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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

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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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OpenAI just made GPT 5.6 available to everyone and I’m here to answer your most burning question:

Is GPT 5.6 better than Claude Fable 5?

The answer is, it depends. 😅

Watch my tutorial to see me test both models head-to-head for building a travel website, 3D Star Fox game, mobile app, and more.

Timestamps:

1. (00:00) GPT-5.6 vs Claude Fable 5 compared

2. (01:45) Build an interactive travel website

3. (05:44) Create a 3D Star Fox-style game

4. (09:39) Edit and publish short videos with browser use

5. (15:10) Plan and build a mobile app feature

6. (19:34) Get personalized life and business advice

7. (22:17) Improve and clean up a personal AI OS

8. (24:37) My takeaway on each model’s strengths and weaknesses

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GPT 5.6 vs. Fable benchmark and cost comparison

GPT 5.6 comes in three models: Sol, Terra, and Luna.

Sol is the flagship model and based on TerminalBench it actually performs slightly better than Claude Fable 5 at coding:

But I think OpenAI’s biggest advantage is model efficiency and cost. GPT 5.6 Sol is 50% cheaper than Claude Fable 5 at API prices:

That’s a big deal because enterprises are increasingly looking for the most cost-effective model. However, benchmarks only tell half the story, let me show you how both models perform on the 6 use cases below:

1. Build an interactive travel website

GPT has historically lagged behind Claude on front-end design so I wanted to see if GPT 5.6 has closed that gap. I’m planning a trip to Japan in December so I gave both models my Google Doc itinerary and the prompt below:

Tip: Ask AI to “add a 3D WebGL element to the hero banner” if you want to create websites with beautiful animated 3D graphics

I ran the same prompt through GPT 5.5, GPT 5.6, and Fable:

1. GPT 5.5 created a functional website but botched the 3D hero image.

2. GPT 5.6 created an animated torii gate and found more relevant photos.

3. Fable 5 created beautiful animated snowflakes in a winter scene.

Watch my video to see how all 3 websites look.

Verdict: Tied because GPT 5.6 has really closed the gap with Fable on frontend design.

P.S. We’ve started hosting regular live workshops in my Slack community for AI builders. Upcoming workshops include:

1. July 17 - AI native design (design.md, components, etc) with Julie Xu

2. July 24 - Building production apps with Colin Matthews

3. August - Deep dive on AI evals with Hamel Husain

All workshop recordings will be shared here for paid subscribers.

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2. Create a 3D Star Fox style game

Growing up, Star Fox was one of my favorite games on the Super Nintendo. So naturally, I had to ask both GPT 5.6 and Claude Fable 5 to build a fully playable Star Fox level complete with enemies, power-ups, and even a boss battle.

Both produced incredibly impressive results:

Here’s the exact prompt that I used to build these games in one shot:

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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

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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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Dear subscribers,

Today, I want to share 18 hot takes on where I think the AI market is headed.

AI is in a weird place right now. The government is restricting access to frontier models, enterprises are becoming conscious of token costs, and everyone’s trying to rebuild their product for agents first instead of humans.

I’ve interviewed dozens of AI leaders and spent far too much time following these topics on X/Twitter. Here are 18 hot takes on where I think AI is headed next:

1. The frontier-only AI stack is collapsing

2. The AI super app era is here

3. Traditional software risks becoming a dumb pipe for agents

4. Cloud agents and collaboration are the next wave

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The frontier-only AI stack is collapsing

1. Tokenmaxxing at frontier API prices makes no sense. Uber burned through its entire 2026 AI budget in 4 months, Microsoft moved engineers off Claude Code due to cost, and companies are realizing that running everything on frontier models can get expensive fast. Tokenmaxxing makes sense when you’re on a subsidized $200/month plan but is unsustainable at API rates.

2. Companies will rely on a portfolio of models. Coinbase recently cut its AI spend nearly in half by switching engineers to Chinese open-source models like GLM and Kimi. Airbnb and Pinterest have done the same with Alibaba’s Qwen models. I believe that this will be the default path forward — using frontier for high-stakes work and cheaper models for everything else.

   

3. China’s open-source strategy is working. Chinese models are taking market share from frontier models at US companies. China is also building the full AI stack — from energy (e.g., solar, nuclear) to data centers to domestic chips. The Chinese government is planning a $295B investment in AI data centers with at least 80% of the chips built domestically.

4. Frontier labs are in a catch-22 situation. If they release great open-source models, they might undercut their own frontier API revenue. If they gate the best models behind a trusted list, companies will just lean on open alternatives more. The last major US open-source model was OpenAI’s gpt-oss series back in August 2025, which already feels like decades ago in the AI space.

   

5. The US needs to think about its AI strategy holistically. I believe that restricting access to frontier models will only hurt American innovation. Banning US companies from Chinese models won’t work either. Just look at how China took over the global electric vehicle market. To compete, we need the best closed and open models while scaling our energy grid and data centers much faster.

The AI super app era is here

6. Anthropic and OpenAI want to transform all knowledge work. Both labs want to remake how white-collar work gets done with agents. What started with coding has expanded to design, finance, legal, science, and more. Knowledge workers are now adopting Codex three times faster than developers.

7. The race to build the best AI super app is on. Whoever wins it will take massive market share away from pre-AI knowledge work tools. The main players are Codex, Claude, Cursor, and a few others. With browser and computer use, I can already get Codex to handle almost everything I want to do on my computer.

8. OpenAI is well positioned to win the super app race. ChatGPT has 800M+ weekly active users and Codex is arguably the best AI super app right now. OpenAI has a clear opportunity to merge ChatGPT and Codex into one app and push agents into the mainstream. I think this is imminent.

   

9. Anthropic leads enterprise but has a fragmented product suite. Long term, I don’t think it makes sense for Claude, Cowork, Claude Code, and Claude Design to all exist as separate products. Working with Claude needs to feel more like working with a single capable coworker instead of a product suite. Claude Tag, Anthropic’s new agent for Slack, is a step in the right direction.

Software risks becoming a dumb pipe for agents

10. Agents are becoming the default user. Most products are still designed for humans clicking buttons, but agents will very soon read, write, buy, and do a lot more on our behalf. Cloudflare’s data shows that bot traffic to websites already exceeds human traffic. This matches my personal experience — I rely on agents like Hermes and Codex to browse websites and steer traditional apps.

    

11. If your product isn’t built for agents, it might as well not exist. Without clear APIs, documentation, and CLIs or MCPs, agents simply won’t discover your product, and neither will the humans who now lean on agents to find things. That’s why the best AI builders I’ve interviewed are all building for agents first.

12. Existing knowledge work products risk becoming dumb pipes. There’s a dark side to building everything for agents first. Take Google Workspace, for example. If I can just use Codex to edit Google Docs for me, I might never touch the Google Docs interface. Today’s apps risk becoming tomorrow’s dumb pipes for agents, the same way cable became the dumb pipe for internet apps.

13. Adding an AI chatbot to your product isn’t going to save you. A chatbot trapped inside a single app, with only that app’s context, can’t compete with an agent like Hermes or Codex that carries my personal context and gets work done across many apps. For example, I get a daily morning brief from Hermes that pulls info from my calendar, unread emails, and Slack.

14. Pure-play SaaS is losing to services with SaaS bolted on. Companies don’t want another SaaS tool to onboard to and maintain. Instead, they want an outcome delivered to them. Pure-play SaaS will struggle to compete with AI skills that users and teams can build and personalize inside their favorite AI super app.

    

Cloud agents and collaboration are the next wave

15. Startups can compete with AI labs by going deep and using a portfolio of models. For example, coding startups like Devin and Factory are doing well because their harnesses route across multiple models to help customers save on cost without giving up performance. They’re also focused on a few specific use cases instead of trying to be everything to everyone.

16. Always-on cloud agents will be accessible from any device. Running agents with your laptop half-open will feel silly soon. Instead, many builders are already launching cloud agents using Cursor, Devin, and others. Codex and Claude Code are no doubt working on the same experience, and this will be the default soon.

17. Collaboration between agents and teams has yet to be figured out. The default agent harness with a list of threads on the left is still very much a single-player experience. The multiplayer experience is still being defined — whether that’s Claude Tag in Slack, agents in Linear, or something else.

18. “The future isn’t 20 terminal tabs. It’s better loops.” I watched Peter Steinberger share this in his keynote at yesterday’s AI Engineer conference. He no longer watches the agents code. Instead, he defines the requirements, and let agents plan, execute, and validate by themselves. I’m a bit wary of loops since they can lead to AI slop, but I’m sure better planning and models will make them more mainstream.

The AI market is about to get a lot more interesting

Here are my 18 takes summarized in four bullets:

1. The frontier-only AI stack is collapsing. Companies will use a portfolio of models to save costs, with everyday tasks defaulting to low-cost open models from China.

2. The AI super app era is here. Codex, Claude, Cursor, and others are competing to disrupt knowledge work by using agents for everything you can do on a computer.

3. Traditional software risks becoming a dumb pipe. Agents will become the default user. They’ll use APIs and browser use to access your website and app without humans ever seeing them.

4. Cloud agents and collaboration are the next wave. The future is agents living in the cloud, accessible from any device, and collaborating closely with both you and your team. This will happen very soon.

Let me know what you think about these takes in the comments!

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’m excited to launch my beginner-friendly Hermes course with 17 opinionated lessons to set up my favorite AI chief of staff in a single afternoon.

This course covers everything from installation to integrations (e.g., email, calendar, Google Workspace) to cron jobs (e.g., morning briefing, health check).

Watch my free 45-minute walkthrough on YouTube. If you want the written version with all 17 lessons and copy-paste skills, consider upgrading to paid at behindthecraft.com.

What the course covers:

• (00:00) Why Hermes is the best 24/7 AI chief of staff

• (01:16) Where and how to install Hermes safely

• (02:55) The best AI model to pick for Hermes

• (05:04) Connect to Telegram for mobile chats

• (11:08) How Hermes uses tools, memory, and skills

• (12:39) How to personalize Hermes by editing its user and soul files

• (15:49) Two best ways to troubleshoot Hermes

• (18:15) Set up Hermes to respond via voice

• (22:47) Connect Gmail, Calendar, Docs, Drive, and Sheets step by step

• (38:25) Create routines like morning briefing, health check, and more

Why Hermes and what’s inside the course?

Hermes is, in my opinion, the best AI chief of staff available today. It’s reliable, free to use (you just need a $20 ChatGPT subscription), and very powerful.

That’s why I spent 20+ hours writing 17 opinionated lessons covering three tracks:

1. Quickstart: Get Hermes running in less than 30 minutes.

2. Integrations: Let Hermes work in your favorite apps (e.g., email, calendar, docs).

3. Cron jobs: Set up morning briefings, health checks, business reports, and more.

Upgrade to Get My Full Hermes Course

Here’s what each track covers.

Quickstart: Get Hermes running in less than 30 minutes

Learn how to install Hermes safely, connect it to your phone via Telegram, and personalize it to be yours by editing its user and soul files.

This track covers:

1. Where to install: Set up Hermes on your laptop, Mac Mini, or virtual server.

2. Install Hermes: Download the desktop app and pick your AI model.

3. Connect Telegram: Create your bot and chat with Hermes from your phone.

4. How Hermes works: A beginner-friendly guide to memory, tools, and more.

5. Personalize Hermes: Edit its user and soul files to make it genuinely useful.

6. Troubleshooting: My two favorite ways to fix Hermes if you get stuck.

Integrations: Connect the apps you already use

Integrations are what make Hermes more useful than a chatbot. Start with voice replies and Google Workspace, which I think are must-haves. Then consider linking Granola for meetings, GitHub for backups, YouTube for research, and more.

This track covers:

7. Voice replies: Talk to Hermes while on the go.

8. Google Workspace: Connect Gmail, Calendar, Docs, Sheets, and Drive.

9. WhatsApp: Use Hermes from WhatsApp with a second phone number.

10. Slack: Add Hermes to Slack for threaded discussions.

11. Granola: Let Hermes analyze your meeting notes.

12. GitHub: Back up Hermes and let it work with your repos.

13. YouTube: Let Hermes summarize videos and research for you.

Cron jobs: make Hermes proactive

Last but not least, cron jobs (or scheduled routines) let Hermes do work for you proactively. I cover how to set up my favorite cron jobs including:

14. Weekend planner: Find fun, time-sensitive family activities for the weekend.

15. Morning briefing: A daily check of your email, calendar, and more.

16. Business review: A weekly insights email with your financials and more.

17. Health check: A weekly review with your weight, workouts, and more.

My goal is to create the most practical AI membership online

With today’s update, paid subscribers can now access all of the following on the Behind the Craft membership portal:

1. 12+ skills and 20+ prompts handcrafted for personal, product, and creator work.

2. 17 lessons on setting up your AI chief of staff with Hermes.

3. Full Codex and Claude Code courses coming later this year.

We’ve also started hosting monthly expert-led live AI workshops in our AI Slack community, starting with my walkthrough of my Codex setup last Friday.

Again, I’ve made the full 45-minute walkthrough free on YouTube to make it easy for you to set up Hermes. If you’d like the written course, my AI skills, live workshops, and upcoming Codex and Claude Code courses, consider upgrading to paid.

Thanks!
Peter

These are more informal sessions that we’ll do once in awhile via the community. Below are the notes and takeaways from this session:

How to decide what to automate

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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

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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 show you how to build an AI advisor that helps you think through important life and career decisions.

This is my favorite AI skill because it knows my goals, gives me useful feedback, and gets better the more I talk to it.

Watch my tutorial now to see how my /advisor skill works and how you can build your own in 13 minutes.

Timestamps:

• (00:00) Why I built a personal AI advisor

• (02:09) skill.md: How to define the advisor’s behavior

• (03:31) plan.md: The context file with goals, principles, energy, and life details

• (07:54) learnings.md: How the advisor remembers patterns over time

• (09:22) eval.md: What AI checks before giving me advice

• (10:15) Demo: Using Anthropic's Fable for deeper advice

• (11:31) How to build your own advisor skill now

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How my AI advisor helps me make decisions

I recently made a video sharing why I decided to leave my job to bet on myself.

What I didn’t share is that I talked to my AI advisor for over 3 months leading up to that decision. It gave me advice like:

“Your decision is psychological and about letting go of the person you spent a decade becoming.”

“Your kids are eight and four and will grow up fast. Your goal isn’t to maximize income, it’s to make enough so you can spend more time with them.”

My /advisor skill has been so useful because it has context about my goals, family, work, financials, and energy to help me think more clearly.

The whole setup is basically a folder with a few text files:

Let’s walk through each file and how you can build your own version.

1. Skill: How you want the AI to give you advice

Your skill.md should not contain a bunch of personal information about yourself.

Instead, it should tell AI what role to play, what context to read, how to advise you, and what to remember over time. Here’s the full skill.md for my AI advisor:

This file defines the advisor’s behavior. The next file gives it your personal context.

2. Plan: Important context about your work and life

Paid subscribers can keep reading for the full /advisor setup, including my plan.md structure, learnings.md examples, eval.md checklist, and more.

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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

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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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Last year, I made seven figures at my product job. Last week, I left that job to bet on myself.

This wasn’t an easy choice to make. After over a decade in product, walking away from a great job felt like giving up a core part of my identity.

What finally helped me decide were 5 principles I wrote down after a lot of reflection.

Watch my video now to hear how I made this decision and perhaps it can help you think through your next career move too.

Timestamps:

• (0:00) Why I quit my high-paying product job

• (0:23) Go where work feels like play

• (3:44) Give yourself permission to build

• (5:35) At some point, trading time for wealth just isn't worth it

• (8:33) Surround yourself with people who share your curiosity

• (10:11) The people you love won't be there forever

• (13:57) What I'm doing next (and a promise)

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1. Go where work feels like play

I became a PM over a decade ago because I wanted to ship great products. I loved the loop of understanding problems, identifying solutions, and executing to craft something great.

This loop is exactly what I got to do at Roblox. I have a lot of fond memories from shipping products like thumbnail personalization, configs & experiments, and AI analytics with my talented team of engineers, designers, and PMs. Most of all, I loved watching Roblox creators light up as we put these products in their hands.

But there’s another side of product management that I didn’t love, and it exists in some form at all the great companies that I’ve worked at. I call it product management theater, and it’s when the focus shifts to:

1. Polishing internal docs over the actual user-facing product.

2. Getting stakeholder alignment over doing what’s right for customers.

3. Climbing the career ladder over shipping products you’re proud of.

For example, early in my career, I spent 4 hours in a meeting with other PMs aligning on quarterly OKRs. That was one of the longest days in my PM career. Being stuck in that room is what pushed me to write my very first newsletter post. I desperately needed to ship something, even if it was just a blog post. That post became this newsletter, which now has almost 150K subscribers.

There’s a framework I come back to whenever I’m making a big career decision called the Zone of Genius. I use it to sort my work into 4 zones:

• Zone of genius is what you’re great at and love doing.

• Zone of curiosity is what you suck at but still love learning about.

• Zone of excellence is what you’re great at but hate doing.

• Zone of incompetence is what you suck at and have no desire to improve.

My zone of genius is building products with customers and sharing what I learn with you all. My zone of curiosity is getting better at building with AI agents. But all that PM theater I just described falls squarely into the other two zones.

I realized that I don’t want to spend my day in back-to-back meetings, no matter how much money or prestige comes with those jobs. I also realized that I was waking up at 6 AM every day to write this newsletter, even though I didn’t make any money from it for years.

Of course, I also loved crafting products and sharing memes with my team, and honestly I miss that already. But life is about trade-offs, and I wanted to spend more of my day in my zones of genius and curiosity.

So I encourage you to map out your own zones and be honest about what you actually enjoy, even if you’re not making money from it right now.

For me, I love building and get antsy if I can’t ship anything in a week, which brings me to the second principle.

2. Give yourself permission to build

The career ladder pushes everyone to be a leader, but I just want to be a builder.

The reality is that a lot of product leaders have calendars that look like this:

Because of this calendar, the only time that I really had to build was on nights and weekends.

Now don’t get me wrong, I enjoy helping people grow and think I’m good at it. But I never want to give up the craft of making great products. I love being hands-on and obsessing over every little detail, from the copy to the UX to the marketing, because that’s what it takes to make a product great.

I also loved being able to check in code myself as a PM with AI’s help. Here’s the tongue-in-cheek GitHub profile that I used at work:

Yet, as you climb the ladder at most companies, you’re expected to step away from building and fill your time with product reviews, cross-functional alignment, managing up, and performance calibrations.

The good news is that this is finally changing. Companies are rewarding builders and ICs more than ever, and even managers are increasingly expected to do IC work too.

But becoming a good builder takes reps, and it’s just hard to put in those reps when you’re in back-to-back meetings all day.

So if you’re a builder at heart, embrace it. You don’t have to give up what you’re good at to be a “leader.”

It doesn’t matter if you’re an engineer, PM, marketer, or designer. Learn how to build.

3. At some point, trading time for wealth just isn’t worth it

Most people in tech are obsessed with total compensation and getting that next title.

If you’ve spent any time on Blind, you know what I’m talking about. Everyone is comparing how much they make, what their title is, and whether they work at OpenAI, Anthropic, or FAANG.

Look, I get it. Put in a few years at one of these companies and you can make generational wealth. But the people who hit all of these goals often have the least control over their time.

If you’re a VP, for example, chances are you’re in back-to-back meetings from morning to night. Kudos to anyone who can do that job well, but it’s just not the life I want. Instead, here’s what I figured out after the highest-earning year of my career:

Past a certain point, more money is not worth giving up agency over your time.

Real agency inside a company is rare. You might pour yourself into a promising product that gets canceled. You might have a great manager who gets re-orged out. So much of it is out of your hands.

And while I have a lot of respect for VC-backed founders, that life also sounds very stressful. You’ll have investors, a board, and payroll to satisfy every month whether you like it or not. With a VC-backed company, you have to go big or go home. You can’t just hit profitability and call it a day. Your investors will push you to 10x or 100x the business.

The path I want to try is being a bootstrapped founder of a small business. As long as I can pay the bills, I’m willing to trade off hundreds of thousands of dollars to be able to decide who I work with, what I work on, and how hard I work. It’s only been a week, but I love waking up to a calendar with no meetings before the afternoon and enough time to chase my curiosity and share what I learn with you all.

The funny thing is, growing up, I didn’t even know this was a real path. I chased prestigious schools and brand-name companies and thought there were only two options: climb the ladder in big tech, or raise money and build a unicorn.

But I think bootstrapping makes a lot of sense now for many people. It’s cheaper than ever to start a business, and a single person with AI tools and agents can get a lot done.

To wrap up this section, there’s this image from Wait But Why that brings into perspective just how little time we all have:

I’m in my early 40s so almost half of my time is already over. If you’re in my shoes, realize that:

The hedonistic treadmill of chasing money and prestige can become a trap. Time and agency are the most valuable assets we have.

4. Surround yourself with people who share your curiosity

One of the best ways to spend your time is with people who share your values and goals.

These days, I mostly make new friends through my kids’ playdates or online. X can be toxic if you’re just doom scrolling, but I’ve made some good friends there who gave me the courage to make this bet. I think everyone should have a few group chats with people who are on the same journey as them.

Two group chats that I rely on are:

1. A YouTube learning group started by my friend Steve Huynh, who spent 14 years as a principal engineer at Amazon before building his own business. I joke that Steve’s group is bad for the economy, because everyone in it eventually walks away from their high-paying tech job to build their own thing.

2. A PM creator group started by Ben Erez, with folks you might know like Lenny, Elena, and others. Everyone in it is a PM or product leader who’s also teaching, creating, and building.

My point is that it’s important to find people who share your values and goals. People who are honest, encouraging, and open, who won’t put up a front, and who’ll give you the courage to make decisions like this.

Courage is contagious. Hang out with people who’ve done some version of what you want to do and who are honest about the parts that are great and the parts that suck.

5. The people you love won’t be there forever

I think about my parents a lot these days. They sacrificed a lot to move to Canada and give me a good education. Now, they’re retired and honestly living the good life, going on cruises all the time.

The thing is, once you’re retired, nobody cares what your title was, how much money you made, or which company logos are on your LinkedIn.

And the reality is that at most companies I’ve worked at, there are only a handful of coworkers I still keep in touch with. Almost everyone else moves on the moment your employment ends.

So if work isn’t the relationship that lasts, then it has to be the people we too often put second: our family and close friends.

Being a PM and a creator at the same time made me feel productive, and the dual income was great, but I hadn’t had breakfast with my kids in months. I was just working all the time.

Over the past week, I’ve been walking my 8-year-old daughter to school. It’s twenty minutes each way, twenty minutes that I used to spend stuck in traffic or on Slack. Now I spend it with her, and she tells me about her friends, her dreams, and the things she’s worried about. This walk is now the highlight of my day.

Before I quit, I promised my wife I’d spend more time being present with my kids. Parenting is hard and I’m far from perfect. Sometimes I just want to shut myself in a room and play video games. But I now have a lot more time to try to keep that promise.

Nobody wants to put on their tombstone that they got divorced and neglected their family, but at least they made VP at Meta.

The people you love won’t be there forever, so spend more time with them while you can.

The 5 principles that helped me make my decision

Here are the five principles again that led to my decision to leave a high-paying job to bet on myself:

1. Go where work feels like play. Map out your zone of genius and be honest with yourself about what you actually enjoy doing.

2. Give yourself permission to build. If you're a builder at heart, embrace it, because you don't have to give up what you're good at to become a "leader."

3. At some point, trading time for wealth just isn’t worth it. Real wealth is having agency over who you work with, what you work on, and how hard you work.

4. Surround yourself with people who share your curiosity. Courage is contagious, so find people who are on a similar journey as you.

5. The people you love won’t be there forever. Make time for your family and close friends while you still can.

One more piece of practical advice if you’re feeling the same itch to bootstrap:

Don’t take a blind leap. It took me three years to get here, and I didn’t make the jump until I was making around the same from my business as my product salary. Start by tinkering and building things on the side while showing up fully at your day job. I took pride in my work at Roblox until my last day.

Here’s what I’m doing next

For now, my focus is on delivering the most practical AI tutorials and interviews available online. That means:

1. Making this channel and my newsletter the best place to learn AI online without the hype.

2. Building behindthecraft.com into a valuable membership where you can get my personal AI operating system, AI tools, and community access.

3. Building products with AI myself and sharing all the wins and the mistakes along the way.

One more thing — it’s so easy to pump out AI slop these days, but I think trust is the only currency that matters for creators and builders. I’ll use AI to save time, but I promise that everything I publish will be reviewed by my own eyes and AI slop free.

And if I ever get too cringe, you should hold me accountable.

Watch my video and I’m happy to answer any questions in the comments.

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.

Try Linear for Free

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:

Read more

Today, I want to show you how to build Claude Skills that catch their own mistakes and get better the more you use them.

I’ve become completely AI-skill pilled. I love encoding my knowledge and taste into skills and then reusing them to save time every week.

Watch my video now to see me build a skill from scratch in 5 steps complete with an eval loop and memory.

Timestamps:

• (00:00) What an AI skill is and the 5 steps

• (01:56) Step 1: Give AI your personal context and examples

• (05:00) Step 2: Edit the description to trigger the skill reliably

• (05:59) Step 3: Build an eval loop to have AI fix its own mistakes

• (11:54) Step 4: Add memory so the skill improves itself over time

• (13:40) Step 5: Build a skill editor to improve all your skills

• (17:44) Where human taste still plays a role

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.

Try Linear for Free

A quick recap of AI skills

As I shared before, a skill is just a folder with instructions that AI can trigger for a given task. In this tutorial, I’m going to build an /edit-post skill that can edit any newsletter or long-form piece. Here’s what the skill folder will look like at the end:

edit-post/
├ skill.md
├ example-tutorial.md
├ example-personal.md
├ example-product.md
├ evals.md
├ memory.md

1. skill.md tells AI what the skill does and when to trigger it.

2. The example files include my best posts so the skill can learn my writing style.

3. evals.md will have pass/fail checks the skill uses to grade its own output.

4. memory.md will log lessons from past chats so the skill gets better over time.

Now let’s build these files in 5 steps.

1. Create the skill using your examples and personal context

My newsletter has three types of posts: tutorials like this one, personal essays on career and life, and deep dives on AI trends and concepts. I saved my best posts for each type in separate md (text) files below:

Then I gave Claude Code all three example files and this prompt:

The most important thing here is to give AI as much personal context and examples of good output as you can. You should also keep these context files separate from the main skill.md for two reasons:

1. It keeps the skill.md lean because AI can decide which example posts to load (e.g., tutorial vs. personal post) based on your draft.

2. It lets you share the skill more easily without revealing your personal posts or information.

2. Be explicit about when AI should trigger the skill

When you ask AI a question, it doesn’t load the whole skill by default. Instead, it only reads the name and description to decide whether to load the full skill:

So it’s very important to include explicit “Use when…” instructions so AI knows when to trigger the skill automatically. You can always trigger it by hand too (type /edit-post).

3. Test the skill manually, then build an evals.md loop

Once you have a skill drafted, test it manually a few times.

For example, I ran /edit-post on a real draft, then gave it feedback to improve. After a few manual iterations, tell AI:

An eval is just written instructions that asks the AI to check its own work. I prefer pass/fail over scoring evals (like 4/5) because AI can’t reliably tell a 3/5 from a 4/5. Here’s a snippet of the evals that it generated for me:

Before we run the evals, give AI one more prompt:

This sets up the evals loop where one agent edits the post, a second agent grades it, and because the grader has a clean context window, it isn’t biased by the first agent’s work. If anything fails, the two will keep working until everything passes:

When I ran the loop on a newsletter draft, it took five rounds between the two agents to pass all the evals. AI stripped every em dash and “X, not Y” phrase along the way.

I think setting up this evals loop makes a big difference in leveling up your skills. You can just kick it off, go get coffee, and come back to a cleaner draft. You should still review and edit the final output manually of course.

4. Give the skill a memory.md so it improves over time

Evals improve the skill’s output and memory improves the skill itself.

To set it up, ask AI:

A memory.md is just a text file with a reverse-chronological log of what the skill learned from your past chats. Here’s an example:

Setting this up is optional, but I’ve found it useful for feedback that doesn’t fit a clean pass/fail check, like “make the voice more authentic.” Make sure skill.md references both evals.md and memory.md.

5. Build a skill that builds skills

I’ve been skillmaxxing for days, and I do worry that relying on AI to write skills could lead to a mess of slop that drags down the model’s response quality.

That’s why I built /skill-editor and /no-ai-slop skills to make all my skills concise and strip AI slop like em dashes, “X, not Y” phrasing, and duplicate instructions. Here’s a snapshot of my /no-ai-slop skill:

When I ran these skills on the /edit-post skill we just built, it caught a few em dashes, removed some redundant formatting, and made the skill more concise.

Quick plug: I’ve made this full post free, but if you want to copy my /skill-editor and /no-ai-slop skills, along with a dozen other handcrafted skills, consider becoming a paid subscriber to unlock all my skills and prompts at behindthecraft.com.

Follow these 5 steps to build skills that improve themselves

Here’s how you put this guide into practice. First, reflect on your past week and brainstorm with AI workflows that you can streamline with skills. Then create each skill by following these 5 steps:

1. Give AI your personal context and best-in-class examples to draft the skill.

2. Trigger the skill reliably by including “use when XYZ” in the description.

3. Build an evals.md with pass/fail checks so AI can loop to improve its own output.

4. Add a memory.md with lessons from your chats so the skill improves over time.

5. Build (or copy) a /skill-editor skill to keep all your skills clean and concise.

Watch the video now and subscribe to my channel if you enjoyed this tutorial. Let me know if you have any questions in the comments!

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.

Read more

Today, I want to show you how to build beautiful slide decks with Claude Code.

Instead of making PowerPoint and Google Slides manually, I built a /slides skill that turns a rough outline into a fully animated HTML deck in minutes.

Just install the skill and type “/slides (your rough outline)” to generate a beautiful slide deck. Watch my tutorial now for the complete walkthrough.

Timestamps:

• (0:00) Why building slides with AI is better than PowerPoint

• (1:30) My /slides skill: 12 formats and 3 templates

• (4:20) The 6 steps that make the skill actually work

• (5:16) Watch me generate a full deck in 3 minutes

• (8:13) What's actually inside my /slides skill file

• (11:57) The one lesson that matters when building with AI

I’m proud to partner with Oceans Talent

I hired Char through Oceans a few months back to help with podcast production. What I didn’t expect was how quickly he’d take it further. He’s now creating clips and picking up Claude Code and Codex on his own to deliver more every week.

Oceans Talent places operators, not assistants. Their talent is AI-fluent and delivers what a senior US hire would at 3-5x less cost. If you need help with marketing, ops, finance, or an EA, book a call at oceanstalent.com/peter.

Get Started for Free

Why you should build slides in HTML

Getting AI to generate slides in HTML has many advantages, including being able to:

1. Go from 0 to draft deck in minutes with a rough outline.

2. Create live, interactive charts with Chart.js.

3. Drop in images and have AI automatically resize them.

4. Add subtle motion to capture attention.

5. Run a visual QA loop where the AI polishes the slide for you.

6. Ship with zero dependencies since all slides render in a single HTML file.

12 beautiful slide formats and 3 templates

My /slides skill comes with 12 ready-to-go formats (cover, agenda, two-column, stat grid, feature bento, comparison, timeline, chart, and more) and three visual templates:

1. Default: Editorial and warm with a sharp red accent.

2. Dark: Bold and contemporary with a black background and yellow accents.

3. Light: Apple-style clean and minimal.

Here’s an example chart slide generated using the dark template:

How to start using the /slides skill now

You have two options:

1. Copy and start using the skill right away by visiting behindthecraft.com to unlock it and 10 other handcrafted skills.

Upgrade to Get Instant Access

2. Keep reading to understand how I built the skill in 6 steps.

How I built the skill in 6 steps

Here's how the skill works under the hood in 6 steps:

1. Read skill.md and styles.md to understand the guidelines and slide formats.

2. Clarify the topic by asking the user questions about what the contents should be.

3. Research online to supplement what the user provided.

4. Generate the deck as a single HTML file.

5. Run a visual QA loop to take screenshots of each slide and fix layout issues.

6. Deliver the final deck that you can present in your browser.

To build this skill yourself, follow my tutorial and iterate with AI on the steps above.

With AI, you have to build the system first

If you remember just one thing from this tutorial, make it this:

With AI, build the system once instead of doing the work every time.

Creating my /slides skill took me longer than making a deck from scratch. But now that it’s up and running, I can use it to save time on every deck I make from here on out. The same idea applies to any workflow or routine you do more than once.

Watch my tutorial now and let me know if you have any questions in the comments or get a Behind the Craft membership to unlock the skill now.

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:

Read more

Here’s what you get when you sign up:

1. 10 hand-crafted AI skills that I rely on for personal (Personal Advisor, Health Coach, Weekend Planner), product (Strategy Writer, Spec Writer, Exec Reviewer), and creator work (Social Writer, Infographic Designer, No AI Slop).

2. 3-6 months free for Wispr Flow, Granola, Replit, Linear, and more ($270 value) for new paid users of each AI tool. Plus 20+ prompts that I use personally.

3. An invite to my member-only Slack community with 700+ builders.

Upgrade Now to Get Instant Access

• Already a paid subscriber? Sign in with your paid subscriber email.

• Not yet a subscriber? Upgrade now and you’ll get an invite link in your email.

Watch this 8 min video to see me give a tour of behindthecraft.com:

I plan to add full guides for OpenClaw, Hermes, Claude Code, and Codex — the 4 products that have changed how I work. Prices will go up once all of these guides are live, so subscribe to lock in the early bird price.

10 handcrafted AI skills that you can set up now

Most AI skill packs dump 100 skills on you and let you sort the slop yourself. The skills below are what I actually use every week and all 10 are available now in the Behind the Craft portal.

Personal

1. Personal Advisor: My favorite skill. Get honest advice grounded in your goals, energy, and life situation.

2. Health Coach: Get evidence-based tips on workouts, meals, and sleep, based on your health metrics and routines.

3. Weekend Planner: Find 10 time-sensitive things to do on an upcoming weekend personalized for your location, preferences, and constraints.

Product

4. Strategy Writer: Shape a crisp one-pager document with vision, principles, strategy, and milestones.

5. Spec Writer: Turn rough notes into a clear spec with my personal PRD template.

6. Exec Reviewer: Set up a profile for your CEO or leader and get their feedback before the real review.

Creator

7. Social Writer: Turn your draft into 3 LinkedIn/X posts based on my most viral hits.

8. Infographic Designer: Create a simple design system before generating beautiful, consistent infographics for your content.

9. No AI Slop: Removes 10+ types of AI slop from your writing.

I’ve also included Skill Editor, the skill I use to build and edit every other skill. More on that below.

Upgrade Now to Get All 10 Skills

How I created these AI skills

Skills are the best way to share your expertise with others, so I treat building one like creating a real product. Specifically, I go through 5 phases:

1. List the most repetitive, manual tasks. The ones I do by hand or that require a lot of copying and pasting into AI are the best candidates. For example, I often ask AI for business advice (Personal Advisor) or to remove AI slop (No AI Slop).

2. Separate the instructions from the personal context. I split each skill's instructions (skill.md) and my personal context (plan.md) into two files. The context file auto-updates from past chats so the skill improves over time.

3. Get the AI to ask questions to gather context. Many skills tell the AI to ask a few questions first. For example, the first time you run Weekend Planner it’ll ask you about your location, preferences, and constraints.

4. Spin up sub-agents to test the skill and run evals. A set of agents test the skill with common and adversarial queries, while another set grades each response pass/fail. The AI keeps iterating until everything passes.

5. Test and polish the skill by hand. I always test each skill and make manual edits to improve it. This is the difference between slop and a skill that’s worthwhile.

I’ve found that these steps lead to much better skills. You can use the Skill Editor skill in the portal to follow the same process to create your own.

What’s coming next

I’ve been creating content for 3 years, and it’s only because of my paid subscribers that I can keep doing this. Over the next few months, I plan to deliver to them:

1. Builder skills, starting with skills to create beautiful slides and landing pages.

2. My full personal OS for Hermes / OpenClaw and Codex / Claude Code.

Visit behindthecraft.com to unlock all my skills and prompts plus $270 in free AI tools now.

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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

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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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Tell me if you’ve seen this before:

Today is a hard day. We’ve made the difficult decision to reduce our workforce by 1,000+ employees. Our business has never been stronger, but AI has changed how we work, and we’re restructuring to win in the agentic era...

Whenever I read one of these, I mentally translate it to what’s actually happening:

Today is a hard day. We screwed up and overhired during the zero-interest era. Now, we need to cut costs. AI agents are easy to blame, so…

According to levels.fyi, over 80,000 tech employees have been laid off in Q1, the highest level since the 2022-23 recession. Here’s a rant about why layoffs continue to happen and what you can do to take back control.

1. The “AI made us do it” copy-paste memo is an excuse

Every layoff memo looks the same these days:

1. “The way we work has changed...Today’s actions are not a cost-cutting exercise; they are about defining how a growth company operates in the agentic AI era.”

2. “This generation of AI is the most transformative technology we’ve seen...We need to be organized more leanly, with fewer layers and more ownership.”

3. “AI tools have changed what it means to build and run a company. A significantly smaller team, using the tools we’re building, can do more and do it better.”

The pattern is always the same: "Today is a hard day → but the business is strong → AI changes everything." It's almost as if the CEOs writing them have a group chat where they share the same script. I think they should just admit that…

2. The real reason is overhiring and empire building

Here’s how headcount grew in the ZIRP era at companies that did Q1 layoffs:

1. Meta: 45,000 in 2019 → 86,000 in 2022

2. Amazon: 341,000 in 2016 → 1.6M in 2021

3. Salesforce: 49,000 in 2020 → 80,000 in 2023

4. Block: 3,800 in 2019 → over 12,000 at peak

Let’s be real — the leaders who grew headcount this quickly messed up. Jack Dorsey, to his credit, basically admitted this the day after cutting 40% of Block’s staff:

There are two main drivers of organizational bloat:

1. Companies prioritized growth at all costs. Money was free and companies chased users and ARR instead of retention and profitability.

2. Empire building was the norm. Leaders were rewarded for growing their scope and headcount instead of keeping their team lean.

Growing headcount this fast is a one-way door change because…

3. The alignment tax is real

If you've ever worked inside big tech, you’ve probably experienced something like this:

You have a product idea that should ship in a week. You just have to draft a memo or deck to get approval from stakeholders and your management chain. By the time you finish the process, months have passed and the market has moved on.

I've worked at great companies with slogans like "it's still day one" and "move fast and break things." But no matter how catchy the slogan is, I now believe that:

As a result…

4. There are basically two types of companies now

If you're a tech employee and worried about keeping your job, start by asking yourself:

Type 1 companies grow headcount as a last resort, even though business is doing well

For example, Anthropic is hiring a lot of people, but only because revenue grew 80x in Q1 2026. When I asked Boris (creator of Claude Code) how his team thinks about hiring, he said he only wants to hire if they've genuinely hit a ceiling on what work AI can take on.

Another example is Linear. Karri (CEO) set a rule to at most double the team every year, even though they've been profitable since 2021:

Type 2 companies are growing headcount despite a stagnating business

You can tell if you’re working in one of these companies if the hallway conversations include a mix of:

• “I need more headcount to do X.”

• “I need to be a manager to grow my career.”

• “Why are we still hiring?”

Unfortunately, I think many tech employees are at type 2 companies where the traditional career ladder is breaking at the seams. From my friend Elena:

Elena’s point is that climbing to VP isn’t the flex it used to be. Smart operators are choosing to go back to IC work instead.

The next generation of great companies have learned from the mistakes of the previous generation. They’re rewarding builders over managers because they know that one person with AI leverage can avoid the alignment tax and do an entire department’s work.

5. How you can take back control as an employee

As an employee, I think the bottom line is:

Take whatever you’re most reliable at and figure out how to do it faster with Claude Code, Codex, or another AI tool that fits your workflow. The goal is to free up your time to do work that’s higher-leverage and harder to commoditize.

I hate seeing employees be at the whim of their employer, so here's what I recommend:

1. Read the signals. If business growth is stagnant and leadership is suddenly obsessed with "flatter orgs," "AI-native teams," or "restructuring for the agentic era," you already know what's coming.

Read more

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

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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:

Read more