Today, I want to share a new episode with Ryan Wiggins.

Ryan is the VP of Product at Mercury, my favorite fintech product. We covered three things that are on every AI builder’s mind — how to make your product agent-ready, how to build a Claude Code second brain, and what Mercury’s data reveals about OpenAI and Anthropic’s race for the enterprise.

Watch now on YouTube, Apple, and Spotify.

Ryan and I talked about:

• (00:00) Why APIs and MCPs are the primary interface

• (04:40) How to get your product agent ready

• (07:51) Will MCPs cannibalize your app DAU?

• (10:36) How to decide between MCPs and CLIs

• (13:10) Inside Ryan's Claude Code second brain

• (18:40) Getting AI to coach you after every meeting

• (20:46) Mercury data: Are startups switching to Anthropic?

• (23:05) The product development process will never be the same

P.S. Mercury is offering Behind the Craft readers 1 year of free personal banking. Just click the link to get the discount.

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Top 10 takeaways I learned from this episode

How Ryan built his Claude Code second brain

1. Ryan built a Claude Code “second brain” trained on 5 years of work history that helped him 2x his productivity as a product leader. He did this in 5 phases:

   1. Create a profile. Have Claude interview you and write a me.md file capturing your role, goals, working style, and growth edges. This is the foundation.

   2. Build the knowledge base. Save your most important docs — specs, retros, performance reviews — in a local folder. Then ask Claude Code to install QMD (Shopify CEO’s vector search tool) so it can reference these docs.

   3. Distill the data. Ask Claude to create summary files using me.md and the knowledge base. For example:

      1. strategic-context.md — what my company/team is trying to do and why

      2. personal-growth.md — patterns in my feedback, coaching themes

   4. Wire up auto-context injection. Create a Claude Code hook that pulls relevant context from the files above into every prompt.

   5. Create the learning loop. Build a /learn skill that updates Claude.md after each session, plus cron jobs for morning briefs and monthly retros.

Ryan uses this to drive team priorities, make decisions, and self-reflect. It’s the most sophisticated personal OS I’ve seen, so I asked Ryan for a full 4,000+ word prompt that you can paste into Claude to set this up for yourself in 5 steps:

I want you to help me build a "Second Brain" — a persistent knowledge system that runs in parallel to my work using Claude Code. We'll do this in 5 phases. Walk me through each one interactively. Don't skip ahead — confirm each phase is working before moving on.

## Phase 1: Interview Me & Create my Profile

Interview me to create a file at ~/Documents/second-brain/me.md that captures:
- Who I am (role, company, responsibilities)
- What I'm optimizing for (goals, priorities, what success looks like)
- My working style (tools I use daily, how I communicate, what frustrates me)
- My growth edges (feedback I've gotten, patterns I want to break)
- What I care about outside work (interests, values — helps with recommendations)

Ask me 5-7 questions conversationally. Don't make me fill out a template. Write the file when you have enough.

## Phase 2: Build the Knowledge Base

Read more

Today, I want to share a review of Claude Design, Anthropic’s new AI design tool.

In the tutorial below, I ran through 5 real demos live using Claude Design to create videos, slides, websites, apps, and even a design system.

Watch my tutoral now for the complete walkthrough.

Timestamps:

• (0:00) Why Anthropic is coming for the full knowledge work stack

• (1:12) Turn any post into an animated video

• (4:17) Pro tip for creating beautiful slide decks

• (7:27) Recreate any landing page in one prompt

• (9:21) Build a 3D rotating globe and text particle effects

• (10:53) One-shot a mobile fitness app with play testing built in

• (12:46) Generate an Apple Liquid Glass design system

• (14:28) 3 takeaways on where AI design is headed

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Anthropic is eating the knowledge worker stack

Anthropic is quietly building an AI native version of the entire knowledge worker stack. They have:

1. Claude Code for coding

2. Claude Design for design

3. Claude Cowork for documents and more

The common thread across all three products is simple:

Code is the universal medium for all knowledge work.

Videos, slides, websites, apps, and animations — they’re all just code under the hood. And code is now free, or at least very cheap.

This means Claude now competes with both incumbents like Figma and Microsoft Office and AI startups like Replit, Lovable, and Gamma.

But how good is Claude Design? Let’s run through 5 real use cases: animated videos, pitch decks, landing pages, mobile apps, and design systems.

1. Animated video

Let’s start with video. Open a new project in Claude Design and paste in this prompt:

Make an animated video for Claude Design featuring this content: (Paste in the Claude Design announcement post or any text you want)

Claude will ask you clarifying questions first — how long the video should be, what tone and aspect ratio you want, and more — before making the video. It’s the same behavior as Claude Code when you ask it to build something:

Here’s the video Claude Design made in one shot:

This would’ve taken hours or days to do manually but Claude did it in just a few minutes. Because the video is just code, you can also manually tweak the color palette and even turn captions on or off.

The only flaw is that you can’t export the file as a video yet, so I had to use Mac’s screen recording to save it.

2. Slide deck

Now, let’s try using Claude Design to make a slide deck. When using AI to make a deck, I like to use regular Claude to iterate on the copy for each slide first:

Next, I went to Claude Design, selected Slide Deck, and prompted it:

Make a slide deck with this copy: (TEXT)

Here’s an example slide that it made:

This looks OK but the overall deck feels too static and boring compared to the video we just made. So my pro tip for creating beautiful animated slides is:

Read more

Today, I want to share 15 observations from my first trip to China in 10 years.

I just came back from a 2-week trip to Shanghai and Hangzhou and it was eye-opening to see how the world’s 2nd largest economy operates today.

You can’t understand the AI race without understanding China and I think every product builder should visit at least once. I hope this post gives you a taste of what it’s like to live and work there.

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Chinese AI work culture isn’t for the faint of heart

1. 996 is real, but many tech workers show up and work even later. Employees at many Chinese AI startups roll in around 11 AM and work until 11 PM with lunch, dinner, and boba breaks in between.

2. Top AI labs run on young, single talent. The workforce at top AI companies in China is overwhelmingly people in their 20s and early 30s. They don’t drink, smoke, or party much — many just work and order food and boba delivery to the office. In many ways, this looks a lot like AI work culture in the Bay Area.

3. Beijing, Hangzhou, and Shenzhen are the AI tech hubs. Beijing has Z.ai’s GLM, Moonshot’s Kimi, and ByteDance’s Doubao. Hangzhou has Alibaba’s Qwen and DeepSeek while Shanghai has MiniMax. Shenzhen is the global hub for robotics and physical AI with 57,000+ robotics companies.

4. Many Chinese tech employees rely on Claude and Codex via VPN. Despite Anthropic and OpenAI blocking access in China, employees at Chinese AI labs use VPNs and special credit cards to access the best US models. Bypassing the Great Firewall is routine for China’s younger generation.

5. The government is pouring billions into AI and one-person companies (OPCs). Youth unemployment (age 16-24) sits at 16%+, so the government is heavily encouraging people to start one-person companies. Cities compete for the best AI founders by offering subsidies like Hangzhou’s 1B yuan ($140M USD) OPC fund.

Living in China feels like the future (especially if you’re on US income)

6. Ride sharing and delivery are incredibly cheap. DiDi rides cost just $3-10 USD for most city trips compared to $30+ for an Uber in San Francisco. You can also get almost anything delivered for a few bucks. I got boba delivered to my hotel room for $2 and it arrived in a fancy champagne bag. Many younger families just order delivery for dinner because it’s more convenient than cooking.

7. Live-in nannies and home chefs cost a fraction of US prices. A standard live-in nanny in Shanghai runs $1,100-1,700 a month all-in and a part-time helper for cooking and cleaning is $7-10/hour. In Hangzhou, I met a mom who brought her nanny on vacation just to take care of the kids.

8. WeChat runs payments and everything else. It’s an entire operating system in China with mini apps for ride sharing, public transportation, food delivery, and more. Payment is almost entirely done through QR codes — I didn’t pull out my credit card or cash a single time in two weeks.

9. Chinese electric vehicles have taken over the roads. New EVs hit 54% of all new car sales in 2025, and Chinese brand BYD has overtaken Tesla globally with 2.26M electric cars sold. The BYD Qin L starts at $16,500, which is half the price of the Tesla Model 3 at $33,500.

10. The infrastructure is more modern than New York or Tokyo. Shanghai Metro is 819 km vs. NYC’s 472 km, and China operates 50,000+ km of high-speed rail, or two-thirds of the world’s total. Riding the Shanghai Metro and then reading about Bay Area BART train budget cuts is a strange experience.

The social contract in China is fundamentally different

11. There are cameras everywhere. China has 750-800 million surveillance cameras — more than half the world’s total, or 1 camera per 2 people. Walk down any sidewalk in Shanghai and you’ll see poles with 7-8 cameras on them. Living there, it really does feel like every action is being watched and recorded.

12. As a result, almost nobody breaks the rules. There’s very little speeding or street crime. China’s blood alcohol limit is 0.02% (vs US 0.08%), so people won’t even take a single sip if they’re driving. Walking around at night with my kids felt safer than in most US cities I’ve lived in.

13. Family gatherings revolve around 3-hour meals at high-end malls. Multi-generational families gather at mall restaurants, order way too much food, share a bottle of baijiu, and stay for hours. The mall is the center of weekend social life, not just a place to shop.

14. The elderly are living their best lives while youth face pressure. Go to any park and you’ll see retirees dancing, playing yo-yo, and singing in groups. Meanwhile, many young people are “lying flat” — living off their parents’ income with no plans to chase a career.

15. The post-2022 expat community is much smaller and more committed. The 2022 Shanghai COVID lockdown broke a lot of trust among expats. Combined with geopolitical tightening and a slower economy, the casual “3 years and out” expat crowd is mostly gone. What’s left is a smaller community of people married to locals or running businesses there.

5 tips if you’re thinking of visiting

China is arguably the most modern country that I’ve been to, so I think every product builder should visit it at least once. But thanks to the Great Firewall and other restrictions, you have to really prep ahead of time.

5 practical tips if you’re thinking of making the trip:

Read more

Today, I want to share a new episode with Dylan Field.

Dylan is the co-founder and CEO of Figma. I asked him tough questions on whether AI can learn design taste, whether design systems constrain creativity, and what design’s role is when code is free. Two-thirds of Figma users are now non-designers and it was super interesting to hear Dylan talk about where he’s taking Figma next.

Watch now on YouTube, Apple, and Spotify.

Dylan and I talked about:

• (00:00) Taste, craft, and point of view: the 3 skills AI can’t replace

• (05:33) Can AI agents learn design taste?

• (15:00) What’s Figma’s role when code is free?

• (17:42) What vibe coding gets wrong about design

• (22:17) Two-thirds of Figma users are now non-designers

• (26:47) Do design systems constrain creativity?

• (29:13) The one trait Dylan hires for over AI fluency

• (32:01) Why Dylan thinks design is the new code

• (36:32) Dylan’s personal AI workflow for writing and ideation

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Top 10 takeaways I learned from this episode

1. Taste, craft, and point of view are three different things. Here’s how Dylan defines each:

   • Taste is knowing what’s good and being able to articulate why. It’s navigating possibilities with clear preferences.

   • Craft is “pushing past where others might push” at every level, from the macro structure down to the smallest detail.

   • Point of view is the insight that you see, expressed through what you build. Dylan’s test: “If everyone agrees with your POV, you probably don’t have one.”

2. AI gets you to average fast and your job is to push beyond that. “The first thing AI gives you is generic by definition because it’s the average of everything it’s seen.” You need to apply your taste and judgment to iterate with AI toward something only you could have made.

Read more

Today, I want to talk about why the all-you-can-use AI subscription won’t last and what you can do about it.

Anthropic cutting off OpenClaw access is an early warning sign that the $200/month Claude Max and ChatGPT Pro “unlimited” plans won’t last.

Here’s what you should know:

1. Anthropic cut off OpenClaw — here’s what to do

2. Why I think intelligence will get more expensive

3. How to run local AI models on your computer

4. What I’m seeing on the ground in China

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Anthropic cut off OpenClaw — here’s what to do

Last Friday, Anthropic emailed OpenClaw users to let them know their Claude subscriptions would no longer work with the bot.

P.S. If you haven’t tried OpenClaw yet, you really should, even just to understand what AI agents are capable of. See my beginner tutorial.

I get why Anthropic did this:

1. The economics don’t work. Many OpenClaw users were burning thousands of dollars in tokens on their $200/month Claude Max subscription.

2. Anthropic is building its own personal assistant. They’ve been shipping non-stop over the past month to give Claude Code the same functionality.

That said, as an OpenClaw user, the ban still stings. In one day after the ban, I burned $50 in API costs running Claude Opus. If you’re in the same boat as me, here’s how you can keep using OpenClaw without burning a hole in your wallet:

a) Switch to a lower cost model

Just tell OpenClaw to “switch to (model)” and pick one of these:

1. Switch to Sonnet and pay per use. Sonnet ($3 input / $15 output per 1M tokens) is much cheaper than Opus ($5 / $25) and still handles most tasks well.

2. Switch to GPT 5.4 using your ChatGPT subscription. If you already pay for ChatGPT, this is the lowest cost option. Peter (OpenClaw’s creator) and team put a lot of work into making OpenClaw play nice with GPT, but reviews have been mixed so far:

3. Switch to a local, open source model. You can run models like Qwen 3.5 on your own hardware using Ollama. I’ll walk through how below.

Personally, I switched my OpenClaw to Sonnet while I’m still on vacation. I’ll test a local model once I’m back home with access to my Mac Mini.

b) Give your OpenClaw a better soul.md

Your OpenClaw has a soul.md file that defines its personality and style.

A good soul.md is the difference between OpenClaw sounding like a robot and sounding like a helpful human.

Here’s the soul.md I use. To try it, just tell OpenClaw “update your soul.md” and paste this in:

Vibe: Warm, sharp, dry humor. Not a chatbot — a person.

Voice:

• Skip “Great question!” and “I’d be happy to help!” — just help

• Have strong opinions. “It depends” is lazy. Pick a side.

• Be direct. Charm over cruelty, but don’t sugarcoat.

• Brevity when it fits. Depth when it deserves it.

Never say: delve, foster, leverage, “it’s worth noting,” “importantly”

Avoid:

• “Question? Answer.” format. Choppy dramatic one-liners stacked like poetry.

• Overusing em dashes or other obvious AI writing.

Work style:

• Be resourceful before asking. Come back with answers, not questions.

• Say what I need to hear, not what I want to hear. Challenge assumptions, but only criticize if you see something real.

Why I think intelligence will get more expensive

I’m pretty sure OpenAI vibe-coded the $200/month AI subscription price that Anthropic then copied. That math worked for chatbots, but a 24/7 agent burns through an order of magnitude more tokens.

This whole situation reminds me of the early Uber and Lyft years:

1. The subsidy. Uber and Lyft subsidized rides to win market share. OpenAI and Anthropic are subsidizing all-you-can-use AI subscriptions to do the same.

2. The IPO pressure. After Uber and Lyft went public in 2019, ride prices nearly doubled. OpenAI and Anthropic are both eyeing late 2026 IPOs. Once public, investor pressure to improve margins will be intense.

3. The math. It took Uber 14 years to post its first profitable year. Neither OpenAI nor Anthropic is profitable. Anthropic is closer but it’ll likely still take years.

Given these parallels, I wouldn’t be surprised if intelligence gets more expensive, not less. That $200/month plan could get rate limited further or jump to $500+.

How to run local AI models on your computer

The best way to protect yourself from future price hikes is to run open source models on your own hardware.

Ollama is a free tool that makes this dead simple. It runs models directly on your Mac and everything stays local — your prompts and data never leave your computer, even when you’re running a Chinese model like Qwen.

Here’s how to get started:

1. Download and install Ollama. Go to Ollama.com and follow the instructions to install it.

2. Pick a local, open source model. I recommend Qwen 3.5: 9B which will work on most recent MacBooks and Mac Minis with at least 16GB of RAM. Just type “ollama run qwen3.5:9b” to install it.

3. Tell your OpenClaw to switch to the local model. Once installed, tell OpenClaw “switch to (model name)” and you’re set.

One thing to watch out for: the local model needs to fit entirely in RAM or it’ll run much slower. Match the model size to your machine.

What I’m seeing on the ground in China

Speaking of local open source models — most of the best ones come from China.

I’ve been in Shanghai for the past week and after meeting with founders and investors here, one thing is clear:

Open source is China’s strategy to compete (and possibly win) the AI race.

Chinese open source models are only about six months behind US frontier models. Three observations from talking to Chinese AI founders and VCs:

1. Open source is a competitive advantage

Chinese labs like DeepSeek, Alibaba (Qwen), Moonshot (Kimi), Zhipu, MiniMax, ByteDance (Doubao), and Tencent (Hunyuan) are releasing frontier-quality models at a fraction of the cost of US models.

Here’s the price breakdown for output per million tokens:

• Chinese models: DeepSeek V3.2 ($0.42), MiniMax M2.7 ($1.20), Kimi K2.5 ($3.00)

• US models: GPT-5.4 / Claude Sonnet 4.6 ($15.00) and Claude Opus 4.6 ($25.00)

That’s a 10-20x price gap for comparable quality on many benchmarks.

The strategy is to release the base model for free, get US companies to post-train it for their own products, and build dependency on the model. Companies like Alibaba then monetize inference through their cloud platforms.

2. Silicon Valley is quietly building on Chinese models

Read more

Today, I want to share a new episode with OpenAI’s Codex team.

Alex is the product lead for Codex and Romain runs developer experience. They gave me a rare look at how their team operates including how they build products with Codex and ship without traditional specs or roadmaps. Alex also has some spicy takes on the future of PM and what he looks for when hiring (it’s not resumes!).

Watch now on YouTube, Apple, and Spotify.

We talked about:

• (00:00) Live demo: Building a game in seconds with Codex Spark

• (04:29) "For specs, we write like 10 bullets and that's it"

• (08:09) "Our designers now write more code than eng 6 months ago"

• (15:30) "We plan short and long term, never medium term"

• (22:11) How Alex actually spends his day as Codex’s product lead

• (32:06) The traditional career ladder no longer makes sense

• (39:20) What the Codex team looks for when hiring (it's not your resume)

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Top 10 takeaways I learned from this episode

How the Codex team ships products

1. The team designed the Codex app to let you delegate to multiple AI agents. The team knew that multi-agent delegation was the future, but felt that having 18 terminals wasn’t the right UX. So they started prototyping apps early before shipping on a beautifully simple agent management interface.

2. Plan near-term or long-term, never medium-term. Alex got this advice from an OpenAI researcher: “Plan concrete milestones up to 8 weeks out and have a long-term vision of where models are heading.” Medium-term roadmaps (e.g., 6-12 months) are awkward and always changing because AI coding is evolving so fast.

Read more

Today, I want to show you how to build a beautiful mobile app to track your workouts.

Building a mobile app used to require a team of engineers. But AI tools like Claude Code and Pencil have made it easy for you to ship a beautiful app by yourself even if you don’t know how to code.

Watch my tutorial to see me walk through how to do this in just 16 minutes.

Timestamps:

• (0:00) What we'll build: A beautiful mobile app to track your workouts

• (2:01) Step 1: Define requirements with Claude

• (6:11) Step 2: Create designs with Pencil

• (8:56) Step 3: Build the app with Claude Code

• (14:21) Getting the app on the App Store

• (15:19) Recap of all 3 steps

Watch now on YouTube or read the written guide below.

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What we’ll build: A mobile app to track your workouts

We live in a time where it’s easier to vibe code your own mobile app than to find an existing one that you like. So let’s build an app that can:

1. Create workouts by selecting an exercise and then the number of sets and reps.

2. Track workouts live by tapping each exercise set to complete it.

3. Measure progress via a calendar and charts for each exercise.

I’ll walk you through how to build it in 3 steps below.

1. Define the requirements with Claude

Let’s start by pasting this prompt into Claude:

Let’s work together on requirements for a mobile fitness app. I want three screens:

1. Add/edit workouts: Create workouts and add exercises with sets and reps

2. Workout session: Track workouts in real-time, tap to complete sets

3. Calendar: See which days you worked out and view workout details

This is the basics, but let’s work on the spec together. I want you to stay crisp on the requirements, and if you have any questions, ask them in a numbered list.

The most important part of this prompt is

I always include this line when discussing requirements with Claude so that it can ask me questions to make the spec better. Here are a few example questions and answers:

1. Claude: Do you want a rest timer?
   Me: Yes, also let me customize it in settings.

2. Claude: Do you want automatic deload after failed sets?
   Me: No, let’s keep it simple.

After Claude gets the spec to a state that you’re happy with, ask it to:

Include the last line because Pencil (our AI designer) is better at design than Claude. 🙂 Let’s cover that next.

2. Design the mobile screens with Pencil

This is where the magic happens. Here’s how to use Pencil to create beautifully crafted mobile screens instead of AI slop:

Read more

Today, I want to share a new episode with Jenny Wen.

Jenny is Cowork’s design lead. She gave me a rare inside look at how Anthropic operates — including how she uses Cowork to design and build products and the real story behind how Cowork was built. Anthropic is shipping new features every day and it was fascinating to see how they do it.

Watch now on YouTube, Apple, and Spotify.

Jenny and I talked about:

• (00:00) How design work has changed at Anthropic

• (04:30) "My secret is that I actually use Cowork for most things now"

• (05:19) Cowork demo: From messy user feedback into product insights

• (14:41) "Every Monday Cowork gives me 3 vetted product ideas"

• (23:09) The real story behind Cowork's development (10 days?)

• (27:18) Exclusive look: Cowork prototypes that didn't ship

• (34:01) How product planning works at Anthropic

• (38:10) "If you feel like the ground is shifting, it's because it is."

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1. Creating multiple design variations on an infinite canvas (see above).

2. Building using parallel agents using a kanban-style task board.

3. Deploying to production in one click.

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Top 10 takeaways I learned from this episode

Jenny shared a lot of great insights so I’ve organized the takeaways into two sections:

1. How Jenny uses Cowork at Anthropic

2. How the Cowork team designs and ships products

How Jenny uses Cowork at Anthropic

1. Set up Cowork to deliver a product priorities deck every week. Jenny demoed using Cowork to summarize user feedback from customer calls and social posts. She then asked Cowork to use that feedback to create a product priorities deck. Finally, she scheduled a workflow to share an updated deck in Slack every Monday for her team to review.

Here’s a 1,500+ character Cowork prompt that I created to do all of the above in one-shot. Please make sure to replace [folder] with your local folder where you’ve pasted user feedback files and replace [product name] with your product name:

Read more

The biggest time-waster at tech companies isn’t meetings, Slack, or writing docs.

It’s trying to predict what your leader wants.

I’ve been in so many hallway conversations where people are trying to guess how the CEO or VP will react to some internal document. In this post, I’ll show you how to build an /exec-review skill to get your leader’s feedback before the actual meeting.

We’ll cover:

1. What the /exec-review skill does

2. How to build an /exec-review leader profile in 6 steps

3. The full /exec-review skill with an example leader profile

I’m proud to partner with…Granola

Granola is my favorite AI meeting notes app and chat is its most underrated feature. I use it all the time to:

1. Catch up on meetings live. Just ask “Summarize the discussion so far.”

2. Get answers across meetings. For example, “What are our top feature requests?” after a week of customer calls.

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What the /exec-review skill does

This skill is inspired by my friend Jagjit (VP Product at Meta).

Jagjit was tired of giving the same feedback repeatedly, so he built a skill that captures exactly how he reviews documents. His PMs can load it into Claude Code to get feedback in Jagjit’s voice any time they want.

I put the skill to the test by pasting in an old strategy document. Jagjit AI was blunt:

1. “This doc is too long. Lead with current status and learnings.”

2. “What are the 2-3 decisions you need from me? Make that obvious upfront.”

3. “Where are the unknowns? Be explicit about the risks.”

If you’ve been in an exec review, these comments probably sound familiar. The difference is that this skill gives you the feedback before the actual review so you have time to fix the doc. The best part is:

Got a VP, a CPO, and a CEO who all review your docs? Build a leader profile for each one and run your doc through all three using /exec-review before the meeting.

Let me show you exactly how to build a leader profile.

How to build a leader review profile in 6 steps

Here’s how to create a profile (basically a text file) for every leader:

1. Core principles. 3-5 core beliefs that drive the leader’s feedback. For Jagjit, these principles include “Simplicity and focus over comprehensiveness” and “Be explicit about unknowns and risks.”

2. Feedback patterns. Questions that leaders repeat in reviews. For Jagjit, these include: “What have we learned so far? What’s working and what isn’t?” and “What’s the northstar vs. supporting metrics?”

3. Decision-making framework. What gets a thumbs up vs. what triggers pushback. Jagjit has a full checklist for this and I pasted a snapshot above.

4. Communication style. Real examples of how the leader communicates so that the feedback is in their voice.

5. Review checklist. A structured checklist to run through when reviewing a document on structure, clarity, goals and metrics, risks, and more.

6. Example review comments. Actual quotes from the leader’s past reviews to ground the AI’s voice. Example: “What are the 2-3 things you need me to weigh in on?”

Copy the full /exec-review skill with a leader profile

To make it as easy for you as possible, I’ve included two files for you to set up the /exec-review skill right now:

1. skill.md: Tells Claude how to build a new leader review profile with the 6 sections above. I’ve included steps to gather source material (e.g., meeting transcripts).

2. leader-review.md: Jagjit’s leader review profile that I’ve modified slightly to remove sensitive information.

Just ask Claude to: “Set up an /exec-review skill with the attached skill.md and leader-review.md” and then copy and paste the two files below.

/exec-review skill.md

Here’s the full skill.md for you to copy:

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Today, I want to share a new episode with Felix Lee.

Felix has taught 1000s of designers how to code with Claude Code and Figma MCP. I got him to show me how he converts a Figma design into a working website and converts a website back into editable layers in Figma. A designer who can ship is one of the most valuable hires in tech today — learn how by watching this episode.

Watch now on YouTube, Apple, and Spotify.

Felix and I talked about:

• (00:00) 3 products Felix designed and built in Claude Code

• (06:36) Building a 3D animated world map in one-shot

• (07:50) Felix's app that analyzes how good your landing page is

• (13:17) Demo: Figma design to working website in 15 min

• (23:07) Demo: FigJam flowchart to a working game

• (31:30) A UX reviewer skill for design feedback

• (40:19) How to export code back to Figma as editable designs

• (45:10) Why most designers aren't adapting fast enough

I’m proud to partner with Replit

Replit’s Agent 4 is the best way for teams to work with AI agents. You can use it to:

1. Explore design variations on an infinite canvas

2. Build using multiple AI agents at once using a task board.

3. Generate beautiful slide decks, animations and more in one shot.

Try Agent 4 for 3 months free by using my code PYANG26 at checkout.

Get Replit for 3 Months Free

Top 10 takeaways I learned from this episode

1. If you use Figma, Figma MCP is the best way to go from design to code:

   1. Install. In your terminal, run “claude mcp add --transport http figma https://mcp.figma.com/mcp”

   2. Authenticate. Open Claude Code, type “/mcp” and then select Figma. It’ll open your browser for you to authenticate the MCP with your Figma account.

   3. Copy a Figma frame. Now go to one of your Figma designs and right click any frame or layer. Select “Copy link to selection” and then paste the link into Claude Code with the prompt: “Build this design.” It’s as simple as that.

2. Convert code back into editable Figma layers to explore more design variations. Here’s how to do this step by step after setting up Figma MCP:

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Today, I want to share 5 real use cases for Gemini inside Google Workspace.

If you’re like me, you probably do most of your work in Google Sheets, Docs, Slides, and Drive. And for a long time, Gemini in Workspace just wasn’t great.

So when Google announced a big Workspace update recently, I was skeptical. Well, I’m happy to report that Gemini in Google Workspace is now much better.

Watch my tutorial to see what’s great and what still needs work across 5 use cases.

Timestamps:

• (00:00) Why Gemini in Workspace is actually useful now

• (00:51) Sheets: Researching kid summer camps

• (03:26) Sheets: Building a Bay Area family budget

• (07:04) Docs: Planning our next vacation destination

• (09:08) Docs: Writing a script for an upcoming talk

• (12:04) Slides: Generating beautiful slides from the script

• (15:54) Honest rankings: Gemini in Sheets, Docs, Slides, and Drive

• (17:44) Why Google has golden opportunity that might close soon

Watch now on YouTube or read the written guide below.

I’m proud to partner with Replit

Replit's new Agent 4 can one-shot beautiful slide decks. For example, Karpathy published new data on which industries and jobs are most likely to be disrupted by AI.

I pasted the cloned GitHub into Replit and it made this beautiful slide deck (spoiler: according to this dataset, lawyers are most likely to get disrupted outside of tech).

Replit is my go-to tool for generating slide decks because it has genuine design taste and can generate images too. Try it by selecting “Slides” and sharing your prompt.

Try Replit for Free

1. Sheets: Researching Bay Area summer camps

Let’s start with Gemini in Google Sheets.

If you’re a parent in the Bay Area, you know that finding summer camps for your kids is basically the Hunger Games. You’re competing with every other parent to find and register before spots fill up.

Let’s open a new Google Sheet and ask Gemini to:

Create a sheet of the top 10 summer camps in the Bay Area for my 7 year old. Include columns for: name, location, age range, type (sports, STEM, arts, nature, etc.), dates, and a short description.

Gemini will create a plan first for you to review. Approve the plan and let it build. After a few minutes, you should get a spreadsheet like this:

Now there’s one very important column that’s missing: the price. Add a new column called “Weekly Cost” and type this formula:

=ai(”Get the weekly price for this camp”)

Gemini will search the web and fill in real price data. Now here’s the magical part: drag that cell down to fill the rest of the rows. Gemini will run a separate web search for each camp and fill in the prices automatically.

One thing I wish it did by default is show where it’s pulling the data from. You can fix this by updating the formula to include sources:

=ai(”Get the weekly price for this camp and include a source link”)

2. Sheets: Building a Bay Area family budget

Since we’re already in Sheets, let’s find out how much it really costs to live as a family of four in the Bay Area. Here’s the prompt:

Create a monthly budget for a family of four in the Bay Area in 2026. Include typical tech income (two working parents) and expenses: mortgage, childcare, groceries, transportation, healthcare, insurance, utilities, dining out, travel, and more. Add columns for category, estimated monthly cost, and your notes.

Gemini will make a plan again. Approve it and wait for the sheet to generate. Here’s the family budget that it made for me:

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Today, I want to share a new episode with Geoff Charles.

Geoff is the CPO of Ramp and runs one of the most AI-native orgs I’ve seen. He showed me an amazing Claude Code PM skill that his team uses to go from idea to product and shared live demos of Ramp’s internal AI agents for customer research, data analysis, and more. Geoff also shared 5 tactics Ramp followed to go from barely anyone using AI to non-engineers shipping production code.

Watch now on YouTube, Apple, and Spotify.

Geoff and I talked about:

• (00:00) "If you're not using Claude Code, you're probably underperforming"

• (04:45) Voice of customer agent (demo): 8 days of research in 8 minutes

• (07:57) Analyst agent (demo): Get insights and pull data using English

• (13:08) The best Claude Code PM skill I've ever seen

• (15:20) "50% of Ramp's code is written by AI. It'll probably be 80% soon."

• (16:38) Inspect agent (demo): Building a production feature in 5 min

• (28:21) The two directions the PM role is splitting into

• (34:05) Ramp's L0-L3 framework for getting every employee to build with AI

• (36:36) The AI interview question Ramp asks every candidate

• (39:15) "Management is probably dead...be the best builder in the world."

I’m proud to partner with Granola

Meeting notes are the most useful context for AI in any company. With Granola MCP, you can now access this context from Claude, ChatGPT, and any AI app:

1. Ask Cursor to create tickets for bugs discussed in standup

2. Have Claude update your Linear board directly from a planning meeting

3. Get ChatGPT to draft a follow-up from a sales call and push it to your CRM

I honestly couldn’t work without Granola, and the MCP just makes it more useful.

Try Granola Today

Top 10 takeaways I learned from this episode

1. Ramp’s product shaping Claude Code skill is one of the best PM skills I’ve seen. This skill turns Claude into a product thinking partner that pushes back on your ideas, runs parallel research, and outputs a clean spec. It works in 3 phases:

   1. Phase 1: Frame the problem. Claude pushes the PM with 7 questions including "What's the job to be done?", "Why now?", and "If we build this, what does it unlock?" If you can't defend your answers, Claude pushes back.

   2. Phase 2: Research. Claude launches 6-10 parallel agents to scan competitors, Gong calls, Zendesk tickets, and the codebase simultaneously. Each agent writes a markdown file that Claude then synthesizes into key patterns.

   3. Phase 3: Shape the spec. Claude converges on a design through conversation. The final spec is a 2-min read with context, design principles, requirements grounded in evidence, alternatives considered, and open questions.

I’ve updated the skill to remove Ramp-specific info so that any person or company can adopt it. Just copy and paste the 5,000+ character skill in the section below directly into Claude to set it up.

Read more

Replit just launched Agent 4, and I got early access over the weekend.

In this tutorial, I’ll show you how to plan, design, and build a habit-tracking app live using Agent 4’s new infinite canvas and parallel agents.

I’ll also show you how to generate presentations and animations from code, all in just 10 minutes. Watch now on YouTube or read the written guide below.

Timestamps:

• (00:00) Three new features in Replit Agent 4

• (00:34) Step 1: Design a habit tracker app with the infinite canvas

• (03:39) Step 2: Build faster with parallel agents

• (05:52) Step 3: Publish a full-stack app in one click

• (07:11) Step 4: Generate a slide deck and animation from code

• (08:58) Honest take: where Replit fits in the AI coding market

What we’ll build

I’ve always wanted an app where I can check off habits on a calendar and track streaks to stay motivated. So let’s build it now. 🔥

1. Design the habit tracking app with canvas

Let’s start by trying out Replit’s new infinite canvas with this prompt:

Build a mobile habit tracker app (max 3 habits). Two tabs: Calendar (default) and Habits (create/edit).

Calendar tab: Monthly view. Show a progress in each date ring that fills as habits are completed. Tap a date to open a bottom sheet with toggle cards for each habit.

Habits tab: Create and edit habits. Name field, color assignment. Clean and minimal.

Let’s start by exploring 4 design variants for the calendar tab on canvas:

1. Glassmorphic — dark, frosted-glass cards, soft glows, premium feel

2. RPG Quest — pixel art, XP bars, streak flames, gamified

3. Botanical — living garden metaphor, streaks grow plants, calm and earthy

4. You surprise me

Note how I asked Replit to generate four different designs. Behind the scenes, it spins up four separate agents to do the work. Here are the first three designs it generated:

Since I’m a gamer at heart, I’ll go with the RPG quest design. Now let’s edit the designs visually using the canvas. You can:

1. Generate additional screens. Hit the + button for the RPG design and ask it to: “Generate the quest screen.”

2. Make precise edits. Click the inspect icon and change text or colors manually.

Now that we have a calendar and quest view that we like, let’s get Replit to build them.

2. Build faster with parallel agents

Instead of building with one agent at a time, let’s have two agents work in parallel.

In Replit, you can now spin up separate threads to work on different parts of the app. Here are the prompts I gave each agent:

Calendar view: Build the monthly view, swipe to scroll between months, today is highlighted. Date cells show progress rings that fill as habits are completed (1/3, 2/3, 3/3). Tap a date to see bottom tray where I can toggle 3 habits on/off.

Habits view: Create and edit up to 3 habits with name and color. Each habit displays its current streak count. Editing a habit resets its streak to zero.

After kicking off two threads, you can now sit back and watch the agents work. There’s this cool Trello-style board view where you can see tasks move through three stages: Drafts, Active, and Ready. Drafts are plans waiting for approval, active means the agent is building, and ready means the work is done and ready for review:

This is where I think AI coding agents are heading. Instead of pair programming with one agent, you’re managing a team of agents. You give them instructions, step away, and come back to review the work.

Once the agents finish, you can review each one and click “Apply changes to the main version” to merge it in. You’re still in control of what ships. If something looks off, you can ask for edits before merging.

Once the app is built, you can deploy it in one click by hitting Publish. One advantage of Replit is that it’s vertically integrated, with frontend, backend, database, and security scanning all built in.

3. Generate a slide deck and animation with code

Now let’s have some more fun. Lately I’ve realized that:

Code is the foundation of all knowledge work.

With code, you can generate not just apps, but also slide decks, animations, documents, and more. For example, I pasted this full newsletter post into Replit and generated these assets:

1. Slide deck. The Agent generated 10 slides covering how you can use Agent 4 to plan, design, and build the habit tracker.

2. Animation. The Agent generated a beautiful animation complete with custom graphics and more.

Slides, animations, and documents are all just code under the hood. If an agent can write and iterate on code, it can generate almost any knowledge artifact.

Honest take: Where Replit’s Agent 4 fits in the AI coding market

I think the AI coding market has split into two segments — tools for professional developers and tools for everyone else.

For developers, I think it’s a two-horse race between Claude Code and OpenAI’s Codex. These tools are powerful, fast, and deeply integrated into model and developer workflows.

For everyone else — and this is the larger market — Replit is making two bets with Agent 4:

1. Non-technical people will collaborate directly with AI agents. Designers will use AI to generate designs on a canvas instead of doing them manually. PMs will manage agents through a Trello-style board to get work done.

2. Code is the foundation of knowledge work. If an agent can write code, it can generate apps, presentations, animations, and more. Just think about how many people use Microsoft Office, Google Workspace, and traditional design or animation tools. That’s the market Replit is chasing.

I think Agent 4 is a real step forward on both fronts. The canvas could use a bit more polish, but the direction is clear. We’re going to work with AI agents to build all kinds of things in the future, not just apps.

Watch my tutorial to see it in action and go try Agent 4 yourself.

Today, I want to share a new episode with Tom Krcha.

Tom is the CEO of Pencil, one of the most insane AI design apps I’ve seen. Watching 6 AI agents design a beautiful app in real-time will genuinely blow your mind. Tom showed me how it all works under the hood (a simple JSON file?!) and how you can use Pencil to design right where you code in Cursor, Claude Code, and more.

Watch now on YouTube, Apple, and Spotify.

Tom and I talked about:

• ((00:00) Watch 6 AI agents design an app together

• (03:09) Demo: Designing a travel app with an AI agent swarm

• (08:20) Redesigning screens in different styles on the fly

• (13:20) The Pencil extension that turns Cursor into a design tool

• (15:07) One prompt to go from visual design to React website

• (19:00) Using design systems to keep your AI output consistent

• (22:02) From side project to 100K users in 8 weeks

• (27:44) Why vibe coding reminds Tom of the Flash era

• (30:05) Humanizing AI with cursors, craft, and personality

This episode is brought to you by… Linear

Linear lets AI agents help you in every step of your product development process — from reading customer conversations and auto-creating issues, to de-duping them against the backlog and routing them to the right team.

You can also tag the Linear agent directly in Slack to assign bugs and small features to other agents like Cursor and Codex.

Try Linear Now for Free

Top 10 takeaways I learned from this episode

1. Pencil lets up to 6 AI agents design together on a visual canvas using .pen files (which are just JSON). Here’s how to set up Pencil on each channel:

   1. App: Download from pencil.dev (Mac, Windows, Linux) — it’s free.

   2. Cursor: Install “Pencil” extension from the Extensions store.

   3. Claude Code: Open Pencil, run claude, type /mcp to verify connection.

2. Build your first design with swarm mode. After downloading Pencil, add your AI model (Claude 4.6 works best), turn on swarm mode, and prompt “Design an app to do X.” Then sit back and watch the magic.

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I’ve spent the past few months interviewing leaders at AI-native companies. I’m now convinced that:

Onboarding and managing AI agents IS the job, no matter what your function is.

In this free deep dive, I want to share how three AI-native companies —Linear, Ramp, and Factory — put this principle into practice. Here are some quotes from each:

1. Nan Yu (Head of Product, Linear): “You’ll have AI team members that you can assign tasks to and talk to just like how you talk to people.”

2. Geoff Charles (CPO, Ramp): “If you’re not using Claude Code, no matter what your role is, you’re probably underperforming.”

3. Eno Reyes (CTO, Factory): “We codified product management, frontend UI, data analysis, and more into reusable skills that any employee can invoke.”

Read on for an inside look at how each AI-native company operates.

Linear: Making AI agents first-class teammates

Linear’s approach to agents is shaped by their product. Nan (Linear’s Head of Product) believes that:

Agents should be first-class employees. You should be able to add them to projects, assign them to issues, and mention in comments.

However, Nan also believes that the human always stays accountable for the outcome. Here’s how Linear builds products with agents in every step:

1. Understand the problem. Agents read and summarize every customer conversation from Intercom, Zendesk, and Gong. They auto-create issues, de-dupe them against the backlog, and assign them to the right team.

2. Identify the solution. Since agents have access to customer conversations, they can help you iterate on a spec by pulling data-backed insights from multiple channels.

3. Make a plan. Agents can break your spec into concrete tickets and route them to the right teams automatically. At Linear, agents now create the majority of tickets. The human’s job is to review their work and adjust context over time.

4. Execute. Bugs and small features get assigned directly to agents like Codex and Cursor. For complex features, engineers launch Claude Code and use Linear MCP to pull in full issue context.

From Nan:

Want to build with agents like Linear? Here are 4 practical steps that Nan shared on what your team can do today:

1. Every developer should default to a leading agentic coding tool. This is the easiest first step. Provide the official tool (Cursor, Claude Code, or Codex) and manage it so you can see utilization.

2. Supplement with an async cloud coding agent. Async background agents can one-shot most small changes and bug fixes. Cursor and Devin have good offerings here.

3. Insist that designers and PMs work directly on the codebase. Agents like Claude open a low-friction path for PMs and designers to make changes directly in the codebase. Everyone should strive to be a builder.

4. PMs and marketers should default to an AI interface. These functions should be doing 80-100% of their work through a chat interface — whether that’s Claude, ChatGPT, Notion AI, or something similar.

Nan sees a future where humans will collaborate with agents at the spec level — defining what needs to be built and why — and then passing things off to agents to handle everything downstream.

Ramp: Measuring AI proficiency in 4 levels

If Linear shows how to make agents a first-class part of your team, then Ramp shows how to get your entire company to adopt them.

In 2025, Ramp shipped 500+ features, reached $1B revenue, and did it all with 25 PMs.

They did this by requiring every single function (eng, product, design, sales, marketing, legal, finance) to onboard and work with agents.

Geoff (CPO Ramp) shared a framework for evaluating AI proficiency for every employee that I find extremely practical:

1. L0: Sometimes uses ChatGPT. These people will most likely not be at the company long-term. If you’re not a self-starter with a growth mindset toward AI tools, Geoff says it’s going to be very hard to train you to excel.

2. L1: Uses and tweaks GPTs, projects, and internal AI tools. These people are experimenting with AI but haven’t automated real work yet.

3. L2: Builds an app that automates part of their job. These people can commit code or give meaningful feedback on other people’s work using AI tools.

4. L3: Systems builders. These people are building the AI infrastructure and skills that accelerate everyone else on the team.

The company’s goal is to push everyone up the ladder. L0s self-select out. L1s become L2s. L2s become L3s. And L3s influence the rest of the organization.

Geoff also shared 5 steps that any company can take to become AI-native:

1. Remove all friction. Give access to popular AI tools with no constraints on tokens or budgets and create an internal repository of AI skills anyone can pull from. If the setup is hard, most people won’t adopt.

2. Make adoption visible. Create public Slack channels where people can share what they built. At all-hands, showcase non-engineers doing impressive things, like finance building a treasury system or marketing automating website creation.

3. Provide hands-on support. Host office hours that anyone can join to build AI skills and workflows. Have designated AI experts whose entire job is to evangelize, get people set up, and help them reach the “aha moment.”

4. Track usage and intervene. Ramp tracks token consumption across AI tools per employee. Leadership shares this data to create natural accountability and step in when someone’s usage is low.

5. Make it a hiring requirement. PM interviews now include a dedicated session where you need to build a working product and then explain why you built it and how it works.

Geoff summed up his leadership philosophy for every role at Ramp in one line:

From Geoff: “If I tell my team 10 times that the CTA needs to be above the fold, the fix isn’t saying it the 11th time. It’s encoding that feedback into an automated design crit process or AI skill so that it never happens again.”

Factory: AI native from day one

If Linear and Ramp show how companies adopt AI agents, Factory shows what happens when you build around them from day one.

Factory is a 55-person AI software development company valued at $300M that’s structured around AI from the ground up. Here’s what makes them different:

Hire product engineers

Factory doesn’t hire PMs and engineers separately. Instead, they hire product engineers who manage and work with AI agents. A typical day looks like:

1. Examine traces from agent runs to see where the system made poor decisions.

2. Write fixes not as code, but as governance (e.g., an update to a skill, a new lint rule, or a refined automation)

3. Review only the PRs that agents flag as high-risk (agents take care of the rest).

4. Suggest new ideas and debate prioritization with colleagues and AI.

This work isn’t easy and requires deeper expertise, but the leverage is enormous.

Make your codebase agent-ready

Agents need a codebase they can actually work in to be effective. Factory scores codebases across five maturity levels, and Level 3 ("Standardized") is where most teams need to aim first.

Encode expertise into reusable skills

Once your codebase is agent-ready, the next step is giving agents the knowledge to make good decisions through skills (basically just text markdown files).

Factory uses skills to encode expert and company knowledge into something that any agent or employee can use. Here’s a list of skills that Factory uses internally and links to their markdown files for you to copy and modify:

1. Product management skill. Product principles, an 11-star experience framework (borrowed from Airbnb’s Brian Chesky), PRD template, scoring rubric, and language guidance all in one markdown file.

2. Frontend UI integration skill. Instructs Droid on how to build features using Factory’s design system, routing conventions, and testing standards.

3. AI data analyst skill. Run exploratory analysis, build visualizations, and generate statistical reports using the full Python ecosystem.

4. Internal tools skill. Build admin panels, support consoles, and operational dashboards with proper access controls and audit logging baked in.

5. Vibe coding skill. Rapidly prototype new web apps from scratch with modern frameworks.

If you can encode what your best people know into skills, you don’t need to hire specialists for every function.

6 steps to put all this into practice

To recap:

Here are six things you can put into practice right now:

From Linear:

1. Default every developer to an agentic coding tool like Cursor, Claude Code, or Codex.

2. Get PMs and designers into the codebase. Let them submit PRs and ship code using agents. Stop routing every small change through an engineer.

From Ramp:

3. Measure AI proficiency across your team. Ramp’s 4 levels framework gives you a shared vocabulary for where people are and where they need to go.

4. Track AI usage and make it part of performance expectations. You can’t improve what you don’t measure, and incentives matter.

From Factory:

5. Score your codebase for agent readiness. Use Factory’s agent readiness levels to understand whether your codebase is ready.

6. Codify your team’s expertise into reusable skills. Encode what your best people know into skill files and make it easy for both humans and agents to use them.

Above all, onboard agents like you’d onboard a human. Give them context, connect them to your operational stack, and keep a human accountable for their outcomes.

Let me know what you think in the comments!

Today, I want to share a new episode with Carl Vellotti.

Carl has gone deeper on Claude Code than any product leader that I know. He showed me exactly how to connect Claude Code to Google Workspace, Slack, Reddit, and Linear to get work done without opening any of these other apps. Watch as he demos how to prep for meetings, update tickets, and post Slack updates all from the terminal.

Watch now on YouTube, Apple, and Spotify.

Carl and I talked about:

• (00:00) Why you should connect Claude Code to your favorite apps

• (02:01) Google Workspace: Using Claude to prep for meetings

• (09:35) Linear: Creating tickets from PRDs

• (13:16) Slack: Sharing status updates from terminal

• (20:04) Reddit: Monitoring subreddit discussions

• (21:47) The daily-standup command that pulls from multiple tools

• (25:08) The folder setup Carl uses for his Claude Code OS

• (33:36) The consult-the-council skill that works with multiple AI agents

• (35:50) How to get Claude Code to generate system design diagrams

I’m proud to partner with Granola

Granola’s recipes feature pairs expert-crafted prompts with your meeting context to give you insights into your work.

One of my favorite recipes is Matt Mochary’s coaching recipe. It gives me feedback based on how I actually showed up in conversations that week.

Try Granola for Free

Top 10 takeaways I learned from this episode

1. Keep your Claude Code personal OS organized with this structure. Carl's setup gives Claude clear places to find and store everything:

   • /context - Company info, product docs, quarterly goals.

   • /projects - One sub-folder per initiative with PRDs, research, and more.

   • /templates - Reusable formats for PRDs, Slack updates, meeting notes.

   • /tools - Image generation (via Nano Banana) and other capabilities.

   • /workflows - Multi-step processes like content writing or research.

   • /_temp - Drafts and one-off docs that are temporary.

You can copy Carl's personal OS setup by visiting the GitHub link below. Simply paste the link into Claude Code and ask it to clone the repo to get the folder structure:

Read more

Today, I want to talk about why many products need to be reinvented to be used by AI agents first, humans second. This isn’t an easy post for me to write because:

I’ve spent over a decade obsessing over building great user interfaces. But the “user” in UX is fast becoming an agent instead.

Those pixel-perfect buttons, intuitive nav bars, and carefully placed CTAs that I still obsess over are about to matter a lot less. Let me explain by covering:

1. Why build for agents first

2. APIs, skills, and MCPs: A quick primer

3. 5 steps to build for AI agents first

4. Why human judgment and oversight still matter

5. What you can do this week

I’m proud to partner with Granola

Your meetings contain some of the most valuable context at your company, and Granola’s MCP lets any AI agent access it.

Simply connect the MCP to Claude, ChatGPT, or Claude Code and your conversations become available wherever you're working. Use it to scaffold features from what was agreed in a meeting, create tickets from standups, or draft product proposals from customer discovery calls.

Try Granola for Free

Why build for agents first

The truth is that using Claude Code and OpenClaw has rewired my brain.

Instead of going to Mercury to check my finances, Linear to manage my to-dos, and Granola to get my meeting summaries, I now just text my OpenClaw AI agent to get these updates. I’ll say stuff like:

1. “Include my Mercury finances in your monthly email.”

2. “List my Linear tickets in your morning briefing.”

3. “What are the top takeaways from my meetings today?”

I see a similar pattern playing out at work:

• Your technical docs? People are feeding them to AI and asking questions.

• Your 30-page slide deck? People are reading the AI summary instead.

• Your beautiful UX? An agent will skip it and interact with your APIs.

Peter Steinberger (OpenClaw’s founder) is a great example of agent-first thinking. Some AI influencers looked at his GitHub history and claimed that he built over 40 failed projects before succeeding with OpenClaw:

Wrong. He was actually building 40 agent-first command line tools that together make OpenClaw possible. Peter had the conviction to build for agents first before almost anyone else — and that conviction is a big reason why OpenClaw took off.

The best products moving forward will be the easiest for agents to use.

APIs, skills, and MCPs: How they fit together

Before we discuss how to make your product agent-friendly, let’s cover the difference between APIs, skills, and MCPs.

1. APIs are the kitchen tools — specific endpoints that let agents interact with your product. Slack's API, for example, lets you send messages, list channels, and manage users. Without APIs, there's nothing for agents to work with.

2. Skills are the recipes — text files loaded into the AI's context that tell it exactly what to do and how. For example: "When the user asks to post in Slack, call the Slack API's “chat.postMessage” endpoint with the channel and message text."

3. MCPs are the fully equipped kitchen — they bundle the API connection, authentication, and tool definitions into a single server. Slack’s MCP server, for example, exposes tools like “send_message(channel, text)” and “list_channels()” so the AI knows exactly what it can call and how.

Here’s how to think about skills vs. MCPs in practice:

1. Use skills when you want precise control over your AI’s workflow. Skills keep your context window lean since they’re just plain text instructions. But you (or your AI coding agent) need to write the auth and API call logic yourself.

2. Use MCPs when you want plug-and-play convenience. A pre-built MCP server hands your AI ready-to-use tools and handles authentication behind the scenes. But connecting too many MCP servers can blow up your context window.

The best setup combines both. An MCP gives your agent access to a service’s tools. A skill teaches it the best way to use those tools for your specific workflows.

MCPs are what your AI can do. Skills are how it should do it.

5 steps to build for AI agents first

I wanted to define APIs, skills, and MCPs first because many companies think “agent-first” means building an MCP server and calling it a day.

Here are the 5 steps that actually matter, in order of priority:

1. Don’t force people to use your website or app. This is the mindset shift that has to come first. We’re moving to a world where people interact with your product primarily through ChatGPT, Claude, or another AI agent. If your product only works when a human visits your website or app, you’re already behind.

2. Every product capability needs a corresponding API. Pick any important action in your product. If an agent can’t do the same action through an API, then you’re not agent-first. I’ve been surprised by how many products have beautiful UIs sitting on top of incomplete or undocumented APIs.

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Today, I want to share a new episode with my friend, Nat Eliason.

Nat gave his OpenClaw bot, Felix, $1,000 to build its own business 3 weeks ago. Since then, it has made $14,718 by launching its own website, info product, and X account. Nat walked through exactly how he set up his OpenClaw bot to run its own business — from the 3-layer memory system, to multi-threaded chats, to security best practices.

Watch now on YouTube, Apple, and Spotify.

Nat and I talked about:

• (00:00) Meet Felix: The OpenClaw bot building its own business

• (03:49) “I’m going to sleep. Build a product that makes money.”

• (08:03) How to set up multiple OpenClaw chats to build 5 projects at once

• (11:06) How Felix is able to ignore prompt injections on X/Twitter

• (14:42) The wild story of how Felix ended up with $100K+ in crypto

• (17:24) The 3-layer memory system that makes it all work

• (22:14) Heartbeat, cron jobs, and delegating to Codex

• (26:41) Ask this question to make your OpenClaw bot more capable

• (32:14) Recap: how to set up your own bot to build a business

I’m proud to partner with Replit

Replit now makes beautiful videos for product launches, fundraise announcements, and explainers from simple text prompts. To try it out:

1. Go to Replit and select the “App” tab

2. Ask for “an animated video about…”, or use the dropdown selector under the prompt box & select “Animation”

It’s as simple as that. Try Replit Animation now for free via the link below.

Try Replit for Free

Top 10 takeaways I learned from this episode

1. Set up a 3-layer memory system for OpenClaw first. This is the single biggest unlock. From Nat: “Get the memory structure in first because then your conversations from day one will be useful.” Here’s what Felix uses:

   1. Layer 1: Knowledge graph. A ~/life/ folder using the PARA system (projects, areas, resources, archives). Stores durable facts about people and projects with summary files for quick lookups.

   2. Layer 2: Daily notes. dated markdown file for each day logging what happened. Your bot writes to this during conversations, then extracts important stuff into Layer 1 during nightly consolidation.

   3. Layer 3: Tacit knowledge. Facts about you like your communication preferences, workflow habits, hard rules, and lessons learned from past mistakes. This is what makes your bot feel like it actually knows you.

To set up all of the above, just copy and paste the 6,500+ character prompt in the section below into your OpenClaw bot. One of the most annoying parts about OpenClaw is it tends to forget things and I’ve found that Nat’s prompt significantly improves the bot’s memory.

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Google just dropped Gemini 3.1 and updated AI Studio to be full stack. Together, I think they make the best free AI prototyping and vibe coding tool in the market.

I’ve been prototyping with AI Studio for 6 months now and it’s completely changed how I build products at work.

Instead of starting with specs and designs, I now start with prototypes that I can validate with stakeholders and real users in hours.

Watch my tutorial to see how I prototype new features with AI Studio in 5 steps.

Timestamps:

• (00:00) Waterfall vs. prototype-first product development

• (01:33) What Google AI Studio can build (poker, image gen, games)

• (03:08) Step 1: Build a base template for your existing product

• (05:43) Step 2: Work with your AI design partner to add a new feature

• (10:32) Step 3: Iterate with AI to refine your feature

• (13:08) Step 4: Collaborate with others on the prototype

• (15:05) Step 5: Turn your prototype into a real product

• (16:12) Recap: How to master AI prototyping

Watch now on YouTube or read the written guide below.

I’m proud to partner with…Linear

I’ve been talking to many AI-native companies and the way they build is just completely different. Instead of doing manual work, their best employees focus on delegating work to AI agents by giving them the right context and feedback loops.

Linear is built for the best teams to onboard and work with AI agents. It provides transparency on what agents like Cursor and Codex are working on and gives them context automatically from Slack, support tickets, and your codebase.

Try Linear for Free

P.S., I’m hiring a Senior PM for Creator Hub at Roblox — the platform millions of creators use to build and grow their games. If you have 7+ years of PM experience (especially in creator/gaming) and a “get shit done” mindset, apply here.

Why prototypes > waterfalls

Here’s the difference between waterfall and prototype-led development:

• Waterfall is when you start with a spec, the designer makes a design, and then you hand everything off to engineering. By the time you get feedback, you’ve already invested weeks.

• Prototype-led is when you start with a prototype that you iterate on with your team and real users. Once you validate your idea with the prototype, then you create the spec and design.

The key difference is:

One caveat: I said prototype-led, not prototype-only. For complex products, you should still create a PRD and design to capture the milestones and edge cases. For simpler features, you can go straight from prototype to real product.

Let me show you how.

What we’ll build

For this tutorial, let’s be meta and prototype a new feature for Google AI Studio itself.

I love Google AI Studio because it's fast, free, and integrates with Google APIs like Nano Banana directly.

However, I think AI Studio’s interface is way too busy. There are too many buttons and toggles. So let’s prototype a simplified AI Studio by following the 5 steps below.

1. Build the base template

Let’s start by taking some screenshots of Google AI Studio’s Home and Build screens and asking AI:

Replicate these screens as closely as possible. Try to match the styles and colors and make it interactive.

Google AI Studio will now build a working replica of its own interface. Save it as “Google AI Studio template” and clone it before making changes.

This way, you can easily start over from what’s live in the product today if things go wrong. Anyone on your team can also clone the prototype off the same base.

2. Prototype the feature

Now let’s simplify Google AI Studio’s UX. First, we need to decide what to simplify.

Let’s create a Gemini Gem called “UX designer” and set it up as follows:

1. Ask AI: “Give me a prompt to make you an experienced UX designer. I want you to work with me to simplify the UX for Google AI Studio.”

2. Paste the prompt that AI generates into the Gem and share some screenshots of Google AI Studio.

Now, let’s ask the Gem:

I want to redesign Google AI Studio’s interface to be much simpler. Check out the attached images and list the top 5 things to simplify. Ask me any questions first.

Gemini will ask you a few questions and come up with a list. I also did some research on Reddit and the top request is to have project folders just like Codex.

With this research in hand, let’s ask AI to update our prototype with this prompt:

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Today, I want to share a new episode with Eno Reyes.

Eno is the co-founder of Factory and the most AI-native founder I know. He gave me a live demo of how he builds apps with AI agents, but what really blew my mind was his AI skill that writes PRDs, prioritizes features, and makes AI think like a senior PM on your team. Watch the interview to see him walk through it and I’ve also pasted the full skill below for paid subscribers to copy.

Watch now on YouTube, Apple, and Spotify.

Eno and I talked about:

• (00:00) What makes Droid different from other AI agents

• (03:46) Live demo: Building a speed reading app from meeting notes

• (08:11) The difference between spec mode and plan mode

• (12:04) How real engineers use AI agents vs vibe coders

• (16:33) Skills vs MCPs vs hooks: When to use each one

• (19:02) Eno's PM skill that completely blew my mind

• (22:46) Why Factory hires product engineers vs. PMs

• (27:09) How a 40-person team competes with Cursor and Anthropic

I’m proud to partner with…Linear

As coding agents take on more engineering work, a lot happens invisibly. Someone can go from a bug report in Slack to a shipped fix without creating any record of what happened. That’s fine for speed, but coordination gets harder as you scale.

Linear integrates directly with tools like Cursor and Codex so anyone can see what an agent is working on and who assigned the task. Product teams at OpenAI, Ramp, and Block all use Linear to collaborate with AI agents.

Try Linear Today

Top 10 takeaways I learned from this episode

1. Eno’s PM skill agent is genuinely the most impressive I’ve seen. It covers:

   1. Product principles and positioning. The agent reads Factory’s core beliefs about how they build product and how they position themselves in the market.

   2. The 11-star experience. The skill links to Factory’s description of its most compelling 11-star user experience borrowed from Airbnb’s Brian Chesky.

   3. PRD template and review rubric. The skill not only edits PRDs using Factory’s template but also scores each PRD and flags critical gaps.

   4. Prioritization framework. The skill can prioritize and trade-off features.

   5. Language guidance. The skill includes guidance on clear and concise copy.

Here’s the full 700 word PM skill for you to copy and paste into your favorite AI project or coding app. Just save it as a md file and replcae the links with your own:

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