The Growth Podcast

Today’s Episode
90% of teams have adopted AI, yet 56% of CEOs say they saw no major financial benefit. Both metrics are accurate, and I’m pretty sure you fall somewhere between them.
With new AI tools flooding the market every week, it is safe to say that every team has its own AI setup by now. But are any of those setups in sync with each other?
I’ve been building this argument in stages. First, Carl Vellotti showed you how to build a personal OS, and Hannah layered it with building a team OS. Jiaona and Mikhail vouch for a full-blown company OS as well.
So, today you get a screen share.
My guests are the product team at Together AI that raised $800M at an $8.3B valuation and sells inference and fine-tuning to developers. Charles Zedlewski is their CPO who brought Necoline, Pavneet, and Hassan on the call to talk about how to build a shared context repo.
Their customers are already agents and their engineers are already agent-first. Their product team had no choice but to catch up.
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10 Key takeaways
1. Individual productivity can move a company backwards - The team's starting question was not how to make each person faster. It was whether everyone generating unlimited code and content actually added up to progress. Charles called the failure mode flooding your coworkers' context windows, where everyone launches slop at each other.
2. The shared repo holds context and skills, not code - Markdown and YAML files covering customer intelligence, sandboxes, and the output of strategy meetings broken down by mission and milestone. Anything tied to a specific codebase stays out of it. The point is that a PM can read another team's context and draft a real proposal before taking up that PM's time.
3. Skills live closest to the work they touch - If a skill references code inside one team's repo, it stays colocated there. Everything else goes to a personal or shared repo. Test it on a branch, use it a few times, and only push to main once it proves repeatable. Niche ones never get pushed.
4. Shared context is a hierarchy, not a flat pool - The team abandoned the idea that everyone should carry everyone's context. Most people have no motivation to learn the depth of someone else's area. They want the one answer they came for. Some people live at the bottom of the hierarchy, most just traverse the top.
5. The PRD stopped being a gate - Historically it was the document everyone aligned on before building started. Together treats it as a trigger for ideation and problem solving instead. One to two pages, defining the customer problem, a few solution options, and a sample user journey. That is enough to argue about whether the thing is worth building.
6. A prototype replaces the bulk of the long document - A separate skill takes the one pager and produces a prompt for a design tool, and that visual is where the sharpest feedback shows up, from engineering and marketing alike.
7. Discovery collapsed from half a day to five minutes - The research agent pulls from the support platform, the project tracker, and internal docs at once. It surfaced 19 tickets filed in two months, flagged that the feature had been partially built and abandoned, and gave verbatim quotes with sources. The value is not the summary. It is not duplicating work someone already started.
8. Automate execution, keep decisions human - Defining the feature, the API surface area, and the abstraction layer stay hands on. Code writing is the part that runs on its own. The PRD skill is explicitly instructed to challenge the PM's assumptions rather than accept them.
9. Agents are already the majority user, so validate for them - Agent evals spins up a sandbox, gives an agent a real task against the product, and watches. It caught that agents could not find the fine-tunable models page because it was not linked from the quick start. Dozens of docs fixes came out of this. Charles calls agent success the new bar for UX.
10. They refused to oversell the gains - No story points, so no proof, but velocity is up more than 5%. Charles finds 3x claims suspicious, because discovery, debate, and coordination do not get magically better with AI. Costs stayed sane partly through open weight models, partly because optimizing for collective output never produced the runaway token budgets others report.
Go Deeper
As promised, I put together this section to help you become the best version in your product management journey. If you’re starting from zero on this, begin with How to build a Team OS in Claude Code with Hannah Stulberg, then scale it up with How to build a Company Operating System with Hermes and OpenClaw. For the skills layer specifically, 3x CPO Oji Udezue on the Essential Claude Skills for PMs is the one to read. And if section 3 sold you on agent evals, go to How to Build Frontier-Lab Quality Evals with Daniel McKinnon. Finally, Pavneet’s take on the one-page PRD lines up almost exactly with what Srini Raghavan showed from Freshworks.
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If you want to advertise, please email - productgrowthppp[at]gmail[dot]com.


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

Today’s episode
Most PMs have automated something with AI by now. A PRD review. A weekly status update. The problem is that every session starts from scratch.
Loops are what fix that. A skill that runs the same way forever is just a skill. A skill that takes the log of what happened and rewrites itself is a loop. Everyone keeps saying loops are the new prompts. Almost nobody shows you how to build one.
Tyler Folkman is Chief AI Officer and Head of Product at JobNimbus. In this episode he builds a loop live on screen. He runs a preflight check on his own podcast recording, spins up three prototype variants while answering questions, writes a decision skill from scratch, and closes the loop on camera.
He also covers the loops every PM should be running, the hooks that stop Claude from doing real damage, and the point where vibe PMing breaks.
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10 Key takeaways
1. A skill becomes a loop when you feed the learning back. A static skill sits there and stays the same forever. The loop closes when you hand the AI the log of the whole session and ask what to improve. Skip that step and you are just running a skill.2. The gate is the most important part of the loop. Fetch inputs, do work, pass a gate, write the artifact. The gate is where you validate the work was correct, and making it deterministic matters more than anything else in the chain.
3. Agents make decisions, prompts wait for you. The difference is not the model. An agent gets a task plus a way to verify itself and executes as far as it can alone. Prompting keeps you in the loop, which caps how far you can scale yourself.
4. Write the first version of a skill by hand. Human authored skills tend to beat AI authored ones because you know more about what you actually want. Once you are on the AI loop it moves fast enough that injecting your own thinking gets hard, like promising you will still pedal on an ebike.
5. In product, the gate is a customer, and that breaks the loop. Code loops run fast because the gate is a test. You cannot lock customers in a room and iterate on them. The workaround is mining the research calls and transcripts you already have to build a cheap first filter.
6. Prototypes are free now, so generate variants instead of one answer. The internal standard is at least three variants per idea. One minimal, one full featured, one creative. Then narrow a hundred ideas down to five before anyone talks to a real customer.
7. Synthetic customers filter, real customers decide. Customer research transcripts loaded into a warehouse let AI inspect a prototype as your customer. It is not a high bar, but it catches low hanging failures fast, which is the whole point of a gate.
8. Write docs for AI, keep human docs to three pages. Part of onboarding is not written for people to read. It is context so the AI can answer questions. Anything a human is expected to read should be one to three pages, visual, and cut down by hand.
9. Hooks add determinism that a prompt cannot. Telling Claude never to delete everything or never to share credentials only works if it reads that instruction today. A hook fires on the bash command itself. Session close hooks can also force the improvement step you would otherwise forget.
10. Ship AI output you have not thought about and it costs you. Passing unreviewed AI work up the chain just moves the effort to someone busier than you. Answering a question in a meeting with what Claude said is the failure mode. Use AI to push your thinking, not to replace the part where you do it.
Related Content
If you don't know where to start on Claude loops, I got you covered with my ultimate guide on Loops for PMs. And then you can follow it up with The Complete PM Guide to /goal in Claude Code. Tyler mentioned the evolving role of PMs and how it is shifting into a Product Builder role, so you can read all about it in my How to Become a Builder PM deep dive.
If you want to advertise, please email productgrowthppp[at]gmail[dot]com. 
P.S. Please subscribe on YouTube and follow on Apple and Spotify. It helps!


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

Today’s episode
Today I’m showing you how to build a company operating system, with Mikhail Shcheglov, CPO at OLX Classifieds.
After 5 months of continuous building on OpenClaw and Hermes, his entire product team now runs on it.
His knowledge graph covers 54% of the company's product, business and customer context. That is a number he tracks as a personal KPI. At that level the agent already makes backlog decisions. Stakeholders pitch feature requests to it before they’re allowed near a PM. It runs his email, his calendar, his recruiting funnel and his design system.
He opens his IDE on camera and shows all of it. 2 findings cut against everything you’ve been told. Summarizing your meeting transcripts costs you 20-25% recall, so he stores every single one raw. And letting Hermes write its own skills off repeated tasks produced a 31% accuracy lift in controlled testing.
We also get into something heavier than architecture.
What happens to PM headcount when one PM covers four domains? And what does a CPO actually screen for now when hiring?
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Key Takeaways
Context coverage is a CPO level KPI - Mikhail tracks what percentage of the company's industry, business model and customer knowledge his agent actually holds. It sits at 54%. That is enough for it to operate like a junior to mid PM and make backlog calls. At 70 to 90% he expects strategy level work.
The real problem AI solves is knowledge leakage - A domain expert leaves and takes five years of context with them. Every company has this hole and almost nobody measures it. One store of business, customer, product and technical knowledge closes it, and the better your AI knows that context the more you can hand it.
Do not summarize your transcripts - Summarization cost them 20 to 25% recall. You lose the granular detail where the answer usually lives, and you force every conversation into a template it was never shaped like. Store everything raw.
Memory needs three layers, not one - A knowledge graph for structure, a vector database for fuzzy retrieval, and raw daily transcripts in MD files. Exact keyword matching fails on most real queries because real questions are ambiguous. The vector layer carries the load.
Auto generated skills lifted recall by 31% - Hermes watches what you keep asking for and decides on its own that a skill is worth writing. Tested across five core topics with ten questions each, control group against treatment group. Plus 31% accuracy.
Imperatives matter more than prompts - Their rules file runs 700 lines. No fabrications. Think before you act. Facts over guesswork. And a ban on what he calls fake helpful, where the agent can't do the thing so it explains how you could do it yourself.
CLAUDE.md stays short, SOUL.md goes long - CLAUDE.md holds under 100 lines and carries the highest priority. SOUL.md runs 800 and sits second. Some of those 800 lines contradict each other and it still produces his most accurate output, because every imperative gets tested against real queries.
Make the agent the gatekeeper - Stakeholders are trained to pitch the agent first. It asks clarifying questions, checks the request against priorities already set, declines politely if it doesn't clear the bar, and routes it to the right PM if it does. The org chart is mapped internally so it knows who owns what.
Half of PM time is process, not thinking - Weekly reports, stakeholder updates, demos. Delegate that layer and one PM does the work of two, pointed entirely at discovery. He now runs one PM across three or four customer facing domains, and only keeps dedicated owners on monetization and search.
Own the agent yourself or lose the advantage - Feedback arrives daily and he pushes changes from his phone straight into the repo. Hand it to an AI ops hire or an engineering team and you keep the tool but lose the speed. Nobody without skin in the game iterates fast enough.
Related content
I’ve already created all of the resources Mikhail mentioned in this episode. But I never mapped them all to one place. That changes today in this edition. PFA the map sorted by the parts you want to build.
Memory first, I Built You Memory for Claude Code, Hermes and OpenClaw
Once you've got a memory layer, put it in version control. GitHub for PMs has the 3-repo setup I use for skills and eval
The Hermes Agent Guide for PMs is a 20-minute setup and a 30-day rollout with persona and skill templates included
For OpenClaw, start with Mahesh Yadav on becoming a Builder PM
For the org-wide version, watch my episode with Jiaona Zhang, CPO at Laurel, on building a Company OS
My conversation with Hannah Stulberg of DoorDash on building a Team OS is the same thing one floor down, with a free starter kit
👨‍💻 Where to find Mikhail Shcheglov:
LinkedIn: https://www.linkedin.com/in/scheglovm1/
Substack: https://corpwaters.substack.com/
👨‍💻 Where to find Aakash:
Twitter: https://x.com/aakashgupta
LinkedIn: https://www.linkedin.com/in/aagupta/
Newsletter: https://www.news.aakashg.com/
If you want to advertise, email productgrowthppp@gmail.com.


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

Check out the conversation on Apple, Spotify and YouTube.
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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:
Bolt.new - Ship AI-powered products 10x faster
Product Faculty - Get $150 off their #1 AI PM Certification: code AAKASH150
Customer.io - Send smarter messages using your product data
Ariso - Ship AI agents and features faster, with fewer regressions
Land PM Job - 12-week live course to master the PM job search
Get More of Oji
LinkedIn
ProductMind
The open-source skills repo
Shipyard
Building Rocketships
Go Deeper
I’ve done extensive testing on what makes a good skill and how to build a PM OS for paid newsletter subscribers (that go much deeper than this podcast).
→ Get the founding plan to get access.
I also have several free podcasts on Claude Code that you may find helpful: my 3 part series with Carl Vellotti, PM OS with Dave Killeen, Team OS with Hannah Stullberg, Company OS with Jiaona Zhang, and Claude Code for CPOs.
→ To never miss an episode, subscribe on YouTube and follow on Apple & Spotify.
Finally, in my live course, I teach you how to do extremely advanced PM automation Claude Code.
→ Join us.