Today, I want to share a new episode with Nan Yu and Jacob Shumway.

Nan and Jacob built Linear Agent, which has transformed how the $1.25B company gets work done. In this episode, they walk through how they built an AI agent from the initial memo to launch, including how to give it tools to find the context it needs and how to design evals that make the agent more reliable. If you want a concrete, behind-the-scenes example of how to build a production agent end to end, this episode is a must-watch.

Watch now on YouTube, Apple, and Spotify.

We talked about:

• (00:00) What’s holding AI agents back as models improve?

• (00:51) Three things every good agent needs

• (03:00) Demo: The internal memo that started Linear Agent

• (09:50) The biggest mistake to avoid when building agents

• (11:29) Demo: From Slack to code in 6 minutes with the agent

• (17:01) When prototyping, “use the biggest model until it works”

• (24:02) Give agents tools to find context, not more context

• (34:07) The first thing to do before building an agent

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Top 10 takeaways I learned from this episode

1. The first step to building a good agent is to map the actual workflow. Identify where work starts, which systems hold context, what actions complete the job, what “done” means, and where a person needs to review. If work starts in Slack, make Slack the on-ramp instead of forcing users into a separate chatbot.

2. Give agents tools to find context instead of stuffing context into the prompt. Jacob’s advice: “Give it as little instruction as possible, to be honest. Give it the tools to load context. Don’t give it context.” Start small, then let the agent pull only the instructions and data it needs for the current task.

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Today, I want to share a new episode with Karan.

Karan is the co-founder of Nous Research, the company behind Hermes, the #1 personal agent and AI app on OpenRouter. We had a great chat about why open-source AI must win and how Hermes gets better the more you use it. Karan also shared a live demo of how he used Hermes to modify his favorite childhood game.

Watch now on YouTube, Apple, and Spotify.

Karan and I talked about:

• (00:00) How Hermes differs from Claude Code and Codex

• (02:20) Why every AI model now sounds exactly the same

• (11:16) How to stop AI from saying “you’re absolutely right”

• (15:16) How Hermes creates skills without bloating its context

• (16:40) The open vs. closed-source debate on AI safety

• (23:40) Using one Kanban board for humans and AI agents

• (26:45) Demo: Rebuilding a childhood Sonic world with Hermes

• (36:07) How two Discord friends started Nous Research

• (42:40) How Hermes became its own biggest contributor

I’m proud to partner with Linear

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Top 10 takeaways I learned from this episode

How to make Hermes work for you

1. The “personal” in personal agent is not the model. Instead, it’s the agent’s memory of your conversations and the skills that you’ve built with it. Karan barely notices any difference in Hermes’ personality when he swaps models because his agent has accumulated so much context about him. The model is interchangeable, but the context layer is what you should cherish and preserve.

2. Customize Hermes’ soul.md to the personality that you want. After using Hermes for 6+ months now, I’ve customized its soul.md to have:

   • Core truths like be genuinely helpful and avoid sycophancy.

   • Boundaries like respecting my privacy especially in any group chats.

   • The right vibes that make it fun to talk to.

   Here’s my complete soul.md that you can copy and paste into Hermes or another AI agent to improve its personality:

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Today, I want to share a new episode with Jason Liu.

Jason is a DevEx engineer on the Codex team. In our episode, he showed me how he runs his entire workday using Codex, from his chief of staff thread for Slack and email to setting up long-running tasks that have verifiable goals. Jason wrote the official OpenAI handbook on how to get the most out of Codex, so don’t miss this episode.

Watch now on YouTube, Apple, and Spotify.

Jason and I talked about:

• (00:00) The only job left after AI can do the work

• (02:40) Turn pinned threads into long-running workspaces

• (05:50) Build a chief of staff across Slack, email, and Linear

• (10:12) Teach Codex to write like you for every audience

• (13:33) Mine past sessions for new skills and workflows

• (20:01) How Codex revised a launch video while Jason was out

• (24:23) Build a drum practice site around how you learn

• (25:59) Goal, plan, and worklog for long-running Codex tasks

• (33:52) Why taste starts with knowing what feels wrong

I’m proud to partner with Google AI Studio

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Top 10 takeaways I learned from this episode

Build a Codex system that improves itself

1. Build a chief of staff Codex thread that manages your email, Slack, and more for you. At 9 am, 1 pm, and 5 pm, Jason’s chief of staff thread reviews his Slack, emails, Twitter DMs (through browser use), and Linear before surfacing priorities with links and draft replies. Here’s a prompt you can modify to set this up:

2. Ask Codex to look at your past sessions to build new skills and workflows. Codex has memory of all your past conversations, so the best way to save time is to ask it to find workflows from your manual iterations that it can build new skills to automate. Here’s a prompt you can paste to do this right now:

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Today, I want to share a new episode with Thariq Shihipar.

Thariq works on the Claude Code team, and I’ve wanted to see how he builds for a long time. In our episode, he showed how to use /goal to keep Claude working, how he plans with Claude to remove unknowns before building, and how he runs a team of agents in Slack. He also shared why his team cut Claude Code’s system prompt by 80% and how he creates polished launch videos with Claude.

Watch now on YouTube, Apple, and Spotify.

Thariq and I talked about:

• (00:00) Goals, loops, and workflows to make Claude work longer

• (04:48) Live demo: Creating a polished video with Claude

• (08:17) Plan by removing unknowns before you build

• (14:32) How Anthropic uses HTML artifacts for planning

• (18:35) Running parallel Claude tasks through Slack

• (27:34) Using separate creator and verifier sub-agents

• (31:08) How Thariq stays focused while Claude works in parallel

• (34:07) Why Anthropic cut Claude Code’s system prompt by 80%

• (37:01) How to become more technical when AI writes the code

I’m proud to partner with Wispr Flow

When I’m building with Claude Code, I use Wispr Flow to dictate all my prompts instead of typing. It’s more accurate than other AI voice dictation tools and saves me hours every week. Try it out for free with my exclusive link below.

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Top 10 takeaways I learned from this episode

1. When a better AI model is released, the first thing you should try is to remove instructions. Thariq and the Claude Code team cut Claude Code’s system prompt by 80% because hard rules and examples were constraining newer models. When a new model is released, audit your CLAUDE.md and skills to remove deterministic instructions.

2. Plan with Claude to discover and remove unknowns. Before building his one-shot video workflow, Thariq asked Claude to explain how voice transcription tools like Whisper could fail. This helped him clarify the plan before getting Claude to create the video. Perhaps the most important learning here is that he actually reads the plan manually instead of just trusting AI.

3. Use /goal only if you have a verifiable finish line. I asked Claude to “/goal Build me a great game” and the results were…not great. Thariq shared how to use /goal to create polished videos with Claude by providing a verifiable end state (e.g., a rendered video). Here’s the exact prompt he used to create the video in one shot:

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Dear subscribers,

Today, I want to share a new episode with Jared Zoneraich.

Jared is a Builder in Residence at Cognition, the team behind Devin. He showed me why teams often overbuild their first agents, and how simple prompts, better tools, and self-testing work together instead. Then he demoed one manager agent breaking down a task, directing 10 cloud agents, and deciding what a human needs to review.

Watch now on YouTube, Apple, and Spotify.

Jared and I talked about:

• (00:00) Why teams overbuild their first AI agent

• (02:50) Let the model cook before adding rules

• (03:16) Why tools matter more than better prompts

• (10:51) Why you should build evals after shipping

• (16:06) What cloud agents can do that local agents can’t

• (20:30) How to get one agent to manage other agents

• (25:39) Demo: One agent launches a team of 10

• (28:03) How agents prove their work before you review it

• (33:18) The best tasks for agent teams to work on

I’m proud to partner with Riverside

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Top 10 takeaways I learned from this episode

How to build great AI agents

1. Start by expressing your intent to the model in a simple prompt. “Let the model cook” by expressing your intent and constraints clearly instead of forcing it to follow deterministic instructions.

2. Give the model relevant tools to pull context. “Tool engineering” is giving the model ways to search your code, read logs, run tests, open the app, and inspect what it changed.

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Dear subscribers,

Today, I want to share a new episode with Rohan Varma.

As a product manager on OpenAI Codex, Rohan has a firsthand look at how one of the most AI-native teams in the world works. He showed me how he uses Codex to automate work from Slack replies, prototype designs with image generation, replace stale PRDs with live sites, and more. Watch this episode if you’re tired of all the nonsense around “AI PM” and want to see how a real one actually operates.

Watch now on YouTube, Apple, and Spotify.

Rohan and I talked about:

• (00:00) Codex can do so much more than you think

• (01:53) Use Codex to pull context from Slack, docs, Linear, and email

• (03:28) How Codex flips planning from docs to demos

• (13:14) Demo: Do the task once with Codex, then automate it

• (14:30) Demo: Trigger Codex automations from Slack replies

• (17:26) Demo: Use Image Gen to explore design variants fast

• (19:58) Demo: Turn any Codex thread into a reusable skill

• (29:55) Demo: Turn project context into a shareable Site

• (33:33) Use one Codex thread to manage other Codex threads

• (37:18) Aim to be 10x more ambitious than feels reasonable

I’m proud to partner with Oceans Talent

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Top 10 takeaways I learned from this episode

Set up Codex as your work layer

1. Connect Codex to the tools where your work already happens. Rohan connects Codex to Slack, Notion, Linear, Gmail, Drive, and other tools. The magic starts when it can pull context from multiple connected tools and navigate to webpages that it has permission to access.

2. Use Codex to get up to speed on any project fast. Rohan said he can get added to a project last minute and quickly catch up by asking Codex to gather the Slack history, docs, Linear issues, email, and Drive context he is allowed to access.

3. Codex handles the manual work so PMs can spend more time with users. Since Codex can handle the manual PM work around information synthesis and follow-ups, Rohan has more time now to spend meeting enterprise customers and teams.

Automate the repetitive work

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Today, I want to share a new episode with Jess Yan.

As product lead at Anthropic, Jess has a first-hand look at how Anthropic teams are moving from prompting AI to building long-running agents that work overnight. In our episode, Jess showed me how to build a Claude analytics agent from scratch, then covered how Anthropic PMs use agents internally to understand the codebase, monitor customer feedback, and make better product decisions.

Watch now on YouTube, Apple, and Spotify.

Jess and I talked about:

• (00:00) AI agents that work while you sleep

• (03:46) Claude Managed Agents help for long-running tasks

• (05:22) Demo: Building a Claude analyst agent from scratch

• (09:28) Traces: How to see where an agent got stuck

• (15:11) Evals: How agents grade their own work

• (20:20) 5 ways in which Anthropic PMs use agents internally

• (28:06) Processing a 4,000-company waitlist with agents

• (32:50) To build your first agent, focus on helping one person

• (36:54) What changes when agents can work overnight

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Top 10 takeaways I learned from this episode

Jess and I covered two topics in the interview:

1. How Anthropic PMs use agents internally

2. How to build long-running Claude agents

How Anthropic PMs use agents internally

Anthropic PMs use agents internally for at least five PM workflows:

1. Understanding the codebase. Instead of asking engineers what shipped or how something works, Jess can inspect pull requests, deployments, and product behavior directly. “Access to our codebase has been the biggest unlock for me.”

2. Synthesizing user feedback from multiple channels. Jess has agents monitor customer Slack channels to get more signal before deciding what to build next.

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Today, I want to share a new episode with Bin and Jake.

Bin and Jake built HyperFrames, a free open-source tool that turns HTML into product launch videos that used to cost thousands of dollars ot make. In this episode, they showed me how to one-shot a video from any website. Jake also revealed his full 5-step video workflow to make polished videos with Codex or Claude Code.

Watch now on YouTube, Apple, and Spotify.

We talked about:

• (00:00) From HTML to professional launch videos with AI

• (02:16) Getting started: Install HyperFrames for free

• (03:50) /website-to-video: Turn any URL into a launch video

• (08:28) Jake’s 5-step playbook to generate better videos

• (10:03) Step 1: Gather assets and create a frame.md

• (11:46) Step 2: Create a storyboard.md with the scenes

• (12:45) Step 3: Pull animations from HyperFrames launches

• (15:43) Step 4: Generate and review static frames

• (17:20) Step 5: Build the video and polish in Studio

I’m proud to partner with Wispr Flow

When I’m building with Codex or Claude Code, I use Wispr Flow to dictate all my prompts instead of typing. It’s more accurate than other AI voice dictation tools and saves me hours every week. Try it out for free with my exclusive link below.

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Getting started with Hyperframes

1. Install HyperFrames for free to make videos from HTML. Hyperframes is a free and open source repository for making videos. To install it, just type “npx skills add heygen-com/hyperframes” in terminal or point Codex or Claude Code at the repo.

2. After HyperFrames is installed, try the website-to-video skill first. In Codex or Claude Code, prompt “Use /website-to-video to turn [paste URL] into a 30s promo video.” Paste a website you like, and HyperFrames will make the launch video in one shot by pulling the site’s screenshots, assets, copy, and brand look.

Jake’s 5-step playbook to generate better videos

While Hyperframes makes it easy to generate basic videos, it still takes a bit of work to generate professional animated videos like the above. Jake shared his 5-step playbook with me to do just that:

Here’s a detailed walkthrough of each step:

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Today, I want to share a new episode with Matt Van Horn.

Matt doesn’t have a CS degree, know how to code, or even read the plans that AI writes. Despite this, he’s built an incredible open source track record with projects like last30days (#1 trending on GitHub with 44K+ stars) and Printing Press (a tool that lets agents use any website or app). Matt has been a huge inspiration for my AI builder journey, and I think you’ll feel the same after watching our conversation.

Watch now on YouTube, Apple, and Spotify.

Matt and I talked about:

• (00:00) "I don't read code and the plans are for AI"

• (01:19) Compound Engineering: Matt’s go-to agentic loop

• (07:07) How Matt builds without a CS degree or reading code

• (11:18) Printing Press: Turning any website into an agent CLI

• (15:38) How Printing Press finds hidden APIs and GitHub projects

• (22:44) Why CLI + skill beats MCP for agent workflows

• (32:49) How Matt contributes to Python, Go, and other top repos

• (44:28) Why Matt is still afraid to launch finished projects

I’m proud to partner with Riverside

I’ve used Riverside for years because it records each person locally in up to 4K, so my podcast audio and video stay clean even when a guest’s wifi gets shaky. Now it also helps me edit from the transcript, clean up audio, generate captioned clips, and publish everywhere from one place. Use code PeterYang at checkout for 1 month free.

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Top 10 takeaways I learned from this episode

1. Use Printing Press to give your AI access to almost any website or app. Matt open sourced Printing Press to let anyone generate a CLI (command line interface) for any website or app. It works by researching docs, finding niche GitHub repos, and sniffing hidden API requests, so agents can use sites like Google Flights, Suno, OpenArt, and more.

2. If you’re non-technical, use Compound Engineering skills to build. Compound Engineering is a set of AI coding skills that lets anyone build with AI in a more structured way. Matt’s go-to skills include:

   1. /ce-plan to research the codebase, edge cases, and implementation path.

   2. /ce-work to execute the plan while following the project’s patterns.

   3. /ce-brainstorm to think through the feature and produce a requirements doc.

   4. /lfg to run the full loop from planning to implementation to PR.

To install it, point your favorite coding agent (e.g., Codex / Claude Code) at this link:

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Today, I want to share a new episode with Kun Chen.

Kun is an ex-L8 principal engineer at Meta who now ships up to 40 PRs a day while rarely reviewing code. In our episode, he walked through the free tools he built to make that possible: Lavish for visual planning in HTML, Treehouse for parallel agents, and No Mistakes for catching AI’s errors before they make it to production.

Watch now on YouTube, Apple, and Spotify.

Kun and I talked about:

• (00:00) Why he doesn't review code anymore

• (01:04) Agentic engineering: Plan, code, validate

• (06:22) Demo: Fixing an AI tutor screen with agents

• (08:40) Demo: Why HTML is better than markdown for planning

• (19:53) How to turn a rough idea into an AI-ready spec

• (23:21) How Kun runs 20-30 agents in parallel

• (32:04) No Mistakes: Kun's free AI code review tool

• (45:19) What Kun checks before merging AI-written code

• (50:18) How to get better at agentic engineering

I’m proud to partner with Linear

As teams ship faster with AI agents, the bottleneck shifts from building to deciding what to build.

Linear’s new Agent understands your full workspace context: roadmap, issues, customer requests, and code. Ask it to surface patterns across feedback, scope out a spec, or catch you up on team progress.

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Top 10 takeaways I learned from this episode

1. Code faster with a team of AI agents. Kun sees himself as the manager of an always-on engineering team. His job is to create plans, validate work, and improve the overall system. Specifically, he runs the same three phases each time:

   1. Plan: Building clear plans is now the most important phase.

   2. Implement: Coding is now handled almost entirely by agents.

   3. Validate: Agents check the work first and only escalate to Kun if needed.

Plan: Spend most of your time here

2. Plan quality determines how long agents run on their own. A one-line prompt might get the agent to work for a few minutes while a detailed plan can keep it working for hours. To delegate more work to agents, move up these three planning levels:

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Today, I want to share a new episode with Josh Pigford.

Josh sold his last startup for $4M and is now building 5 products solo with agents. He showed me his exact AI skills stack, including a 4-step /build skill, an /adversarial-code-review skill where he pits Opus against GPT, a /but-for-real skill that makes the AI catch its own mistakes, and a /learnings skill that improves AI’s output over time. Josh has been a solo builder for 25 years and it was fascinating to watch how he works.

Watch now on YouTube, Apple, and Spotify.

Josh and I talked about:

• (00:00) Why launching fast beats months of planning

• (01:02) A tour of the 5 AI products Josh built solo

• (05:22) /build skill: Research, plan, track, implement

• (10:52) /adversarial-code-review skill: Getting GPT to review Opus' code

• (16:02) /learnings skill: Make AI update its own CLAUDE.md

• (16:47) /but-for-real skill: Make AI fix its own mistakes

• (19:11) Live demo: How to design non AI slop websites

• (28:55) Advice for builders without technical experience

If you enjoyed my chat with Josh, check out his new community for AI builders.

I’m proud to partner with Wispr Flow

There are many AI voice dictation tools out there but I’ve found Wispr Flow to be the most accurate at transcribing words. My kids love using it to dictate to Codex and Claude Code to build games and apps. Try it out with my free link below.

Try Wispr Flow for Free

Top 10 takeaways I learned from this episode

1. Keep shipping even though the fear of being embarrassed never goes away. After 25 years as a solo builder, Josh still thinks “It’s terrifying launching something. Every single time it’s just like, what if zero people care?” But he ships anyway: “The idea of spending months working on something before you put it out for other people to use, I think that’s a real bad idea.”

2. Use Josh’s /but-for-real skill to force AI to check for mistakes thoroughly before pushing a change. Josh finds that the skill surfaces at least 3-5 additional bugs that AI missed each time he runs it. The skill (see snippet above) is hilarious because it bullies the AI to work harder. You can find the full skill to copy at the end of this post and in my new skills library at behindthecraft.com.

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

Ryan is a serial founder who’s building his latest startup solo with only AI agents. It was fascinating to watch him demo the exact skills and routines he uses to get OpenClaw, Codex, and Devin to manage his inbox and calendar, do sales outreach, and build features while he sleeps.

Watch now on YouTube, Apple, and Spotify.

Ryan and I talked about:

• (00:00) Agents are just cron jobs and text markdown files

• (02:25) Demo: How Ryan set up OpenClaw to be his AI chief of staff

• (08:14) The setting most people get wrong in OpenClaw

• (13:46) How to onboard your agent like you would an employee

• (17:06) The cron job that books cold meetings while Ryan sleeps

• (24:36) Why Ryan prefers Devin over other AI coding tools

• (28:33) Demo: How Ryan ships 10 PRs a day solo

• (33:08) Google Ads agencies and a $6K/month designer

• (37:11) Why building the system is the real startup work

I’m proud to partner with Wispr Flow

There are many AI voice dictation tools out there but I’ve found Wispr Flow to be the most accurate at transcribing words accurately. My kids love using it to dictate to Codex and Claude Code to build games and apps. Try it out with my free link below.

Try Wispr Flow for Free

Top 10 takeaways I learned from this episode

Build your AI chief of staff with OpenClaw

1. Make OpenClaw your AI chief of staff with these 4 skills. Each skill is a markdown file that teaches OpenClaw how to do one job:

   1. executive-assistant: Checks email every 15 minutes, watches your calendar, and pings you on Slack when there’s an important email or calendar conflict.

   2. business-development: Searches for sales prospects nightly, adds them to a Google Sheet, and sends personalized email outreach as your agent.

   3. daily-task-manager: Keeps one canonical task list instead of guessing from chat history.

   4. daily-task-prep: Preps your task list before you wake up, pulling from calendar, unread emails, and outstanding outreach.

To copy these skills, first install OpenClaw (or Hermes). Then paste this link into Codex or Claude Code to duplicate Ryan’s full chief of staff system:

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Today, I want to share a new episode with Alex Albert.

Alex is a research PM at Anthropic building the next Claude model. He gave me a rare inside look at how the research team operates — including how they turn user feedback into model training, how they decide which capabilities to prioritize, and how they improve Claude’s personality. If you’re wondering how frontier model development actually works, then this interview is a must-watch.

Watch now on YouTube, Apple, and Spotify.

Alex and I talked about:

• (00:00) How Anthropic treats each new model as a product

• (04:58) Building adaptive thinking into Claude

• (07:07) Why Claude is starting to dream

• (11:15) "If it's not a one-way door, then it's essentially free"

• (15:40) How Alex uses Claude Cowork to pressure test his docs

• (17:05) Inside Anthropic's eval process for new models

• (21:02) How Anthropic trains Claude's character

• (32:39) The consciousness question Anthropic is quietly working on

I’m proud to partner with Oceans

I have to give a shout out to Char, my executive assistant from Oceans, for helping me run the podcast. Here’s how we work together:

1. I gave him my AI skills for drafting show notes and clips

2. He’s iterated on them across episodes to make them better

3. We both do a manual review at the end to apply human taste

Now we have a system that saves us hours every week. If you need help with marketing, operations, finance, or an executive assistant, I highly recommend giving Oceans a try. Book a call at oceanstalent.com/peter.

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Top 10 takeaways I learned from this episode

I’ve organized these takeaways into 3 sections:

1. How Anthropic is building the next Claude

2. How to get your AI product ready for the next model

3. How Anthropic research PMs work

How Anthropic is building the next Claude

1. Anthropic decides what each model should be good at before training. Alex’s team picks the big capability bets upfront before pre-training (e.g., coding, knowledge work). These bets come from talking to enterprise customers and from Anthropic employees hitting roadblocks in their own work.

2. Think about the model and the harness together. The model and the harness are coupled. Each surface wraps the model in a different prompt and tool setup, so the same model can give different responses depending on where it runs. As a research PM, Alex has to think through how the model will perform across Claude, Cowork, Claude Code, and more.

3. Anthropic has full-time researchers thinking about Claude’s consciousness. Anthropic has people whose whole job is to think about what it means for Claude to be a conscious actor. There’s no official position on whether it is or isn’t, but the question is taken seriously as agents take on more autonomous work.

How to get your AI product ready for the next model

4. Test on the latest model and build minimal scaffolding. The two most common mistakes that Alex sees teams make when building AI products:

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Today, I want to share a new episode with Moritz Kremb.

Moritz is an AI creator who built a personal OS in Claude Code to manage his emails, create content, and even buy groceries. In this episode, he walks through everything you need to set up your own: folder structure, tools, memory loops, skills, and routines. We also talked about the pros and cons of OpenClaw vs. Claude Code.

Watch now on YouTube, Apple, and Spotify.

Moritz and I talked about:

• (00:00) The honest tradeoffs: OpenClaw vs. Claude Code

• (09:03) The reliability problem that made Moritz switch

• (15:40) The folder structure that runs Moritz's entire OS

• (20:03) The memory loop that makes Claude Code remember everything

• (23:47) CLI vs. MCP vs. API: which to pick and why it matters

• (26:02) Skills walkthrough: automated grocery runs and video uploads

• (29:03) Local vs. remote routines (and when to use each)

• (31:51) The full content pipeline from idea to posted video

• (41:43) Tips to start building your first personal OS

I’m proud to partner with Linear

As teams ship faster with AI agents, the bottleneck shifts from building to deciding what to build.

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Top 10 takeaways I learned from this episode

1. Build your Claude Code personal OS in 4 layers. Moritz runs his email, content pipeline, and grocery shopping through his Claude Code OS. It has 4 layers:

   1. Folder (with memory): A set of files Claude Code reads at the start of every session so it knows who you are, what tools it has, and what it remembers from past chats.

   2. Tools: The APIs, MCPs, and CLIs that let Claude complete tasks like send emails, post to social, search Google Drive, and more.

   3. Skills: Detailed instructions for tasks that Moritz does over and over like “upload my video files” or “refill my grocery cart.”

   4. Routines: Scheduled jobs that run proactively.

Let's walk through how to set up each layer below:

2. Set up your folder structure first. Get this right before setting up tools or skills so that your OS stays organized. Critical files include:

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Today, I want to share a new episode with Ravi Mehta.

Ravi is a top AI instructor and former CPO of Tinder. In our episode, he did a live demo of using his 3-layer context engineering system (functional, visual, data) to build a beautiful music app instead of the usual AI slop. You’ll never prompt AI the same way again after learning about Ravi’s approach.

Watch now on YouTube, Apple, and Spotify.

Ravi and I talked about:

• (00:00) Why most AI prototypes feel like slop

• (01:08) Context engineering, explained from first principles

• (05:12) Layer 1 demo: Functional context

• (09:38) Layer 2 demo: Visual context

• (13:31) Layer 3 demo: Data context

• (15:47) Building a custom MCP server in Claude Code

• (19:54) The full stack prompt: all 3 layers in one shot

• (24:21) Why separating the data layer is the real unlock

• (28:59) Should PMs prototype or just ship to production?

• (33:51) Where PRDs still fit in AI native product development

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Top 10 takeaways I learned from this episode

1. Apply three layers of context to create polished AI prototypes. Ravi built the same music app in 3 ways to highlight what each context layer adds:

   • Functional context. First, he wrote a spec listing the feature requirements (hero, albums, etc) and got a generic app with placeholders.

   • Visual context. Next, he added a wireframe from Figma to the prompt. This led to better UX and layout but the product was still missing real data.

   • Data context. Finally, he asked AI to generate a JSON with genre, artist, and album names and used a custom MCP server to pull in album cover art from Deezer. This time, AI generated a complete music discovery app.

The best thing about this system is that you can swap the data layer (JSON) at any time to have your app highlight completely different content (e.g., from rock to hip hop albums) without touching the other two layers.

Here’s a detailed prompt that Ravi shared with me to generate all 3 layers in one-shot for any app or idea that you have (just replace the music app details with yours):

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Today, I want to share a new episode with Tibo Louis-Lucas.

Tibo is a solo founder making $1M+ a month (!) after bootstrapping 5 AI products. I got him to walk through his exact playbook for validating ideas fast, acquiring users, and pricing AI products. If you want to bootstrap an AI business from scratch, this interview is a must watch.

Watch now on YouTube, Apple, and Spotify.

Tibo and I talked about:

• (00:00) The right mindset for solo AI founders

• (04:48) Tibo's 5-rule playbook to reach $1M/month

• (09:55) The product pivot that unlocked $600K/month

• (13:08) Why you should charge money right away

• (20:50) The acquisition channel everyone says is dead (it's not)

• (26:58) Running 5 businesses without a real team

• (36:34) How to price your AI product

• (37:20) What's a good churn rate for AI products?

• (40:16) 9 products failed. The 10th hit $600K/month

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Top 10 takeaways I learned from this episode

1. Tibo’s 5 rules behind his $1M/month AI portfolio. After launching 5 successful AI products (and many failed ones), Here’s Tibo’s rules to build an AI product that customers want:

   • Charge money from day one. Collect money early so you know if people actually want what you're building.

   • Follow user behavior, not your plan. When people twist your product into something unexpected, build for that instead.

   • Price for quality customers. Charging $50-100/month keeps away high-churn users who cancel fast and flood customer support.

   • Build free tools that rank on Google. Target each tool to a specific search query (e.g., “audio to video”) to win SEO traffic.

   • Launch fast and kill what doesn't work. Accept that most ideas will fail, so move quickly and double down on what sticks.

Let's dive into these rules with real examples 👇

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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

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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.

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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.

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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."

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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:

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