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.

I’m proud to partner with…Granola

Granola just launched Spaces — a new way for your team to organize and share meeting notes with each other. For example, you can create a “User Calls” space with your interviews to let anyone ask questions about user needs. You can even ask questions directly in Claude, ChatGPT, or Gemini with Granola’s new APIs and MCP.

Try Granola for Free

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:

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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."

I’m proud to partner with Replit

Replit’s new Agent 4 is the best way for teams to collaborate with AI agents.

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.

Get 3 months free to Replit by using my code PYANG26 at checkout.

Get Replit for 3 Months Free

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:

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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.

Try Granola for Free

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.

Read more

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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Today, I want to show you how to go from AI novice to native in 5 levels.

Everyone throws around the term “AI native” without explaining how to get there.

You don’t have to read 100+ articles or take $1,000+ courses. I went from AI novice to native in a year while balancing a job, creator work, and kids, but you can navigate all 5 levels in a single week if you’re focused.

Watch my tutorial where I demystify all 5 levels and share my personal journey.

Timestamps:

• (0:00) The 5 levels to become AI native

• (0:47) Level 1: Where most people are stuck

• (1:16) Level 2: Voice, meetings, and projects (80% of my value)

• (5:05) Level 3: Why I prototype before specs now (live demo)

• (8:02) Level 4: All the apps I've built with AI

• (15:10) Level 5: AI as your personal agent (3 examples)

• (20:15) How to go from Level 1 to 5 in a week

Watch now on YouTube or read the written guide below.

I’m proud to partner with Granola

One of Granola’s most useful features is letting you chat with your meeting notes as the meeting is happening. I use this all the time with requests like:

1. “Catch me up on this conversation”

2. “What are the top requests from this call?”

3. “What’s the sentiment in this meeting so far?”

I don’t even know how to work without Granola anymore, so give it a try below.

Try Granola Today

Level 1: AI for everyday answers

This level is where 99% of people are today.

You’re using ChatGPT to get everyday answers instead of Google Search. For example, the other day, I fixed my toilet by sharing a few questions and photos with ChatGPT. My wife doesn’t call me the least handy person in the world for nothing 🙂

This is where we all started, and that’s fine. But if this is all you’re doing, you’re barely scratching the surface. You need to at least get to…

Level 2: AI for daily work

This level is a massive step up from level 1.

To unlock level 2, use AI for:

1. Voice dictation (Wispr Flow). It’s much faster to speak than to type and AI is excellent at understanding your stream-of-consciousness thoughts.

2. Meeting notes (Granola). Having AI take notes has been a massive time saver for every type of meeting from product reviews to interviews.

3. Projects. I have a dozen projects covering everything from product work to creator flows to weekend trip planning with my family (watch a live demo). Most of my AI chats flow through these projects now.

I even helped my 65+ year old parents set up all 3 tools above so they can use AI to monitor their health and plan their vacations.

Level 3: AI for prototyping

This level changed how I work as a product manager.

Instead of following the broken waterfall process (spec → design → build), I now:

1. Start with a prototype. I create a prototype first using Google AI Studio or Replit by cloning my product’s existing UI and adding a feature on top.

2. Gather feedback fast. I share the prototype with my team, execs, and stakeholders to get feedback and iterate quickly.

3. Create the spec and design. Once I feel confident in the prototype’s direction, THEN I create the spec and design to iron out the edge cases and more.

If you’re still stuck in a culture where people are chasing waterfalls, just start creating and sharing prototypes in meetings. I’ve yet to meet a leader who doesn’t prefer playing with a prototype over looking at a static doc or design. It just brings what you’re trying to build to life so much better. The bottom line is this:

Watch my tutorial with Logan (Google AI Studio lead) for a live tutorial.

Level 4: AI for building apps

This level is when you can’t help but think about building apps with AI all day.

I’ve built over 16 apps including a retro shooter, a personal website, and a board game finder. Check out my GitHub for more projects.

At this point you may be thinking: “But I have no idea how to code or what a terminal even is. Someone needs to show me how!”

Look, you’re just chatting with an AI agent using plain English. If you’re just vibe coding a personal app, you don’t have to look at the code at all. And the terminal is arguably a cleaner interface than an IDE with all sorts of features you won’t use. Just take a look at this cute Claude Code crab:

How to start building

Follow these three tips:

1. Don’t know what to build? Just tell AI your problems. Ask it: “What are some simple apps that we can build to help me take time back?”

2. Create a robust plan first. Ask it to “Create a plan with 3 clear milestones” to avoid mistakes during coding. Spend half your time planning with AI.

3. You’re the manager, AI does the work. Give it feedback and direction like you would a human employee. Be patient with it 🙂

Which AI coding tool to use

Just start instead of worrying about which tool to use. But here’s my rough guide:

Read more

Today, I want to share a new episode with Kieran Klaassen.

Kieran my favorite Claude Code power user and teacher. In our interview, he walked through his Compound Engineering system that makes Claude Code better every time you use it. This same system has been embraced by the Claude Code team and others. Kieran is like Morpheus introducing me to the matrix, so don’t miss this episode 🙂

Watch now on YouTube, Apple, and Spotify.

Kieran and I talked about:

• (00:00) The compound engineering loop: Plan, work, assess, compound

• (03:40) Live demo: The /workflows command to plan your app

• (13:25) How to use Claude Skills as just-in-time context

• (25:27) Why you should always ask Claude to ask you questions first

• (30:00) Live demo: Using Playwright MCP as your AI QA team

• (35:03) Code reviews by AI agents playing security, architect, and more

• (40:33) The LFG command: one prompt to go from 0 to production

• (46:39) Slash commands vs sub-agents vs skills — when to use each

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. Kieran’s compound engineering system makes Claude Code smarter every time you use it. Here's the 4-step loop:

   1. Plan. Sub-agents research your codebase, best practices, and framework versions in parallel before any code is written.

   2. Work. Claude asks clarifying questions first, then builds the feature and writes tests from the plan.

   3. Assess. Review agents check security, architecture, and code quality, then triage findings by priority.

   4. Compound. Capture learnings into docs that Claude reads next time. Simplest version: “add this to claude.md.”

The best part: Kieran packaged this entire system into a free Claude Code plugin with slash commands for each step. Install it here and try it yourself:

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Today, I want to show you how to use OpenClaw to manage your calendar, edit your documents, send yourself personalized briefings, and much more.

Ever since I set up my bot, I’ve barely touched my other AI apps. It’s just so much easier to text my AI employee to do things while on the go.

Watch my tutorial to see me demo 5 use cases, Google Workspace setup, and more.

Timestamps:

• (00:00) Why OpenClaw is my favorite AI bot

• (00:54) How to set up your bot safely in 5 steps

• (03:01) Use case 1: Manage Google calendar

• (05:40) Use case 2: Edit Google Docs and Sheets

• (08:31) Use case 3: Give your bot a voice

• (11:15) Use case 4: Get personalized briefings

• (13:50) Use case 5: Get weekly insight reports

• (15:48) How to connect to Google Workspace

• (21:47) How to personalize your bot’s memory and soul

Watch now on YouTube or read the written guide.

I’m proud to partner with…Linear

My newsletter is called Behind the Craft and few companies exemplify craft like Linear. They’ve built the best platform for teams to work with AI agents. You can:

1. Assign bugs and tickets to coding agents

2. Pull in context from Slack, your support tickets, and your code base

3. Turn that context into specs and plans automatically without manual work

I’m managing my creator business with Linear and you should try it too. Use the link below to get 6 months free to Linear Business now.

Try Linear for Free

Recap: How to set up OpenClaw safely

Let’s first recap how to set up OpenClaw safely so that you don’t get compromised:

1. Run it on a dedicated computer. I installed my bot on a Mac Mini but any old MacBook will do. Keep your Mac on 24/7 using a free app called Amphetamine.

2. Give it its own credentials. My bot (I call her Zoe) has her own Apple and Gmail ID.

3. Run OpenClaw’s security audit. In terminal, type “clawdbot security audit --deep” and then follow its instructions to make it more secure.

4. Give it read access to your main account and write access to select files. For example, Zoe can read my calendar and edit select Google Docs and Sheets but can’t access my entire Google Drive.

5. Never share your bot with anyone else. Don’t add it to any group chats or websites. Zoe can only talk to me.

5 everyday tasks I’ve delegated to my bot

When I talk about OpenClaw, I get questions like:

1. “Why can’t you just vibe code an app for this?”

2. “Is this really better than Claude Code?”

Using my bot doesn’t feel like using an app. It feels like onboarding an AI employee that I can train to do whatever I want her to do.

Here are 5 tasks that I’ve delegated to Zoe:

1. Managing calendar invites

Zoe has view access to my calendar, so I can simply text her to schedule things for me.

For example, I recently texted Zoe to send me a calendar invite for a family trip to SF. It’s way easier than navigating the Google Calendar app:

2. Editing Google documents

Zoe can edit select Google Docs and Sheets that I’ve shared with her.

For example, I asked Zoe to draft a plan for my weekend visit to the city in a Google Doc. She included Caltrain schedules and lunch options at the Ferry Building:

Zoe can also edit specific Google Sheet cells and Google slides just through texting. I demo this in my video tutorial.

3. Reply via voice

Zoe is more fun to talk to when she can reply with voice. To set this up, just ask:

I want to let you reply to me with voice. What are some options to do this?

She found a few voices in Microsoft’s Edge TTS service, which is free to use. I ended up giving her a soothing British voice to tell me corny dad jokes:

4. Daily morning briefs

One of Zoe’s most useful capabilities is that she can schedule recurring tasks that run at a set date and time (also known as cron jobs).

Every morning at 6:30 AM, Zoe sends me a personalized briefing with weather, calendar events, priority tasks, trending AI tweets, and a personalized thought to start my day. She pulls from this info from Google Calendar, Linear, X, and her memory.

Her personalized thought is my favorite. Here’s what Zoe shared with me recently:

5. Weekly insight report

Zoe can also send me emails from her dedicated Gmail account.

Every Friday, she sends me an email with stats for my latest YouTube videos and Substack articles:

1. For YouTube, she uses yt-dlp (free) to pull public video data for me and similar channels.

2. For Substack, there’s no public API access. So I added Zoe as an admin to my Substack and she just uses the browser to pull stats for me!

Here’s what the report looks like:

To try this, just ask your bot:

I want you to send me a weekly email that includes (fill this in).

You probably see where I’m going with this. Zoe (OpenClaw) is the first AI product that I’ve used where it feels like I can just ask her to do anything.

That’s a remarkable feeling.

Setting up your bot with Google Workspace

If you’re like me, you probably run your life using Google Workspace. I’ve been waiting for Google to launch an AI that can send me emails, manage my calendar, edit my Google Docs, and more for years.

It turns out that you can set up your OpenClaw bot to do all this in 10 minutes.

Let me walk you through exactly how in 3 steps:

Read more

Today, I want to share a new episode with Peter Steinberger.

Peter is the creator of OpenClaw (formerly Molt - the name keeps changing 😅), the hottest AI right now with 2M visitors in a week. In our interview, Peter shared is personal favorite use cases including using Claw to check in to flights, control his home, and more. We also talked about his hot takes such as no plan mode or MCPs.

Watch now on YouTube, Apple, and Spotify.

Peter and I talked about:

• (00:00) “It’s like having a new weird friend that lives on your computer”

• (03:52) “It sent me a voice message but I never set that up”

• (15:06) “It watched my security camera all night and found this”

• (16:15) Using OpenClaw to check in flights, change lights, and adjust his bed

• (19:42) Why 80% of your phone apps will disappear

• (22:51) The agentic trap: Why fancy AI workflows produce slop

• (29:42) Peter’s AI coding hot takes: No plan mode, MCPs suck, and more

• (36:56) The way to learn AI is to play

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Top 10 takeaways I learned from this episode

1. Peter’s AI workflow rejects most popular “best practices”:

   1. Default to Codex for coding (not Opus). Codex handles big codebases better with fewer mistakes and less handholding. Opus is great for personality.

   2. No plan mode. From Peter: “Plan mode was a hack for older models. I just write ‘let’s discuss’ and have a conversation.”

   3. No MCPs. “Most MCPs should be CLIs. The agent will try the CLI, get the help menu, and from now on we’re good.”

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Today, I want to show you how to set up OpenClaw (formerly called Clawd), the most impressive AI product I’ve tried since Claude Code.

OpenClaw is an AI employee that you can text at any time to manage emails, edit documents, push code, and so much more on command.

Watch my tutorial now to see my top 5 use cases and learn how to set up OpenClaw safely.

Timestamps:

• (00:00) What is OpenClaw and why everyone's losing their minds

• (02:30) Live demo: Using OpenClaw to send emails, read docs, push code

• (09:27) Do you actually need a Mac Mini to run OpenClaw?

• (10:26) How to set up OpenClaw to run safely (important!)

• (12:41) Step 1: Run the install script and setup wizard

• (13:26) Step 2: Connect OpenClaw and your messaging apps

• (16:10) Step 3: Give OpenClaw access to GitHub, X, and Google Workspace

Watch now on YouTube or read the written guide below.

P.S. I recently interviewed Peter Steinberger, the developer who built Molt, so subscribe to get our exclusive interview this Sunday. 🙏

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1. Granola lets me customize exactly how I want to share my meeting notes.

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What is OpenClaw and what can it do?

OpenClaw is a free, open-source AI assistant that lives in your messaging apps.

It's like having a really smart employee that you can text and assign tasks to. You just need to give it your Claude subscription and install it on a computer for it to work.

I’ll walk through how to set up OpenClaw safely later. First, here’s what it can do:

1. Read X/Twitter timeline

I asked OpenClaw to check my X timeline and it pulled the best tweets from people I follow along with the hot topics of the day.

2. Read, draft, and send emails

I asked OpenClaw to email me a summary of the setup process from its point of view. It wrote the email and sent it to my Gmail in a few seconds.

3. Send me personalized weekly briefings

I asked OpenClaw to look at my Gmail, Google Calendar, and Google Drive to send me a briefing for the upcoming week. It pulled together a briefing with important meetings, emails that need attention, key focus areas, and more.

4. Build and deploy a website

I asked OpenClaw to build and deploy a website covering our interactions so far. It deployed the website in under a minute from a chat message. 🔥

5. Remember context about me

Finally, I asked OpenClaw to read my Google Drive to update its memory based on my Plan and Health documents. Now when I ask OpenClaw questions, it knows my goals, my health priorities, and what I’m working on. Onboarding my new AI employee is complete 😉

How other people are using OpenClaw

I’ve only been playing with OpenClaw for a few days and it’s already blown my mind, but others have customized it to do much more:

1. Health tracking. Connect OpenClaw to Whoop to get daily fitness summaries.

2. Flight check-in. Get OpenClaw to use your browser to check in to flights for you.

3. Smart home control. Control your lights and home appliances with OpenClaw.

One person even set it up to control their smart oven although I have some serious hesitations about doing this. You don’t want AI to burn your house down. 😅

Step by step guide to install OpenClaw in 20 minutes

First things first, you can run OpenClaw on any of the following:

1. Your regular laptop. Your old MacBook works fine to start. Just make sure it stays plugged in and on (no sleep mode) or OpenClaw will go silent.

2. A virtual private server (VPS). Services like Hetzner cost $5-10/month and keep OpenClaw running 24/7 without leaving a computer on at home.

3. Mac Mini. It costs $500 to buy one. The advantage here is that you can just leave your Mac Mini plugged in as a dedicated home for OpenClaw.

I made this guide just running OpenClaw on my old Macbook Pro, so start with that before you decide to invest in new hardware.

How to install OpenClaw safely

If you want to run OpenClaw safely, here’s what I recommend:

1. Run it on a dedicated computer. This can be a Mac Mini that’s powered on 24/7 or just an old laptop. You can try it on your main machine, but I definitely recommend switching to a dedicated computer as soon as possible.

   Give it its own credentials. I set up my Mac Mini with its own Apple ID, Gmail, and Google ID. This way OpenClaw doesn’t have access to my main accounts. Instead, I’ve shared specific Google Docs, Sheets, and Drive files for it to read and edit.

Again, OpenClaw is still early software. You don’t want it to read a malicious email that has a prompt injection to delete all your files. That’s why I recommend doing the above.

Now let’s walk through the install process step by step:

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Today, I want to share a new episode with Logan Kilpatrick.

Logan is the Product Lead for Google AI Studio. I got him to give me an inside look at how he uses AI Studio to build AI Studio (very meta) and how his team ships at startup speed inside Google. I’ve been using AI Studio non-stop for prototyping and it has genuinely made my PM job more fun, so don’t miss this tutorial.

Watch now on YouTube, Apple, and Spotify.

Logan and I talked about:

• (00:00) Tour of Google AI Studio's Build mode

• (06:29) Live demo: Building a social media content generator

• (09:31) How Logan uses AI Studio to build AI Studio

• (11:34) Cloning the AI Studio UI in 68 seconds

• (12:58) Getting AI to show you 5-6 design options in one UI

• (17:20) Live demo: Building a restaurant finder with Google Maps

• (21:12) Using annotate mode to give visual feedback

• (34:09) "There's one mode and the mode is we ship fast"

• (36:00) What Logan looks for when hiring PMs

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Top 10 takeaways I learned from this episode

1. Clone existing UIs instead of prompting from scratch. Logan’s go-to workflow is screenshotting his existing AI Studio interface, pasting it into AI Studio, and saying “clone this UI exactly.” He then prototypes new features on top of this template.

2. Explore multiple UI options in a single view. Instead of building separate prototypes, he asks the AI to add a button that toggles between design variations in one UI. Simply prompt AI Studio: “Add a widget so I can click through these styles: [describe 3-5 different styles].”

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