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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:
* The AI PDLC they embraced
* The AI Harness they created in Cursor, so it can run on any models
* How the PM role shifted to Product Builder
If you’re a product leader, this is a great example of how to become AI-native.
If you’re a PM, it’s a great harness (using Grok models!) to learn from.

I hope you enjoy it as much as I did:
Apple | Spotify. | YouTube

I’ve written up the key takeaways for newsletter subscribers as well.
1. The AI PDLC they Embraced
How do you go from shipping every 6 months to every 2 weeks?
It’s not about becoming AI first! Most teams try that: they open Cursor or Figma Make and start prompting.
Srini made the point that it actually all begins with being Data First:
When you’re data first, you create the right foundation to actually be AI first. At Freshworks scale, with 300 million end users, you can’t afford a hallucination.
For Freshworks, that was three things:
* A design system structured so agents can parse it
* Coding standards written down explicitly
* A single repo as the source of truth
They wrapped that all into a system they call Prism. It’s a knowledge hub that knows the product and its dependencies inside out, plus a context hub that passes feature context between phases, and a central library of what they call AI builder artifacts. These are the skills, rules, commands, and agents that describe how Freshworks specifically builds.
The AI PDLC sits atop all of that. And it looks like this:
It’s the same lifecycle every SaaS company runs from discovery through release, with two changes. First, there’s a governed AI agent working inside each. Second, there’s an evals phase at the end.
And that’s how their release cycle went from 6 months to 2 weeks.
But even in the episode, there were some cracks. Figma Make skipped a few design system components. Srini noted that those are the places where humans still have a role.
2. The AI Harness in Cursor they created
I began in engineering. After 14 years of not touching code, now I’m spending a lot of time in Cursor.
Srini is now spending lots of time in Cursor, and he showed us. Everything starts with a slash command, /fw-innit. Here’s what that kicks off:
The harness asks for the business unit, Epic ID, and feature team. Then it runs 12 phases, all the way from idea brief through to prototyping and QA.
Srini did the whole demo on Grok models! He explained:
I used to use Claude, but Grok seems to be working really well, and it’s really really fast. Nothing takes more than 10-15 seconds.
And indeed, that’s what we found. If you really need speed, consider Grok.
He live built a performance dashboard for their new EX Agent Studio. After he described the feature in plain English, the agent came back with questions a good PM would ask:
* How deep should the drill down go?
* Who is the primary persona?
* What does success look like in 6 months?
It then wrote its own SQL against Bel, their Databricks data lake with usage from all 75,000 customers. It assembled this into quantitative evidence in the PRD:
It looked across all 4,358 active ITSM accounts and showed its work.
At the end of the process is a step called CPO check which reviews the draft the way Srini would. His team named it after him.
From there, the PRD goes straight into Figma Make. Because of being data-first, they have a design system built into it. And after a few prompts we had a working prototype:
That’s the end-to-end process. No Claude Code or Codex. Just Cursor + any model.
3. How the PM role shifted to product builder
Which brings us back to where we started: what roles will execute work like this in the picture?
Srini manages 200+ people with titles like PM, UX researcher, and designer. His prediction is:
The titles Product Manager, Product Designer, and Engineer will go away in the next 5 years.

He didn’t say that to get clicks on Social Media. That’s how he’s running Freshworks. When he joined, Freshworks ran 1 PM and 1 designer for every 10 to 20 engineers.
Now, their new workflow is 1 PM to 1 engineer. Some teams have no dedicated designer at all.
Here’s why: those titles marked stations on an assembly line. The PM would write the PRD, hand it to design, and design would hand it to engineering. Now, like in section 2, one person can do the whole assembly line.
If you want to prepare for this future, I’ve put together the roadmap:
Pay close attention to step 8. Since one person can now run this assembly line, that person also owns whether the thing made any money.
In the full episode, he also covers how Freshworks’ agent builder works, how CPOs can replicate his playbook more. Check it out: Apple, Spotify., YouTube.

Get more of Srini
Freshworks is hiring and you can find Srini on LinkedIn. He also generously shared his deck.
Since the episode was recorded, Srini actually left Freshworks. With his tenure as CPO at RingCentral, SVP at Five9, and Director at Cisco, I’d expect you see him at another high-powered role soon.
Go Deeper
* Hear from more CPOs on becoming AI native: Rachel Wolan, Jiaona Zhang
* Learn more about becoming a Builder PM: Mahesh Yadav
* Explore other Coding Harnesses: Codex, Claude Code
PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!


This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe

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

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



This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe

Check out the conversation on Apple, Spotify, and YouTube.
Brought to you by
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* Product Faculty - Get $550 off their #1 AI PM Certification with code AAKASH550C7
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Today’s episode
Every startup founder picks up Zero to One. Reads the chapter on Delaware C-Corps. Files the paperwork. Moves on.
That paperwork will outlast every product decision they ever make.
I sat down with Eric Ries, the man who created Build, Measure, Learn. NYT bestselling author of The Lean Startup. Co-founder of Answer.AI with Jeremy Howard. Founder of the Long-Term Stock Exchange. Dario Amodei called him before Anthropic’s seed round.
His new book Incorruptible drops May 26. It is the blueprint for building a company that the financial system cannot capture.
He also demoed live how he wrote the book using Solve It, the AI platform from Answer.AI. Not prompting. Not generating. Editing the model’s responses directly.
If you are building anything you want to outlast the next funding round, this is the one episode to watch.
Check out the episode on Apple Podcast and Spotify.
If you want access to my AI tool stack, grab Aakash’s bundle.
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Key Takeaways:
1. Governance has four dimensions - Compliance is table stakes. Purpose, coherence, and integrity are the three most boards ignore. Companies that nail all four outperform the market over decades.
2. Financial gravity destroys good companies - The unconscious reflex to comply with the values of those who have more than you. Jim Senegal called it heroin. You compromise once and it gets baked into the forecast.
3. Costco's governance fortress is the blueprint - Staggered board terms, poison pills, fiduciary hierarchy. $10K at the Costco IPO is worth $8.7M today versus $151K in the S&P 500.
4. Stone does not enforce itself - Johnson & Johnson carved values into limestone. Asbestos ended up in the baby powder. $10B settlement. Structure protects ethos but does not create it.
5. Mission lock vehicles create 6x survival - A separate entity holding the for-profit board accountable. Novo Nordisk, IKEA, Patagonia, Hershey, Vanguard all use this structure. 60% survival to year 50 versus 10%.
6. Anthropic's LTBT took two years to defend - AI safety experts appoint board seats. The trust gains power as the company hits milestones. Structural protection is why Anthropic can afford to be courageous.
7. Public Benefit Corporations write mission into the charter - Legal permission to pursue purpose over shareholder value. Not the B-Corp certification sticker. A legal structure.
8. LLMs are conformity machines - They produce the center of the outcome distribution. For competitive advantage you must change how you use AI.
9. Solve It enables human-in-the-loop writing - Edit the model's responses directly. 600 test readers, 10K structured comments, Python scripts organizing feedback per chapter.
10. Build Measure Learn works at any timescale - Not about absolute speed. Relative velocity versus your industry convention. The AI labs that release more quickly create decisive trust advantages.
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Where to find Eric Ries
* LinkedIn
* Incorruptible
* Answer.AI / Solve It
* Long-Term Stock Exchange
* Lean Startup
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Related content
Podcasts:
* AI Product Strategy with Aman Khan
* The Marty Cagan Episode on Product Management
* How to Succeed as a Head of Growth with Dan Olsen
Newsletters:
* AI product strategy in 2026
* How Cursor grows
* PLG in 2026
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PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!
If you want to advertise, email productgrowthppp at gmail.


This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe

Today’s episode
“This one’s too complex. I’m still stuck on ChatGPT.”
I get some version of that DM every week, usually right after I publish something on the PM OS or the Team OS. And every time, I feel it, because those guides do assume you’re already up and running.
So I made this episode for the person sending the message. Jyothi Nookula has been an AI PM since before that was a title, through Netflix, Meta, and Amazon, and I asked her to take a PM from zero to eighty on the entire Claude stack in one sitting.
She walks through:
The five-layer Claude stack, so you finally know which surface and which model to reach for and when
A chief of staff you build in Claude Code that reads your meetings and quietly learns your org, your people, and your politics
The self-improving agent loop she used to beat 30 engineering teams at an internal hackathon, as a PM
Everything she shows is something she runs at work, which is the only reason any of it holds up.
Send this to the PM in your life who keeps saying they’re behind. Two hours from now, they won’t be.
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Brought to you by:
Hyper Agent: Turn your recurring PM work into reusable agents
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If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, Relay.app, Magic Patterns, Speechify, Bolt.new and Mobbin - become an annual subscriber ($150), and grab Aakash’s bundle.
If you want access to my AI PM customizations - PM OS, Job Search OS, and Prompt Library - become a founding subscriber ($250).
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Key Takeaways:
1. Match the model to the job - Sonnet handles ninety percent of PM work at the best cost. Save Opus for genuinely hard reasoning, and hand fast bulk jobs to Haiku. Defaulting to the smartest model for everything just burns time and money.
2. Stop skipping the knowledge layer - Projects, skills, and memory are what make Claude know your actual work instead of guessing from a blank slate. Almost everyone underinvests here, and it is the difference between a chatbot and an assistant that knows you.
3. A skill beats a prompt - A skill is a saved playbook Claude picks up on its own when it fits the task. It only loads when needed, so it never clogs the context window. Build one once and stop re-explaining the same task forever.
4. Write your skills yourself - Human-written skill files consistently beat AI-written ones. Draft with Claude to move fast, then layer in the domain knowledge only you have. That last step is what makes it actually work.
5. Automate your time based work - A morning brief, a standup summary, and an end-of-day wrap can all run on a schedule while you sleep. You walk in already knowing what needs your attention. It clears the busywork that eats your mornings.
6. Give your automations guardrails - Cap the length, tell them to stick to facts, and never let them hallucinate. Left unchecked, an AI brief will pad itself and invent things. A few hard rules keep it sharp and trustworthy.
7. Build a chief of staff that learns your org - Point Claude at your meeting notes and let it build a picture of your people, your priorities, and your politics over time. Feed it transcripts first, since they carry the richest signal. It compounds into something no generic chatbot can match.
8. Keep that knowledge base on your own laptop - Your most personal work data does not belong in someone else's cloud. When you leave a company, it walks out with you. You keep full control of your most sensitive context.
9. The PM job is changing fast - The ratio is shifting from one PM per eight engineers toward two PMs per one. Building is becoming part of the role, and the PMs who can ship are pulling ahead.
10. Building is easy now, taste is scarce - When anyone can build, the edge moves to knowing what is worth building and what good actually looks like. That judgment is the one skill you cannot download.
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Related content
Github repo: https://github.com/fibbonnaci/ai-builder-skills
Podcasts:
How to Become an AI PM - YouTube | Spotify | Apple
How a VP Uses Claude Without Producing Slop - YouTube | Spotify | Apple
This CPO Uses Claude Code to Run His Entire Work - YouTube | Spotify | Apple
Newsletters:
I Built You Memory for Claude Code, Hermes, and OpenClaw
I spent 100s of hours building a PM OS for you
How to build a Team OS in Claude Code
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Where to find Jyothi Nookula:
LinkedIn: https://www.linkedin.com/in/jyothinookula/
NextGen Product Manager: https://nextgenproductmanager.com/

Where to find Aakash:
Twitter/X: https://x.com/aakashgupta
LinkedIn: https://www.linkedin.com/in/aagupta/
Newsletter: https://www.news.aakashg.com
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PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!
If you want to advertise, email productgrowthppp at gmail.



This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe