

You could maintain that all yourself, a series of vibe codes in Cowork, but there’s a maintenance cost involved.

That’s why basically every company out there eventually gets on some tool. The big one’s are Jira Product Discovery, Productboard, Pendo, and… Airtable.

2. Intro to Airtable Lingo

Airtable has its own lingua franca, so let’s clear that up.

Put simply:

• Bases are systems

• Tables are a thype of thing

• Records are rows in tables

• Fields are columns within each row

• Linked records are how you connect tables

• Views are the views you build on top of them

• Forms are how you collect outside data cleanly

• Automations are “if, then” actions powered by connectors

• Interfaces sit atop everything to give your data an app home

Here’s all that in a handy visual:

3. How to set up Airtable for Product Teams

You have two paths:

1. AI Agent

2. Templates

I recommend the AI agent. I find it pretty good:

I recommend starting with your product roadmap:

From one simple prompt, you’ll get an input of yor roadmap:

You can then start to leverage automations, like an email when a new issue is created:

Then you can start using things like the Kanban board to actually manage the work:

The final part is forms. You can create a project intake form for stakeholders, for example:

Then you’ve got a working basic product system in Airtable! Get to work getting the most out of it:

4. Airtable vs the competition

Compared to the competition, here’s where Airtable stacks up:

The short of it:

5. How to get Airtable free with the Bundle

1/ Upgrade to a yearly paid or founding member:

Subscribe now

2/ Head to bundle.aakashg.com to redeem your codes:

Get your bundle now.

3/ Use that code in your checkout window on airtable:

Airtable Checkout

6. Putting it all together into Aakash’s stack

Here’s how I’d put together all the tools in my Bundle into a proper stack:

• Front-end Prototypes: Magic Patterns

• Full-featured Prototypes: Bolt.new

• Design Inspiration: Mobbin

• Video editing: Descript

• Dictation: Speechify

• Research: Dovetail

• Tasks: Linear

• Evals: Arize

I hope this helps supercharge your work with AI. $27,467 in products for $150.

See you next week,

Aakash

I want to add more phenomenal tools to the bundle. If you work at a great tool company, please hit reply to this e-mail. I’d love to chat.

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They ran into the same round at both companies:

“Both had technical round where they asked me about my AI depth. How transformers work, that kind of thing.”

So then I went looking online for resources on the topic.

To my surprise, I didn’t find anything:

So then I went about the process of creating the guide for you: learning the questions they ask, solidifying the topics you need to learn, and understanding what a winning answer is.

Today’s Post

1. The technical questions PMs are actually asked

2. 🔒 The technical concepts PMs need to know

3. 🔒 What a winning answer is

4. 🔒 AI tools to practice

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Two quick notes before we get into today’s post

Getting AI PM Interviews

While today’s newsletter is about cracking the technical rounds of AI PM interviews, if you’re still stuck on getting interviews, join my free webinar tomorrow morning, 9AM pacific:

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Our Coaching

If you want to go further than this newsletter on technical topics, you should consider the AI PM course Prasad and I teach with guest instructor Ankit Virmani:

The next cohort starts on August 3rd. Join us:

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1. The Technical Questions PMs are Actually Asked

12,397 AI product jobs were posted in the US in the first 6 months of 2026.

That’s a lot of jobs, and that means there’s a lot of variability. Here’s the lowdown on reports of real questions from real companies:

• OpenAI: Define hallucinations in LLMs, You have a model with 10x the capability at 10x the cost. What do you do with it?

• Anthropic: How would you handle hallucinations in a generative AI model deployed to users?, How would you define a “redline” for a model capability?

• Google DeepMind: What AI agents have you built to make yourself more productive?, Design a high-level system for Gemini responding to a user query

• Nvidia: Explain how GPUs are used in deep learning applications, Design a RAG system on Nvidia infrastructure with latency, relevance, and cost trade-offs

• Perplexity: Explain how RAG works

• Glean: Have you built any end-to-end agentic systems?

• Microsoft: Explain trade-offs in model selection, Walk me through the system design of an AI-powered experience

• Amazon: Tell me about a time you had to go several layers deep into an ML system or AI infrastructure to diagnose and solve a problem

• Meta: Whiteboard an evaluation framework, plus human-in-the-loop feedback systems and prompt chain prototyping

I’ve combined those reports into an infographic for you to practice with:

👇 Now that you understand the questions, it’s time to learn the core concepts.

Then, I’ll cover how to structure the learnings into winning answers. And end with some AI tools to practice (worth the cost of the subscription themself).

This post was written entirely by hand:

And leverages insider knowledge from my coaching 150+ candidates over the past year.

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Check out the conversation on Apple, Spotify, and YouTube.

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Today’s Episode

Eric Ries is the Lean Startup Guy. He was an early call from Dario Amodei when he was founding Anthropic.

Dario could afford that call and make the connection. But what about you?

I’m making that call for you today. Eric spent the last few years creating his next movement after Lean Startup, Incorruptible, and I get the low down:

• How to structure your company with a trust

• Governance structures you can copy

• And the underlying culture you need

He goes behind the scenes to show how he wrote Incorruptible with his AI tool Solveit and live screenshares his writing workflow with Claude.

Check it out:

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🎊 To celebrate, I’m giving away 10 copies of Eric’s book Incorruptible:

All you have to do is fill out this short survey on PM salaries.

🆓 I’m doing a free webinar Thursday on getting AI PM interviews. Join me:

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The next cohort of my LandPMJob program starts August. If you want my 1:1 coaching, sign up.

Today’s Deep Dive

I’ve written up the PM’s Guide to Governance:

1. The four dimensions of Incorruptibility

2. The Costco case study to become Incorruptible

3. How to structure your next company (if you’re a founder)

Plus, two bonus sections:

1. [Bonus 1] How Eric Ries Uses AI to write

2. [Bonus 2] The Lean Startup methodology in the AI era

1. The four dimensions of Incorruptibility

If you start studying the world’s most lasting companies, the first thing you’ll learn is that:

Most spectacular startup collapses were governance failures, not product failures.

So how do you avoid them? Four things:

2. The Costco Case Study

Let’s talk about Costco for a second. It’s one of the best performing companies of all time:

How has Costco had such longevity? Well, it actually starts with disappointment. You see, Costco was co-founded by a guy named Sol Price.

Sol originally created FedMart. It became a very successful company, but he gave control to investors. Eventually, the board wanted growth while Sol would do things like tell customers to go buy from a competitor down the street if they had a cheaper price.

So, the board locked Sol out of his own company. It didn’t work well for them. By 1982, FedMart was in liquidation.

Sol, and his mentee Jim Sinegal, didn’t want to follow in FedMart’s footsteps. So when they built Costco (a merger of their two companies), they built a governance fortress, with staggered board, poison pill, fiduciary hierarchy, and a cultural fortress:

The cultural fortress is perhaps the hardest to capture part. How do you build an Incorruptible culture like Costco?

Eric takes inspiration from Jim Sinegal:

Compromising is "literally like taking heroin," one small price increase, stock goes up, you get hooked. Now it's baked into the forecast, so you have to do it again next quarter.

You have to avoid “financial gravity.”

3. How to structure a company from day one

Dario Amodei called Eric when Anthropic was still figuring this out.

Here’s the advice you can get from that call - without the access:

Seed Stage

1. Convert to a Public Benefit Corporation. Write the corporate mission directly into the charter. It is a legal structure that gives directors permission to pursue purpose even when it conflicts with shareholder value.

2. Designate a mission guardian. Someone with the authority and responsibility to keep the company on course. At Answer.AI, the founders hold that role.

Series A-B

Like Anthropic created and defended a Long-Term Benefit Trust, you will have to do the same.

Investors may want to change the terms. Hold the line.

Series C+

At larger companies, a mission lock vehicle can take the role of mission guardian:

This is what Answer.AI, Eric’s company did, and what Anthropic did.

Just remember, structure isn’t everything.

So that covers it on our short tour through governance for PMs. Now, two bonuses.

Bonus 1: How Eric Ries uses AI to write

The thing to avoid in the AI era is being called slop. How does Eric do it?

The book Incorruptible was written inside Solve It, the AI platform from Answer.AI. The approach is fundamentally different from prompting.

How it works:

1. Three block types. Notes (raw markdown you write), prompts (LLM instructions), and code (Python). You mix and match freely.

2. Context control. The LLM only sees messages above the current one. You choose what it knows.

3. Direct editing. If the model writes something wrong, instead of saying “try again,” you replace what it said with what it should have said. The model learns from the corrected version, reducing the mistake-correction cycle.

4. The writing style guide. A document loaded into every session listing phrases to avoid, patterns to follow, and the author’s specific voice characteristics. The generic AI gets banned before it starts.

Eric then took it to the next level. He had 600+ test readers leave structured comments, which the tool helped him organize and do months of daily turns on.

Bonus 2: The Lean Startup methodology in the AI era

No. But you have to understand what it actually is.

Build, Measure, Learn is a principle for operating under uncertainty. Its essence is:

• Learn as quickly as possible

• Turn every iteration into feedback

• Shorten the time between building, measuring, and learning

• Repeat faster than everyone else

The framework has been used on power plants, cancer therapies, even the long-term stock exchange. It works in the AI era too.

What matters is whether you can go faster than the convention in your industry.

If everyone else in your space takes 18 months between major iterations and you take 12, you have the advantage.

Claude Code is emerging as a standard partly because Anthropic’s product organization has unparalleled velocity.

Shipping velocity does more than accelerate learning. It compounds trust through two effects:

1. The cooking effect. When a company is shipping consistently, getting better visibly, you start to feel safe building on top of their platform. Your app will magically get better over time because their platform keeps improving.

2. The standardization effect. Once enough developers choose a platform, everyone else has an incentive to do the same. Enterprise customers sign multi-year contracts. Partners build around it. Hiring shifts toward it. Over time, the platform becomes the default choice.

And how does Claude Code ship so fast?

And that’s how we come full circle.

Get Transcript

Finally, here’s a one-sheeter you can print on governence:

Where to find Eric Ries

• LinkedIn

• Incorruptible

• Answer.AI / Solve It

• Long-Term Stock Exchange

• Lean Startup

PS1. If you want access to my AI tool stack - Airtable, Dovetail, Arize, Linear, Descript, Reforge Build, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.

PS2. Please subscribe on YouTube and follow on Apple & Spotify. It helps!

“I don’t prompt Claude anymore. I have loops running. My job is to write loops.”

That was Boris Cherny, the creator of Claude Code.

Peter Steinberger, who built OpenClaw also said something similar on X:

These two are arguably the best AI product builders in the whole world. So I’ve had a big question on my mind:

That’s the question we answer today.

Today’s Post

Today’s post is the result of 5 weeks of testing loops to figure out what the alpha really is for PMs:

1. What a PM loop actually is

2. 🔒 The 6 pieces of a great PM loop

3. 🔒 12 heavily tested PM Loops you can steal

4. 🔒 A skill to generate 6-piece loops that work easily

5. 🔒 How to maintain loops over time

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Before we get into the long deep dive, here’s a quick summary of what you need to know.

1. What a PM loop actually is

Loops fit the PM work you repeat:

• Weekly business review

• Weekly Sprint prep

• Monthly user interview themes

These are the types of things you may have built into Relay.app or Lindy before. Now they can run straight out of Claude Code or Codex.

Real Loop Example

Here’s an example. Most PMs in B2B companies only hear about sales deals in a Quarterly Review with sales. So they can’t react in time.

That’s the perfect use case to set up a Sales Monitoring loop with a simple prompt to Claude Code or Codex:

That’s it. That’s what a basic PM loop is. You’ll get a basic AI output like this:

I find that you don’t really ‘get it’ till you do that. So go build a loop now (use my prompt above or your own) before we go any further so you really grok it.

What should be a Loop

Ask yourself 4 questions. If the answer to all 4 is yes, you can make a loop:

Here are my suggestions:

That’s everything you need to build basic loops.

2. The 6 pieces of a great PM loop

What you’ll quickly run into with a basic loop like those in section 1 is:

1. The output could be better

2. It doesn’t have memory

After months of testing to solve these 2 problems, I’ve learned each loop needs 6 elements:

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Google is so different. They have case interviews and specific rounds, but literally nowhere else I have interviewed has them.

That’s one candidate I’m coaching, who’s interviewed at leading companies like Roku, Amazon, and Snapchat.

And the craziest part? Google’s PM interviews have just been updated.

I talked to 6 people: 1 candidate who has recently completed the Google process, 1 currently in the process, 2 current hiring managers, and 2 former hiring managers.

Then, I sat down with one of them, Gal Eshel, to put together the ultimate sequel to my Google PM interview guide.

Gal spent six years as a PM at Google and now coaches PMs through the process.

Today’s Post

We’ve recorded a full video for you all (complete with mock interview):

And written up an even more comprehensive deep dive:

1. What the 2026 Google PM loop actually looks like

2. How to crack the Google PM interview

   • What Googleyness actually is

   • The 1 skill that decides the offer

   • How to nail “Tell Me About Yourself”

3. How to crack Google AI PM roles specifically

4. A “Google Hiring Manager” AI agent you can use to practice

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Do you want deep coaching to crack Google? The fourth cohort of my Land PM Job program goes through everything:

1. PM Interviews and Job Search Mastery ($6,000 value)

2. AI PM: From Evals to Prototyping ($3,000 value)

3. PM Fundamentals + Leadership ($2,000 value)

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Here’s the most important takeaways from the article in an infographic. Save it. Print it:

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1. What the 2026 Google PM loop actually looks like

Everyone disagrees about what is tested in the Google PM Interview. I wrote a post saying they ask vibe coding interviews, then a Google Director commented they don’t. Some took this as proof I was wrong.

So I went and asked a Google AI PM Director, Satyajeet Salgar. Here’s what he said:

We used to put all PMs through a standardized interview process. Now, there are two ways into Google PM.

One is through that standardized process, which has no vibe coding round, and is followed by a team matching process.

The other is a specialized process, where the hiring manager has control of the process. Hiring managers in my org have used these rounds for vibe coding.

Turns out we were both describing real loops. Google’s standard process has no vibe coding round. But hiring managers control specialized loops, and some have used those rounds for vibe coding.

As for the standard process?

For instance, you know what you won’t encounter? Technical interview questions. I myself got a technical round in 2017: “How would you improve page load of Google Search?”

But that isn’t asked anymore.

I’ve got my hands on the official Google PM prep materials being distributed right now. Let’s take a close look:

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Today’s episode

If you’ve got an enterprise Claude subscription, you have access to one of the most powerful tools ever invented.

But at the same time, many PMs report using AI but not feeling any more productive.

Here to help is Jyothi Nookula. She just used Claude Code to beat 30 teams at her company’s hackathon. She’s getting productivity out of Claude that’s measurable.

And today she breaks down everything.

I’ve done plenty of advanced guides on PM OS, Team OS, and company OS. I’ve also broken down Claude Code for non-technical PMs and the Claude ecosystem.

Today’s piece is the first principles, bottoms-up explanation of everything Claude:

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Newsletter Deep Dive

As a thank you for having me in your inbox, here is the full workflow, aligned to the five layers of the Claude Ecosystem.

1. Models - when to choose which

2. Surfaces - when to use which

3. Knowledge - how to build it out

4. Integrations - how to manage them

5. Agents - the one’s to build

   • How to build an AI Chief of Staff

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The 5 Layers of the Claude Ecosystem

Claude has a dizzying array of features and surfaces. You can think of the Claude ecosystem as a stack of 5 layers:

1. Models at the bottom. These are the raw intelligence.

2. Surfaces sit on top of models. These are things like the the web app, desktop app, mobile app, or Chrome extension you actually type into.

3. Knowledge sits above that. This is your projects, skills, memory, and custom instructions.

4. Integrations comes next. Your MCP connections to Slack, Drive, and Jira; plus, your own files.

5. Agents and orchestration sit at the top.

You want to move up the layers one by one.

Layer 1 - Choosing the right model for the job

This is the first skill: knowing when should you which of the three models.

Here’s what Jyothi recommends:

She walked through each in detail:

Haiku is your speed machine. Cheapest, fastest, best when you need volume over depth. Triaging a pile of docs, tagging, quick classification, generating fifty variants of something. In the episode, Jyothi runs her scheduled automations on Haiku specifically to save tokens.

Sonnet is where ninety percent of PM work lives. The best ratio of quality to cost. PRDs, user research synthesis, competitive analysis, stakeholder briefs, roadmap thinking. Start here for almost everything.

Fable/Opus for the high-stakes reasoning. Genuine trade-off analysis, contradictory research, long-horizon planning where you need second and third order implications.

Layer 2 - Picking the right surface for the task

So you know the model. Now, what surface? Here’s what Jyothi recommends:

• Chat on the web is your thinking-out-loud surface.

  Quick questions, a smarter replacement for a Google search.

• Desktop is where the real work runs, because it can touch your files, your connected systems, and your scheduled tasks.

  Cowork and Claude Code both live here.

• Mobile is for kicking off a run and checking on it from a walk.

• Chrome Plugins is for computer use.

  Competitive research, pulling from sites agents cannot otherwise reach, and user testing where you tell Claude to behave like a real person and report back where your product confused it.

• Claude Design for decks and prototypes.

Upload your brand guide, a Figma file, or a GitHub link, and everything it generates matches your company’s colors instead of looking like a generic template.

It also has a real visual editor built in. You can mark up a slide, drop a comment the way you would in Figma, and Claude executes the edit (as shown in the above image).

In the episode, Jyothi mentioned that she built a CEO-level deck in the hour before a meeting that looked like it took her all day. For a quick design, Claude Design is enough.

• Claude Code when you want a clickable app that real users can poke at and give you feedback on.

Layer 3 - Build out your knowledge base

This is the layer Jyothi says PMs underinvest in most, and I agree.

It’s what turns Claude from a generic chatbot into something that actually knows your world.

We need to cover three pieces: projects, skills, and memory.

Projects are your folders

You want one project per role. A project should hold all your context through memory and custom instructions.

If you want to run three different things at once, make three projects, each carrying its own context as project instructions.

Skills beat prompts

A prompt is a one-time instruction. A skill is a saved playbook Claude reaches for on its own.

Jyothi’s customer interview synthesis skill is the template to steal. It has a when-to-use trigger, a checklist, and numbered steps: inventory the inputs, extract observations with citations so it hallucinates less, use the speaker’s own words, and keep behavioral observations separate from stated preferences.

Since early this year, skills can be multiple files. If your main skill file is one giant markdown doc pushing past 500 lines, you’re doing it wrong.

Two more things Jyothi flagged:

1. Write your skills yourself. Her read of the research: AI-written skill files underperform human-written ones. Draft with Claude, then layer in your domain knowledge, your template, your structure.

2. Update on drift. Review skills when your domain changes or when output quality slips. Declining output is your cue that the playbook went stale. Quarterly is a fine default.

The highest-value skills to build first: prioritization, PRD writing, customer interview synthesis, and turning support tickets into Jira issues.

Memory is the compounding piece

If you’re using projects in Claude web, then tell the project all of your context and tell it to commit to memory:

Here’s my context. Commit this to the project’s memory:

1. My role:

2. My company:

3. Our strategy (attached doc)

4. Our OKR attainment last quarter (attached doc)

5. The big features we’re going after:

If you’re using Claude Code, grab this memory system I built for you.

It captures as you work, with every claim traced to its source and conflict markers.

Layer 4 - Integrations

MCP is how Claude connects to the systems your work lives in. Slack, Drive, Jira, Gmail, your CRM, your analytics.

Two types:

1. Remote MCPs are the ones you click to connect. Customize, connectors, hit the plus, authenticate. Done.

2. Local MCPs run on your machine. This is how Jyothi’s chief of staff knowledge base works. We’ll build it below.

Which ones do you need? Look at the tools you already touch daily. Email, calendar, Slack, meeting transcripts, your Jira board, your analytics.

Jyothi’s most exotic connection at work: NVIDIA’s BioNeMo model for drug prediction.

Layer 5 - Build out agents to do things for you

There are three parts to agents that you want to build:

1. Automations that kill your busywork

2. Self-improving agent loops

3. A chief of staff

Let’s talk about each.

Agent Category 1 - Automations that kill your busywork

Start with Cowork. You need the desktop app and at least a Pro plan, around twenty+ dollars a month, and you can schedule Claude to run on its own.

Jyothi recommends a few standing automations:

• A morning brief at 9am wired to Calendar, Gmail, Drive, and Jira. It pulls the day’s meetings with attendees and attached docs, the inbox threads that need her, and the Jira items on her plate, then hands her the top three things to focus on.

• An end-of-day wrap at 5pm that reads the morning brief, checks what actually happened, and previews tomorrow. What shipped/missed, what is new.

• A standup brief on the active sprint. Done since yesterday, in progress, blocked, new, and sprint health, rendered as a clean dashboard.

Two things to know before you build.

These automations only run when your laptop is on, so time them for when you are actually working.

And this quietly replaces the Relay, Lindy, Gumloop, and n8n flows where one failed node kills the chain.

Agent Category 2 - Self-improving Agents

This is the ceiling, and it is where Jyothi’s hackathon win came from.

She went up against 30 engineering teams and won with one idea from an Anthropic blog post (Adversarial agents).

The idea comes from an older AI technique called GANs. One AI creates something. A second AI tries to find everything that's wrong with it. The feedback goes back to the first AI, which improves its work. The cycle repeats until the output is good enough.

Now apply the same idea to agents. One agent builds, another looks for flaws, and the first agent keeps improving until it passes your quality bar:

The promp to kick it off looks like this:

Agent Category 3 - Your AI Chief of Staff

Connect Claude to your sources and it can read your calendar and act on your Jira. What it still does not have is a model of your world.

It does not know who your allies are, which relationships are load bearing, or which message is too sensitive to send before you loop someone in.

A chief of staff closes that gap.

Jyothi built a personal agent that reads every meeting transcript and learns her org over time.

After one meeting, chief of staff told her to make a particular colleague an ally because they were strong in an area she was trying to grow into.

Another time it stopped her before she sent something and told her who to inform first. That’s not possible with just connectors, you need a knowledge graph of your working life.

How to Build It

The shape is straightforward:

• A context KB, structured into folders for people, meetings, documents, company, your priorities, and the patterns it spots over time.

  

• A set of extraction templates so a person profile captures how they operate and what they care about, and a document gets its metadata, key points, and relevance to you.

• Then an MCP server on top, so you can talk to the whole thing from the desktop app instead of digging through files.

Feed it so it compounds

The system is only as good as what you put in it. Rank your inputs by signal density and feed the richest first.

• Meeting transcripts are the richest source. Pushback, reactions, who went quiet, what got decided. Wire an automation so every new Granola or Google Meet transcript that lands in your inbox auto-logs to the KB.

• Key strategy docs you write and receive.

• Slack threads, where a surprising amount of real context lives.

There is a fair objection here - Log every email and every transcript and you will burn tokens.

That’s true.

The payoff is a knowledge base rich enough to connect dots you would have missed, and you can throttle it to only the sources that carry real signal.

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[Bonus] Takeaway

Here’s the things to remember to use Claude better. Print it out:

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Where to find Jyothi Nookula

• LinkedIn

• NextGen Product Manager

Go Deeper

1. To get access to my AI tool stack - Airtable, 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.

2. To get access to my AI PM customizations - PM OS, Job Search OS, and Prompt Library - become a founding subscriber ($250).

PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!

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Check out the conversation on Apple, Spotify, and YouTube.

Brought to you by: Arize

Trace, evaluate, and fix your AI agents before broken behavior ships to users.

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Today’s Episode

Meng To is one of the world’s leading AI design thought leaders, with 170K+ followers on X and 16K+ on Instagram. He told me something wild:

I barely use Claude anymore.

What does he find is more powerful? Codex. So today’s episode is a masterclass in designing with Codex.

I’ve covered the Codex harness, PM use cases, and design basics already. Today’s episode goes the next layer deeper:

I hope you enjoy the episode as much as I did.

Apple Podcast

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Want My Coaching?

You should know the story of Sourav Yadav from my Land a PM Job course. He closed 5 offers in 2 months and became Head of Product at Times of India.

I’m starting my next cohort next month. We’re teaching three courses:

1. AI PM ($3000 value)

2. PM fundamentals ($2000 value)

3. How to get a PM Job ($6000 value)

Here’s the syllabus:

This cohort features two 1:1s with me (first time ever).

So, what’s stopping you?

Apply now

Want a taste of the course? I’m putting on a free webinar on behavioral & AI PM interview mastery next Thursday. Sign up.

Newsletter Deep Dive

Now, thanks for having. me in your inbox. Here’s a complete guide to AI Design for PMs.

1. How to Setup Codex for AI Design

2. The AI Design workflow that works

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If you don’t have time for the full deep dive, here’s the most important takeaways.

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1. How to Set up Codex for Great Design

Codex is ChatGPT on Steroids

What makes Codex ChatGPT on steroids? Three things:

a. Context
b. Skills
c. Connectors

If you cover these correctly, you get a much more powerful harness for AI. So let’s briefly cover each.

1a. You need to build out great context

If you’re using my PM OS, you know this already. If you don’t, here’s what you need to do:

We recommend 1 folder per project, grouped under a single parent directory:

This works because you don’t have to re-explain yourself to AI all the time: what you do, what your company is going after, what your team’s product strategy is.

It all loads in for free.

Now here’s the trick: it’s a Goldilocks problem.

Too much undifferentiated context and the AI burns tokens figuring out what matters before it even starts the task you asked for.

Too little context and you spend forever iterating on the output when a bit more upfront context could’ve one-shotted what you want.

So get really good at managing that context. Even ask Codex:

1b. You need to enable it with the right skills

I’ve been talking about skills a lot. So you probably already know they are a lightweight MD of instructions. And you should have them for everything.

To design AI that’s not slop, you should know about this taste skill that has 60K+ stars on Github:

Also install the Playwright skill to take screenshots of UI for GPT to iterate on itself.

1c. You need to connect it to the right tools

You’ve heard about MCPs for the last year. Use all of the MCPs! PostHog, Linear, etc.

Designing for AI with Codex has two nuances when it comes to connectors:

1. You want to use plugins (not MCPs) to connect to Figma and Slack.

2. You also want to install ChatGPT Atlas so Codex can use computer use on websites for tasks that don’t have an API (eg browse LinkedIn).

How these three features come together

Context + Skills + Tools suddenly transforms your LLM into an agent harness that is much more powerful.

And two new Codex features make this more powerful than ever:

1. Codex Mobile sits inside your ChatGPT app and connects to whatever is running on your desktop, so you can check in on a build from a taxi or a coffee line.

2. The screenshot shortcut (cmd + cmd) lets you select any window and drop it straight into your current chat as visual context, no separate upload step required.

2. The AI Design Workflow to Avoid Slop

So now that you have the setup, here’s the workflow:

1. Plan with context

2. Build with the taste skill

3. Iterate with a human touch

It sounds simple but the power is in the nuance.

Step 1 - Plan with context

You’ve probably heard of plan mode. Use it!

The fastest way to lose an afternoon with Codex is to skip straight to building, since it commits to a direction within seconds and rarely doubles back on its own.

Plan mode forces a pause between your request and the AI’s first action.

Read that plan the way you’d read a junior PM’s spec before a kickoff. If something is missing, ask before approving. “Can we expand on this part” or “what if we add this constraint” costs you thirty seconds.

Give it the right context

Screenshots are the highest leverage form of context you can give the AI, more useful than a paragraph of typed description.

Two things are worth knowing here.

1. Simply select a window, hit the screenshot shortcut (Press both Command keys at the same time), and the image drops straight into your chat as the thing to fix or match.

2. If you have an app on different devices, run the app in a simulator, capture every screen in the flow, sign in, payment, editor, and hand the AI the full set so it can validate the design end to end instead of one screen at a time.

A screenshot tells the AI what you mean in one shot. A paragraph of description tells it what you think you mean, and those are not always the same thing.

Step 2 - Invoke the taste skill

The model matters less than the taste layer you give it. Use the taste skill from 1b and then look at the outputs.

Usually the skill will give you several - soft, brutalist, etc. If it doesn’t for whatever reason, prompt it:

Once you have a design you like, the next decision is output format:

• HTML is the fastest and most controlled option, the default unless you have a specific reason not to.

• Figma takes longer and assumes Figma fluency, but it’s the right call when a designer needs to pick up your file and keep iterating.

• Keynote or PowerPoint only makes sense when the final deliverable has to literally be a slide file someone opens in that app.

The rule of thumb is to keep your blast radius small. Every extra tool you add is a subscription, a login, and a context switch the AI cannot do for you.

Step 3 - Steer it mid-flow and iterate

One of the coolest features of Codex vs ChatGPT is once the build is running, you are not locked out!

You can steer mid-task, queue a follow up command while the current one finishes, or interrupt with new direction the moment you see it heading somewhere you didn’t intend.

So do that. And then iterate more on the AI output. The difference between slop and tasteful design is typically found in this final 20% of iterations.

And that’s it - now you’ve got design that isn’t slop!

Get Transcript

Downloadables to Take Away

1. Taste Skill - The Anti Slop Frontend Framework for Al Agents (60k+ stars)

2. The 15 tools running Meng To’s Al Workflow

   

Follow Meng Online

• Design+Code

• LinkedIn

• YouTube

• Aura

• X

Go Deeper

Tools:

1. To get access to my AI tool stack - Airtable, 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.

2. To get access to my AI PM customizations - PM OS, Job Search OS, and Prompt Library - become a founding subscriber ($250).

Podcasts:

1. How to Design with AI - YouTube | Spotify | Apple

2. How to Use Codex Like an OpenAI PM - YouTube | Spotify | Apple

3. The Ultimate Guide to ChatGPT Codex - YouTube | Spotify | Apple

Newsletters:

1. OpenAI’s Codex is the Best Way to Use ChatGPT

2. I spent 100s of hours building a PM OS for you

3. How to build product strategy in the age of AI

PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!

Refer a friend

Last month, your Claude setup worked perfectly.

This month, it feels like the AI is actively gaslighting you. It ignores your skills details and writes docs so lazy you end up rewriting them yourself.

If you’re like me, you find yourself scrolling through past chats, desperately trying to find that one version of a prompt that actually worked a few months back.

Where did it go? What skill version was it? Did I accidentally overwrite it when I updated my CLAUDE.md yesterday?

This is the exhausting reality of AI workspace drift.

When you don’t track your AI infrastructure, a single word shift can completely break your OS’s behavior. Top product managers are stopping this bleeding by moving their workspaces to GitHub.

Introducing Shubham Saboo

Shubham is a Senior AI Product Manager at Google and the mastermind behind Awesome LLM Apps - an open-source powerhouse with over 120 production-ready templates covering everything from multi-agent teams and MCP configurations to complex agent skills.

If his name sounds familiar, it’s because I recently wrote an entire breakdown piece on how his Github got him hired at Google. His repository has crossed an absurd 115,000 GitHub stars.

Today’s Post

Hannah Stulberg and Sidwyn Koh wrote an excellent GitHub 101 for non-engineers. My Ship Your First PR piece teaches the code PR workflow. Today, we are opening up the critical infrastructure layer that sits directly underneath both my PM OS and Team OS frameworks:

1. Three repos that cover 90% of PM work

2. The daily workflow: pull, branch, commit, push, review, merge

3. Four version control workflows for PM artifacts

4. The practice repo and your Monday move

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New to Claude Code? Quick glossary: skills are reusable instruction files for Claude Code tasks. CLAUDE.md is the persistent context file Claude reads every session. Eval criteria are scoring functions. These files change constantly. Managing them is what the rest of the piece covers.

If you’re new to GitHub entirely, I put together the web’s first guide on PM Githubs.

1. Three Repos, One PM

Imagine your laptop dies tonight. How will you access your important files?

You’ve got a CLAUDE.md. You’ve got skills. Maybe eval criteria, prompt templates, project configs. They’re scattered across your machine. Some in the PM OS folder. Some in project directories. Some are in Downloads because you grabbed them from Slack two weeks ago.

If you’re not careful, all of that effort goes down the drain. This is why you need GitHub repos. Here are three repos that cover your whole PM workflow.

You won’t need all three when starting. You only need Repo 1 if you’re just a beginner. Repo 2, when you have skills worth sharing. Repo 3, when you’re shipping code with the PR workflow.

Your Workspace (Repo 1)

This is your private workspace containing everything that requires your personal context. This is where your PM OS lives.

Inside this repository, you store your personal CLAUDE.md (pre-loaded with your specific stakeholder list, company strategy, and communication preferences), your private skills/ folder, and your autoresearch configurations. When you open Claude Code inside this folder, your PM OS architecture boots up instantly, making every single custom skill available on launch.

Why GitHub instead of just a folder on your machine?

Backup. Machine dies, you clone the repo on a new one. Back to full setup in 2 minutes. Same repo across machines, zero reconfiguration.

History. Every change to every file is tracked. When your CLAUDE.md was working better last month, you can see exactly what changed. When a skill breaks after a tweak, you restore the previous version in one command.

Phone access. Claude Code on the iOS app can read from your GitHub repo. You can start agents from your phone that read your CLAUDE.md and run tasks against your project files. Requires the files to be on GitHub, accessible from anywhere.

Your Shared Tools (Repo 2)

This is where your Team OS plugins live. It houses the skills, templates, and configurations that other PMs can fork, adapt, and run inside their own workspaces.

The core difference from your private workspace is simple: shared tools have all personal and company-specific context completely stripped out.

Hannah’s shared repo - her Team OS at DoorDash - has nested CLAUDE.md navigation files, shared skills (customer-call-summary, decision-log-entry, feature-launch-gate), analytics templates, and team context that any engineer, designer, or PM can query.

When her strategy partner started contributing, she cloned the repo and had the full team toolkit in 30 seconds.

On the public side, this category is exploding:

• Garry Tan’s gstack: 23 specialist skills

• Pawel Huryn’s pm-skills: 100+ skills across 8 plugins

• Carl Vellotti’s claude-code-pm-course: interactive Claude Code course

My Steal 6 Skills piece shows what shareable skills look like.

Your Projects (Repo 3)

These are projects you are currently working on.

PLANNING.md for the feature. Eval scoring criteria from the experimentation piece. The code. One repo per initiative because different projects have different collaborators and different lifespans.

When the project ships, the repo is the complete record of what was built, why, and how it was measured. PLANNING.md revisions, eval criteria updates, prompt iterations, tracked in the commit history.

The Decision Guide

How This Connects

Coming from PM OS? You already have the folder structure: skills, context library, templates. Git is what keeps that folder managed over time. The /upgrade-to-team-os command from the Team OS starter kit scaffolds the team structure around your existing PM OS, and it assumes your PM OS is already in a git repo. The setup in Section 2 gets you there.

Coming from Team OS? You know the architecture: shared context, shared queries, shared discipline. The GitHub mechanics underneath are what make it work. Team OS says, “push to GitHub so you have version control,” and “I’ve had to revert my own CLAUDE.md multiple times after changes degraded performance.”

The pruning workflow in Section 3 is exactly that revert process. Team OS’s Phase 2 (the team contributing shared skills via PRs) runs on the same PR workflow taught here. When Hannah’s strategy partner submitted her first PR, she was committing a skill change with a descriptive message, getting it reviewed, and merging it so the whole team got the update.

Team OS is the architecture. The workflows below are the plumbing.

2. The Daily Workflow

Seven steps. The same rhythm every time you change a PM artifact.

Every command works by talking to Claude Code in natural language. No git syntax to memorize.

Pull

Before you start working, get the latest version. If a teammate updated the shared repo overnight, you want their changes before you start editing.

Claude Code: “Pull the latest changes from main.”

Branch

A branch is your own copy of the files. Changes on your branch don’t affect anyone else until you merge.

Name it after what you’re doing.

Claude Code: “Create a new branch called improve/prd-reviewer-v7.”

For solo work on your private workspace, you can skip branching and work directly on main. Branching matters most when you’re sharing a repo with your team (Team OS).

Edit

Make your changes. Update the skill file. Add a line to your CLAUDE.md. Modify the eval criteria.

The actual work happens here.

Commit

A commit saves a snapshot of your work with a message explaining what you did.

The message matters. “Update” tells you nothing in three months. “Add behavior contract check to PRD reviewer” tells you exactly what changed and why.

Claude Code: “Commit my changes with the message: add behavior contract check to PRD reviewer.”

Commit after every meaningful change. Not after every keystroke. Not after a full day of work. After each change you’d want to be able to roll back to individually.

If you’re unsure, commit more often.

Push

Upload your commits to GitHub. Until you push, your work exists only on your machine.

Claude Code: “Push my changes.”

After pushing: backed up, accessible from other machines, visible to teammates if the repo is shared.

Pull Request (PR)

A PR says: “Here are my changes. Please review before they become official.”

For PM artifacts in a shared repo, the review is about quality, not syntax. Does this skill improvement actually produce better output? Does this CLAUDE.md change follow team conventions?

Claude Code: “Open a pull request.”

Write a short description of what changed and why. The CONTRIBUTING.md in the practice repo has a template.

For your private workspace, you can skip PRs and push directly to main. PRs matter when the repo is shared.

Merge

Reviewer approves. You click merge on GitHub. Your changes are now part of the official version. Delete the branch.

Working solo? Merge your own PRs. The PR still serves as a record of what changed and why.

Seven steps, same rhythm every time. Pull, branch, edit, commit, push, PR, merge.

Hannah’s GitHub 101 goes deeper on each step with screenshots and troubleshooting. Ship Your First PR applies this cycle to code changes specifically. The four workflows in the next section build on top of this rhythm.

🔒 Below, you’ll read:

• Loop Engineering for your workspace asset
• The exact repo structure Aakash uses
• Four version-control workflows that save hours every month (skill rollback, CLAUDE.md pruning, autoresearch tracking, eval criteria versioning)
• A practice repo you can fork today
• The PM GitHub starter kit

If you’re serious about building a long-term AI operating system, the rest of the guide is for you.

Read more