I once asked a group of sales professionals at a Fortune 500 company a question that sounded almost too simple:

“What do you actually do all day?”

One answer was:

“I meet with customers.”

That was true. It was also several levels too high to reveal where AI could help.

So I kept asking questions.

What happens before the meeting? How do you decide which customers to see and when? How do you set the meeting up? What do you learn about the customer beforehand? What information do you look for? What happens after the conversation?

The broad answer started to break apart. Meeting with customers included scheduling, communication, research, preparation, synthesis, and follow-up. It included learning what was happening in a customer’s industry and what might affect demand for our products. It included deciding which information mattered and how to use it in a human conversation.

We had to go almost six levels down before the work became visible.

Once it did, the AI opportunities were much easier to see.

Subscribe now

Most AI advice starts with someone else’s context

I came to this idea after spending a lot of time learning from AI content. I watched YouTube videos, read tutorials, and followed people who understood the technology far better than I did.

A lot of that material was useful. There was also a lot of it.

Many of the examples focused on writing and content creation. That made sense. Full-time creators were showing people how they used AI in the work they did every day.

Their examples were good. Their context was different from mine.

A sales leader, product manager, founder, lawyer, or operations director can learn something from a creator’s workflow. But they still have to translate that example into the tasks, decisions, information, and goals that shape their own work.

As I learned more about how language models respond to instructions and context, I started trying something different. Instead of waiting for someone to tell me how I should use AI, I gave the AI more information about my situation, my role, and what I was trying to accomplish. The responses became more specific.

My question changed. I stopped looking for a universal list of AI use cases and started examining the work in front of me.

Many people get stuck while translating an example into their own work.

We don’t have a shortage of AI use cases. We have thousands of them. The harder problem is finding the few that make sense for a particular person, in a particular role, trying to reach a particular goal.

No teacher can know all of that context in advance. And no list of “top AI use cases” can possibly cover it.

The Opportunity Canvas in 24 seconds: your context, five possibilities, one useful place to start.

Go below the job title

Job titles hide work.

“I work in sales” is a role. “I meet with customers” is a responsibility. Neither tells us enough to design a useful way of working with AI.

The useful starting points show up when we ask what happens before, during, and after that responsibility.

In the sales example, customer research became one of the clearest places to start. Account executives already wanted to understand a customer’s business, industry, priorities, and pressures before walking into a meeting. The problem was the amount of material available and the time required to read it.

AI could help them study public 10-K filings, sustainability reports, and other company information. It could help organize the evidence, compare themes across sources, and prepare questions for a human to check.

AI research doesn’t replace the account executive. The relationship, judgment, and conversation still belong to the person. Better research improves the preparation behind that conversation.

The sales sessions also changed the way some people felt about AI. A vague promise of “AI for sales” can sound like another tool to learn or a threat to a job someone already knows how to do. A specific research task feels different. People can see the inputs, the output, the review step, and the part where their expertise still matters.

They start to see AI as something they can direct.

A quick way to examine your own work

You can try this without the Canvas.

Choose one broad sentence you use to describe your work. It might be “I manage a team,” “I talk to customers,” “I launch products,” or “I run the business.”

Then work through four questions:

1. What happens before, during, and after this responsibility?

2. What information do you collect, compare, summarize, or create?

3. Which decisions require your judgment, and what would help you make them better?

4. Which part repeats often enough that a reusable AI workflow could help?

Write the answers in plain language. Keep going until you reach work with a clear input, a useful output, and a person who can judge whether the result is good.

Then choose one place to test. A small, repeated task with a clear review step is usually a better starting point than a sweeping plan to “use AI” across an entire role.

Context changes the answer

You can open ChatGPT and ask, “What are some AI use cases for a product manager?” That is a reasonable place to start. You will probably get several useful suggestions.

The Canvas starts with your goals, responsibilities, business, and context.

Now add the missing context.

Say what kind of company you work for. Name the product you are trying to launch. Explain your 90-day goal, the measures that define success, the recurring work on your calendar, the tools you can use, and the information you are allowed to share.

The answer has a better chance of being useful because the AI has more of the information that makes your situation different.

We built the AI Opportunity Canvas to make that context easier to collect and use.

The Canvas walks each AI Strategy Exchange member through that context. It asks about the person’s role, goals, recurring work, current friction, available tools, and information boundaries. The member confirms the profile before anything is generated.

The Canvas then returns exactly five ranked opportunities. Each one describes a repeatable piece of work, the information it needs, the result it should produce, and what the person must check. The member chooses one.

Once the member chooses, the Canvas creates a portable AI Skill. A Skill is a reusable set of instructions that tells an AI how to perform a specific kind of work. They can download it, install it in a supported AI tool, or use the included prompt version when Skills aren’t supported.

You leave with one specific thing to try.

Humans control the loop

There is a phrase people use often in AI: human in the loop.

I think that gives the human too small a role.

Humans design the loop. Humans control the loop. Humans own the loop.

You decide which work is worth improving. You choose what information AI can use. You define a good result, check the output, and own what happens next.

AI can help with research, comparison, drafting, planning, and synthesis. It can process more material than most of us have time to read. That can increase the quality and reach of our work. It doesn’t remove our responsibility for judgment.

If AI makes you feel a little defensive, this creates a useful permission structure. You’re still the expert. You’re still in control. You decide where AI helps and where it doesn’t belong.

That’s a healthier starting point than either fear or blind enthusiasm.

The Canvas is the start

A personalized opportunity gives you a place to begin. It can’t answer every question that comes after the first attempt.

The work changes as people test it. They learn that an input is missing, a review step is weak, or the output needs a clearer standard. They also learn from people in other roles who have found approaches they would never have seen on their own.

We built the Canvas inside AI Strategy Exchange because the first attempt creates new questions.

The community brings together founders, professionals, and business leaders who are trying to get better with AI. We hold four live strategy calls or workshops each month, along with implementation sessions, shared Skills, and member discussions.

Nobody has this technology all figured out. Some people have more experience with a tool, method, or kind of work, and that experience is useful. A good community lets people share what worked, ask better questions, raise concerns, and improve the work together.

The exchange matters to me. AI has me feeling like a kid again. I wake up curious about what I can learn and what I might be able to do today that I could not do yesterday.

We’re learning too. We spend a lot of time studying the tools, the methods, and the questions that help people use them well. We’d like to learn from your work as much as we’d like to help with it.

If that sounds useful, come check out AI Strategy Exchange for seven days. Try the Opportunity Canvas. Join a live call. Meet the people inside and see if the community is for you.

No pressure. We’d be glad to have you.

Subscribe now

Try AI Strategy Exchange for seven days

Three, maybe five minutes in, Bill Glazer put his hand up.

“𝙃𝙖𝙣𝙜 𝙤𝙣 𝙖 𝙨𝙚𝙘𝙤𝙣𝙙. 𝙏𝙝𝙖𝙩’𝙨 𝙖𝙡𝙡 𝙜𝙤𝙤𝙙, 𝙗𝙪𝙩 𝙛𝙤𝙧𝙜𝙚𝙩 𝙖𝙡𝙡 𝙩𝙝𝙖𝙩 𝙨𝙩𝙪𝙛𝙛. 𝙔𝙤𝙪’𝙫𝙚 𝙜𝙤𝙩 𝙖 𝙜𝙧𝙚𝙖𝙩 𝙗𝙧𝙖𝙣𝙙. 𝙔𝙤𝙪’𝙫𝙚 𝙜𝙤𝙩 𝙖 𝙜𝙧𝙚𝙖𝙩 𝙧𝙚𝙥𝙪𝙩𝙖𝙩𝙞𝙤𝙣. 𝙔𝙤𝙪’𝙫𝙚 𝙗𝙚𝙚𝙣 𝙙𝙤𝙞𝙣𝙜 𝙩𝙝𝙞𝙨 𝙖 𝙬𝙝𝙞𝙡𝙚, 𝙖𝙣𝙙 𝙛𝙤𝙡𝙠𝙨 𝙜𝙚𝙩 𝙜𝙤𝙤𝙙 𝙧𝙚𝙨𝙪𝙡𝙩𝙨 𝙛𝙧𝙤𝙢 𝙮𝙤𝙪. 𝙄𝙛 𝙮𝙤𝙪 𝙬𝙖𝙣𝙩 𝙩𝙤 𝙙𝙤𝙪𝙗𝙡𝙚 𝙮𝙤𝙪𝙧 𝙞𝙣𝙘𝙤𝙢𝙚, 𝙩𝙝𝙚𝙣 𝙟𝙪𝙨𝙩 𝙜𝙤 𝙙𝙤𝙪𝙗𝙡𝙚 𝙮𝙤𝙪𝙧 𝙡𝙞𝙨𝙩. 𝙄𝙩’𝙨 𝙩𝙝𝙖𝙩 𝙨𝙞𝙢𝙥𝙡𝙚.”

Malinchak tells this story in all his seminars now. He says he stood there dumbfounded because it seemed so simple. His words:

“𝙄 𝙬𝙖𝙨 𝙨𝙤 𝙛𝙖𝙧 𝙞𝙣𝙩𝙤 𝙩𝙝𝙚 𝙛𝙤𝙧𝙚𝙨𝙩, 𝙄 𝙘𝙤𝙪𝙡𝙙𝙣’𝙩 𝙨𝙚𝙚 𝙩𝙝𝙚 𝙩𝙧𝙚𝙚𝙨.”

Everybody else was throwing out great suggestions. Bill saw right through all of it and got back to foundations.

So they focused on doubling the list and the results followed.

Something similar happened to me last week...

A men’s suit wholesaler in LA got referred to me by his brother. The pitch that landed in his ear was “this guy is good with AI, he can help you get customers.” So that’s what he asked me for on our first call.

“𝙂𝙞𝙫𝙚 𝙢𝙚 𝘼𝙄 𝙩𝙤 𝙜𝙚𝙩 𝙘𝙪𝙨𝙩𝙤𝙢𝙚𝙧𝙨.”

He told me about his actual business. $3 million of inventory at cost and about 1/3 of it sitting “dead” on hangers. He does two trade shows a year that bring in almost all his new accounts.

Halfway through our call, one of his best buyers walked into the warehouse to pick up more suits, so he stepped away from the Zoom to handle it.

His best customer was standing in the doorway while he asked me about AI.

I ended the call there and sent him eleven questions.

𝗛𝗶𝘀 𝗮𝗻𝘀𝘄𝗲𝗿𝘀 𝗵𝗲𝗹𝗱 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗜 𝗻𝗲𝗲𝗱𝗲𝗱.

• The tradeshows cost $50k a year and bring in 10 to 15 new store accounts.

• A good account buys $50-80k of product in its first year

• He’d happily clear a dead suit at $40 to anyone who would buy it.

𝗡𝗼𝗯𝗼𝗱𝘆 𝗵𝗮𝗱 𝗲𝘃𝗲𝗿 𝗽𝘂𝘁 𝘁𝗵𝗼𝘀𝗲 𝘁𝗵𝗿𝗲𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝗻𝗲𝘅𝘁 𝘁𝗼 𝗲𝗮𝗰𝗵 𝗼𝘁𝗵𝗲𝗿.

$50k in tradeshow fees for 10 to 15 accounts means he already pays $3,300 to $5,000 for every new account he lands.

Now, let’s look at this from another angle.

At his own $40 floor, a bundle of 25 to 50 dead suits costs him $1,000 to $2,000.

What if he offered that bundle as a signing bonus and a new account, costing him less than half of what the shows charge, paid in stock he was about to dump anyway.

• Ship bonus suits with every new account’s first order

• Put consignment racks of the dead styles into the 200 stores that already buy from him

• Thank his ten biggest buyers for referrals in suits instead of cash

(𝘾𝙖𝙣 𝙮𝙤𝙪 𝙩𝙚𝙡𝙡 𝙅𝙖𝙮 𝘼𝙗𝙧𝙖𝙝𝙖𝙢 𝙞𝙨 𝙧𝙪𝙗𝙗𝙞𝙣𝙜 𝙤𝙛𝙛 𝙤𝙣 𝙢𝙚?)

The plan I sent him had six ideas. Zero of them had anything to do with AI.

That’s not to say AI wasn’t involved at all... I used it a ton. Research agents dug through marketplace fees, closeout broker networks, and the reasons his old $80k ad spend never had a chance.

If you’re reading this, you know I love AI. My entire business is built around it. But that doesn’t mean AI is the silver bullet for every problem.

Before you go rushing off to put an agent in charge of every problem you have, first figure out what problem you are actually solving. And then always remember...

𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐟𝐢𝐫𝐬𝐭. 𝐓𝐡𝐞𝐧 𝐭𝐚𝐜𝐭𝐢𝐜𝐬.

-Max

Subscribe now

“I see why I feel like I do nothing all day.”

She runs a business. She builds funnels, apps and email series for clients, and she’s the one who remembers to hook up the resend and double-check the automation before it goes live.

She isn’t just chillin’ all day.

And yet, when we started talking about saving your workflows so AI can run them for you, she said she couldn’t picture what she’d even save.

“I’m so scattered. I don’t have regular things I do in a particular fashion.”

I told her what I believe... she does far more than she realizes.

What she’s missing is the map.

Drawing that map is the whole reason the AI Strategy Exchange exists, so let me show you what it looks like using the call itself.

The part where we admit we were doing too much

We opened with a confession. When “The Zains” and I built the calendar for this community, one or two calls a week with a fresh topic each time looked about right. Put it next to any other community and it’s a generous amount of material.

After almost a month of running it, we realized we were teaching six new, heavy, topics a month. That’s a ton. If I were a member and I walked away with one workflow I used every day, or one that moved the business, that alone would be worth the membership. What we don’t want is six half-finished things.

So September cuts the new topics in half. The number of calls stays the same but one week we teach something, the next week you bring what you built and we refine it.

Then we take what came out of those calls, polish it, and turn it into step-by-step playbooks that live inside the community. The avatar work, the brand work, and Lil Z’s context mapping already snap together into one of those.

My favorite part of all of this is we’re figuring this out in front of you. When you watch the replay you will see us literally planning future sessions with the members.

Atomic units

Big Zain built the Opportunity Canvas almost two years ago with Lil Z, back when the only thing anyone shared was prompts. No skills, no agents. Lots of prompts and n8n.

The problem it solves hasn’t changed. You watch a YouTube video about some AI use case, you learn something, and then you sit back down at your own computer and none of it applies to you. The tutorials are either very general or built for content creators. So Zain got into the habit of using AI to figure out where he should use AI.

One line of his was great.

Nobody thinks about their own work in atomic units. Nobody sits down and asks, what do I actually do all day? We’re too automatic for that. But when you describe where you work, what your 90-day goals are, what you review and decide day in and day out, the openings show up on their own.

I ran the canvas live. 90-day goal, what I create and review, the part that takes more time than it should, what I never want it to produce (I wrote “AI slop”). It came back with a ranked list of opportunities by impact and effort. I picked the one I’d been complaining about in Slack that same morning, turning a topic into a teachable lesson, and it handed me a skill file plus a quick-start prompt. Built from my own answers instead of someone else’s workflow.

Judi’s reaction was great. She said it’s still foundational for her, that she’s trying to get her workflow organized first, and she can see coming back to it once that foundation is in. That’s the right order. The canvas is a thought starter. It resets every 90 days because your near-term goals change about that often, and so do the tools.

The canvas is members-only right now. Your login is tied to your community email.

The muscle

Mo raised the objection I hear a ton. When you start running prompts, he said, you get carried away fast. The AI jumps from step A to step Z and you never learn the process in the middle.

He’s right that you need the checklist. The checklist is the easy half.

The muscle you’re building is what you do when step 4 doesn’t come out the way the checklist said it would. It almost always deviates a little. So you troubleshoot. You figure out how to talk to it differently, how to still get to the answer you wanted. Each wall you hit teaches you a new way of doing something, and the next wall is easier.

That’s why you can’t learn this from videos alone. I’ve watched Suzie hit wall after wall on her own builds this year, and every one of them taught her a new way to talk to AI.

Then Zain showed what the muscle produces over time. He keeps a snippet called Decision Log. When a chat has sprawled in five directions, he runs it and the AI recaps the decisions that got him there, and he saves that log to Obsidian. He has another one called catch-up. Come back from a 30-minute lunch with seven sessions open, type /catch-up, and it tells you what you were working on, what was decided, and what’s still open.

Suzie’s response, word for word.

“Oh my god, Zain, this is gonna help me so much.”

That’s the map. A four-hour session one day becomes a slash command you run in 30 seconds for years. You run it again, it’s a little off, you tell it what to fix, and the third run is better. Before long you’ve got a library of workflows shaped to how you work. When you watch Zain and me jump between screens, all we’re doing is firing off steps we’ve already validated.

Suzie said she looks forward to that day. She’s closer than she thinks. So are you.

Four things people asked to copy

All four are in the replay with the skill files attached.

1. Nicole’s time audit: runs her own AI community, and this is what she has her clients do. Record two days of your work, with a screen recording or a note-taker narrating each hour. Run the recording through a prompt that breaks your day into percentages. Admin, email, content, outreach, meetings. Then sort every bucket into stays-human, partly assisted, or automate. She offered the workbook and the prompt to anyone on the call. I admitted I avoid doing this on my own time the way I avoid looking at my bloodwork. Ignorance is bliss. It’s still the right thing to do.

2. Judi’s “stupid question” that was anything but: She asked whether she should be scared of Claude Code. My answer was that for the kind of work she does, there’s no reason to move past Cowork. It’s gotten good enough to do nearly everything she’d need, and the deeper tool would pull her away from her goals. I’d rather tell you that than sell you on a tool you don’t need.

3. The picture on the puzzle box: Mo said he loses track of where everything sits when he’s running research, a build, and a proposal at once. Before you run any skill or workflow, paste it in and say, “inspect this skill and lay out how it would work as a workflow, as an ASCII diagram.” You see the lid of the box before you start on the pieces. Codex has a free Visualize plugin that does a prettier version, and I dropped a System Map skill in the chat that maps your whole Claude Code setup, counts your projects and skills, and runs a checkup on all of it.

4. ScreenKite and the branded player: Zain found ScreenKite on Reddit about a week ago. He recorded a 15-minute walkthrough of the Opportunity Canvas, then let Codex take over and edit the whole thing, the zooms, the captions, the little graphics on top. He didn’t know what B-roll was two months ago. I went one step further last night. Two prompts, and my videos now play from watch.maxbernstein.com in a player with my own branding instead of a Loom link. Nicole wants to move every lead-magnet video she gives away onto hers. Lil Z is turning the setup into a skill this week.

The class we didn’t teach

We had a positioning class planned. We never got to it. With 20 minutes left I asked whether people wanted to speed-run it or keep going on this, and the vote was to keep going.

That’s the Exchange on a good day. A canvas, a confession, a question someone was scared to ask, and four tools you can run before the next call. Next month we add the book club, where we’ll show you how we use AI to pull the frameworks out of a book and turn them into prompts you can run the same afternoon.

If you’ve been watching the videos and you still feel like you do nothing all day, come draw the map with us.

-Max

Join The AI Strategy Exchange

And speaking of a good time, I spend way too much of it on X and YouTube, bookmarking all the updates that I will “read later.”

But my bad habits are your good fortune as I do come across lots of good Skills and other AI goodies.

Below are 5+ Skills, including a few collections for you to experiment with this week…

Let’s boogie.

🛠️ The Opus 5 Skills Upgrade Prompt

Creator: Matt Shumer, Something Big Is Happening (somethingbig.ai)

Link: https://somethingbig.ai/skills-upgrade

Every skill you built for an older Claude model could be working against you now.

That’s the pitch, and it’s an “ah hell…” one if you’ve got a .claude/skills folder you’ve been adding to for months. Skills written for older models tend to over-explain and over-constrain, patching around weaknesses the newer model doesn’t actually have anymore.

Paste Shumer’s prompt into Claude Code, wherever your skills actually live, and it gets to work. First it finds every skill you’ve got installed and backs up an untouched copy of each one before touching anything. Then it builds a live HTML dashboard right on your own computer, laying out the whole inventory: what does what, which ones came from where, which ones you’re even allowed to touch, and where two skills are quietly stepping on each other. Then it goes through what he calls a Gauntlet Loop: its own builder, its own blind critic. Beat the original at its own job, it’s an upgrade. Lose, it gets retired.

The fun version: pick the ONE skill in your library you’re proudest of, the one you spent real hours tuning, and send it through the gauntlet alone first. Blind-judged against bare Opus 5, no instructions at all.

✏️ Human Review

Creator: , Behind the Craft (creatoreconomy.so)

Link: https://github.com/petergyang/human-review

You know the paragraph you end up typing just to get one typo fixed? List every change, hope the agent catches all of them, then go double-check its work anyway.

Peter Yang built the fix for that. If his name’s familiar, he’s the guy behind /no-ai-slop, the skill that hit 4,500-plus GitHub stars for hunting down AI writing tics like “it’s not X, it’s Y” and cutting them, while your sentences stay yours. Human Review tackles a different half of the same problem…Reviewing what AI builds, not just what it writes.

• Point it at an HTML or Markdown file, or a page running on localhost, and it opens it right in your browser with a feedback panel next to it.

• Edit the text and basic formatting yourself.

• Drag an image corner to resize it.

• Click any phrase, photo, or section and drop a comment directly on it, the way you’d comment inside a Google Doc.

• Command-click a link to jump to the next page in a multi-page site without losing what you already flagged.

• One click on “Send to agent” and it gets every edit and every comment in ONE batch, not five chat messages.

Nothing leaves your machine. No account, no API key, no cloud upload.

One quirk: HTML edits save the second you make them. Markdown and localhost pages need that Send click before your agent sees the change.

Use it any time you’re reviewing something an agent built instead of something it wrote: a landing page, a planning doc, a multi-page site.

Install by pasting “Install the /human-review skill globally from https://github.com/petergyang/human-review” into Claude Code, then run /human-review index.html or /human-review localhost:3000.

The fun version: leave the kind of blunt note you’d leave on a coworker’s draft. Click the stock photo and just type “no.” Click the paragraph that’s doing nothing and type “cut this.” Send it, and watch your agent act on the note instead of guessing what you meant.

Full Peter write-up here:

Behind the Craft

Use My /Human-Review Skill to Edit HTML and Markdown Files Visually

Dear subscribers…

Read more

a month ago · 49 likes · 3 comments · Peter Yang

📚 The Skill Shelf

Creator: 🙋🏻‍♂️ ←

Link: https://aistrategyexchange.com/skillshelf

I started collecting some of my favorites plus a handful of my other favorite ones that I created myself and “tossed them on the shelf”. I am keeping these updated as I find + create them and are all free.

🎨 Impeccable

Creator: Paul Bakaus (@pbakaus on X)

Link: https://impeccable.style/

I bet you recognize it even if you don’t know you do. The cream background. Oversized italic serif headline. A soft glow sitting behind the hero. These are design defaults every coding model reaches for, and Paul Bakaus built the tool that stops it.

One skill, 23 commands, and it works across nearly every AI coding harness that exists. Claude Code, Cursor, GitHub Copilot, Codex, Gemini CLI, Grok Build, more. It does two jobs.

1. It steers what gets designed in the first place, reading your tokens and components so new work fits your system instead of overwriting it.

2. And it catches the tells if they slip through anyway, a 58-rule detector that can run in CI and fail a pull request, or scan any page in your browser, including a competitor’s.

V4 just shipped and Bakaus’s team ran an experiment, asking a coding model to be creative sixteen different ways. 30 of 35 answers came back as the IDENTICAL concept. So V4 stopped letting the model pick its own favorite direction. Now it deals a hand of curated design “worlds,” 188 of them, human-reviewed out of 360 candidates, as challengers against whatever the model would have defaulted to. You pick the winner yourself on a decision page that opens right in your browser. The core prompt also got rebuilt leaner, on purpose, because newer models like Fable 5 follow short structured instructions better than long insistent prose.

Use it any time an AI agent is building or redesigning your interface and you want it to look considered instead of templated. Install with npx impeccable install, which detects your harness on its own, or grab it through the Claude Code plugin marketplace. Run /impeccable init the first time.

The fun version. Start something low-stakes and let the dice pick your design direction instead of you picking one. The deck’s got a world built on W.E.B. Du Bois’s 1900 data portraits, another modeled on split-flap departure boards, another straight out of Saul Bass movie titles. Deal yourself something you’d never choose and see what happens.

📱 The Ultimate Claude Content Pack

Creator: , founder of Blotato

Link: https://help.blotato.com/claude-skills/claude-skills

Seven free skills, and they’re built to hand off to each other so you only ever have to talk to ONE of them.

Sabrina Ramonov built this and is the one behind Blotato, the tool that handles the posting, but she also built an NLP startup years before ChatGPT existed and sold it to Pega Systems. These days she personally ships 250-plus pieces of content a week (😯) off this exact stack, so the pack had to survive her daily use before it ever reached the public.

The chain kicks off with content-coach. Tell it you don’t know what to post and it checks for a brand brief, runs a five-question intake the first time only (what you sell, who buys, your CTA, a recent story, your vibe), then hands you ideas. Pick one and post-writer drafts the hook, body, and CTA. post-grader scores it, hook strength alone is half the score, since the first three words decide whether the post survives the scroll, and loops the draft until it clears an 8 out of 10. Approve, and post-scheduler ships it.

Two more skills sit on top for when you’ve got more than an idea.

The first, repurpose, turns one long piece, a blog post, newsletter, or transcript, into a week’s worth of platform-specific posts (three for LinkedIn, five X threads, two short video scripts), opening each one with a line pulled from viral-hooks’ library of 100 tested hooks.

You don’t need Blotato for any of this except the very last click (though I’ve heard good things). 6 of the 7 skills run on nothing but Claude and file access. post-scheduler is the only one that touches Blotato, and that one just writes the finished post to a file for you to paste yourself if you’re not connected.

Install the whole pack as one ZIP. Unzip it, drop the seven folders into ~/.claude/skills/, restart Claude Code or Desktop. On claude.ai or Cowork there’s no local skills folder, so you upload each SKILL.md one at a time through Customize > Skills.

The fun version. Hand repurpose your worst, most rambling old blog post and let it try to mine three LinkedIn posts and five tweet threads out of it anyway. Then let post-grader tell you, in a number, exactly how bad your hooks are compared to hers.

📬 P.S. — The Sneaky Useful Update

Link: https://code.claude.com/docs/en/cross-session-messaging

Claude Code sessions can now message each other. Working across a few terminals or worktrees, you can ask one session to pass a heads-up to another instead of retyping the same context yourself. Codex and Cursor have had a version of this for a while. Claude showing up late to the party is fine. Being there is what matters.

One catch: if your session runs with permissions bypassed, which a lot of ours do, Claude Code holds every incoming message for your approval by default. You end up clicking accept all day instead of getting anything done. Fix is one line in ~/.claude/settings.json:

{
  "crossSessionInbound": "accept"
}

Or just tell Claude to add it for you. Saw this fix floating around on X and it’s too good not to pass on.

Go get’m this week!

-Max

https://aistrategyexchange.com/

You.

If something were to happen and you were unable to work for a month, would your business know which client to call first, or why the big renewal never goes through the normal process?

I hear and see situations like this every week, sitting with businesses that are 5, 10, 50 people deep and still running on one person’s memory.

• The pricing logic lives in someone’s noggin.

• The client history lives in a scatter of old email threads and one very specific Post-it note

• The reason a certain vendor gets called first instead of second lives nowhere except a conversation that happened years ago and, ya, zero chance it was never written down

Subscribe now

Hand that business every AI tool on the market and none of it helps, because the tools are hungry for exactly the thing that’s missing…

The actual knowledge, processed somewhere a system can use it. I’ve made this mistake in my own business more than once, buying a tool before I’d bothered to write down the process the tool was supposed to run.

That’s the real problem sitting underneath the AI tool overwhelm going around right now. Picking a chatbot was never the hard part. The hard part is getting what’s stuck in your head onto paper, in a form your team or a piece of software can actually use.

That’s the actual reason three of us built a community around this instead of just another AI tools meet ‘n’ greet.

Which my friends, brings me to the actual new…

The community I run with and , previously called Beware the Defaults, is now AI Strategy Exchange.

It’s still the same for the most part. What’s different is the name, some strategy talk and a bit of much needed structure. Beware the Defaults was/is still a great concept: Don’t take whatever the model hands you out of the box, decide what you actually need first. As a name for a group of people, though, it made everyone a bit confused.

A few concrete things are different starting now:

• A calendar goes out before each month starts, so nobody’s guessing what we’re covering

• The strategy calls run longer, and I teach them with Zain Haseeb. We’re talking about what we are seeing in the “field” and what is working with our clients and jobs

• Zain Merchant runs a separate build call every other week talking about things like getting a system to actually hold up once a real client starts using it, not just look good on YouTube

• Every week, we will each be creating skills and workflows plus detailed walkthroughs of how we are using them.

A Quick “About Us”

One thing that separates us from a lot of communities out there, is there is 3 different “experts” (not 1) bringing something different to the table, irrationality excited to turn you into an AI maniac like us.

I work with 5+ businesses on their AI problems every week, plus one of the top business consultants in the world, so most of what I bring to these calls is being stress-tested in real time.

Zain Haseeb has worked at General Motors for ~10 years helping hundreds of employees use AI, so he knows precisely which parts of a strategy fall apart the second they meet a real org chart.

Zain Merchant builds the systems everyone else’s ideas depend on, and back in sixth grade, before he could legally drive, he was already reverse-engineering online chess competitions for fun. His brain has been doing this a long time.

That 71% number doesn’t go away because a Skool community changes its name. It goes away when the stuff that’s only in your head gets written down somewhere that outlives your memory on a bad week. That’s what we will be doing on these calls, and is a darn good reason to show up.

If you want to see the calendar, sit in on a call, or just check out the group, the page is here: https://www.skool.com/aistrategyexchange/about

We are keeping the price at $47 for one more week as we migrate things over and then it will go up to $97.

We also do a 7-Day trial if you want to pop in and see if it’s for you.

See ya in there!

- Max

¹: National Association of Insurance Commissioners study on founder dependency, reported by Forbes, July 28, 2026: AI Adoption Fails 95% Of The Time. Small Business Leadership Is Why

The bridge was 40 years old. It had been inspected every single year and every inspection produced a report, all of which were signed.

The National Transportation Safety Board (NTSB) found that the steel plates holding the bridge together were half the thickness they should have been. A design error from 1967... forty freakin years earlier. The bridge had been slowly failing for decades while inspectors noted the corrosion, filed their reports, and signed their names at the bottom.

Subscribe now

17 years before it fell, the bridge was flagged “structurally deficient.”

And yet, nobody stopped driving across it.

The inspectors weren’t lazy. They weren’t lying. They did exactly what the process asked.

They looked and they noted.
They filed and they signed.

The process just never included the step where someone says, “This needs to close.”

So the inspection became paperwork. The signature became a rubber stamp and the gap between “inspected” and “safe” grew one year at a time, until it was 456 feet wide and full of cars.

That was the non-obvious, obvious mistake. The checking never stopped. It just stopped meaning anything. And nobody could tell the difference from the outside until the bridge was in the river.

---

Now, think about the last time you reviewed something AI wrote for you. The email. The proposal. The slide deck.

How long did you look?

I know, I know... nobody dies when you paste an AI-generated paragraph into a slide deck. But the same human behavior is happening. It’s become so easy to copy 142 words into a PowerPoint that you end up on autopilot, slide after slide.

When that happens, your judgment and your taste take a backseat. The gap between “good enough” and “quality” gets a little wider every time, and you start to think nobody can tell the difference.

But Zapier can.

They just updated their AI fluency hiring rubric. They’ve been screening every candidate on AI skills for a year now. 100% adoption internally. The original rubric measured three things... mindset, strategy, and building.

They just added a fourth. Accountability.

Their Global Head of Talent put it plainly.

“With AI, you can delegate the work, but not the accountability.”

They’re now screening every hire for four specific behaviors.

1. Define what good looks like before you start

2. Evaluate outputs critically

3. Catch what’s wrong before it ships

4. Own the outcomes

What Erodes First

It starts with speed.

AI generates a draft in 8 seconds, and without realizing it, you compress your review to match. What used to be ten minutes with a pen in hand becomes ten seconds with your thumb hovering over send. You’re still reviewing, technically, but you’ve squeezed it down to the point where it’s creating the illusion of safety.

The cost is that your review becomes a glance. A glance catches a typo, but it doesn’t catch a wrong assumption, a misattributed claim, or a confidently stated number that’s off by 40%.

Then something sneakier happens.

The output starts to look right. Every time. Clean formatting, confident tone, citations that sound real. The surface quality is so consistently polished that your brain starts using the surface as a proxy for the substance.

The horror stories are already piling up. In 2025, a California attorney named Amir Mostafavi used ChatGPT to write an appellate brief. He reviewed it. Filed it. Signed his name to it. Twenty-one of the twenty-three quotes in that brief were completely fabricated. The court fined him $10,000. He didn’t skip the review. He just didn’t verify anything, because the brief looked like a brief. Clean formatting, confident citations, paragraphs that read like someone who knew what they were talking about wrote them.

Raja Parasuraman spent two decades studying this at George Mason University. His research on automation complacency showed that the more reliable an automated system is, the worse humans become at detecting its errors. The monitoring itself stops working. The pattern of “it’s always been right” quietly rewires how hard you look, and the better the tool gets, the worse you get at catching it.

You start to trust it because it’s earned your trust.

The cost is that “looks right” quietly replaced “is right,” and you didn’t notice the swap.

And then comes the part that actually matters. You stop feeling responsible for the output.

“The AI wrote it” becomes an escape hatch you’d never say out loud, but you feel it. A thin pane of glass between you and the thing you just sent. When it works, you take credit. When it’s wrong, there’s a half-second where your brain reaches for the tool instead of yourself.

This can feels like delegation. You’ve finally scaled yourself.

But like the other examples, the bill always comes at the end. The cost is that when the output is wrong (and it will be wrong), you can’t explain why. You can’t walk someone through your reasoning or point to the judgment call that led to the error, because there was no judgment call. There was a blinking cursor and a send button.

The bridge inspectors signed every report right up until the day it fell.

Why This Is Worse Now

A year ago, the default failure mode with AI was bad output. Hallucinations and obvious errors. The tool was unreliable enough that you had to check.

But then those stinkin’ tools got better. The output got cleaner. And the checking got softer.

Anthropic’s AI Fluency Index found that the strongest users aren’t the ones who reach for the most tools. They’re the ones who push back and iterate. The signal is in the friction.

But the default pulls the other way. Every interface update makes it faster to accept and harder to pause. One-click insertions. Auto-applied suggestions. Inline completions that fill your sentence before you finish the thought.

The tools are designing for speed. But who is designing for judgment?

---

One Question

Before you send the next thing AI wrote for you... email, proposal, code, slide, message... ask yourself one thing.

Could I explain why this is right?

Not “does this look right.” Not “did I read it.” Could you sit across from someone, point to any sentence, and explain why it’s right? Could you defend the number? Could you catch the error?

If the answer is no, you didn’t review it. You rubber-stamped it. The form is signed. and the bridge is still standing. For now.

Try it for one week. You’ll be surprised how often the honest answer is “I have no idea if this is right. I just know it looks right.”

That’s the gap. Inspected vs. safe. You can feel it the moment you name it.

---

You’re Scanning This Right Now

Your brain is doing it right now. You’re three paragraphs from the end and you’ve already filed this under “accountability, got it, moving on.”

But you haven’t done the thing. You haven’t thought about the specific last thing you sent. Not a general “yeah, I should be more careful.” The specific email, proposal, or message where you hit send without understanding what you were approving.

You know which one it is.

The inspectors on the I-35W bridge weren’t careless people. They were experienced professionals doing their job exactly as the system asked. The system just never included the step where someone says stop.

Nobody is going to redesign your send button to include a judgment check. That one’s yours.

-Max

---

The invisible assumptions running underneath the way you work. That’s what Beware the Default is about. If that’s your kind of room, we’d like to have you.

7-Day Trail On Us

---

Sources

- National Transportation Safety Board (2008). *Collapse of I-35W Highway Bridge, Minneapolis, Minnesota, August 1, 2007.* Highway Accident Report NTSB/HAR-08/03.

- Parasuraman, R. & Manzey, D.H. (2010). “Complacency and Bias in Human Use of Automation: An Attentional Integration.” *Human Factors*, 52(3), 381-410.

- Anthropic (2025). *AI Fluency Index.*

- Zapier (2026). “One Year Later: Raising the AI Fluency Bar for Every Zapier Hire.”

By the 1950s, the automatic washing machine had reached most American homes. The promise seemed obvious: all that scrubbing and wringing, gone. Free time would appear.

But Ruth Schwartz Cowan studied what actually happened, drawing on time-use research by sociologist Joann Vanek. The finding was not what anyone expected.

By the 1970s, women were spending more time on laundry than before the machines arrived.

Total housework had gone up, not down.

The machines made laundry easier, so people washed clothes more often.

Sheets that used to last a week now lasted three days. Standards for what counted as “clean” quietly recalibrated upward. The stain your grandmother would’ve ignored became the stain your mother couldn’t tolerate.

Cowan called it the “great irony of household technology”. The machines didn’t free anyone.

They moved the bar.

I know, I know…You already see where this is going.

The AI productivity story runs on the same assumption the washing machine did.

Do the same work faster, get the leftover time back. That’s the pitch. That’s the implicit promise every time someone posts a “saved me 4 hours” screenshot.

But the time doesn’t come back. It gets absorbed.

Subscribe now

How the Bar Moves Without You Noticing

Aruna Ranganathan and Xingqi Maggie Ye spent eight months inside a 200-person tech company watching this happen. Their study, published in Harvard Business Review this February, tracked 40 workers who adopted AI tools.

The findings should’ve been a celebration. Workers got faster. WAY faster.

Then three things happened, in order:

First, scope expanded. Workers who finished tasks faster didn’t log off early. They took on more work. The freed-up time just... filled. The same way a new lane on a highway fills with traffic within months like the extra space never even existed.

The capacity didn’t create slack. It created demand.

Nobody sent a memo. The work just appeared. And because it appeared at the exact moment they felt productive, they called it ambition.

It was absorption.

Second, hours extended. Workers started sending prompts and requests after hours. The tool was always available, so the work became always available. The boundary between “work time” and “free time” didn’t blur. It dissolved. Email did this exact thing 20 years ago. Office workers now spend 28% of their week managing email... a technology that was supposed to make communication faster.

But, as you can see (and probably know), the pattern is the same. Lower the friction of doing a thing, and people do more of the thing. Not less. The bar was moving. Everyone called it dedication.

Third, quality dropped. The cognitive load of broader scope plus longer hours plus constant output evaluation added up. The researchers’ conclusion: AI didn’t reduce work. It intensified it. It felt like a mystery. It was the bill coming due.

The gain was the story people told on the way in. What they found was a trap.

Speed up the work -> Absorb the speed into expectations -> Extend the hours to meet the new expectations -> Burn out under the weight of a bar that moved while nobody was watching.

We are watching the washing machine cycle get compressed from decades into months.

The Part That Actually Costs You Something

There’s a layer underneath the burnout that’s harder to measure but worse to lose.

A team led by Elena Hayoung Lee published a study in Nature this March. They gave 269 people tasks to complete with AI assistance. The variable was:

• One group received AI outputs passively (the AI did the work, they reviewed it).

• The other group collaborated actively (working with the AI, shaping the output together).

The passive group finished faster. They also reported lower self-efficacy, lower sense of ownership, and less meaning from the work.

Sit with that for a second. The people who let AI handle it... felt less capable afterward. Not because the AI failed. Because they succeeded without being involved.

The washing machine took away the physical labor of scrubbing. It also took away the thing that made a clean shirt feel earned. The same exchange is happening in your work right now.

You used to write the first draft. Now you edit the AI’s first draft. The output might be better. The feeling of authorship is different. And that difference goes deeper than feeling. It shows up in how you talk about the work, how much you’ll fight for it in a meeting, how deeply you understand what you shipped.

Ownership is a signal of understanding. When it fades, the understanding goes with it.

And honestly, it’s something you have to experience firsthand to really get it. One AI-generated article is easy to dismiss. One delegated task, one social post you didn’t really write. That’s nothing.

The feeling comes later. When it becomes a habit. When you look up one day and realize you feel disconnected from your own work. Not tired. Disconnected. Like the thing you’re producing stopped being yours and just became more noise.

Until I wrote this, I could feel that. I just couldn’t name it.

Dario Amodei, CEO of Anthropic, raised the same question on the Lex Fridman podcast. In a world of increasingly powerful AI, where does meaning come from? His answer wasn’t complicated. Work is a source of deep meaning for most of us, he said, and that won’t change no matter how capable the tools get. The need for meaning doesn’t get automated away.

The Lee study already told you the answer. The people who felt less capable afterward handed the work over and reviewed the output. The ones who kept their sense of ownership stayed inside the thinking as it happened. Shaping it. Treating the AI as a collaborator.

That’s the variable. Stay inside the work or step outside it.

Use AI to move faster through the parts that don’t require you. Stay inside the parts that do.

Why This Time Is Faster

The washing machine took 50 years to fully recalibrate expectations around cleanliness. Email took about 15 to become the thing it was supposed to replace (meetings, memos, phone calls) plus a dozen things it was never meant to be.

AI is doing it in months.

The cycle is the same. The clock is faster. Your organization adopted AI tools sometime in the last year. The expectations have already moved. The “normal output” for a Tuesday has already been redefined by whoever on your team figured out Claude first. And once one person’s output resets the benchmark, everyone else is running to match a bar they didn’t see move.

The Question Worth Sitting With

Before you fill the time AI freed up, ask who decided it needed filling.

Not “how do I use this time productively.” That question has already been colonized by the same expectation inflation that turned the washing machine into more laundry.

Just: who decided?

If the answer is “my calendar filled itself” or “there was more work so I took it” or “I guess I just... did”... you’re living the washing machine paradox. The time was never yours. The bar moved, and you moved with it, and the gap between where you were and where you are now has been papered over by the feeling of being productive.

Productivity is the feeling. Leverage is the question.

The difference between the two is whether you chose what to do with the hour you got back, or whether the hour got taken before you noticed it existed.

Your Organization (Or Client) Is Reading This Too

Your ego will tell you this is a personal discipline problem. That you just need better boundaries, better time management, a system.

But Cowan’s research showed something the self-help version misses. The washing machine didn’t just change individual behavior. It changed what society expected of a household. The standard shifted, and every individual decision happened inside that shifted standard. You couldn’t opt out of “clean” being redefined.

You could only decide how to respond.

The AI version of this is already in motion. The company that saw one team ship 3x faster with AI didn’t celebrate and go home. It asked why the other teams weren’t doing the same. The bar moved for everyone because it moved for someone.

You’re not going to solve this with a morning routine.

The Oldest Trick in the Technology Book

In 1983, when Cowan published her research, the response was predictable. People said it didn’t apply to them. Their washing machine really did save time. They really were spending less effort on laundry than their mothers.

They were wrong, and they were wrong in the exact way that made the data invisible.

The standard moved slowly enough that each incremental shift felt like a personal choice rather than a structural one.

The AI bar is moving faster. Which means you’ll notice it sooner...if you’re looking.

But most people aren’t looking. They’re too busy filling the time they saved.

-Max

This is what we talk about inside Beware the Default. Not productivity tips. The invisible assumptions underneath them. If that’s your kind of room, we’d like to have you.

Sources

• Cowan, R.S. (1983). More Work for Mother: The Ironies of Household Technology from the Open Hearth to the Microwave. Basic Books.

• Ranganathan, A. & Ye, X.M. (2026). “AI Doesn’t Reduce Work—It Intensifies It.” Harvard Business Review, Feb 9, 2026.

• Lee, E.H., Yin, Y., Jia, N., & Wakslak, C.J. (2026). “Relying on AI at work reduces self-efficacy, ownership, and meaning while active collaboration mitigates the effects.” Scientific Reports (Nature), March 15, 2026.

Starting Friday (tomorrow 3/6), I’m running a 3-day Vibe Coding sprint inside the BTD community. ​

Three sessions. You walk away with a finished app, even if you have zero idea what to build right now.

I ran the first version of this a few months ago. Learned a ton from it and now it’s time to run it back.

Here’s the schedule (booking it for 60 minutes but may run over):

• Day 1: Friday, during the normal Build Lab slot

• Day 2: Tuesday 3/10, 3pm EST

• Day 3: Thursday, 3/12 3pm EST

Everything is recorded. I’ll open a thread each day so you can drop questions and stuck points as you build, + have a day in between sessions to do some deep-vibin-work.
​
There are also three other people that run the community that are much smarter than me, so there will be no shortage of help.

What we’re actually covering:

Starting from scratch on what a good idea is. How to pick one, how to know if it’s real, how to decide if people would pay for it or if it’s just an “annoying” problem, how to position it, how to turn it into something you can ship in a week or two.

This is not JUST a Lovable tutorial. If you know Lovable, great. If you don’t, you’ll be fine. The tools will probably change next week. I want to teach you actual strategy. Skills that you can use no matter what these crazy AI people in fancy offices come up with.

I also built a vibe coding buddy to use during the sprint (his name is Rubber Ducky Robot - it will make more sense once you get in there). It will hold your hand the entire way from idea to finished product no matter how technical you are. It will literally will give you every prompt to give Lovable.

If you’re not a member yet: the trial is 7 days. Sign up today, come to class, and cancel before you’re ever charged. Zero risk (unless you think it’s a risk to hang out with me which I completely think is fair).

​Join Here. Show up Friday. Let’s Vibe.

The argument…

The world as we know it is ending. Your job is next. Start preparing now.

So you can guess what happened next.

People shared it. Everywhere. Developers reported mental health crises on X.

A tech writer who covers this space for a living had to publish a counter-piece just to clear the air. Friends were texting friends. LinkedIn was a wall of anxiety.

I read the whole thing.

Then I closed the tab and went back to work.

Subscribe now

What My Actual Week Looked Like

I connected Claude directly to a client’s Webflow CMS, controlling the entire content management system, building wireframes, uploading assets.

I used Claude Code to build a voice agent designed to interview fifty sales reps, extract their pain points, and help build custom solutions for each of them.

Both of these things were done just by typing in plain english to AI.

But the moment that stuck with me most was MUCH simpler than either of those.

I helped an employee of a retainer client set up a Claude Project. Her job involves reviewing keywords for SEO campaigns. Not a few keywords. Somewhere between six and eight thousand of them. Per client. She has 56 clients.

The process of flagging the negative ones, keywords that were wasting ad spend, used to take her an entire day. Every week. For every client.

I want to be clear about something…

I know almost nothing about SEO. I told her that upfront. She looked at me like I was slightly insane for offering to help.

But here’s the thing. You don’t need to know the industry. You need to know the tool and lean on the domain experts that do.

We sat down and I started asking her questions.

• What does the spreadsheet look like?

• What are you actually looking for when you scan it manually?

• What makes a keyword negative?

She explained it. I listened. Then we built a Claude Project around exactly that logic — her criteria, her process, her standards — and walked through the workflow together.

20 minutes later she could run the whole analysis in ten minutes.

She stared at the screen for a second. Then she said:

“Wait. Can we do this for the other part too?”

That’s the look.

If you’ve done this with someone, you know exactly what I’m describing. It isn’t excitement. It’s reorientation.

The moment someone’s mental model of what’s possible quietly shifts and doesn’t shift back.

The Gap Nobody Talks About

Here’s where it gets strange.

I was at a Supabase hackathon two weeks ago. The developers in that room knew more about code than I will in my entire career. These are people who write software for a living, who have been doing it for years, who are genuinely excellent at it.

They were slow.

Not struggling. Not failing. Slow. Still working the old way. Still treating AI like a slightly faster version of Stack Overflow rather than a fundamentally different way of working.

It turns out this isn’t unusual.

just published a video where he talked about a randomized control trial published last year by METR found that experienced developers using AI tools took 19% longer to complete tasks than developers working without them. The researchers controlled for task difficulty, experience level, and tool familiarity. None of it mattered.

But what’s even weirder…those same developers believed AI had made them 24% faster.

Wrong about the direction.
Wrong about the magnitude.
Wrong in both directions at once.

And that’s the real gap right now. Not between people who use AI and people who don’t.

Between people who’ve rebuilt their workflow around it and people who’ve bolted it on top of the old one.

Deep technical expertise does not automatically translate.
Years of experience do not automatically translate.

The developers at that hackathon had mastery I’ll never have. Didn’t matter. I (and other AI maniacs) knew something they didn’t.

That asymmetry is everywhere right now, across every industry, at every level of seniority.

The people who know the most about their field are often the ones most locked into how they’ve always worked.

The Business You’ve Never Heard Of

On a recent episode of My First Million, Shaan Puri described visiting his friend’s father. The man runs an at-home care company in Missouri. Ninety million dollars a year in revenue. Eighty employees. Seventy-plus years old.

Puri asked him about AI.

The owner had never heard the word. Not skeptical of it. Not dismissing it. Had never encountered it as a concept. Puri walked into the office and described it as eighty employees who all looked like the same person, straight out of Ferris Bueller’s Day Off.

Eight million dollars a year in payroll. Puri’s reaction on the show:

“That’s just one little business you’ve never heard of, in the middle of Missouri, that just unlocked $8 million a year in value that someone can go and do.”

Think about how many of those businesses exist. Not just in Missouri. In every mid-sized city, in every industry you’ve never worked in, run by people who are excellent at what they do and have never heard the word AI used in a sentence that applied to them.

This is the market.

It is enormous.

And almost nobody in this community is talking to it.

Why Most People Are Paralyzed

Cedric Chin, whose response to all of it was that single tweet above, had the right instinct:

Stop consuming emotional reactions to AI and pay attention to field reports from people actually using it.

That reframe matters more than it might seem.

Because 80 million people who read that doom article are now feeling something. Fear, mostly. Paralysis, for a lot of them. The comments are full of people asking what they should do. And the answer, the actual answer, is embarrassingly simple.

Use the tool.

Not read about the tool.
Not worry about the tool.
Not watch videos about people using the tool.

Use it. On real work. With real people. In real situations where something is actually at stake.

The doom scroll doesn’t give you that. The viral article doesn’t give you that. Sitting in a room with someone who reviews eight thousand keywords manually and saying “let me see if I can help.”

That gives you that.

You Are Not Behind

Here’s what doesn’t get said enough in rooms like this one.

You are not behind.

You are so far ahead that the gap is genuinely hard to see from where you’re standing.

The water you’re swimming in feels normal because you’ve been in it long enough. Claude Projects, Skills, custom workflows, these are just Monday for you. But for most of the world, this isn’t water. It’s the deep end, and nobody told them to bring a swimsuit.

The SEO employee had been doing that keyword review for years. She built her entire workflow around it. She was good at it. She had processes, she had spreadsheets, she had a rhythm. She had absolutely no reason to think a stranger who admitted knowing nothing about her industry could help her.

But knowing her industry wasn’t the point.

Sam Parr made this observation on that same My First Million episode. The problem, he said, is that we compare to the two decimal places over. Elon’s doing this. That person raised that. This company shipped that. And we freeze, convinced we’re behind when we’re actually lapping most of the field.

You don’t have to be them to win.

Catching any piece of this puts you ahead of almost everyone. The managing partner of a major law firm who figured this out isn’t using AI because it’s a toy. He’s using it because it works, and he told someone recently that it’s like having a team of associates available instantly. He’s not panicking. But he’s paying very close attention.

The person already experimenting, already building, already paying attention (which is you, reading this right now) is better positioned than almost anyone to take advantage of what’s sitting right in front of them.

What You Can Actually Do This Week

The opportunity doesn’t require expertise in the field you’re helping.

It doesn’t require you to understand SEO, or at-home care, or whatever industry the person across from you has spent their career in. It requires you to know the tool well enough to show someone something they haven’t seen. That’s it. One proof point. One “wait, it can do that?” moment.

Here’s the concrete version of that.

Find someone this week who is drowning in a process they’ve been doing manually for years. It doesn’t have to be a big process. It doesn’t have to be complicated. It just has to be something that takes more time than it should and that they’ve accepted as just the way things are.

Sit down with them. Ask them to explain what they’re actually doing…

• The steps

• The logic

• The decisions they’re making

Listen carefully. You’re not there to impress them with AI. You’re there to understand their problem well enough to show them a better way.

Then build something simple. A Project. A prompt. A workflow that takes their criteria and does the heavy lifting. Don’t overpromise. Don’t pitch. Just show them.

30 minutes. That’s usually enough to change how someone sees what’s possible. And once someone has seen what’s possible, the conversation about what comes next takes care of itself. You don’t have to close anything. You don’t have to sell anything. The proof does that work for you.

A day’s worth of manual work down to ten minutes is its own argument.

The people consuming the doom content are waiting for someone to tell them what to do. You already know what to do.

The doom scroll can wait.

- Max

The Beware the Default community exists for exactly this. People sharing real workflows, real proof points, and real results from actually using these tools. Not doom scrolls. Not hot takes for clicks.

Field reports from people who are out there sitting down with clients, building things, and figuring out what works. If that sounds like your kind of room, we would love to have you.

Join Beware The Default

“Most people avoid ‘magic show’ demos because they can’t predict what will get thrown at them.”

That’s what Charles wrote after Friday’s Build Lab. He wasn’t being critical. He was naming the thing most AI educators won’t admit.

The polished demo is a performance. The presenter knows what’s coming because they rehearsed it. Three times. Maybe five. Until the rough edges disappeared and everything worked on the first try.

It looks impressive. It feels professional.

It teaches you almost nothing about what happens when things don’t work.

Subscribe now

There’s a growing industry built on a simple promise: watch experts, copy their systems.

It sounds reasonable. Find someone who’s figured it out. Learn their workflow. Apply it to your work.

Here’s the problem nobody talks about.

Every person has unique AI use cases. Your context. Your problems. Your way of thinking. Your messy combination of skills and gaps and weird edge cases that no guru anticipated when they built their “ultimate workflow.”

No expert’s system is your system. The best they can do is show you what’s possible. The discovery is yours to make.

But discovery is messy. It doesn’t fit in a 90-minute session with a clean arc and a satisfying conclusion. It involves getting stuck. Trying things that don’t work. Backtracking. Questioning whether you’re even asking the right question.

That’s not content you can rehearse.

Here’s what we’ve been experimenting with.

Every Friday, we turn on the camera. Community members bring problems. Real ones. Problems they’re actually stuck on, not softballs designed to make us look good.

We don’t know what’s coming.

Charles called it “live demo improvisation, tag-team problem solving, and real-time troubleshooting without flinching.”

That last part is generous. We flinch. We get stuck. We try approaches that go nowhere. We backtrack.

But we do it in the open.

Want to see what this looks like? Join the community (7-day free trial) and show up to a Friday Build Lab.

Join The Community ➡️

Most AI education follows a predictable structure.

Expert presents system. System looks clean. Audience copies system. Audience wonders why their version doesn’t work as well.

The gap isn’t skill. It’s context.

The expert built that system for their problems. Their data. Their workflow. Their weird combination of needs that emerged over months of iteration.

When you copy it, you’re copying the output without the journey. You get the what without the why. The answer without understanding why that answer worked for that specific problem.

Polished demos teach polish. They teach you to admire someone else’s system.

Messy problem-solving teaches something different. It teaches you how to think when the first approach doesn’t work. How to notice what’s actually happening instead of what you expected to happen. How to improvise when the script breaks.

That’s the skill that compounds. Not “here’s my system.” But “here’s how I navigate when I don’t have a system yet.”

We’re not claiming to have this figured out.

Three weeks ago, Beware the Defaults was an idea and a group chat. Now we have a community, a handful of Build Labs under our belt, and people like Charles telling us something is working.

Small sample size. Early days.

But the signal is clear enough to keep going.

What we’re building isn’t another place to watch experts perform expertise. It’s a place where practitioners figure things out together. Where the value isn’t in having answers but in showing how you find them.

Four people with different skills. We all think about AI through different lenses: automations, development, use case discovery, workflows. Our approaches are different. Our backgrounds are different. But we’re aligned on the same thing: curiosity, challenging AI’s first response, discovering what’s actually possible instead of accepting what’s handed to us.

We cover ground together because we see different things.

We help you discover your use cases. We don’t hand you ours.

Halfway through one of these sessions, it hit us: this isn’t content. This is apprenticeship.

The old model. Before “content” became a product.

You don’t watch the master give lectures. You watch them get stuck. You see them solve problems they haven’t solved before. You learn how they think by being present while they think.

That’s harder to scale. You can’t pre-record it and sell access to the replay library.

But it’s how people actually develop judgment. Not technique. Judgment. The thing that tells you which technique to use when the situation doesn’t match the tutorial.

AI expertise ages in weeks. The guru who learned six months ago is already behind. The workflow that worked in October might be obsolete by February.

What doesn’t age: curiosity, experimentation, the willingness to get stuck in public and figure it out anyway.

This is what Fridays look like at Beware the Default.

No scripts. No rehearsals. Someone brings a problem. We solve it together. Things break. Things work. Everyone learns more than if we’d prepared.

If that sounds like too much uncertainty, there are plenty of places that offer polished demos and expert systems. That’s not what we’re building.

We’re building something different.

Bring a problem. Watch us figure it out. Figure out your own stuff alongside us.

The people who learn fastest aren’t the ones with the best tutorials. They’re the ones willing to look stupid together.

- Max, Zain H, Tam & Zain M

Every Friday: No agenda. Real problems. Practitioners figuring it out in real time.

Join The Community ➡️

“Everybody has a plan until they get punched in the mouth.”

-Mike Tyson

The plan was simple…

Test Anthropic’s new Cowork feature, see what it could do and share what we found. An hour later, we had something more useful than a demo reel.

We had an honest record of what happens when you actually try to do complex things with a four-day-old product.

Subscribe now

The YouTube Version

You’ve seen the posts. “I automated my entire life in two hours.” “Two months of work in one afternoon.” The demos are clean and the workflows are pre-scripted. Everything works on the first try.

We wanted to know what happens when you don’t have a script.

What We Actually Tried

Organize Downloads Folder

Worked. A couple minutes. Every .txt file migrated into one folder. 127 files I’d forgotten existed, now living together in a place called “Text Files.” Simple. Isolated. Effective.

Multi-Step Content Workflow With Notion MCP.

Struggled. The connector fumbled around looking for tools that didn’t exist. Tried to bail out through the browser. We killed it before it wandered too far into the woods.

Browser-Based Email Unsubscribe Automation

Slow. Painfully slow. Authentication hiccups. Permission pop-ups every few seconds like a toddler asking “can I?” over and over. By the time you’ve approved everything, you could have clicked unsubscribe yourself. Twice.

Parallel Agent Content Multiplication

Spawned six agents simultaneously. Watched them fan out like caffeinated interns. Looked on as the usage bar went up. 8% of my monthly plan evaporated in one session. Expensive lesson…except for Zain who got 50 pieces of content from my Claude tokens.

iMessage database analytics

This one caught us off guard. Zane (AKA lil’Z) pointed it at his local messages. It pulled response time ratios, peak texting hours, contacts he leaves waiting longest. Found a middle school group chat with 20,000 messages he’d never replied to. Not once…In over a decade.

The Pattern

Simple isolated tasks: Solid.

Complex multi-step workflows with MCPs and browser: A mess of pop-ups, failed connections, and the distinct feeling that you’re watching someone parallel park a bus.

The capability ceiling is lower than the hype suggests. For now.

The Product Strategy Hiding in Plain Sight

Cowork has no intention of replacing Claude Code. Cowork wants to get you comfortable enough to eventually open Claude Code.

Look at the design choices:

• The terminal is hidden. You never see the scary code scrolling by. Progress bars and clean UI instead.

• It looks like chat. Same interface you already know.

• It sits right next to the Code button. One click away. Watching. Waiting.

It looks and feels like a gateway drug to Claude Code.

Anthropic built an on-ramp. They’re optimizing for the person who would never open a terminal. The one who finds VS Code and Cursor intimidating. The one who wants to do AI things without feeling like they need a computer science degree and a mass stockpile of energy drinks.

That person can start here.

Get comfortable. Build confidence. And maybe, eventually, click the button next to it.

The limitations serve a purpose. Entry-level product. Fear reduction device.

The Part We Didn’t Expect

We walked into that call as Claude Code users. We had workflows. We had opinions. We knew what “good” looked like.

Cowork broke all of that.

The Notion MCP fumbled. The browser automation crawled. The token burn blindsided us. Our complex workflows collapsed in ways we couldn’t quickly fix.

We couldn’t be experts.

And in that forced not-knowing, something shifted. We stopped trying to impose our existing workflows. We started actually seeing what was in front of us.

The iMessage demo landed because Zane wasn’t trying to prove anything. He pointed it at his actual data and let it show him something about himself. Response patterns. Forgotten conversations. Who he leaves waiting. A middle school group chat he ghosted for a decade without realizing it.

AI as mirror. AI as the thing that tells you what you’ve been avoiding.

We stumbled into that because we couldn’t make our default approach work.

The Default to Beware

The default is to see tool limitations as failures.

We walked in expecting to validate our existing mental models. We expected Cowork to be a simpler version of Claude Code. Same capabilities. Nicer interface.

When that expectation shattered, the friction felt like the tool’s problem.

The friction was the teacher.

It forced us back to basics. Simple tasks. Isolated folders. One thing at a time. It wouldn’t let us be clever.

It made us be curious instead.

There’s a concept in Zen called shoshin. Beginner’s mind. In the beginner’s mind there are many possibilities. In the expert’s mind there are few.

We achieved shoshin through forced failure. The tool wouldn’t let us be experts. So we had to be beginners.

And beginners notice things experts filter out.

Where to Actually Use This Thing Right Now

If you’re expecting to replace your Claude Code workflows: don’t. Not yet.

If you want to:

• Compress 47 images in a folder without opening Photoshop

• Organize files by type while you make coffee

• Analyze a spreadsheet without remembering how VLOOKUP works

• Run simple, isolated tasks while you do something else

• Feel productive without actually doing the hard thing yourself

It’s solid for that.

And if you’ve been intimidated by Claude Code, by terminals, by the whole “agentic AI” thing that sounds like it requires credentials you don’t have. This is your on-ramp.

Start simple. Let it fail. See what the friction teaches you.

-Max, Tam & the Zains

This Is What We Do Every Friday

We hit record on a Friday afternoon expecting to make a demo reel. We made something more useful: an honest record of learning in public.

The friction was the point.

Inside the Beware the Defaults community, this is what Fridays look like. We test new tools. We share what we’re building. We show the experiments that work and the ones that blow up in our faces. No scripts. No polish. Just practitioners figuring it out together.

Join us for $27/month. That price locks in for life before we raise it.

Every Friday: Behind the scenes, what we’re building, what we’re experimenting with, what’s actually working and what’s quietly failing.

Join the Community

The people who learn fastest aren’t the ones with the best tutorials. They’re the ones willing to look stupid together.

Door to door. Thirty-two pounds of books in a leather briefcase. The pitch landed in living rooms across America…

Your children need access to information. These fancy books are how they get it.

Then libraries got better.

Then the internet happened.

The “how do I find information about X” problem got solved for free.

The encyclopedia salesman didn’t pivot. He didn’t rebrand. He vanished. Along with his briefcase, his commission structure, and his optimism about the letter Q.

The same thing is happening to expert content right now. Three shifts. All at once.

Shift 1: Access

“How do I write a cold email.” “How do I structure a presentation.” “How do I analyze this data.”

These questions have answers now. Good answers. Free answers. Answers that show up in a chat box before you finish typing the question.

The person who built an audience on “here’s how to do the thing you already know you want to do” is standing on a shrinking island.

The water rises every time someone opens Claude instead of clicking their tutorial. Every time someone pastes a transcript and gets a summary in four seconds instead of watching a 47-minute YouTube video about summarization frameworks.

How-to content isn’t worthless…yet. But the floor is dropping fast.

And the elevator doesn’t go back up.

Shift 2: Value

In 1963, Julia Child dropped a potato pancake on the stove.

She was filming The French Chef on live television. No second takes. The pancake slipped off the pan, hit the burner, and started to come apart in her hands.

She scooped it back up with her bare hands. Hot pan….on camera.

“When you flip anything, you just have to have the courage of your convictions,” she said. Then she kept cooking.

Before Julia Child, cooking instruction meant recipes.

• Ingredients

• Temperatures

• Steps

“How to make beef bourguignon” was a list in a book. Anyone could print it. Anyone could photocopy it and stick it in a drawer next to seventeen other recipes they’d never make.

She made it a demonstration.

You weren’t learning how to flip a pancake.

You were learning what was possible when you stopped being afraid of the pan.

The dropped pancake taught more than the perfect flip ever could.

When “how” becomes free, value shifts to “what’s possible that you didn’t know to ask about.”

Shift 3: Position

There’s a defensive benefit to this shift.

When you teach “how to,” you invite argument. Someone always knows a better way.

The comments fill with “actually, you should use a different framework” from a guy whose LinkedIn headline contains the word “evangelist.”

When you demonstrate “here’s how I’m doing it,” you’re showing your work. You can’t be wrong about your own process.

“I’m not saying this is the right way. I’m saying this is what I do. Decide for yourself if it’s useful.”

Different posture. Harder to attack. More interesting to watch.

The Vault

and I are testing this.

We turn on the camera and we solve a real problem live using AI. The twist: we don’t know what the problem is until the camera turns on.

Someone submits it. We build a solution. On camera. No prep. No safety net.

Most expert content is theater. The webinar was written three weeks ago. The “live demo” crashed twice in rehearsal so they pre-recorded it. The Q&A questions came from the host’s cousin and two people from marketing. Everyone pretends not to notice.

We’re doing the opposite.

This will either prove we know what we’re doing or expose that we don’t. Both outcomes are useful. The demonstration is the point.

The first one is today at 1pm EST.

Submit a problem: BewareTheDefaults.com/Vault

Get the tools we build: Join the community

The vault locks today at 1. After that, we’re live.

- Max & Zain

He’d just lost his job and Roberts wasn’t about to bring it up so he was surprised when the CEO said:

“You know, when I had to make that key decision, I used the wrong case study.”

He was a Harvard MBA. Years of training in exactly this: match the situation to the case, then act.

So, when the moment came, when everything hung on one “bet-the-company” decision, he did what his training taught him to do. He shuffled through his mental files:

• The ones from business school.

• The ones from case competitions in conference rooms with bad coffee.

• The ones from the last three recessions.

He scanned the library of historical cases he’d built over decades and found the one that seemed to match his situation.

And he picked it...

But he picked wrong.

He had too much experience. That was the problem. His expertise gave him a library and his confidence made him certain he’d pulled the right file.

The combination destroyed his company.

The Version That Looks Like Competence

We talk about ego like it’s arrogance. Chest-puffing. Credit-taking. The loud guy in the meeting who makes everything about himself.

That version is easy to spot.

But there’s another kind. The kind that feels like judgment. Like standards. Like knowing what you’re doing.

The bankrupt CEO wasn’t arrogant. He was competent. He’d built a mental library across a career. When the crisis hit, he reached for it. He had the knowledge. That was never the problem.

The problem was trusting his pattern-matching more than the possibility that this situation might be different.

His expertise became a filter. He saw what matched his existing models and missed what didn’t.

Subscribe now

Your Expertise Doesn’t Want You To See This

Your expertise has been protecting you for years. It sits in the background of every meeting, every decision, every quick judgment you make without thinking. It whispers, “You’ve seen this before. You know what this is.”

Usually, it’s right. That’s why you trust it.

But expertise has a problem. It gets good, then it gets invisible, then it gets defensive. It starts gatekeeping your inputs before you’re aware anything’s been blocked. It assumes the new thing is just a variation of the old thing. It does not raise its hand and say, “I might be wrong about this.”

This is what’s happening right now with AI.

Not to the skeptics. The skeptics aren’t the problem. They’ll come around or they won’t.

The problem is the experts.

The high-agency professionals who use AI every day and have strong opinions about how work should get done. The ones who take their existing workflows and try to plug AI into them like a faster version of what they already had.

They’re not failing to adopt the technology. They’re using it constantly. They’re just not getting better...

And they don’t know why.

We didn’t know why either. For the first year, we kept trying to make AI fit our old ways of working. Same workflows, same outputs, same assumptions about what “good” looked like.

We were fitting a square peg into a round hole.

And were so focused on the fitting that we never stopped to ask what a round peg might make possible.

Three Ways Your Brain Lies To You

Here is what we’ve started to notice. Ego operates on three levels, and each one is harder to see than the last.

Level 1: Identity Protection

This is the obvious one. “I’m the smart one.” “I’m the expert.” “My value comes from knowing things other people don’t.”

When AI produces something good, this version of ego gets uncomfortable. If the machine can do what you do, what does that say about you?

The defense mechanism is subtle. You don’t reject the output. You just find reasons why it’s not quite right. You edit it back toward the version you would have written. You maintain the feeling that you’re still the one who really knows.

• The Cost: Slower adaptation. You’re protecting an identity that’s becoming less valuable by the hour.

• Feels Like: Maintaining standards.

Level 2: Perceptual Filtering

This one is trickier. Your expertise gives you strong pattern recognition. You’ve seen hundreds of situations like this. You know what good looks like.

The problem is your pattern recognition now filters your perception before you’re aware of it. When you read AI output, you’re not seeing what’s actually there. You’re seeing what your mental models expect to see.

A client once told us she couldn’t figure out why Claude kept “missing the point” of her prompts. She’d been using AI for eight months. She considered herself advanced. We asked her to read one of the outputs aloud, slowly, as if she’d never seen it before.

She stopped halfway through.

“That’s actually interesting,” she said. “I didn’t see that the first three times.”

She wasn’t lying. She literally hadn’t seen it. Her expertise had already decided what mattered and what didn’t.

• The Cost: Missed information. You’re not seeing what’s actually there.

• Feels Like: Quality control.

Level 3: Possibility Compression

This is the quiet killer.

When you approach AI with “I know what good looks like,” you’ve already defined the target. The entire interaction becomes about hitting that target. Every prompt, every edit, every judgment bends the output toward what you already had in mind.

You already know how to hit the targets you’ve defined, BUT, AI is useful for finding the ones you didn’t know existed.

The Zen Buddhists have a phrase:

“In the beginner’s mind there are many possibilities, but in the expert’s mind there are few.”

They were talking about enlightenment. They were also talking about working with AI in 2026.

They just didn’t know it yet.

The expert who “knows what good looks like” is compressing the possibility space down to known territory. The beginner who doesn’t know is free to see what’s actually there.

• The Cost: Missed discovery. You’re finding what you expected to find.

• Feels Like: Knowing what you’re doing.

Why This Worked Until It Didn’t

Here’s what ties these three levels together.

In a stable environment, expertise is an asset. You’ve seen this before. You know what works. Your accumulated mental models are correct more often than they’re wrong. Trusting your judgment is the right move.

In this unstable environment, expertise becomes a liability. The ground is shifting. Your mental models were built for a world that no longer exists. Trusting your judgment means trusting a map of a territory that’s been redrawn.

AI has made the environment unstable.

• The frontier moves weekly

• The thing you learned last month may already be obsolete.

• The capability that didn’t exist yesterday might change everything.

Your expertise doesn’t know this. It still thinks it’s 2023... and it still thinks it knows what good looks like.

Every hour you spend defending your priors, gatekeeping your inputs, and squeezing possibilities down to the familiar is an hour someone else spent wandering into territory you’ll never see.

This Isn’t Just You

The same pattern plays out at the organizational level.

Large companies with massive AI budgets are getting outrun by three-person startups. Not because the startups are smarter but because the enterprise has too much to protect.

Existing workflows. Existing processes. Existing definitions of what “good” looks like. The AI implementation becomes a project to make the new thing fit the old thing. Every integration decision filters through “how do we preserve what we have?”

The startup has nothing to preserve so they ask a different question:

“What’s possible now?”

Same mechanism with a completely different scale.

The One Question That Disarms It

The fix is not humility in the soft sense. Not self-deprecation or pretending you don’t know things.

The fix is a different question. Let’s make it concrete.

Before you evaluate AI output, before you decide what’s wrong with it, before you start editing it back toward the document you already had in your head, ask yourself:

“What’s here that I wouldn’t have created?”

• Not: “Is this what I wanted?”

• Not: “What needs to be fixed?”

• Not: “How do I make this sound like me?”

Just: “What’s here that I wouldn’t have thought of?”

This one question disarms all three traps. You’re not asking if AI is better than you. You’re not looking for the familiar. You’re not evaluating.

You’re exploring.

Try it for one week. Before you judge any AI output, find one thing that surprises you. Write it down in a note on your phone, or a sticky note next to your monitor, or the margin of whatever you’re working on. (Yes, even if you’re certain the output is wrong. Especially then.) Don’t evaluate whether the surprise is good or bad. Just notice it.

You will discover that you’ve been filtering out surprises since March. Your expertise has been doing its job so well that you stopped seeing what was actually there.

Your Ego Is Reading This Right Now

Your ego will tell you this doesn’t apply to you.

That’s how you know it does.

• It will feel like standards.

• It will feel like quality control.

• It will feel like knowing what you’re doing.

• It will feel like all the things that made you successful in the first place.

• It will feel like your third cup of coffee telling you that you’re the only one who really understands the assignment.

The question is whether you can see past it.

Beginner’s mind doesn’t end when you become an expert. It’s a discipline you maintain against the gravitational pull of your own success. The Japanese calligraphers practice the same strokes for decades. The Zen monks meditate for years. Steve Jobs talked about staying foolish.

Steve Jobs: Stay Hungry, Stay Foolish

(one of our favorite speeches of all time)

Not because ignorance is good. Because openness requires continuous effort. Ego is gravity. It pulls you toward closure, toward certainty, toward the known.

The AI era rewards the people who can resist that pull.

• High Agency + High Ego = Failure.

• High Agency + High Humility = Discovery.

The choice is yours.

And if you’re reading this on January 1st, you already know why we picked today to say it.

P.S. Speaking of defaults you don't know you're running….

We built something for that. Resolution Override is a free prompt pack that uses James Clear's framework to surface the invisible patterns underneath your goals.

Exclusive Prompt Pack

Grab it here and join the Beware the Defaults community launching January 6th.